Aggregate Insider Trading and Future Market Returns in the US, … · 2018-04-24 · 3 Aggregate...

1 Aggregate Trading by Insiders and Future Market Returns in the US, Europe, and Asia

Trading by Corporate Insiders and Future Market Returns in the US,

Europe, and Asia

Dennis D. Malliourisa, Alphons T.N. Vermorken

b, Maximilian A.M. Vermorken

c*

a University of Oxford, DPhil Student – [email protected] b Altana Wealth Ltd., Portfolio Manager – [email protected] c University College London, Visiting Teaching Fellow - UCL School of Management, UCL – [email protected]

A R T I C L E I N F O

Article history:

Status: Submitted for publication

Progress: Awaiting review

Date: April 2018

Keywords:

Director Dealings,

Insider Transactions,

Insider Trading,

Sentiment.

A B S T R A C T

Using a well-established methodology to measure aggregate insider trading, this exploratory study

examines the relation between future stock market returns and aggregate trading by corporate insiders (in

academy commonly referred to as insider trading and directors’ dealings). Analyzing a unique data set of

more than 1.3 million filings of individual directors’ transactions in 16,893 US, European, and Asian

firms from 2003 to 2017, we provide novel results for a multitude of countries which had not been

thematized before. We find that the null-hypothesis (i.e., aggregate trading by directors is not related to

future stock market returns and corporate insiders cannot forecast economy-wide trends) cannot

conclusively be rejected for all countries in the sample. Only aggregate directors’ dealings in the US,

Luxembourg, Switzerland, Poland, Asia-combined, China, India, and the Philippines is coherently

positively associated with future market returns. Implications and further research opportunities are

discussed.

© 2018 Altana Wealth Ltd. All rights reserved.

1. Introduction

It is well established that corporate insiders profitably trade shares in

their own firms on US (Finnerty, 1976a; Jaffe, 1974; Lakonishok & Lee,

2001; Seyhun, 1986), European (Aussenegg, Jelic, & Ranzi, 2016;

Fidrmuc, Goergen, & Renneboog, 2006), and Asian (Bris, 2005; Jaggi &

Tsui, 2007) markets. It can be assumed that insiders trade for one of three

distinct reasons: randomness, firm-specific factors, or market-wide

factors. Based on extant academic evidence in multiple geographic

regions, and the fact that outside investors trade profitably on publicly

available insider transaction filings (e.g., Altana Wealth Ltd, 2018;

Sabrient Systems LLC, 2018), the information advantage hypothesis can

be accepted. Accordingly, randomness-based trades can be ruled out.

What remains is the conundrum as to whether insiders trade on firm-

specific or economy-wide superior information.

If insiders base their trades predominantly on macroeconomic private

intelligence, it is likely that a majority of insiders possess similar

information and collectively trades in a particular direction. If this is

indeed the case, aggregate insider transactions should be able to forecast

future market returns as the market takes into account economy-wide

changes after they will have substantialized, and a positive correlation

between aggregate insider transactions and future market returns should

become observable.

If insiders do not collectively base their trades on expectations of

market-wide developments, aggregate insider trading will not be

statistically significantly associated with future stock market returns, and

* Corresponding author. Tel.: +44 7807 133 425;

E-mail address: [email protected]

© 2018 Altana Wealth Ltd. All rights reserved

insiders are more likely to trade for private firm-specific cash flow news.

Most evidence on insider trading is on the information content of

individual firm-level transactions. Extent studies on the relation between

aggregate insider trading and future market returns almost exclusively

examined US SEC-regulated insider trades in the 1970s and 1980s

(Chowdhury, Howe, & Lin, 1993; Lakonishok & Lee, 2001; Seyhun,

1988, 1992), and can thus be considered outdated. The scarce evidence on

other, less developed, markets is limited in its geographical scope (Zhu,

Wang, & Yang, 2014). Aggregate insider trading and its relation to stock

market returns with a particular focus on non-US markets thus merits

further analysis. Accordingly, the aim of this exploratory study is to

update and extend findings on the relation between aggregate insider

trading and future market returns. The paper contributes to the literature in

the following ways. This is the first study to examine aggregate insider

trading and its predictive power for future market returns in a multitude of

European and Asian countries. It also updates prior insights on the US

market. Thereby it adds substance to the discussion as to whether insiders

possess superior knowledge pertaining to firm- or economy-specific

developments.

By analyzing a unique data set of more than 1.3 million individual

insider transactions in 16,893 US, European, and Asian firms from 2003

to 2017, we find that outsiders cannot always easily distinguish whether

executives, directors, and other corporate insiders trade on superior firm-

specific or market-wide information. Aggregate insider trading can only

coherently predict future market returns in the US, Luxembourg,

Switzerland, Poland, Asia-combined, China, India, and the Philippines.

Insiders in these countries appear to trade on economy-wide expectations.

In Europe-combined, Germany, France, the United Kingdom, Italy,

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Russia, Spain, the Netherlands, Sweden, Belgium, Austria, Norway,

Ireland, Denmark, Finland, Romania, Greece, Cyprus, Turkey, Hong

Kong, Korea, Australia, New Zealand, Malaysia, Singapore, and Thailand

aggregate insider trading does not predict a coherently positive correlation

with future stock market returns. Insiders in these countries are more

likely to trade on future firm-specific cash flow news. Our findings imply

that outside investors can use publicly available data to inform passive

investment strategies’ decision-making for particular countries. This study

covers multiple insider sentiment aggregation horizons (during which

insiders trade) as well as multiple long-term forecast horizons (during

which future returns substantiate) and discusses potential reasons as to

why results are heterogeneous across countries.

The paper is structured as follows. Section II provides a literature review

and builds up the main hypothesis. Data and methodology used are

presented in Sections III and IV, respectively. Empirical findings are

reported in Section V, followed by a discussion in Section VI, and

conclusions and future remarks in Section VII.

2. Literature Review

Insider trading is defined as corporate insiders (e.g., executives,

directors, and significant stockholders) buying or selling financial

instruments in their own firms’ stocks. In most jurisdictions, it is generally

considered legal, as long as the trades are not based on material non-

public information. Corporate insiders in the EU, the US, and in multiple

Asian countries may trade legally in their own securities, but are obligated

to report their trades to the relevant market authority. Assuming that

insiders are rational economic agents who intend to maximize their private

wealth, they do not trade randomly, but on superior information or

knowledge of future cash flows in their firms. Their advanced insights

into firms’ opportunities, threats, and the competitive position in the

market allow them to perceive mispricings relative to current share prices

and changes in cash flows (Piotroski & Roulstone, 2005). When corporate

insiders deem current prices too low, they are likely to be net buyers.

When prices seem too high, they will turn into net sellers.

Firm-specific insights may stem from multiple sources. Previous

research claimed that insiders may base their transactions on their

interpretation of financial information which may differ from analysts’

expectations, knowledge of internal forecasts, a better understanding of

the company’s competitive position in the market relative to competitors,

or simply a better ‘gut feeling’ (Cohen, Malloy, & Pomorski, 2012;

Finnerty, 1976b; Pope, Morris, & Peel, 1990). Moreover, insiders benefit

from firm-specific cash flow considerations related to proposed mergers

(Keown & Pinkerton, 1981), new issue announcements (Karpoff & Lee,

1991), dividend announcements (John & Lang, 1991), expected R&D

outcomes (Aboody & Lev, 2000; Coff & Lee, 2003), and imminent

breakthrough developments and product announcements (Ahuja, Coff, &

Lee, 2005; Coff, 2010). Additionally, there are studies revealing evidence

for firm-level market timing abilities (Friederich, Gregory, Matatko, &

Tonks, 2002) and for insiders contributing to general market price

discovery efficiency on insider trading days (Aktas, de Bodt, & Van

Oppens, 2008).

Overall, corporate insiders are motivated and incentivized to buy

shares in anticipation of positive firm-specific news and vice versa. They

are able to gather, decipher, and trade on firm-specific information. The

greater the information asymmetries vis-à-vis outsiders, the greater the

ability of insiders to exploit private information.

Corporate insiders do not only seem to be able to perceive mispricing

in their own firms and anticipate changes in their firms’ cash flows based

on firm-specific information. Instead, directors, executives, and other

insiders might also base trades on their sentiment towards future

economy-wide developments and the respective impact on corporate cash

flows. Insiders across firms in a given country may develop similar

expectations of trends in macroeconomic factors and future stock market

corrections. These anticipations are likely to be reflected by aggregate

insider trading, i.e., the net summation of corporate insiders’ transactions

across publicly traded firms. As other investors start perceiving changes in

economy-wide indicators as well, they will alter their valuations and drive

share prices across firms accordingly, resulting in respective market

returns (Seyhun, 1992). Consequently, insider sentiment in terms of

aggregate insider trading would predict future stock market returns.

The connection between aggregate insider trading and its

ability to forecast future market returns is likely to stem from three

different sources. Insiders’ ability to perceive unanticipated changes in

macroeconomic trends earlier, their ability to observe such changes more

effectively, and their ability to detect systematic market misvaluations

induce a “macro information advantage” relative to other investors

(Seyhun, 1988; Zhu et al., 2014).

First, insiders at the operational forefront and those well-connected to

insiders at other firms have preferential access to information pertaining

to, e.g., price movements, capacity utilization, and restructurings. This

allows them to perceive economic trends, e.g., inflation, aggregate

demand, a country’s Gross Domestic Product (GDP), and unemployment

rates earlier than the general public, which can only access trade and

commercial statistics later. Once other market participants will have

picked up on changes in economy-wide activities as well, stock prices

collectively rise (Jiang & Zaman, 2010).

Second, corporate insiders also tend to possess high levels of

education (see Barker & Mueller, 2002), an improved understanding of

their firms and the industry, and experience as to how macroeconomic

trends affect their firms’ cash flows. Consider the following example.

Based on executives’ knowledge of suppliers’ price alterations and

industry structures, they might anticipate shifts in demand for

intermediate goods. Accordingly, these insiders might sense changes in

demand for a range of final goods earlier than outside investors and thus

deduce economy-wide trends (as the market value of final goods

determines a country’s GDP). If such economy-wide trends pertain to a

substantial proportion of the total firm population, corporate insiders

across firms would perceive market-wide mispricings and trade in the

same direction. Later on, outside investors might gain access to similar

trade information, realize that firms’ current prices deviate from their fair

values, and buy or sell shares accordingly. In other words, current

aggregate insider sentiment may predict future stock market returns and a

temporal connection between the two should be observable.

Third, markets may overheat (consider e.g., quantitative easing) or be

overly bearish (consider e.g., market crashes/dips and overreactions) due

to exogenous shocks and irrational behavior. Such systematic overpricing

and underpricing may be perceived by corporate insiders who then trade

their own firms’ shares accordingly. If multiple firms’ stocks suffer from a

particular mispricing, insiders collectively capitalize on the prices, and

their transactions tend to appear in selling or buying waves (Zhu et al.,


2014). For instance, examining the stock market crash of October 1987,

Seyhun (1990) presented evidence that corporate insiders did not predict

the market crash, but correctly predicted the strongly positive market

returns during the subsequent recovery. There was no increased insider

sales activity before the crash, but a record number of net aggregate

purchases following the crash, which indicates that collectively insiders

correctly identified systematic mispricing in the market induced by

outside investors’ previous overreaction. More recently, analyzing insider

sentiment around the 2008 financial crisis indicated that corporate insiders

were able to perceive a general price bubble and traded accordingly, prior

to the crisis’ peak. In February 2018, a slump of more than 8 percent in

the S&P 500 and the Dow Jones Industrial Average was predicted by

insiders, as a high aggregate volume of sales transactions prior to the drop

indicated a strongly bullish sentiment (Altana Wealth Ltd, 2018).

2.1. Empirical evidence of aggregate insider trading

There is some empirical evidence corroborating the theoretical

grounding laid out above. Seyhun (1988) was the first to establish that

aggregate corporate insider sales and purchases can be linked with future

stock market returns. Analyzing US insider trades from 1975 to 1981, the

results suggest that aggregate insider trading conveys information

pertaining to future changes in economy-wide trends not already factored

into current stock prices. The study documented a significantly positive

relation between monthly aggregate insider trading and market returns

during the following two months. Specifically, one standard deviation

change in the standardized aggregate net number of executives’

transaction predicted up to 1.7 percent change in future excess market

returns. Moreover, the author showed that insiders’ transactions in firms

of greater market value carry a greater predictive value for future market

returns. Hence, such insiders seem more informed of future

macroeconomic trends. Insiders in firms with greater market risk trade

more on economy-wide expectations and information (Seyhun, 1988).

Analyzing US insider sentiment 1975 to 1989, Seyhun (1992)

documented a strong relationship between aggregate insider trading and

future stock returns in excess of one-month Treasury Bills. This seminal

paper revealed that using long-term aggregation horizons to predict long-

term forecast horizons is associated with particularly strong prediction

abilities. According to the findings, up to 60 percent of future one-year

market returns’ variation could be predicted by twelve-month aggregate

net numbers of transactions. Moreover, Seyhun (1992) found that

aggregate insider trading is positively associated with future growth rates

of the Index of Industrial Production and the Gross National Product,

which suggests that insiders possess some forecasting ability concerning

economy-wide activity. However, including future real activity as an

additional explanatory variable of future returns does not render aggregate

insider trading insignificant. Insider sentiment does thus retain an

explanatory meaning for market returns in its own right.

There is some discord in the literature as to whether insiders merely

follow a contrarian investment strategy or actually trade on superior

information. Chowdhury et al.’s (1993) results contradict Seyhun’s (1988,

1992) earlier findings. Analyzing the short-term relation between

aggregate insider transactions in 1,361 US firms from 1975 to 1986 and

market returns, the authors documented that the predictive power of

aggregate insider transactions is existent but actually slight. Instead,

Chowdhury et al. (1993) found strong evidence for a reverse relationship.

Current market returns predict aggregate insider trading, i.e., high stock

market returns cause insiders to sell off stock and vice versa.

Examining US insider transactions from 1976 to 1995, Lakonishok

and Lee (2001) reported further findings in support of aggregate insider

transactions’ predictive power for future market returns. Controlling for

contrarian insider investing, they found that aggregate insider trading can

predict future market returns. For instance, Lakonishok and Lee (2001)

reported an 11 percent gap in future twelve-month returns between

months of very low versus very high net purchasing activities. Moreover,

the authors showed that managers’ aggregate trading is associated with

greater predictive power for future stock market returns than large

shareholders’ one. Longer sentiment aggregation horizons and forecast

horizons were both associated with greater predictive powers.

Jiang & Zaman (2010) analyzed the relation between aggregate insider

trading and future market returns using a novel returns model, which

allowed them to distinguish between future market return components

related to insiders’ superior knowledge of economy-wide factors and

those related to contrarian investing. They demonstrated that insiders’

predictive skills are due to their ability to forecast unexpected future cash-

flow news which can be related to changes in economy-wide activity.

Examining US data from 1975 to 2000, they do not find evidence

suggesting that insiders act as contrarian investors.

More recently, Marin & Olivier (2008) presented additional anecdotal

evidence indicating that aggregate insider trading predicts substantial

future market crashes and jumps.

The only study on insider sentiment and future market returns in

emerging markets (Zhu et al., 2014) established that in China, aggregate

insider trading also predicts future market returns, even to a greater extent

than insider trading in the US. Analyzing 5,553 insider transactions

between 2007 and 2011, the authors found that insider trading can forecast

up to 72.7 percent of variation in future market returns. Furthermore, Zhu

et al. (2014) showed that higher levels of operational involvement and

hierarchy are associated with greater degrees of predictive power, which

they attribute to more pronounced abilities to forecast macroeconomic

developments and observe systematic market misvaluation relative to

other insiders. The authors also provided evidence that state-owned

companies’ insiders exhibit lower abilities to predict future market returns

than insiders in firms with different corporate governance structures.

Overall, conceptual and empirical evidence suggests that insiders can

effectively observe macroeconomic developments and systematic

misvaluation in the market and trade own firm shares accordingly.

Aggregate insider trading is a substantial leading predictor of future stock

market returns. Studies show that aggregated insider trading does not

constitute a simple contrarian strategy, but that transactions carry

predictive power. Additionally, there are some groups of insiders which

are associated with greater predictive power than others. Given the

prevalence of varied types of insiders, firm sizes and risk characteristics,

and market dynamics across countries, it is reasonable to assume the

existence of substantial differences in the predictive power of aggregate

insider trading across countries.

2.2. Trades based on firm-specific vs. economy-wide information

Aggregate insider trading’s predictive ability is not a simple

summation of insiders’ trades on firm-specific information. Assume

prevalence of firm-specific news to be near-normally distributed, with


positive and negative information at each respective tail of the

distribution. Assume further that firm-specific news are independent of

changes in economy-wide activity. If insiders trade exclusively on firm-

specific knowledge, their transactions’ directions, amounts, and volumes

are likely to net out in aggregate. In a given sentiment aggregation

horizon, transactions of insiders anticipating favorable news and thus

buying shares would be cancelled out by those based on unfavorable

expected cash-flow news inducing insiders to sell shares. Accordingly, no

distinct insider sentiment would be deducible, and current aggregate

insider sentiment should not predict future market returns. Instead, it

would appear that transactions are predominantly based on firm-specific

information. However, if a large proportion of corporate insiders buy or

sell shares in concurrent waves, trades would appear to be based on

mutually shared information about future market-wide activities (cf.,

Seyhun, 1988; Zhu et al., 2014). It is reasonable to assume that in some

countries, information asymmetries provide for great opportunities for

insiders to exploit firm-specific information and market structures do not

allow insiders to perceive macroeconomic developments. In such

countries, it would be expected that no distinct insider sentiment can

established and no connection between aggregate insider trading and

future stock market returns can be observed. The same logic also applies

vice versa.

In summary, corporate insiders possess superior information or skills

allowing them to perceive mispricings in their firms’ stock, and trade on it

accordingly. Perceived mispricing may originate from two sources:

economy-wide and firm-specific expectations of changes in cash flow.

Divergent insider, firm, and market characteristics across countries may

cause differences in insiders’ ability to trade on firm-specific or

macroeconomic information. We thus hypothesize that aggregate insider

trading can predict future stock market returns in multiple European and

Asian countries and the US. With respect to varying insider forecast

abilities we do not expect homogenous results across countries.

3. Data Sources and Sample Characteristics

Daily-closing indices prices to compute returns as well as exchange

rates to convert all monetary values into US Dollars were downloaded

from Bloomberg. For each country, the most common index featuring the

most liquid stocks and most highly capitalized firms was chosen to

represent the particular country’s stock market returns*. To analyze the

relationship between aggregate Europe-combined and Asia-combined

insider trading and stock market returns, the EURO STOXX 50 and MSCI

AC Asia ex Japan indices are used.

Corporate insider trading data is obtained from a unique data set on

which no prior published research has been carried out. The data set was

constructed by Altana Wealth Limited on the basis of 2iQ Research

GmbH filings and Bloomberg L.P. information. The sample includes all

insider trades in listed firms with a market capitalization of at least

USD250 million at the time of filing domiciled in the US, 21 European,

* Due to space constraints, the utilized stock market indices are not shown here. A

list of indices used for each country can be obtained from the authors upon request.

and 10 Asian countries from 2003 to 2017. The sample consists of three

regional sets of transactions on US, European, and Asian exchanges. Both

cash-market and derivative transactions were included in this study.

Transactions arising from the award of stock-based executive

remuneration are excluded, as these transactions are not grounded in

executives’ perceptions of firm-specific or economy-wide mispricings.

Also, transactions featuring missing data were omitted from the sample.

Table 1 Appendix A

In Table I, overall sample characteristics, the number of firms and

unaggregated filings present in the sample, the aggregate purchase and

sales volumes, and the aggregate numbers of shares bought and sold, can

be observed. The table shows the top-level statistics per country as well as

for Europe-combined and Asia-combined. Firms and their associated

insider transactions were assigned to a country based on a firm’s country

of domicile as listed on Standard & Poor’s Compustat database. This

mapping was chosen to reflect the country in which most corporate

insiders are likely to reside, consume media, and interact with members in

their network, which accumulates into their expectations formation

process vis-à-vis macroeconomic trends. The US sub-sample contains all

insider trades on US exchanges and in firms listed abroad but domiciled in

the US. The Asia-combined and Europe-combined sub-samples are based

on all insider transactions filed on Asian and European exchanges,

respectively†. Table I shows that the overall sample of insider trades in the

time period ranging from 2003 to 2017 contains a total of 1,349,265

insider transactions in 6,093 US, 4,233 European, and 6,567 Asian firms.

The total number of shares assessed in the overall sample amounts to

5.429 trillion shares traded by corporate insiders. Unsurprisingly,

countries featuring a lower number of firms in which insider trades were

conducted are also associated with lower total volumes and amounts of

shares in the sample. However, a country’s economy’s size, in terms of

GDP, is not necessarily correlated with lower numbers of firms, filings, or

shares in the overall sample. The number of firms and filings is also an

indication as to the introduction date of insider trading regulations and the

prevalence of insider trading in the respective country.

As expected, the net total number of shares (i.e., the sum of all shares

bought minus the sum of all shares sold), as well as the net total volume

(i.e., the volume of all buy transactions minus the volume of all sales

transactions per country in the sample) tend to be negative for most

countries. This means that insiders sell more shares than they buy, which

is consistent with previous studies (Aboody & Lev, 2000), and mainly due

to executives selling shares they had previously been awarded with as a

part of their remuneration packages (i.e., liquidity needs).

4. Methodology

The study’s methodology is adapted from the established literature on

aggregate insider trading (Seyhun, 1988, 1992). In order to achieve the

† Due to space constraints, included exchanges are not shown here. A list of all

exchanges and countries represented in the (sub-) samples can be obtained from the

authors upon request.


goal of this paper, to demonstrate the empirical relationship between

aggregate insider trading and future stock market returns in multiple

countries, twelve economic models per country or region were established

as follows.

For each country or region, we use three different measures to

operationalize the independent variable, aggregate insider sentiment;

standardized aggregate net number of transactions ( ), standardized

aggregate net number of shares ( ), and standardized aggregate net

volume of shares ( ). First, for each day and firm all individual

insider transactions are converted into daily events as follows

∑

∑

∑

∑

{

where and are the net event number of shares and

volume of shares for firm on day , respectively, and are

numbers of shares purchased and sold in each individual transaction , and

and are the volumes of shares purchased and sold,

accordingly. The number of transactions in a given firm on a given day is

denoted . If the daily-firm event’s net volume is positive, the event

transaction’s direction, , equals and vice versa.

Second, , , and are summed per firm and month:

∑

∑

∑

where denotes the number of days in a given month, and , ,

and are the monthly firm-level sums of net transaction directions,

net numbers of shares, and net volumes of shares, respectively. Third,

within each country and region sub-sample, , , and are

standardized as follows

where denotes the country-/region-specific sub-sample, and ,

, and are the standardized monthly firm-level transaction

directions, number of shares, and volume of shares traded. Finally, for

each country and region, standardized aggregate net number of

transactions ( ), standardized aggregate net number of shares

( ), and standardized aggregate net volume of shares ( ) are

computed as follows

∑

{ }

∑

{ }

∑

{ }

where denotes the number of firms in a given country, and ,

, and

are the one-month aggregate insider sentiment

indicators. Using standardized indicators allows for more effective

interpretation as it limits the variables’ ranges, allows for improved

comparison of coefficients across models, and smoothens out variation in

the respective sentiment indicator (Seyhun, 1992). Each monthly insider

sentiment indicator is also aggregated over three and six months (

{ }) to smoothen out variability of corporate insider sentiment and to

reduce the influence of short-term trends.

The dependent variables in each model are the future one-month,

three-month, six-month, and twelve-month stock market index returns.

Previous studies conceptualized excess returns of an index relative to risk-

free assets as a measure of future market returns (Lakonishok & Lee,

2001; Seyhun, 1992). We use actual index holding returns to remove one

potential source of sensitivity stemming from the choice of risk-free return

rates. Our results thus allow for statements about the predictability of

insider sentiment for future market returns (the actual relation intended to

show) as opposed to future excess market returns which may differ

substantially. This study’s returns are defined as follows

(

)

{ }

where is the linear index holding return of the forecast horizon

of one, three, six, or twelve months; is the index’s price on the first

trading day of a given month following the end of an aggregation horizon;

and is the index’s price on the last trading day months after

the beginning of the forecast horizon month . Including long-term future

returns in the analysis ensures that the potential influence of short-term

seasonalities in stock returns or insider trading are mitigated.

Operationally, given data availability, all four forecast horizons are

calculated for the first month in the data set. Then, the four forecast

windows’ start and end points are both moved one month ahead. This

implies that forecast horizons can be overlapping. For instance, as the

twelve-month window is shifted one month ahead, the window implicitly

covers eleven months of the previous ’s twelve-month forecast window.

To establish the coefficients of interest, we run ordinary least square

regressions. A lagged return variable is introduced as an additional

independent variable in each regression model to account for serially

correlated residuals resulting from overlapping time periods. The lagged

variable is defined as the dependent variable at , which means that, for


instance, to predict the future six and twelve months index returns starting

February of a given year, the lagged variables used are the future six and

twelve months returns starting January of the same year. The regression

models are set up as follows

∑ { }

where are the returns to be predicted within the one, three, six,

or twelve months forecast horizons; is the coefficient of interest

showing the relation between aggregate insider sentiment and future index

returns; the sigma sign indicates the aggregation horizon, which starts

months and ends one month prior to a forecast horizon’s starting month

; is the lagged variable, i.e., the one, three, six, or twelve month

returns starting at the month before the focal month ;

and is a residual error term.

4. Empirical Results

Key variables’ descriptive statistics for selected countries are reported

in Table II. Descriptive statistics for all countries and regions (i.e., the US,

Europe-combined, Asia-combined, and individual European and Asian

countries) are shown in Table IV in the appendix. For each variable the

mean, standard deviation, minimum, and maximum are shown. The three

sentiment indicators and returns were calculated as laid out above. The

number of observations is the number of calendarmonths for the one-

month explanatory variable and the number of sums for the multi-month

explanatory variables, respectively. A negative (positive) mean sentiment

indicator implies that in aggregate insiders were net sellers (buyers)

during the period of time under consideration. , , and

standard deviations and ranges tend to increase with an increase in

aggregation horizons.

Table 2 Appendix B

Time series regression model results for selected countries are shown

in Table III. Results for all countries and regions are shown in Table V in

the appendix. Each model predicts the dependent variable, future market

return, as a function of the independent variable, insider sentiment. For

each region or country, future one-month, three-month, six-month, and

twelve-month buy-and-hold stock market returns are regressed on the

three different insider sentiment indicators reflecting aggregated

transactions over one, three, or six months ( , , ;

, , ; and , , ). Country- and

region-specific returns and insider sentiment indicators are calculated as

laid out in the methods part above.

Table 3 Appendix C

In each panel, the first columns demonstrate the relation between the

independent variable and the following month’s return, the associated

sample size of individual months or aggregations of months, and the

respective model’s . The following columns reveal the relation

between the sentiment indicator and the following three-, six-, and twelve-

month returns, respectively, the associated sample sizes, and

s. For each country, the first row relates to the one-month sentiment

indicator. The following rows show the model coefficients based on

values smoothed over three and six months, respectively. The

coefficients indicate the strength of the relationship between the

respective sentiment indicator and future market returns. The intercept

coefficients and lagged autoregressive coefficients are not of interest

for this analysis and thus omitted from the table. s are

increasing with the dependent variable’s time frame due to the lagged

variables’ large coefficients. Differences in s across the three

sentiment indicators are negligible.

The first Panel for each country shows the results for models in which

standardized aggregate net number of transactions were used. The results

imply that, for instance, an increase in one-month US by one

standard deviation, is associated with an expected increase of future six-

month S&P 500 returns by 2 percent. The second Panels feature the

results of models using standardized aggregate net number of shares.

Considering, for instance, Luxembourg, the Panel shows that an increase

of six-months by one standard deviation is expected to result in a

2.86 percent increase of future twelve-month LuxX Index returns. In the

third set of Panels, models reveal the results using standardized aggregate

net volume of shares as a proxy for corporate insider sentiment. The

results show that, for instance, an increase in three-month Asia-combined

by one standard deviation is associated with an expected increase in

the MSCI AC Asia ex Japan Index by 1.75 percent.

Overall, the results exhibit absence of one single monotonic trend

persisting across all countries, time frames, and sentiment indicators.

Insider sentiment is coherently significantly associated with future returns

in only some countries. For the US, Luxembourg, Switzerland, Poland,

India, and the Philippines, the presence of at least one significantly

positive coefficient in two sentiment indicators indicates that an

increase in insider sentiment can be reliably linked to higher future

returns. For Asia-combined and China, at least one significantly positive

coefficient in all three sentiment indicators suggests a homogeneously

positive relation. For all other 27 countries and regions in the sample,

results are either inconclusive, convey a mixture of positive and negative

signals across the economic models, or suggest negative associations

between insider sentiment and future returns. For instance, for Canada and

Austria, at least one significantly negative coefficient in each sentiment

indicator coherently implies a negative correlation between insider

sentiment and future market returns.

Comparing the three measurement instruments, it becomes apparent

that captures more associations between insider sentiment and

future returns than the other two ones, as the first Panel in each country

features more significant coefficients than the second and third Panel.

Furthermore, the first Panels show that within one country,

coefficients are mostly internally coherent, i.e., in all countries apart from

Greece, statistically significant s across time frames are of the same

sign. Another trend that can be observed is that for a given , the

sentiment indicators’ time period, coefficients tend to become more

positive as , the future returns’ time period, increases. In other words,

-based models’ forecast ability increases as the time period to be

predicted increases. Consider Hong Kong, where the three-months and

six-months smoothened sentiment indicators are statistically

insignificant in the models predicting future one-month, three-month, and

six-month Hong Kong Hang Seng Index (HSI) returns. Both sentiment

indicators are, however, significantly positively associated with future

twelve-month HSI returns. Similarly, examining Hong Kong’s one-month


coefficients shows a stronger relationship with predicted future

twelve-month returns than with predicted future six-month returns. In this

particular case, all else being equal, if increases by one standard

deviation, future six-month HSI returns are expected to rise by 2.06

percent whereas future twelve-month HSI returns are expected to rise by

2.85 percent.

The second and third Panels show that - and -based models

tend to introduce more noise than -based ones. For instance, for

Sweden, Singapore, and Thailand, -based models coherently

indicate a positive relation between insider sentiment and future market

returns across multiple dependent and independent variables’ time frames.

However, the overall country-level results are rendered ambiguous as

-based models indicate a negative relationship and -based

ones do not feature any statistically significant coefficients.

Furthermore, only transaction count-based results are robust to

changes in . Smoothing sentiment indicators over time by summing

multiple months’ standardized aggregate net number of transactions does

not alter results substantially. and -based models, however,

are sensitive to smoothed values. For instance, summing Germany-based

insiders’ standardized aggregate net numbers of shares over three months

changes the coefficients’ signs from negative to positive. Similarly,

summing Hong Kong-based insiders’ standardized aggregate net volumes

of shares over multiple months changes the coefficients’ sign from

positive to negative.

5. Discussion

We find evidence consistent with what Zhu et al. (2014) considers the

insiders’ “macro information advantage” for some countries in our

sample. The regression results indicate that in the US, Luxembourg,

Switzerland, Poland, India, the Philippines, Asia-combined, and China

aggregate insider trading coherently predicts future market returns.

Insiders seem to be able to observe and trade on market-wide activities

and trends in said countries. This ability may be due to insiders obtaining

macroeconomic information earlier, analyzing information more

effectively, and perceiving systematic stock market mispricings better

than other market participants. In the aforementioned countries, insider

sentiment forecast abilities are economically significant. An increase in

aggregate insider buying by one standard deviation is associated with rises

of around 2 percent. Market participants wishing to implement a low-cost

tool to forecast stock market returns can use publicly available insider

filings to deduce insider sentiments for these countries.

In contrast, our data suggests that insiders in Europe-combined,

Germany, France, the United Kingdom, Italy, Russia, Spain, the

Netherlands, Sweden, Belgium, Austria, Norway, Ireland, Denmark,

Finland, Romania, Greece, Cyprus, Turkey, Hong Kong, Korea, Australia,

New Zealand, Malaysia, Singapore, and Thailand do not base their trades

on macroeconomic expectations and information. Insiders in these

countries rather seem to base their trades predominantly on firm-specific

information. Insiders seem to be more effective in obtaining and trading

on cash flow news related to their own firms. Market participants do not

seem to be able to use aggregate insider trading as a tool to forecast future

market returns in these countries.

Additionally, there are three categories of reasons (market-, firm-, and

insider-level) which provide potential explanations as to why aggregate

insider trading does not predict market returns in these countries. First,

from a market-level perspective there might be differences in insider

trading regulation implementation and enforcement allowing insiders in

the second set of countries to trade on firm-specific cash flow information

without fearing a high risk of litigation. Differences in firm-level

abnormal returns have previously been ascribed to regulatory differences

(Fidrmuc, Korczak, & Korczak, 2013). Other aspects that may harm

insiders’ ability to trade on macroeconomic expectations include the level

of market maturity and efficiency. In modern times, all market

participants have fast access to a plethora of information and means to

analyze data. It is thus likely that insiders’ macro information advantage

relative to other investors has vanished in some countries as every market

participant trades on the same (publicly) available data. Moreover, the

degree of specialization in a given economy might be positively

associated with insiders’ forecasting abilities as specialized firms may

tend to have more interactions with other firms than non-specialized ones,

allowing them to gather macroeconomic information through frequent

interaction. Second, extant research suggests an impact of firm size

(Lakonishok & Lee, 2001; Seyhun, 1988), firm market risk (Seyhun,

1988), and governance and ownership structures (Zhu et al., 2014) on the

predictive power of aggregate insider trading. It is possible that some of

the results presented above are influenced by these aspects. For instance,

Lakonishok and Lee (2001) claimed that insiders in smaller companies

have greater predictive power. It is possible that in the sample, countries

that do not reveal a significant relation between insider sentiment and

market returns feature an unproportionally huge number of large firms.

Third, prior research indicated that managers exhibit higher predictive

power than large shareholders (Lakonishok & Lee, 2001), and that levels

of hierarchy and operational involvement are positively correlated with

insiders’ predictive power (Lakonishok & Lee, 2001; Zhu et al., 2014). It

may be possible that in the sample, countries that do not reveal a

significant relation between insider sentiment and market returns feature a

relatively high amount of large shareholders’ transactions and insiders of

low hierarchical level. Additionally, in countries for which future returns

can be predicted by aggregate insider trading, corporate insiders might be

better inter-connected, allowing them to gather and deduce

macroeconomic information more effectively. All aforementioned aspects

may also interact differently across countries.

In line with e.g. Lakonishok and Lee (2001) we find that the models’

forecast ability becomes of greater magnitude as the forecast horizon

increases. This increase is likely due to insiders observing trends very

early, which only substantiate and become noticed by outside investors

over the course of time. Another noteworthy pattern in the results is that

models for smaller economies in terms of market activity, number of

firms, or GDP tend to reveal more significant relations between aggregate

insider trading and market returns than those for larger economies. This

trend may be due to insiders in smaller countries being more

interconnected, which allows them to build up macroeconomic trends

more effectively as their access to economy-wide information increases.

The second and third Panels of Tables III and V present some

mixed evidence. Accordingly, the overall assessment as to whether

insiders base transactions on superior economy-wide knowledge is

rendered inconclusive for Germany, Sweden, Hong Kong, Singapore, and

Thailand. The nonuniform nature of - and -based models is in

line with previous studies (Seyhun, 1992). Extant research demonstrated

that insider transactions in smaller firms are associated with greater

abnormal returns (Lakonishok & Lee, 2001), which implies that insiders

in such firms tend to possess greater firm-specific insights. Directors in


larger firms, who tend to possess and trade on less firm-specific

information, tend to buy and sell higher numbers and volumes of shares,

which results in - and -based models being biased towards

such firms. The fact that these models provide less conclusive evidence

than -based ones suggests that large-firm insiders do also not

possess superior economy-wide information.

Our findings are generally consistent with past studies. Seyhun (1988)

showed that one standard deviation change in aggregate insider trading

predicts up to 1.7 percent change in future excess market returns. We

documented that an increase in one-month US by one standard

deviation is associated with an expected increase of future six-month S&P

500 returns by 2 percent. Similar to Seyhun (1992), we find that using

standardized aggregate net number of shares as an indicator for insider

sentiment leads produces more noisy future return predictions than the

standardized aggregate net number of transactions. Our findings

pertaining to China and other less mature markets (i.e., India and the

Philippines) are in line with Zhu et al. (2014).

Apart from the novelty of our data and the geographical breadth of our

sample, the main strength of our exploratory study lies in the

methodological rigor applied. By defining three distinct insider trading

measures we avoid potential biases arising from using only one single

indicator. For instance, aggregate dollar volume as a measure of aggregate

trading might be influenced by large firms and a small number of large

transactions, whereas the transaction count may be less biased. Monthly

insider sentiment indicators are smoothened out to reduce the variability

of corporate insider sentiment and to reduce the influence of short-term

trends. Sentiment aggregations of one, three, and six month(s) are

examined, as opposed to other studies (e.g., Chowdhury et al., 1993) who

used short-term sentiment aggregation horizons of as little as one week. A

similar logic applies to long-term forecast horizons, chosen to mitigate the

potential influence of seasonalities. Moreover, we use holding returns as

opposed to, for instance, excess returns, as the dependent variable in order

to analyze the actual predicted relation and to avoid one potential source

of bias introduced by choosing appropriate risk-free assets. One potential

weakness of our study is the high level of analysis. We do not consider

differences in transaction, insiders, or firm types. For instance,

Chowdhury et al. (1993) showed that aggregate insider purchases have a

greater predictive power than aggregate insider sales. However, we do not

effectively account for such potential differences.

6. Conclusion & Future Research

This study examines aggregate insider transactions in 21 European

and 10 Asian countries and the US. We find that only in the US,

Luxembourg, Switzerland, Poland, Asia-combined, China, India, and the

Philippines insiders do coherently predict future market returns. Insiders

in these countries seem to trade on economy-wide expectations, whereas

in other countries in the sample, insiders appear to trade predominantly on

firm-specific private information. For the aforementioned seven countries,

investors can use aggregate insider trading as an effective tool to make

assumptions about future stock market returns and use insider sentiment

to inform passive or index investing strategies. On the contrary, in those

countries in which aggregate insider trading is uncorrelated with future

market returns, insiders appear to rather trade on firm-specific

information. Accordingly, investment strategies focusing on using

individual firms’ insider trading filings to invest in particular stocks may

be more profitable. The findings imply that naively mimicking all insider

transactions is not necessarily a profitable investment strategy.

This high-level study does not examine transaction (e.g., buy vs sell),

firm (e.g., size, risk), or insider (e.g., hierarchical level, operational

involvement) characteristics. The discussion section of this paper provides

multiple reasons as to why insider sentiment in terms of aggregate insider

trading may not be an accurate predictor for some countries in the sample.

Multiple testable hypotheses to be empirically analyzed can be developed

from the discussion. We leave this to further research.


Appendix A. Table 1. Number of firms, number of filings, and net total transaction volume in million USD for each country 2003 to 2017. Aggregate volumes and numbers of shares bought

and sold are in millions.

Country No. of

firms

No. of

filings

Aggregate

Vol. Buy

Aggregate

Vol. Sell

Aggregate

No. Buy

Aggregate

No. Sell Country

No. of

firms

No. of

filings

Aggregate

Vol. Buy

Aggregate

Vol. Sell

Aggregate

No. Buy

Aggregate

No. Sell

Europe 4,233 223,520 433,651 680,165 101,428 110,467 Belgium 94 5,504 8,598 11,013 205 254

Germany 353 9,785 15,042 34,932 1,737 2,512 Austria 61 2,716 4,585 3,991 325 221

France 365 24,954 68,979 68,322 1,482 1,811 Norway 182 4,746 17,032 16,801 6,084 4,605

UK 927 29,900 23,285 40,336 4,433 7,288 Ireland 56 1,605 696 10,772 260 22,347

Italy 258 29,416 72,983 92,188 32,844 16,554 Denmark 96 4,412 3,228 10,044 306 320

Russia 82 1,772 8,885 9,080 14,112 15,165 Finland 91 5,791 2,082 2,930 326 539

Spain 158 16,693 31,547 42,610 2,238 4,498 Romania 23 2,124 417 1,136 8,115 7,787

Netherlands 147 5,483 9,771 16,092 473 846 Greece 103 12,878 17,914 9,265 2,273 1,127

Switzerland 225 18,764 68,794 197,863 718 2,008 Luxembourg 35 1,694 4,284 5,360 482 238

Sweden 284 16,083 18,524 20,306 1,369 1,751 Cyprus 13 351 244 235 103 24

Poland 125 3,585 12,598 13,176 1,742 1,310 Turkey 130 5,214 10,858 12,049 4,087 3,855

Country No. of

firms

No. of

filings

Aggregate

Vol. Buy

Aggregate

Vol. Sell

Aggregate

No. Buy

Aggregate

No. Sell

USA 6,093 478,103 1,404,007 2,222,292 61,663 73,062

Asia 6,567 223,239 699,211 964,478 1,137,815 1,384,683

China 2,606 69,219 192,485 323,598 250,515 401,387

Hong Kong 808 35,210 219,597 251,417 650,910 680,272

India 599 26,782 67,375 73,239 19,176 16,160

S. Korea 690 26,672 75,288 65,568 4,467 3,822

Australia 623 9,083 11,875 134,428 5,192 78,730

New Zealand 66 1,166 231 1,129 132 414

Malaysia 305 25,921 35,544 38,221 41,372 43,391

Singapore 266 8,677 81,712 53,360 63,130 38,551

Thailand 236 14,249 4,488 7,694 25,784 46,922

Philippines 92 3,954 1,611 3,675 4,791 6,211


Appendix B. Table 2. Key variables’ descriptive statistics: selected statistics for seven countries exhibiting coherently positive associations between aggregate insider trading and future market

returns.

Statistic N Mean Standard Deviation Minimum Maximum N Mean Standard Deviation Minimum Maximum

USA Luxembourg

SANT 177 -771.26 665.75 -2,287.44 2,091.57 158 8.01 27.94 -109.82 85.29

SANT 175 -2,333.79 1,560.98 -6,056.43 2,115.96 156 24.36 75.93 -202.00 236.27

SANT 172 -4,711.61 2,586.07 -10,646.70 2,542.22 153 49.91 141.58 -228.52 441.91

SANS 177 -17.27 71.41 -284.3 415.05 158 0.17 6.91 -24.79 81.09

SANS 175 -52.16 125.86 -347.7 511.28 156 0.53 11.87 -28.29 81.00

SANS 172 -105.79 200.3 -517.27 607.22 153 1.10 16.75 -28.72 81.61

SAVS 177 -62.94 153.33 -669.27 317.13 158 0.34 4.36 -17.57 51.45

SAVS 175 -190.55 345.85 -1,376.15 517.19 156 1.04 7.62 -17.56 52.18

SAVS 172 -386.37 606.46 -2,431.89 602.99 153 2.14 10.91 -17.50 53.07

177 1.00% 4.00% -17.00% 14.00% 158 0.10% 5.00% -28.00% 12.00%

176 2.00% 7.00% -30.00% 31.00% 156 1.00% 11.00% -47.00% 26.00%

173 4.00% 11.00% -42.00% 46.00% 153 3.00% 17.00% -57.00% 49.00%

167 8.00% 16.00% -45.00% 58.00% 147 6.00% 26.00% -60.00% 72.00%

Switzerland Poland

SANT 174 -39.68 115.19 -614.84 309.22 133 -1.24 30.59 -111.43 98.62

SANT 172 -120.61 264.57 -1092.82 697.24 131 -3.99 62.80 -187.19 170.70

SANT 169 -248.79 448.12 -1328.91 1044.19 128 -8.27 99.67 -286.74 194.17

SANS 174 3.12 22.63 -76.64 198.14 133 0.13 5.99 -24.28 43.83

SANS 172 9.43 48.47 -105.78 345.97 131 0.55 11.16 -25.15 68.45

SANS 169 15.88 66.44 -107.46 356.33 128 1.51 16.66 -24.52 75.29

SAVS 174 -0.49 26.48 -308.53 100.79 133 0.62 11.98 -67.57 82.96

SAVS 172 -1.49 46.74 -311.23 98.57 131 1.96 19.34 -71.14 76.53

SAVS 169 -3.41 57.97 -309.39 94.58 128 4.13 25.97 -68.38 77.81

174 0.30% 4.00% -9.00% 11.00% 133 0.30% 6.00% -25.00% 20.00%

172 1.00% 7.00% -20.00% 21.00% 131 1.00% 11.00% -33.00% 35.00%


169 3.00% 11.00% -35.00% 40.00% 128 2.00% 18.00% -46.00% 74.00%

163 5.00% 17.00% -37.00% 51.00% 122 4.00% 26.00% -54.00% 78.00%

China India

SANT 157 -54.19 475.12 -2271.41 3105.50 137 -83.66 202.03 -1012.33 326.43

SANT 155 -167.32 1063.31 -3683.34 6049.11 135 -251.08 499.28 -1969.93 596.92

SANT 152 -355.25 1706.31 -5843.60 7401.30 132 -496.18 936.00 -3588.95 1053.58

SANS 157 0.41 47.21 -498.74 156.64 137 -0.12 32.00 -167.27 242.38

SANS 155 1.07 92.27 -695.40 171.20 135 -0.73 55.02 -185.28 219.15

SANS 152 2.04 139.24 -691.01 184.34 132 -3.15 74.69 -199.20 199.73

SAVS 157 -1.10 48.15 -437.80 105.91 137 -2.28 29.68 -236.19 87.53

SAVS 155 -3.41 89.60 -542.21 259.59 135 -7.60 54.32 -261.49 133.01

SAVS 152 -6.65 127.37 -535.20 375.85 132 -17.65 76.46 -254.50 136.69

156 1.00% 8.00% -23.00% 27.00% 136 1.00% 6.00% -25.00% 21.00%

154 3.00% 17.00% -38.00% 50.00% 134 3.00% 12.00% -37.00% 70.00%

151 8.00% 30.00% -54.00% 107.00% 131 6.00% 19.00% -44.00% 82.00%

145 19.00% 55.00% -71.00% 221.00% 125 11.00% 25.00% -54.00% 91.00%

The Philippines

SANT 147 -2.08 25.48 -83.76 102.00

SANT 145 -6.43 50.08 -164.63 117.67

SANT 142 -14.72 81.39 -223.20 169.38

SANS 147 0.10 9.84 -60.05 55.43

SANS 145 0.29 17.05 -59.56 73.92

SANS 142 0.06 23.25 -55.72 77.03

SAVS 147 0.25 10.33 -56.02 55.70

SAVS 145 0.76 19.68 -62.81 89.39

SAVS 142 1.09 26.18 -73.38 97.12

146 1.00% 5.00% -25.00% 15.00%

144 3.00% 10.00% -28.00% 31.00%

141 7.00% 16.00% -32.00% 55.00%

135 14.00% 24.00% -48.00% 67.00%


Appendix C. Table 3. Time series regression of future market returns on aggregate insider trading activity: selected results for seven countries exhibiting coherently positive associations

between aggregate insider trading and future market returns.

USA SANT

0.001

(-0.0005)

176 0.001 0.002***

(-0.001)

175 0.491 0.003***

(-0.001)

172 0.698 0.003***

(-0.001)

166 0.851

0.0003

(-0.0002)

175 0.0002 0.0003

(-0.0003)

174 0.462 0.0003

(-0.0003)

171 0.674 0.0003

(-0.0003)

165 0.831

0.0001

(-0.0001)

172 -0.003 0.0001

(-0.0002)

171 0.458 0.0001

(-0.0002)

168 0.67 0.0003

(-0.0002)

162 0.832

SANS

-0.001

(-0.004)

176 -0.011 0.003

(-0.006)

175 0.459 0.01

(-0.007)

172 0.678 0.015**

(-0.007)

166 0.836

-0.0001

(-0.002)

175 -0.011 0.0004

(-0.003)

174 0.457 0.003

(-0.004)

171 0.673 0.007*

(-0.004)

165 0.833

0.0004

(-0.0020)

172 -0.011 0.001

(-0.0020)

171 0.456 0.002

(-0.0020)

168 0.672 0.005*

(-0.0030)

162 0.834

SAVS

0.001

(-0.002)

176 -0.01 0.002

(-0.003)

175 0.459 0.003

(-0.003)

172 0.676 0.005

(-0.003)

166 0.834

0.0001

(-0.001)

175 -0.011 0.0003

(-0.001)

174 0.458 0.001

(-0.001)

171 0.673 0.002

(-0.001)

165 0.831

0.00003

(-0.0010)

172 -0.011 0.0002

(-0.0010)

171 0.456 0.001

(-0.0010)

168 0.671 0.001

(-0.0010)

162 0.831

Luxembourg SANT

-0.001

(-0.015)

157 0.074 0.018

(-0.02)

155 0.596 0.019

(-0.024)

152 0.768 0.045*

(-0.025)

146 0.905

0.002

(-0.005)

156 0.073 0.005

(-0.007)

154 0.588 0.005

(-0.009)

151 0.763 0.011

(-0.009)

145 0.903

0.002

(-0.0030)

153 0.067 0.002

(-0.004)

151 0.587 0.001

(-0.005)

148 0.771 0.005

(-0.005)

142 0.902

SANS

-0.089

(-0.059)

157 0.088 -0.033

(-0.08)

155 0.594 0.046

(-0.097)

152 0.767 0.081

(-0.093)

146 0.903

-0.029 156 0.076 0.137*** 154 0.609 0.157*** 151 0.774 0.171*** 145 0.908


(-0.035) (-0.046) (-0.056) (-0.053)

0.005

(-0.025)

153 0.066 0.063*

(-0.033)

151 0.597 0.041

(-0.041)

148 0.773 0.034

(-0.041)

142 0.902

SAVS

-0.165*

(-0.094)

157 0.093 -0.028

(-0.128)

155 0.594 0.141

(-0.153)

152 0.768 0.171

(-0.146)

146 0.904

-0.048

(-0.055)

156 0.077 0.225***

(-0.071)

154 0.612 0.270***

(-0.086)

151 0.777 0.252***

(-0.083)

145 0.908

-0.001

(-0.0390)

153 0.065 0.097*

(-0.051)

151 0.597 0.075

(-0.063)

148 0.773 0.04

(-0.062)

142 0.902

Switzerland SANT

0.001

(-0.002)

173 0.001 0.006**

(-0.003)

171 0.561 0.004

(-0.004)

168 0.757 0.010**

(-0.004)

162 0.889

0.001

(-0.001)

172 0.005 0.002

(-0.001)

170 0.551 0.001

(-0.002)

167 0.752 0.003*

(-0.002)

161 0.884

0.0001

(-0.001)

169 -0.0005 0.0004

(-0.001)

167 0.534 0.001

(-0.001)

164 0.75 0.002**

(-0.001)

158 0.886

SANS

-0.005

(-0.012)

173 -0.00002 -0.001

(-0.015)

171 0.55 -0.031

(-0.031)

168 0.757 0.005

(-0.033)

162 0.884

-0.002

(-0.005)

172 0.003 0.001

(-0.008)

170 0.546 -0.026

(-0.016)

167 0.755 0.011

(-0.017)

161 0.882

-0.002

(-0.004)

169 0.0004 -0.004

(-0.006)

167 0.534 -0.005

(-0.011)

164 0.748 0.024**

(-0.012)

158 0.885

SAVS

-0.008

(-0.01)

173 0.002 -0.009

(-0.013)

171 0.551 -0.013

(-0.016)

168 0.756 -0.006

(-0.017)

162 0.884

-0.005

(-0.006)

172 0.007 -0.0005

(-0.008)

170 0.546 -0.013

(-0.009)

167 0.754 -0.001

(-0.01)

161 0.882

-0.004

(-0.005)

169 0.004 -0.010*

(-0.006)

167 0.541 -0.008

(-0.007)

164 0.749 0.01

(-0.008)

158 0.883

Poland SANT

0.032**

(-0.016)

132 0.031 0.023

(-0.021)

130 0.556 0.027

(-0.026)

127 0.75 0.052**

(-0.026)

121 0.879

0.009

(-0.008)

131 0.009 0.006

(-0.01)

129 0.563 -0.006

(-0.012)

126 0.754 0.002

(-0.013)

120 0.874

0.006

(-0.005)

128 0.015 -0.002

(-0.006)

126 0.563 -0.01

(-0.008)

123 0.754 0.008

(-0.008)

117 0.888


SANS

0.006

(-0.081)

132 0.001 0.001

(-0.114)

130 0.552 0.072

(-0.14)

127 0.748 0.05

(-0.142)

121 0.875

0.039

(-0.044)

131 0.004 0.041

(-0.059)

129 0.563 0.086

(-0.073)

126 0.756 0.016

(-0.077)

120 0.874

0.045

(-0.029)

128 0.024 0.055

(-0.039)

126 0.569 0.07

(-0.05)

123 0.755 -0.013

(-0.05)

117 0.887

SAVS

0.049

(-0.041)

132 0.012 0.053

(-0.053)

130 0.555 0.048

(-0.066)

127 0.749 -0.002

(-0.067)

121 0.875

0.048*

(-0.025)

131 0.025 0.012

(-0.033)

129 0.562 0.009

(-0.041)

126 0.754 -0.024

(-0.043)

120 0.874

0.039**

(-0.019)

128 0.038 0.019

(-0.025)

126 0.564 0.041

(-0.032)

123 0.754 -0.035

(-0.033)

117 0.888

China SANT

-0.0001 155 -0.008 0.004* 153 0.551 0.003 150 0.789 0.006** 144 0.908

(-0.001) (-0.002) (-0.002) (-0.003)

0.0002 154 -0.006 0.0005 152 0.54 0.001 149 0.789 0.002 143 0.906

(-0.001) (-0.001) (-0.001) (-0.001)

0.00001 151 -0.007 0.0005 149 0.541 0.001 146 0.788 0.001 140 0.907

(-0.0004) (-0.001) (-0.001) (-0.001)

SANS

-0.01 155 -0.004 0.045** 153 0.555 0.004 150 0.787 0.026 144 0.905

(-0.014) (-0.019) (-0.023) (-0.029)

0.004 154 -0.005 0.013 152 0.544 0.009 149 0.787 0.01 143 0.906

(-0.007) (-0.01) (-0.012) (-0.015)

-0.0002 151 -0.007 0.005 149 0.54 0.006 146 0.786 0.002 140 0.906

(-0.005) (-0.007) (-0.008) (-0.01)

SAVS

-0.013 155 -0.002 0.046** 153 0.557 0.003 150 0.786 0.029 144 0.906

(-0.014) (-0.019) (-0.023) (-0.028)

0.007 154 -0.001 0.018* 152 0.548 0.012 149 0.787 0.016 143 0.906

(-0.007) (-0.01) (-0.012) (-0.015)


0.002 151 -0.006 0.008 149 0.542 0.009 146 0.787 0.005 140 0.906

(-0.005) (-0.007) (-0.009) (-0.011)

India SANT

-0.001 135 -0.005 0.002 133 0.478 0.005 130 0.703 0.013** 124 0.84

(-0.003) (-0.004) (-0.005) (-0.006)

-0.0004 134 -0.005 -0.0001 132 0.477 0.001 129 0.705 0.003 123 0.835

(-0.001) (-0.002) (-0.002) (-0.003)

-0.0002 131 -0.006 <0.0001 129 0.5 0.0005 126 0.706 0.002 120 0.833

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

0.003 135 -0.005 -0.003 133 0.477 0.018 130 0.701 0.038 124 0.836

(-0.017) (-0.025) (-0.029) (-0.028)

-0.008 134 -0.001 -0.004 132 0.477 0.005 129 0.705 0.024 123 0.836

(-0.01) (-0.015) (-0.017) (-0.017)

-0.005 131 -0.003 -0.001 129 0.5 0.007 126 0.706 0.022* 120 0.836

(-0.008) (-0.011) (-0.013) (-0.012)

SAVS

0.002 135 -0.006 0.033 133 0.483 0.038 130 0.703 0.072** 124 0.84

(-0.019) (-0.027) (-0.032) (-0.032)

0.004 134 -0.005 0.01 132 0.478 0.014 129 0.706 0.03 123 0.837

(-0.01) (-0.015) (-0.018) (-0.018)

0.001 131 -0.007 0.006 129 0.501 0.01 126 0.707 0.018 120 0.834

(-0.007) (-0.01) (-0.013) (-0.013)

The Philippines SANT

-0.004 145 -0.008 0.035 143 0.559 0.058** 140 0.764 0.104*** 134 0.877

(-0.018) (-0.024) (-0.029) (-0.031)

-0.009 144 -0.003 0.017 142 0.558 0.027** 139 0.764 0.040*** 133 0.875

(-0.009) (-0.012) (-0.014) (-0.015)

-0.003 141 -0.007 0.007 139 0.554 0.011 136 0.764 0.024** 130 0.873

(-0.006) (-0.007) (-0.008) (-0.009)


SANS

-0.022 145 -0.007 0.018 143 0.552 0.067 140 0.759 -0.008 134 0.867

(-0.044) (-0.056) (-0.067) (-0.076)

-0.002 144 -0.009 0.024 142 0.553 0.044 139 0.76 0.04 133 0.869

(-0.026) (-0.033) (-0.039) (-0.044)

0.007 141 -0.008 0.018 139 0.553 0.009 136 0.761 0.045 130 0.869

(-0.019) (-0.025) (-0.029) (-0.032)

SAVS

-0.047 145 0.0002 0.019 143 0.552 0.109* 140 0.762 -0.015 134 0.867

(-0.042) (-0.054) (-0.063) (-0.071)

-0.005 144 -0.009 0.055* 142 0.563 0.075** 139 0.766 0.013 133 0.868

(-0.022) (-0.028) (-0.034) (-0.038)

0.02 141 0.001 0.044** 139 0.564 0.013 136 0.761 -0.002 130 0.867

(-0.017) (-0.022) (-0.027) (-0.029)

Appendix D. Table 4. Key variables’ descriptive statistics: all countries and regions analyzed.

Statistic N Mean Standard Deviation Minimum Maximum N Mean Standard Deviation Minimum Maximum

Europe Germany

SANT 178 326.51 906.91 -1,214.78 4,011.82 178 14.12 64.86 -177.66 331.87

SANT 176 992.65 2,450.60 -2,250.19 9,204.02 176 42.49 147.48 -294.52 540.58

SANT 173 2,022.87 4,613.22 -2,686.53 17,120.63 173 85.68 261.37 -369.00 859.96

SANS 178 2.45 32.5 -202.76 165.79 178 0.09 12.64 -84.76 80.72

SANS 176 7.46 61.34 -185.92 268.44 176 0.26 19.27 -107.77 82.21

SANS 173 15.11 97.45 -233.01 424.52 173 0.53 27.46 -108.69 84.29

SAVS 178 0.88 96.9 -1,087.26 334.39 178 0.78 24.46 -242.00 101.94

SAVS 176 2.63 177.22 -1,076.48 345.17 176 2.31 46.50 -256.04 191.09

SAVS 173 3.36 231.61 -1,008.27 456.29 173 4.74 69.65 -273.12 227.79

171 0.20% 5.00% -16.00% 13.00% 178 1.00% 5.00% -17.00% 20.00%


169 1.00% 9.00% -28.00% 30.00% 176 3.00% 9.00% -27.00% 33.00%

166 2.00% 13.00% -41.00% 47.00% 173 6.00% 14.00% -40.00% 47.00%

160 4.00% 18.00% -46.00% 45.00% 167 12.00

%

19.00% -43.00% 58.00%

France United Kingdom

SANT 165 47.38 119.23 -302.59 382.92 180 2.29 92.83 -400.21 334.74

SANT 163 143.96 293.95 -407.74 1069.30 178 6.64 208.71 -589.47 804.10

SANT 160 294.24 521.22 -492.30 1937.70 175 13.59 349.05 -1060.80 1042.01

SANS 165 -0.20 49.81 -560.71 189.33 180 -0.09 34.30 -276.39 225.73

SANS 163 -0.65 86.67 -559.70 202.08 178 -0.29 49.85 -277.12 255.10

SANS 160 -1.94 124.89 -572.03 203.55 175 -0.32 74.11 -331.63 288.53

SAVS 165 0.04 55.36 -621.09 211.89 180 0.59 41.34 -353.27 227.63

SAVS 163 0.10 97.86 -621.61 222.03 178 1.77 68.84 -347.99 409.34

SAVS 160 -2.53 144.07 -650.07 224.97 175 4.08 102.20 -342.07 471.80

165 0.20% 5.00% -14.00% 12.00% 178 0.20% 4.00% -12.00% 9.00%

163 1.00% 8.00% -27.00% 27.00% 176 1.00% 6.00% -23.00% 22.00%

160 2.00% 12.00% -40.00% 42.00% 173 2.00% 9.00% -32.00% 35.00%

154 4.00% 18.00% -43.00% 44.00% 167 5.00% 13.00% -34.00% 48.00%

Italy Russia

SANT 178 70.36 189.38 -434.29 1121.09 121 -9.76 41.61 -135.12 78.03

SANT 176 214.55 505.36 -729.44 2297.47 119 -29.81 116.28 -363.26 157.70

SANT 173 441.58 942.56 -845.75 3862.27 116 -61.81 216.81 -693.95 243.11

SANS 178 -1.06 11.22 -61.88 81.33 121 0.11 2.46 -24.02 9.51

SANS 176 -3.20 23.70 -76.51 138.20 119 0.34 4.35 -23.92 9.77

SANS 173 -6.43 38.59 -95.96 191.46 116 0.67 6.20 -23.73 11.01

SAVS 178 3.42 37.95 -251.87 332.23 121 -0.79 5.13 -23.07 23.02

SAVS 176 10.40 70.46 -253.30 338.59 119 -2.41 8.74 -24.73 17.65

SAVS 173 21.19 106.63 -309.46 340.59 116 -5.11 11.78 -38.45 21.66

178 -0.03% 5.00% -17.00% 20.00% 121 -0.20% 7.00% -28.00% 17.00%

176 0.40% 10.00% -31.00% 38.00% 120 1.00% 13.00% -55.00% 47.00%

173 1.00% 15.00% -47.00% 56.00% 117 3.00% 20.00% -68.00% 98.00%


167 1.00% 22.00% -54.00% 47.00% 111 5.00% 30.00% -68.00% 127.00%

Spain The Netherlands

SANT 158 15.62 101.41 -226.60 492.78 178 7.38 43.30 -72.68 177.05

SANT 156 47.17 262.33 -458.67 907.82 176 22.41 103.83 -117.42 471.07

SANT 153 94.76 484.66 -782.64 1419.21 173 46.57 175.25 -188.25 627.57

SANS 158 0.32 18.11 -106.91 39.26 178 1.16 11.14 -41.57 131.45

SANS 156 0.96 35.76 -165.21 70.38 176 3.53 21.50 -41.11 164.26

SANS 153 1.87 58.50 -187.73 116.49 173 7.19 28.41 -39.94 161.22

SAVS 158 3.42 21.59 -71.09 104.47 178 0.52 7.24 -61.53 58.60

SAVS 156 10.38 44.46 -80.70 229.33 176 1.57 12.76 -60.53 66.49

SAVS 153 21.13 72.92 -89.77 301.56 173 3.19 15.11 -60.81 59.81

158 0.30% 5.00% -18.00% 17.00% 178 0.20% 5.00% -20.00% 12.00%

156 1.00% 10.00% -29.00% 30.00% 176 1.00% 9.00% -39.00% 24.00%

153 2.00% 15.00% -35.00% 56.00% 173 3.00% 13.00% -47.00% 42.00%

147 3.00% 21.00% -43.00% 42.00% 167 6.00% 19.00% -52.00% 56.00%

Switzerland Sweden

SANT 174 -39.68 115.19 -614.84 309.22 177 23.07 72.07 -134.68 401.03

SANT 172 -120.61 264.57 -1092.82 697.24 175 70.11 144.68 -196.91 536.62

SANT 169 -248.79 448.12 -1328.91 1044.19 172 139.76 228.66 -274.72 652.78

SANS 174 3.12 22.63 -76.64 198.14 177 1.15 25.79 -184.92 108.52

SANS 172 9.43 48.47 -105.78 345.97 175 3.44 45.05 -167.52 130.33

SANS 169 15.88 66.44 -107.46 356.33 172 6.42 65.26 -163.49 202.18

SAVS 174 -0.49 26.48 -308.53 100.79 177 1.59 28.89 -234.72 144.09

SAVS 172 -1.49 46.74 -311.23 98.57 175 4.79 48.85 -254.56 161.20

SAVS 169 -3.41 57.97 -309.39 94.58 172 9.39 68.89 -254.28 194.68

174 0.30% 4.00% -9.00% 11.00% 177 1.00% 5.00% -17.00% 13.00%

172 1.00% 7.00% -20.00% 21.00% 175 2.00% 8.00% -27.00% 27.00%

169 3.00% 11.00% -35.00% 40.00% 172 5.00% 13.00% -37.00% 46.00%

163 5.00% 17.00% -37.00% 51.00% 166 10.00

%

20.00% -45.00% 58.00%

Poland Belgium


SANT 133 -1.24 30.59 -111.43 98.62 147 6.28 44.61 -173.08 161.48

SANT 131 -3.99 62.80 -187.19 170.70 145 19.46 100.21 -217.23 455.15

SANT 128 -8.27 99.67 -286.74 194.17 142 40.42 162.81 -384.41 552.88

SANS 133 0.13 5.99 -24.28 43.83 147 0.07 10.29 -57.16 42.62

SANS 131 0.55 11.16 -25.15 68.45 145 0.22 19.10 -64.81 48.30

SANS 128 1.51 16.66 -24.52 75.29 142 0.45 31.20 -116.04 62.49

SAVS 133 0.62 11.98 -67.57 82.96 147 0.82 10.26 -50.76 49.10

SAVS 131 1.96 19.34 -71.14 76.53 145 2.51 20.19 -50.66 91.35

SAVS 128 4.13 25.97 -68.38 77.81 142 5.09 32.81 -90.36 108.54

133 0.30% 6.00% -25.00% 20.00% 147 0.10% 5.00% -23.00% 10.00%

131 1.00% 11.00% -33.00% 35.00% 145 1.00% 9.00% -40.00% 29.00%

128 2.00% 18.00% -46.00% 74.00% 142 2.00% 14.00% -49.00% 47.00%

122 4.00% 26.00% -54.00% 78.00% 136 3.00% 22.00% -55.00% 56.00%

Austria Norway

SANT 161 12.66 37.61 -47.86 213.79 159 11.52 34.63 -97.59 210.97

SANT 159 38.49 88.74 -65.26 410.85 157 35.05 79.10 -121.24 413.76

SANT 156 78.47 151.07 -82.14 602.58 154 72.04 130.15 -144.98 554.28

SANS 161 3.96 25.33 -78.26 235.19 159 -0.37 14.32 -150.64 40.89

SANS 159 12.03 52.20 -80.08 269.49 157 -1.12 25.41 -148.27 52.61

SANS 156 24.59 85.64 -81.94 403.10 154 -2.25 37.40 -154.93 103.24

SAVS 161 2.40 13.13 -26.63 107.48 159 1.18 15.00 -57.81 95.47

SAVS 159 7.29 27.31 -18.50 151.23 157 3.56 26.45 -55.37 91.42

SAVS 156 14.89 46.10 -22.82 237.61 154 7.27 44.79 -82.02 139.57

161 0.20% 6.00% -28.00% 20.00% 159 1.00% 5.00% -22.00% 14.00%

159 2.00% 13.00% -50.00% 49.00% 157 3.00% 11.00% -45.00% 38.00%

156 3.00% 19.00% -59.00% 77.00% 154 6.00% 16.00% -54.00% 40.00%

150 6.00% 28.00% -61.00% 72.00% 148 12.00

%

24.00% -53.00% 76.00%

Ireland Denmark

SANT 160 -0.19 17.18 -163.38 42.65 137 -46.31 73.16 -276.28 31.49

SANT 158 -0.53 35.28 -205.70 78.91 135 - 212.93 -754.17 46.01


139.82

SANT 155 -1.23 55.61 -223.93 115.33 132 -

280.61

415.40 -1239.00 79.41

SANS 160 0.12 2.50 -30.64 1.07 137 -1.69 12.13 -67.43 59.42

SANS 158 0.35 4.35 -30.19 2.18 135 -5.12 21.89 -83.86 62.54

SANS 155 0.70 6.23 -29.49 3.75 132 -10.38 33.54 -124.16 64.20

SAVS 160 -0.06 3.42 -29.34 5.13 137 -8.63 40.74 -273.22 24.31

SAVS 158 -0.18 6.21 -32.34 6.33 135 -26.29 77.12 -463.24 34.01

SAVS 155 -0.40 9.09 -33.20 8.82 132 -53.86 126.10 -550.43 41.16

160 -0.10% 5.00% -21.00% 15.00% 137 1.00% 5.00% -19.00% 16.00%

158 1.00% 11.00% -43.00% 37.00% 135 3.00% 10.00% -38.00% 27.00%

155 2.00% 18.00% -57.00% 56.00% 132 6.00% 15.00% -43.00% 43.00%

149 5.00% 26.00% -67.00% 45.00% 126 11.00

%

23.00% -48.00% 61.00%

Finland Romania

SANT 160 -2.01 46.73 -170.20 160.33 141 5.25 25.78 -95.06 105.80

SANT 158 -6.28 93.72 -304.92 242.22 139 15.98 61.26 -210.09 194.57

SANT 155 -15.39 149.69 -361.02 280.71 136 33.40 103.49 -238.76 289.70

SANS 160 0.68 12.09 -94.09 44.07 141 -0.12 3.70 -28.16 21.16

SANS 158 2.05 19.17 -94.31 52.35 139 -0.37 7.44 -31.17 36.80

SANS 155 4.52 25.38 -90.60 79.93 136 -0.68 11.02 -31.43 36.84

SAVS 160 2.05 16.52 -93.60 159.85 141 0.07 8.46 -52.34 17.08

SAVS 158 6.21 28.56 -92.57 167.42 139 0.15 16.71 -69.30 19.15

SAVS 155 12.89 40.04 -95.68 169.65 136 0.09 23.76 -69.38 28.31

160 0.10% 5.00% -18.00% 22.00% 141 0.02% 8.00% -34.00% 26.00%

158 1.00% 10.00% -34.00% 38.00% 139 1.00% 15.00% -52.00% 75.00%

155 3.00% 15.00% -48.00% 48.00% 136 3.00% 24.00% -65.00% 122.00%

149 6.00% 22.00% -58.00% 59.00% 130 6.00% 35.00% -74.00% 178.00%

Greece Luxembourg

SANT 150 55.35 136.28 -163.41 782.28 158 8.01 27.94 -109.82 85.29

SANT 148 167.94 378.66 -398.01 1796.58 156 24.36 75.93 -202.00 236.27


SANT 145 342.21 729.62 -446.77 3291.98 153 49.91 141.58 -228.52 441.91

SANS 150 1.49 26.37 -48.34 303.74 158 0.17 6.91 -24.79 81.09

SANS 148 4.56 46.04 -50.45 301.79 156 0.53 11.87 -28.29 81.00

SANS 145 9.51 60.07 -54.11 292.21 153 1.10 16.75 -28.72 81.61

SAVS 150 2.72 43.76 -56.10 521.88 158 0.34 4.36 -17.57 51.45

SAVS 148 8.31 78.50 -69.53 549.22 156 1.04 7.62 -17.56 52.18

SAVS 145 17.15 112.23 -90.33 572.04 153 2.14 10.91 -17.50 53.07

149 -1.00% 9.00% -27.00% 22.00% 158 0.10% 5.00% -28.00% 12.00%

147 -2.00% 17.00% -41.00% 54.00% 156 1.00% 11.00% -47.00% 26.00%

144 -2.00% 25.00% -54.00% 65.00% 153 3.00% 17.00% -57.00% 49.00%

138 -6.00% 35.00% -66.00% 102.00% 147 6.00% 26.00% -60.00% 72.00%

Cyprus Turkey

SANT 86 1.58 10.32 -36.64 64.05 111 34.63 55.55 -145.41 207.80

SANT 84 4.85 16.62 -55.28 71.26 109 105.49 130.91 -153.58 479.98

SANT 81 9.96 19.04 -52.32 75.68 106 216.32 233.70 -225.37 751.15

SANS 86 -0.26 4.52 -9.18 36.05 111 0.90 5.47 -23.74 33.02

SANS 84 -0.78 9.00 -15.32 38.58 109 2.77 10.94 -26.87 37.52

SANS 81 -1.68 14.44 -29.34 40.26 106 5.83 16.66 -28.31 47.17

SAVS 86 0.01 2.57 -19.91 9.50 111 0.61 4.93 -22.60 23.32

SAVS 84 0.01 4.52 -20.78 10.67 109 1.92 9.13 -24.35 30.84

SAVS 81 -0.10 5.28 -20.52 11.11 106 4.06 13.05 -23.39 38.46

86 -3.00% 13.00% -28.00% 31.00% 111 1.00% 6.00% -13.00% 22.00%

85 -7.00% 27.00% -58.00% 134.00% 109 4.00% 12.00% -26.00% 48.00%

84 -12.00% 36.00% -74.00% 124.00% 106 8.00% 20.00% -28.00% 96.00%

83 -21.00% 48.00% -83.00% 116.00% 100 15.00

%

31.00% -53.00% 118.00%

USA Asia

SANT 177 -771.26 665.75 -2,287.44 2,091.57 178 88.13 595.16 -1,776.21 2,876.85

SANT 175 -2,333.79 1,560.98 -6,056.43 2,115.96 176 266 1,408.06 -2,719.76 7,075.37

SANT 172 -4,711.61 2,586.07 -10,646.70 2,542.22 173 516.88 2,341.54 -3,545.13 9,079.25

SANS 177 -17.27 71.41 -284.3 415.05 178 -2.95 104.57 -1,230.29 192.78


SANS 175 -52.16 125.86 -347.7 511.28 176 -9.19 202.17 -1,457.00 221.56

SANS 172 -105.79 200.3 -517.27 607.22 173 -19.68 295.94 -1,461.59 393.72

SAVS 177 -62.94 153.33 -669.27 317.13 178 -5.34 153.53 -1,918.88 178.51

SAVS 175 -190.55 345.85 -1,376.15 517.19 176 -16.48 291.26 -2,236.34 234.05

SAVS 172 -386.37 606.46 -2,431.89 602.99 173 -34.45 421.22 -2,267.78 321.97

177 1.00% 4.00% -17.00% 14.00% 177 1.00% 6.00% -25.00% 19.00%

176 2.00% 7.00% -30.00% 31.00% 175 3.00% 12.00% -41.00% 60.00%

173 4.00% 11.00% -42.00% 46.00% 172 6.00% 18.00% -53.00% 73.00%

167 8.00% 16.00% -45.00% 58.00% 166 11.00

%

25.00% -59.00% 88.00%

China Hong Kong

SANT 157 -54.19 475.12 -2271.41 3105.50 170 107.87 158.51 -322.71 686.57

SANT 155 -167.32 1063.31 -3683.34 6049.11 168 325.15 364.54 -448.53 1329.91

SANT 152 -355.25 1706.31 -5843.60 7401.30 165 647.38 597.53 -441.71 2149.94

SANS 157 0.41 47.21 -498.74 156.64 170 -0.28 18.70 -82.68 71.95

SANS 155 1.07 92.27 -695.40 171.20 168 -0.86 41.22 -215.85 120.56

SANS 152 2.04 139.24 -691.01 184.34 165 -1.82 65.56 -298.00 155.64

SAVS 157 -1.10 48.15 -437.80 105.91 170 -0.44 51.79 -619.11 110.13

SAVS 155 -3.41 89.60 -542.21 259.59 168 -1.36 80.32 -599.82 150.82

SAVS 152 -6.65 127.37 -535.20 375.85 165 -2.82 104.87 -577.88 155.44

156 1.00% 8.00% -23.00% 27.00% 169 0.50% 6.00% -23.00% 18.00%

154 3.00% 17.00% -38.00% 50.00% 167 2.00% 11.00% -39.00% 48.00%

151 8.00% 30.00% -54.00% 107.00% 164 4.00% 17.00% -47.00% 60.00%

145 19.00% 55.00% -71.00% 221.00% 158 8.00% 22.00% -56.00% 70.00%

India S. Korea

SANT 137 -83.66 202.03 -1012.33 326.43 162 35.56 149.34 -618.95 491.71

SANT 135 -251.08 499.28 -1969.93 596.92 160 105.93 381.22 -1517.53 1132.65

SANT 132 -496.18 936.00 -3588.95 1053.58 157 196.65 648.06 -2020.53 1719.21

SANS 137 -0.12 32.00 -167.27 242.38 162 -0.14 13.47 -46.16 62.16

SANS 135 -0.73 55.02 -185.28 219.15 160 0.10 25.70 -88.12 93.44

SANS 132 -3.15 74.69 -199.20 199.73 157 0.75 37.28 -110.58 110.54


SAVS 137 -2.28 29.68 -236.19 87.53 162 -0.41 20.16 -84.15 160.49

SAVS 135 -7.60 54.32 -261.49 133.01 160 -0.95 38.94 -196.65 164.54

SAVS 132 -17.65 76.46 -254.50 136.69 157 -1.29 59.41 -201.88 189.46

136 1.00% 6.00% -25.00% 21.00% 160 1.00% 5.00% -23.00% 18.00%

134 3.00% 12.00% -37.00% 70.00% 157 2.00% 9.00% -29.00% 37.00%

131 6.00% 19.00% -44.00% 82.00% 152 5.00% 14.00% -42.00% 56.00%

125 11.00% 25.00% -54.00% 91.00% 145 9.00% 20.00% -46.00% 57.00%

Australia New Zealand

SANT 173 8.63 29.04 -92.95 94.90 148 -0.73 10.96 -93.58 30.37

SANT 171 26.19 61.10 -191.56 191.93 146 -2.12 22.09 -127.91 43.15

SANT 168 52.43 91.58 -248.99 269.46 143 -3.89 36.44 -150.78 58.45

SANS 173 -3.09 45.08 -585.99 4.07 148 0.13 2.31 -24.93 5.71

SANS 171 -9.37 85.83 -684.23 6.80 146 0.39 3.98 -25.05 6.35

SANS 168 -19.14 124.25 -679.65 9.63 143 0.76 5.67 -27.73 8.21

SAVS 173 -2.91 43.56 -566.44 4.83 148 0.08 2.33 -26.78 3.43

SAVS 171 -8.86 82.53 -655.67 8.58 146 0.23 4.01 -26.65 3.91

SAVS 168 -18.10 119.24 -650.67 11.95 143 0.44 5.71 -27.01 5.26

172 0.40% 4.00% -16.00% 10.00% 147 0.40% 3.00% -11.00% 8.00%

170 1.00% 7.00% -27.00% 22.00% 145 1.00% 6.00% -20.00% 13.00%

167 3.00% 11.00% -35.00% 38.00% 142 2.00% 9.00% -27.00% 20.00%

161 5.00% 16.00% -43.00% 43.00% 136 4.00% 14.00% -37.00% 27.00%

Malaysia Singapore

SANT 158 30.99 92.47 -233.08 419.05 168 33.96 67.41 -72.09 359.96

SANT 156 93.42 216.07 -477.89 739.57 166 103.12 151.83 -139.04 735.29

SANT 153 184.67 358.89 -513.70 1193.21 163 210.06 247.16 -271.93 1003.77

SANS 158 2.34 29.31 -102.58 216.60 168 4.10 21.83 -70.23 184.58

SANS 156 7.14 45.10 -101.85 222.42 166 12.42 37.49 -75.72 181.71

SANS 153 14.25 65.89 -181.70 181.26 163 25.35 55.35 -76.12 216.90

SAVS 158 3.81 52.87 -172.08 613.46 168 3.16 19.98 -41.47 215.63

SAVS 156 11.60 86.00 -194.55 637.55 166 9.58 34.47 -42.85 215.81


SAVS 153 23.58 120.24 -229.60 609.05 163 19.59 50.79 -42.36 280.80

157 0.50% 3.00% -16.00% 13.00% 167 0.30% 5.00% -24.00% 15.00%

155 2.00% 7.00% -26.00% 25.00% 165 2.00% 10.00% -38.00% 52.00%

152 4.00% 12.00% -34.00% 40.00% 162 3.00% 15.00% -46.00% 69.00%

146 8.00% 19.00% -41.00% 55.00% 156 7.00% 22.00% -52.00% 79.00%

Thailand The Philippines

SANT 167 15.96 78.90 -172.58 283.69 147 -2.08 25.48 -83.76 102.00

SANT 165 48.12 189.56 -357.34 652.35 145 -6.43 50.08 -164.63 117.67

SANT 162 95.70 325.36 -508.43 967.57 142 -14.72 81.39 -223.20 169.38

SANS 167 1.81 13.72 -139.76 40.62 147 0.10 9.84 -60.05 55.43

SANS 165 5.48 23.38 -149.06 60.48 145 0.29 17.05 -59.56 73.92

SANS 162 11.15 33.21 -149.10 91.11 142 0.06 23.25 -55.72 77.03

SAVS 167 3.43 15.52 -86.11 91.03 147 0.25 10.33 -56.02 55.70

SAVS 165 10.41 28.75 -83.48 146.27 145 0.76 19.68 -62.81 89.39

SAVS 162 21.36 42.04 -74.56 205.33 142 1.09 26.18 -73.38 97.12

166 1.00% 6.00% -30.00% 20.00% 146 1.00% 5.00% -25.00% 15.00%

164 2.00% 10.00% -40.00% 39.00% 144 3.00% 10.00% -28.00% 31.00%

161 5.00% 17.00% -51.00% 67.00% 141 7.00% 16.00% -32.00% 55.00%

155 10.00% 23.00% -54.00% 83.00% 135 14.00

%

24.00% -48.00% 67.00%

Appendix E. Table 5. Time series regression of future market returns on aggregate insider trading activity: all countries and regions analyzed.

Europe SANT

-0.001 170 0.004 0.0001 168 0.442 0.001 165 0.682 0.002** 159 0.843

(-0.0004) (-0.001) (-0.001) (-0.001)

-0.0003* 169 0.013 -0.0003 167 0.448 -0.00004 164 0.677 0.0002 158 0.837

(-0.0002) (-0.0002) (-0.0002) (-0.0002)

-0.0001* 166 0.012 -0.0001 164 0.441 0.00001 161 0.673 0.0001 155 0.836

(-0.0001) (-0.0001) (-0.0001) (-0.0001)


SANS

0.004 170 -0.008 -0.003 168 0.442 0.004 165 0.677 0.005 159 0.837

(-0.012) (-0.015) (-0.018) (-0.018)

-0.003 169 -0.008 0.002 167 0.444 0.003 164 0.677 0.013 158 0.839

(-0.006) (-0.008) (-0.009) (-0.009)

-0.002 166 -0.007 -0.002 164 0.438 -0.003 161 0.674 0.002 155 0.835

(-0.004) (-0.005) (-0.006) (-0.006)

SAVS

-0.004 170 -0.002 -0.003 168 0.444 -0.002 165 0.677 -0.001 159 0.837

(-0.004) (-0.005) (-0.006) (-0.006)

-0.003 169 0.002 -0.0002 167 0.443 -0.003 164 0.679 -0.001 158 0.837

(-0.002) (-0.003) (-0.003) (-0.003)

-0.002 166 0.0030 -0.003 164 0.4450 -0.003 161 0.6770 0.001 155 0.8360

(-0.002) (-0.002) (-0.003) (-0.003)

Germany SANT

-0.007 177 -0.004 0.001 175 0.427 0.003 172 0.663 -0.003 166 0.821

(-0.006) (-0.008) (-0.009) (-0.01)

-0.002 176 -0.008 -0.002 174 0.43 -0.002 171 0.66 -0.006 165 0.82

(-0.003) (-0.004) (-0.004) (-0.004)

-0.002 173 -0.004 -0.002 171 0.413 -0.001 168 0.646 -0.002 162 0.817

(-0.001) (-0.002) (-0.002) (-0.003)

SANS

-0.111*** 177 0.062 -0.048 175 0.432 -0.082* 172 0.669 -0.119** 166 0.828

(-0.03) (-0.042) (-0.047) (-0.048)

-0.02 176 -0.006 0.068** 174 0.45 0.065** 171 0.668 0.005 165 0.818

(-0.021) (-0.027) (-0.031) (-0.032)

-0.005 173 -0.009 0.032* 171 0.42 0.002 168 0.646 -0.032 162 0.818

(-0.014) (-0.019) (-0.023) (-0.023)

SAVS

-0.01 177 -0.008 -0.028 175 0.433 -0.007 172 0.663 -0.004 166 0.821


(-0.016) (-0.022) (-0.025) (-0.025)

-0.015* 176 0.006 -0.006 174 0.429 0.008 171 0.66 0.018 165 0.82

(-0.008) (-0.012) (-0.013) (-0.013)

-0.007 173 -0.002 -0.002 171 0.411 0.001 168 0.646 0.007 162 0.816

(-0.006) (-0.008) (-0.009) (-0.009)

France SANT

-0.003 164 -0.004 -0.001 162 0.421 0.004 159 0.675 0.013** 153 0.839

(-0.003) (-0.004) (-0.005) (-0.005)

-0.002 163 0.001 -0.002 161 0.435 0.0004 158 0.674 0.002 152 0.849

(-0.001) (-0.002) (-0.002) (-0.002)

-0.001 160 0.006 -0.001 158 0.436 0.001 155 0.68 0.002 149 0.851

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

-0.003 164 -0.01 0.001 162 0.421 0.01 159 0.675 0.006 153 0.832

(-0.007) (-0.01) (-0.011) (-0.012)

-0.003 163 -0.007 -0.002 161 0.43 0.007 158 0.676 0.003 152 0.848

(-0.004) (-0.006) (-0.006) (-0.007)

-0.002 160 -0.006 0.002 158 0.434 0.001 155 0.679 -0.001 149 0.848

(-0.003) (-0.004) (-0.005) (-0.005)

SAVS

-0.004 164 -0.009 0.006 162 0.423 0.012 159 0.676 0.006 153 0.832

(-0.007) (-0.009) (-0.011) (-0.011)

-0.001 163 -0.008 -0.001 161 0.429 0.006 158 0.675 0.002 152 0.848

(-0.004) (-0.005) (-0.006) (-0.006)

-0.002 160 -0.005 0.001 158 0.434 0.0004 155 0.679 -0.001 149 0.848

(-0.003) (-0.004) (-0.004) (-0.004)

United Kingdom SANT

-0.001 177 -0.002 0.003 175 0.371 0.005 172 0.633 0.007 166 0.826

(-0.003) (-0.004) (-0.005) (-0.005)

-0.0002 176 -0.003 0.0004 174 0.366 0.001 171 0.629 0.001 165 0.823


(-0.001) (-0.002) (-0.002) (-0.002)

-0.0002 173 -0.002 0.0001 171 0.359 0.00004 168 0.625 0.001 162 0.823

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

0.005 177 -0.001 -0.006 175 0.37 0.01 172 0.632 0.019 166 0.826

(-0.008) (-0.01) (-0.012) (-0.013)

0.005 176 0.001 -0.01 174 0.373 -0.008 171 0.63 0.005 165 0.823

(-0.005) (-0.007) (-0.009) (-0.009)

0.002 173 -0.001 -0.003 171 0.36 -0.001 168 0.625 0.004 162 0.823

(-0.004) (-0.005) (-0.006) (-0.006)

SAVS

0.003 177 -0.002 -0.008 175 0.371 0.01 172 0.633 0.008 166 0.824

(-0.007) (-0.009) (-0.01) (-0.01)

0.003 176 0.001 -0.004 174 0.368 -0.002 171 0.628 0.003 165 0.823

(-0.004) (-0.005) (-0.006) (-0.006)

0.003 173 0.007 0.001 171 0.359 0.001 168 0.625 0.001 162 0.823

(-0.003) (-0.004) (-0.004) (-0.004)

Italy SANT

-0.003 177 0.003 0.001 175 0.478 0.005 172 0.701 0.005 166 0.857

(-0.002) (-0.003) (-0.004) (-0.004)

-0.001 176 0.011 -0.001 174 0.481 -0.001 171 0.698 0.0001 165 0.854

(-0.001) (-0.001) (-0.001) (-0.001)

-0.001* 173 0.017 -0.001 171 0.482 -0.001 168 0.698 0.0002 162 0.854

(-0.0004) (-0.001) (-0.001) (-0.001)

SANS

0.043 177 0.003 -0.022 175 0.478 0.062 172 0.7 0.087 166 0.857

(-0.037) (-0.049) (-0.057) (-0.055)

-0.005 176 -0.004 -0.034 174 0.484 -0.001 171 0.698 0.037 165 0.856

(-0.017) (-0.023) (-0.027) (-0.027)

-0.002 173 -0.003 -0.007 171 0.477 0.009 168 0.697 0.033** 162 0.857


(-0.011) (-0.014) (-0.017) (-0.016)

SAVS

-0.003 177 -0.004 0.005 175 0.478 0.018 172 0.7 0.005 166 0.855

(-0.011) (-0.014) (-0.017) (-0.016)

-0.001 176 -0.004 -0.006 174 0.479 0.001 171 0.698 0.001 165 0.854

(-0.006) (-0.008) (-0.009) (-0.009)

-0.001 173 -0.003 -0.002 171 0.477 0.002 168 0.696 0.004 162 0.854

(-0.004) (-0.005) (-0.006) (-0.006)

Russia SANT

-0.011 120 0.041 -0.019 119 0.481 -0.012 116 0.569 -0.025 110 0.777

(-0.015) (-0.021) (-0.03) (-0.033)

-0.006 119 0.044 -0.008 118 0.478 -0.004 115 0.568 -0.007 109 0.775

(-0.005) (-0.008) (-0.011) (-0.012)

-0.003 116 0.052 -0.002 115 0.481 -0.002 112 0.571 -0.002 106 0.787

(-0.003) (-0.004) (-0.006) (-0.006)

SANS

-0.124 120 0.038 -0.367 119 0.483 -0.288 116 0.57 -0.59 110 0.776

(-0.248) (-0.351) (-0.492) (-1.209)

-0.044 119 0.035 -0.138 118 0.476 -0.063 115 0.567 -0.322 109 0.775

(-0.141) (-0.201) (-0.329) (-0.689)

-0.021 116 0.044 -0.045 115 0.48 -0.039 112 0.57 -0.309 106 0.788

(-0.1) (-0.154) (-0.295) (-0.502)

SAVS

-0.132 120 0.046 -0.138 119 0.481 -0.189 116 0.571 0.206 110 0.777

(-0.118) (-0.168) (-0.238) (-0.254)

-0.051 119 0.038 -0.139 118 0.482 0.0001 115 0.567 0.091 109 0.775

(-0.071) (-0.101) (-0.144) (-0.156)

-0.097* 116 0.07 -0.076 115 0.484 0.002 112 0.57 0.111 106 0.789

(-0.053) (-0.077) (-0.109) (-0.115)

Spain SANT


-0.002 157 -0.006 0.004 155 0.478 0.006 152 0.705 0.005 146 0.848

(-0.004) (-0.006) (-0.007) (-0.007)

-0.0003 156 -0.007 0.0001 154 0.479 -0.001 151 0.704 -0.0002 145 0.847

(-0.002) (-0.002) (-0.003) (-0.003)

-0.0001 153 -0.008 -0.0003 151 0.475 -0.001 148 0.704 -0.0002 142 0.847

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

-0.021 157 -0.002 -0.023 155 0.478 -0.028 152 0.705 -0.047 146 0.849

(-0.024) (-0.032) (-0.036) (-0.037)

-0.009 156 -0.004 0.011 154 0.481 0.001 151 0.704 -0.009 145 0.848

(-0.012) (-0.016) (-0.019) (-0.019)

-0.002 153 -0.007 -0.001 151 0.475 -0.012 148 0.706 -0.016 142 0.849

(-0.008) (-0.01) (-0.011) (-0.012)

SAVS

0.015 157 -0.004 -0.033 155 0.482 -0.021 152 0.705 -0.017 146 0.848

(-0.02) (-0.027) (-0.031) (-0.031)

-0.004 156 -0.007 0.008 154 0.481 0.002 151 0.704 -0.006 145 0.847

(-0.01) (-0.013) (-0.015) (-0.015)

0.004 153 -0.005 0.004 151 0.476 -0.006 148 0.704 -0.014 142 0.849

(-0.006) (-0.008) (-0.009) (-0.009)

The Netherlands SANT

0.005 177 -0.004 -0.011 175 0.495 0.001 172 0.712 0.004 166 0.858

(-0.008) (-0.011) (-0.013) (-0.013)

-0.003 176 -0.004 -0.008* 174 0.506 -0.005 171 0.72 0.005 165 0.861

(-0.004) (-0.005) (-0.005) (-0.005)

-0.002 173 0.001 -0.003 171 0.492 0.001 168 0.711 0.006* 162 0.863

(-0.002) (-0.003) (-0.003) (-0.003)

SANS

0.006 177 -0.006 -0.021 175 0.492 -0.003 172 0.712 0.004 166 0.858

(-0.033) (-0.042) (-0.049) (-0.049)


0.005 176 -0.008 -0.029 174 0.502 0.007 171 0.719 0.014 165 0.861

(-0.017) (-0.022) (-0.025) (-0.025)

-0.0003 173 -0.006 0.004 171 0.488 0.015 168 0.712 0.021 162 0.861

(-0.013) (-0.017) (-0.019) (-0.02)

SAVS

0.023 177 -0.005 -0.051 175 0.494 -0.032 172 0.712 -0.02 166 0.858

(-0.05) (-0.064) (-0.075) (-0.076)

-0.002 176 -0.009 -0.055 174 0.503 -0.005 171 0.719 0.016 165 0.861

(-0.029) (-0.036) (-0.042) (-0.043)

-0.018 173 -0.003 -0.012 171 0.488 0.01 168 0.711 0.03 162 0.86

(-0.024) (-0.031) (-0.036) (-0.036)

Switzerland SANT

0.001

(-0.002)

173 0.001 0.006**

(-0.003)

171 0.561 0.004

(-0.004)

168 0.757 0.010**

(-0.004)

162 0.889

0.001

(-0.001)

172 0.005 0.002

(-0.001)

170 0.551 0.001

(-0.002)

167 0.752 0.003*

(-0.002)

161 0.884

0.0001

(-0.001)

169 -0.0005 0.0004

(-0.001)

167 0.534 0.001

(-0.001)

164 0.75 0.002**

(-0.001)

158 0.886

SANS

-0.005

(-0.012)

173 -0.00002 -0.001

(-0.015)

171 0.55 -0.031

(-0.031)

168 0.757 0.005

(-0.033)

162 0.884

-0.002

(-0.005)

172 0.003 0.001

(-0.008)

170 0.546 -0.026

(-0.016)

167 0.755 0.011

(-0.017)

161 0.882

-0.002

(-0.004)

169 0.0004 -0.004

(-0.006)

167 0.534 -0.005

(-0.011)

164 0.748 0.024**

(-0.012)

158 0.885

SAVS

-0.008

(-0.01)

173 0.002 -0.009

(-0.013)

171 0.551 -0.013

(-0.016)

168 0.756 -0.006

(-0.017)

162 0.884

-0.005

(-0.006)

172 0.007 -0.0005

(-0.008)

170 0.546 -0.013

(-0.009)

167 0.754 -0.001

(-0.01)

161 0.882

-0.004

(-0.005)

169 0.004 -0.010*

(-0.006)

167 0.541 -0.008

(-0.007)

164 0.749 0.01

(-0.008)

158 0.883

Sweden SANT

< 0.00001 176 -0.01 0.006 174 0.511 0.024*** 171 0.755 0.017** 165 0.87


(-0.005) (-0.006) (-0.007) (-0.008)

-0.002 175 -0.004 0.002 173 0.508 0.005 170 0.739 0.005 164 0.864

(-0.002) (-0.003) (-0.004) (-0.004)

0.00001 172 -0.009 0.002 170 0.501 0.002 167 0.732 0.003 161 0.865

(-0.001) (-0.002) (-0.002) (-0.002)

SANS

-0.012 176 -0.005 -0.028 174 0.516 -0.021 171 0.74 -0.033 165 0.868

(-0.013) (-0.017) (-0.02) (-0.022)

-0.016** 175 0.017 -0.009 173 0.509 0.001 170 0.735 -0.016 164 0.864

(-0.007) (-0.01) (-0.012) (-0.013)

-0.007 172 0.003 0.001 170 0.499 -0.0002 167 0.73 -0.011 161 0.864

(-0.005) (-0.007) (-0.009) (-0.01)

SAVS

0.003 176 -0.01 -0.01 174 0.509 -0.004 171 0.738 0.006 165 0.866

(-0.012) (-0.015) (-0.018) (-0.02)

-0.001 175 -0.009 0.012 173 0.511 -0.001 170 0.735 -0.001 164 0.863

(-0.007) (-0.009) (-0.011) (-0.012)

0.006 172 -0.0002 0.005 170 0.501 -0.007 167 0.731 -0.008 161 0.864

(-0.005) (-0.007) (-0.008) (-0.009)

Poland SANT

0.032**

(-0.016)

132 0.031 0.023

(-0.021)

130 0.556 0.027

(-0.026)

127 0.75 0.052**

(-0.026)

121 0.879

0.009

(-0.008)

131 0.009 0.006

(-0.01)

129 0.563 -0.006

(-0.012)

126 0.754 0.002

(-0.013)

120 0.874

0.006

(-0.005)

128 0.015 -0.002

(-0.006)

126 0.563 -0.01

(-0.008)

123 0.754 0.008

(-0.008)

117 0.888

SANS

0.006

(-0.081)

132 0.001 0.001

(-0.114)

130 0.552 0.072

(-0.14)

127 0.748 0.05

(-0.142)

121 0.875

0.039

(-0.044)

131 0.004 0.041

(-0.059)

129 0.563 0.086

(-0.073)

126 0.756 0.016

(-0.077)

120 0.874

0.045

(-0.029)

128 0.024 0.055

(-0.039)

126 0.569 0.07

(-0.05)

123 0.755 -0.013

(-0.05)

117 0.887


SAVS

0.049

(-0.041)

132 0.012 0.053

(-0.053)

130 0.555 0.048

(-0.066)

127 0.749 -0.002

(-0.067)

121 0.875

0.048*

(-0.025)

131 0.025 0.012

(-0.033)

129 0.562 0.009

(-0.041)

126 0.754 -0.024

(-0.043)

120 0.874

0.039**

(-0.019)

128 0.038 0.019

(-0.025)

126 0.564 0.041

(-0.032)

123 0.754 -0.035

(-0.033)

117 0.888

Belgium SANT

-0.005 146 0.035 0.009 144 0.542 0.003 141 0.748 0.012 135 0.887

(-0.009) (-0.012) (-0.014) (-0.014)

-0.003 145 0.036 -0.009* 143 0.55 -0.003 140 0.751 0.0001 134 0.885

(-0.004) (-0.005) (-0.007) (-0.007)

-0.006** 142 0.067 -0.005 140 0.548 0.002 137 0.75 0.003 131 0.884

(-0.002) (-0.004) (-0.004) (-0.004)

SANS

0.028 146 0.036 -0.019 144 0.541 -0.024 141 0.749 -0.038 135 0.886

(-0.037) (-0.05) (-0.058) (-0.06)

0.006 145 0.033 -0.013 143 0.54 -0.02 140 0.751 -0.036 134 0.886

(-0.02) (-0.027) (-0.032) (-0.034)

-0.003 142 0.034 -0.014 140 0.545 -0.019 137 0.752 -0.021 131 0.884

(-0.013) (-0.017) (-0.02) (-0.022)

SAVS

0.024 146 0.035 0.036 144 0.542 0.005 141 0.748 -0.011 135 0.886

(-0.037) (-0.05) (-0.058) (-0.061)

0.02 145 0.04 -0.014 143 0.54 -0.013 140 0.751 -0.028 134 0.886

(-0.019) (-0.026) (-0.03) (-0.036)

-0.003 142 0.034 -0.01 140 0.544 -0.016 137 0.751 -0.005 131 0.884

(-0.012) (-0.016) (-0.019) (-0.024)

Austria SANT

-0.025* 160 0.053 -0.008 158 0.558 0.025 155 0.747 0.03 149 0.884

(-0.013) (-0.019) (-0.023) (-0.023)

-0.015*** 159 0.077 -0.012 157 0.564 0.008 154 0.746 0.01 148 0.882


(-0.005) (-0.008) (-0.01) (-0.01)

-0.007** 156 0.06 0.0004 154 0.552 0.007 151 0.745 0.011* 145 0.878

(-0.003) (-0.005) (-0.006) (-0.006)

SANS

0.003 160 0.032 -0.017 158 0.558 0.002 155 0.745 -0.067** 149 0.886

(-0.018) (-0.026) (-0.031) (-0.031)

-0.005 159 0.033 -0.01 157 0.559 -0.007 154 0.745 -0.060*** 148 0.893

(-0.009) (-0.013) (-0.015) (-0.015)

-0.004 156 0.033 -0.004 154 0.553 -0.015 151 0.747 -0.038*** 145 0.885

(-0.006) (-0.008) (-0.009) (-0.011)

SAVS

0.006 160 0.032 -0.019 158 0.558 0.001 155 0.745 -0.107* 149 0.885

(-0.036) (-0.05) (-0.06) (-0.059)

-0.009 159 0.033 -0.019 157 0.559 -0.02 154 0.746 -0.110*** 148 0.892

(-0.017) (-0.025) (-0.029) (-0.029)

-0.009 156 0.035 -0.012 154 0.554 -0.027 151 0.747 -0.067*** 145 0.884

(-0.01) (-0.015) (-0.018) (-0.02)

Norway SANT

-0.007 158 0.004 0.018 156 0.504 -0.012 153 0.71 0.015 147 0.849

(-0.013) (-0.018) (-0.02) (-0.022)

-0.003 157 0.005 -0.002 155 0.501 -0.014 152 0.714 -0.012 146 0.849

(-0.005) (-0.008) (-0.009) (-0.01)

-0.005 154 0.017 -0.007 152 0.504 -0.006 149 0.711 -0.012* 143 0.848

(-0.003) (-0.005) (-0.006) (-0.007)

SANS

0.018 158 0.004 0.014 156 0.501 -0.007 153 0.709 0.044 147 0.85

(-0.03) (-0.042) (-0.047) (-0.051)

0.006 157 0.004 -0.00003 155 0.501 -0.01 152 0.71 0.023 146 0.849

(-0.017) (-0.024) (-0.027) (-0.029)

0.001 154 0.005 -0.006 152 0.497 -0.0005 149 0.709 -0.004 143 0.844


(-0.012) (-0.017) (-0.018) (-0.02)

SAVS

0.056** 158 0.027 0.007 156 0.501 -0.002 153 0.709 -0.012 147 0.849

(-0.028) (-0.041) (-0.045) (-0.049)

0.004 157 0.004 -0.005 155 0.501 -0.021 152 0.711 -0.033 146 0.849

(-0.016) (-0.023) (-0.026) (-0.029)

0.002 154 0.005 -0.009 152 0.498 -0.019 149 0.712 -0.037** 143 0.85

(-0.01) (-0.014) (-0.015) (-0.017)

Ireland SANT

-0.042* 159 0.048 -0.039 157 0.592 0.014 154 0.776 0.001 148 0.919

(-0.024) (-0.033) (-0.039) (-0.034)

-0.022* 158 0.052 0.0004 156 0.589 -0.001 153 0.776 0.01 147 0.92

(-0.012) (-0.016) (-0.019) (-0.017)

-0.006 155 0.035 -0.005 153 0.589 -0.009 150 0.776 -0.003 144 0.921

(-0.008) (-0.011) (-0.012) (-0.011)

SANS

0.066 159 0.031 -0.405* 157 0.597 -0.166 154 0.776 -0.09 148 0.919

(-0.168) (-0.225) (-0.265) (-0.233)

-0.14 158 0.044 0.065 156 0.589 0.079 153 0.776 0.031 147 0.919

(-0.097) (-0.132) (-0.154) -0.135

-0.051 155 0.035 0.017 153 0.588 -0.04 150 0.776 -0.069 144 0.921

(-0.069) (-0.094) (-0.11) (-0.095)

SAVS

-0.023 159 0.03 -0.367** 157 0.602 -0.074 154 0.776 -0.043 148 0.919

(-0.123) (-0.164) (-0.194) (-0.171)

-0.118* 158 0.049 -0.012 156 0.589 -0.0003 153 0.776 -0.002 147 0.919

(-0.068) (-0.093) (-0.108) (-0.095)

-0.037 155 0.035 -0.025 153 0.589 -0.051 150 0.776 -0.082 144 0.921

(-0.047) (-0.064) (-0.075) (-0.065)

Denmark SANT


-0.003 136 0.05 -0.006 134 0.516 -0.01 131 0.738 -0.003 125 0.874

(-0.006) (-0.008) (-0.009) (-0.01)

-0.002 135 0.054 -0.003 133 0.518 -0.003 130 0.736 -0.001 124 0.874

(-0.002) (-0.003) (-0.003) (-0.003)

-0.001 132 0.057 -0.001 130 0.524 -0.0004 127 0.737 0.0001 121 0.888

(-0.001) (-0.001) (-0.002) (-0.002)

SANS

0.002 136 0.047 -0.080* 134 0.524 -0.061 131 0.738 -0.117** 125 0.878

(-0.034) (-0.048) (-0.056) (-0.058)

-0.03 135 0.066 -0.043 133 0.523 -0.028 130 0.737 -0.057* 124 0.877

(-0.019) (-0.027) (-0.032) (-0.035)

-0.022* 132 0.07 -0.023 130 0.528 -0.028 127 0.741 -0.034 121 0.89

(-0.013) (-0.018) (-0.022) (-0.023)

SAVS

-0.003 136 0.048 -0.025* 134 0.525 -0.011 131 0.737 -0.018 125 0.875

(-0.01) (-0.014) (-0.017) (-0.017)

-0.011* 135 0.075 -0.009 133 0.519 -0.002 130 0.735 -0.006 124 0.875

(-0.005) (-0.008) (-0.009) (-0.01)

-0.005 132 0.066 -0.003 130 0.524 -0.004 127 0.738 -0.002 121 0.888

(-0.003) (-0.005) (-0.006) (-0.006)

Finland SANT

-0.006 159 0.008 -0.004 157 0.521 0.014 154 0.724 -0.001 148 0.864

(-0.009) (-0.012) (-0.014) (-0.015)

-0.004 158 0.011 0.002 156 0.522 -0.001 153 0.723 -0.002 147 0.865

(-0.004) (-0.006) (-0.007) (-0.007)

0.0004 155 0.013 -0.0002 153 0.524 -0.002 150 0.727 -0.001 144 0.867

(-0.003) (-0.004) (-0.004) (-0.005)

SANS

0.009 159 0.006 -0.076* 157 0.529 0.021 154 0.722 0.002 148 0.864

(-0.033) (-0.045) (-0.054) (-0.056)


-0.042** 158 0.031 -0.028 156 0.525 -0.015 153 0.723 0.005 147 0.865

(-0.021) (-0.029) (-0.035) (-0.036)

-0.007 155 0.014 -0.012 153 0.525 -0.005 150 0.726 0.019 144 0.867

(-0.016) (-0.022) (-0.026) (-0.027)

SAVS

-0.005 159 0.005 -0.021 157 0.522 0.012 154 0.722 -0.015 148 0.864

(-0.024) (-0.033) (-0.039) (-0.041)

-0.022 158 0.02 -0.021 156 0.526 -0.017 153 0.724 -0.019 147 0.866

(-0.014) (-0.02) (-0.023) (-0.025)

-0.011 155 0.02 -0.013 153 0.526 -0.018 150 0.728 0.007 144 0.867

(-0.01) (-0.014) (-0.017=) (-0.0190)

Romania SANT

0.039 140 0.049 0.029 138 0.565 0.102*** 135 0.769 0.033 129 0.85

(-0.025) (-0.034) (-0.039) (-0.047)

0.005 139 0.034 0.02 137 0.568 0.033* 134 0.765 -0.001 128 0.85

(-0.01) (-0.014) (-0.017) (-0.02)

0.012** 136 0.061 0.019** 134 0.578 0.01 131 0.76 0.002 125 0.85

(-0.006) (-0.009) (-0.011) (-0.012)

SANS

0.017 140 0.032 -0.408* 138 0.572 -0.204 135 0.758 -0.129 129 0.85

(-0.171) (-0.232) (-0.27) (-0.315)

-0.084 139 0.039 -0.091 137 0.564 -0.007 134 0.758 0.001 128 0.85

(-0.085) (-0.118) (-0.136) (-0.169)

-0.034 136 0.035 -0.025 134 0.564 0.027 131 0.758 -0.061 125 0.85

(-0.059) (-0.081) (-0.093) (-0.124)

SAVS

0.021 140 0.032 -0.008 138 0.562 0.043 135 0.757 0.029 129 0.85

(-0.074) (-0.103) (-0.119) (-0.139)

-0.007 139 0.033 0.001 137 0.562 0.024 134 0.759 0.0002 128 0.85

(-0.038) (-0.053) (-0.06) (-0.073)


0.004 136 0.033 0.018 134 0.564 0.006 131 0.758 -0.008 125 0.85

(-0.027) (-0.038) (-0.043) (-0.053)

Greece SANT

-0.012** 147 0.027 -0.004 145 0.537 0.013* 142 0.763 0.017** 136 0.85

(-0.005) (-0.007) (-0.008) (-0.008)

-0.004** 146 0.028 -0.002 144 0.537 0.004 141 0.762 0.004 135 0.847

(-0.002) (-0.003) (-0.003) (-0.003)

-0.002 143 0.009 0.0003 141 0.537 0.002 138 0.759 0.001 132 0.846

(-0.001) (-0.001) (-0.001) (-0.002)

SANS

-0.03 147 0.002 0.013 145 0.536 0.014 142 0.758 0.044 136 0.847

(-0.027) (-0.035) (-0.039) (-0.042)

0.001 146 -0.007 -0.004 144 0.536 0.037 141 0.763 0.011 135 0.845

(-0.016) (-0.02) (-0.022) (-0.025)

-0.002 143 -0.007 0.022 141 0.543 0.030* 138 0.76 0.011 132 0.846

(-0.012) (-0.016) (-0.017) (-0.019)

SAVS

0.0004 147 -0.006 -0.012 145 0.537 0.01 142 0.758 -0.004 136 0.846

(-0.016) (-0.021) (-0.023) (-0.026)

-0.014 146 0.009 0.0002 144 0.536 0.001 141 0.758 -0.002 135 0.844

(-0.009) (-0.012) (-0.013) (-0.015)

-0.005 143 -0.002 -0.003 141 0.537 -0.008 138 0.756 -0.001 132 0.845

(-0.007) (-0.009) (-0.009) (-0.011)

Luxembourg SANT

-0.001

(-0.015)

157 0.074 0.018

(-0.02)

155 0.596 0.019

(-0.024)

152 0.768 0.045*

(-0.025)

146 0.905

0.002

(-0.005)

156 0.073 0.005

(-0.007)

154 0.588 0.005

(-0.009)

151 0.763 0.011

(-0.009)

145 0.903

0.002

(-0.0030)

153 0.067 0.002

(-0.004)

151 0.587 0.001

(-0.005)

148 0.771 0.005

(-0.005)

142 0.902

SANS

-0.089 157 0.088 -0.033 155 0.594 0.046 152 0.767 0.081 146 0.903


(-0.059) (-0.08) (-0.097) (-0.093)

-0.029

(-0.035)

156 0.076 0.137***

(-0.046)

154 0.609 0.157***

(-0.056)

151 0.774 0.171***

(-0.053)

145 0.908

0.005

(-0.025)

153 0.066 0.063*

(-0.033)

151 0.597 0.041

(-0.041)

148 0.773 0.034

(-0.041)

142 0.902

SAVS

-0.165*

(-0.094)

157 0.093 -0.028

(-0.128)

155 0.594 0.141

(-0.153)

152 0.768 0.171

(-0.146)

146 0.904

-0.048

(-0.055)

156 0.077 0.225***

(-0.071)

154 0.612 0.270***

(-0.086)

151 0.777 0.252***

(-0.083)

145 0.908

-0.001

(-0.0390)

153 0.065 0.097*

(-0.051)

151 0.597 0.075

(-0.063)

148 0.773 0.04

(-0.062)

142 0.902

Cyprus SANT

-0.061 85 -0.0005 -0.101 84 0.346 0.164 83 0.631 0.1 82 0.814

(-0.139) (-0.233) (-0.229) (-0.209)

-0.152* 84 0.035 0.071 83 0.342 0.292** 82 0.653 0.126 81 0.81

(-0.087) (-0.149) (-0.139) (-0.13)

-0.045 81 0.002 0.260** 80 0.371 0.216* 79 0.623 0.079 78 0.775

(-0.078) (-0.126) (-0.125) (-0.116)

SANS

0.07 85 -0.002 0.227 84 0.346 0.32 83 0.63 0.193 82 0.814

(-0.311) (-0.522) (-0.52) (-0.489)

0.121 84 0.006 0.243 83 0.347 0.348 82 0.642 0.213 81 0.81

(-0.158) (-0.267) (-0.263) (-0.257)

0.173* 81 0.033 0.239 80 0.352 0.287* 79 0.623 0.228 78 0.778

(-0.103) (-0.173) (-0.171) (-0.174)

SAVS

-0.244 85 -0.0004 -0.568 84 0.347 -0.4 83 0.63 -0.389 82 0.814

(-0.548) (-0.918) (-0.914) (-0.841)

-0.217 84 0.004 -0.063 83 0.341 0.374 82 0.636 0.41 81 0.81

(-0.317) (-0.533) (-0.522) (-0.481)

0.129 81 0.0003 0.283 80 0.339 0.659 79 0.619 0.413 78 0.776

(-0.28) (-0.467) (-0.449) (-0.43)


Turkey SANT

0.011 110 -0.009 0.009 108 0.441 0.014 105 0.664 0.012 99 0.699

(-0.011) (-0.016) (-0.02) (-0.03)

-0.001 109 -0.017 0.004 107 0.462 0.006 104 0.655 0.003 98 0.692

(-0.005) (-0.007) (-0.009) (-0.013)

0.0003 106 -0.019 0.002 104 0.44 0.005 101 0.692 0.003 95 0.818

(-0.003) (-0.004) (-0.005) (-0.005)

SANS

-0.01 110 -0.017 0.069 108 0.44 0.057 105 0.662 0.037 99 0.699

(-0.115) (-0.163) (-0.202) (-0.298)

-0.029 109 -0.016 -0.01 107 0.461 0.016 104 0.653 0.085 98 0.693

(-0.058) (-0.079) (-0.104) (-0.151)

-0.022 106 -0.016 0.002 104 0.439 0.026 101 0.689 0.11 95 0.822

(-0.038) (-0.054) (-0.065) (-0.072)

SAVS

-0.08 110 -0.014 0.182 108 0.444 -0.031 105 0.662 0.087 99 0.699

(-0.127) (-0.182) (-0.226) (-0.337)

-0.013 109 -0.018 0.108 107 0.468 0.045 104 0.653 0.174 98 0.695

(-0.069) (-0.094) (-0.125) (-0.183)

-0.015 106 -0.018 0.032 104 0.44 0.017 101 0.689 0.095 95 0.819

(-0.049) (-0.069) (-0.085) (-0.096)

USA SANT

0.001

(-0.0005)

176 0.001 0.002***

(-0.001)

175 0.491 0.003***

(-0.001)

172 0.698 0.003***

(-0.001)

166 0.851

0.0003

(-0.0002)

175 0.0002 0.0003

(-0.0003)

174 0.462 0.0003

(-0.0003)

171 0.674 0.0003

(-0.0003)

165 0.831

0.0001

(-0.0001)

172 -0.003 0.0001

(-0.0002)

171 0.458 0.0001

(-0.0002)

168 0.67 0.0003

(-0.0002)

162 0.832

SANS

-0.001

(-0.004)

176 -0.011 0.003

(-0.006)

175 0.459 0.01

(-0.007)

172 0.678 0.015**

(-0.007)

166 0.836

-0.0001

(-0.002)

175 -0.011 0.0004

(-0.003)

174 0.457 0.003

(-0.004)

171 0.673 0.007*

(-0.004)

165 0.833


0.0004

(-0.0020)

172 -0.011 0.001

(-0.0020)

171 0.456 0.002

(-0.0020)

168 0.672 0.005*

(-0.0030)

162 0.834

SAVS

0.001

(-0.002)

176 -0.01 0.002

(-0.003)

175 0.459 0.003

(-0.003)

172 0.676 0.005

(-0.003)

166 0.834

0.0001

(-0.001)

175 -0.011 0.0003

(-0.001)

174 0.458 0.001

(-0.001)

171 0.673 0.002

(-0.001)

165 0.831

0.00003

(-0.0010)

172 -0.011 0.0002

(-0.0010)

171 0.456 0.001

(-0.0010)

168 0.671 0.001

(-0.0010)

162 0.831

Asia SANT

-0.001 176 0.014 0.002** 174 0.573 0.004*** 171 0.768 0.006*** 165 0.865

(-0.001) (-0.001) (-0.001) (-0.001)

-0.0002 175 0.013 0.0001 173 0.555 0.001* 170 0.753 0.001** 164 0.851

(-0.0003) (-0.0004) (-0.0005) (-0.001)

-0.0002 172 0.012 0.0002 170 0.544 0.001* 167 0.747 0.001** 161 0.854

(-0.0002) (-0.0003) (-0.0003) (-0.0003)

SANS

0.003 176 0.015 0.004 174 0.561 0.009 171 0.756 0.014** 165 0.851

(-0.004) (-0.006) (-0.006) (-0.007)

0.0002 175 0.011 0.003 173 0.558 0.010*** 170 0.763 0.003 164 0.846

(-0.002) (-0.003) (-0.003) (-0.004)

0.001 172 0.012 0.006*** 170 0.566 0.008*** 167 0.759 0.0004 161 0.848

(-0.002) (-0.002) (-0.002) (-0.003)

SAVS

0.005* 176 0.027 0.002 174 0.56 0.006 171 0.756 0.004 165 0.848

(-0.003) (-0.004) (-0.004) (-0.005)

0.001 175 0.014 0.003 173 0.562 0.006** 170 0.757 -0.001 164 0.845

(-0.002) (-0.002) (-0.002) (-0.003)

0.002* 172 0.027 0.004*** 170 0.563 0.004** 167 0.751 -0.003* 161 0.852

(-0.001) (-0.001) (-0.002) (-0.0020)

China SANT

-0.0001 155 -0.008 0.004* 153 0.551 0.003 150 0.789 0.006** 144 0.908


(-0.001) (-0.002) (-0.002) (-0.003)

0.0002 154 -0.006 0.0005 152 0.54 0.001 149 0.789 0.002 143 0.906

(-0.001) (-0.001) (-0.001) (-0.001)

0.00001 151 -0.007 0.0005 149 0.541 0.001 146 0.788 0.001 140 0.907

(-0.0004) (-0.001) (-0.001) (-0.001)

SANS

-0.01 155 -0.004 0.045** 153 0.555 0.004 150 0.787 0.026 144 0.905

(-0.014) (-0.019) (-0.023) (-0.029)

0.004 154 -0.005 0.013 152 0.544 0.009 149 0.787 0.01 143 0.906

(-0.007) (-0.01) (-0.012) (-0.015)

-0.0002 151 -0.007 0.005 149 0.54 0.006 146 0.786 0.002 140 0.906

(-0.005) (-0.007) (-0.008) (-0.01)

SAVS

-0.013 155 -0.002 0.046** 153 0.557 0.003 150 0.786 0.029 144 0.906

(-0.014) (-0.019) (-0.023) (-0.028)

0.007 154 -0.001 0.018* 152 0.548 0.012 149 0.787 0.016 143 0.906

(-0.007) (-0.01) (-0.012) (-0.015)

0.002 151 -0.006 0.008 149 0.542 0.009 146 0.787 0.005 140 0.906

(-0.005) (-0.007) (-0.009) (-0.011)

Hong Kong SANT

-0.003 168 < 0.0001 0.007* 166 0.524 0.013*** 163 0.723 0.018*** 157 0.833

(-0.003) (-0.004) (-0.005) (-0.005)

-0.002 167 0.005 -0.0003 165 0.513 0.003 162 0.711 0.005** 156 0.823

(-0.001) (-0.002) (-0.002) (-0.002)

-0.001 164 0.008 0.0004 162 0.505 0.001 159 0.718 0.003** 153 0.825

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

0.005 168 -0.004 -0.017 166 0.516 -0.005 163 0.711 0.023 157 0.818

(-0.024) (-0.033) (-0.038) (-0.041)

-0.003 167 -0.004 -0.022 165 0.519 -0.002 162 0.707 0.002 156 0.817


(-0.011) (-0.015) (-0.017) (-0.018)

-0.007 164 0.001 -0.006 162 0.506 0.006 159 0.717 0.021* 153 0.822

(-0.007) (-0.01) (-0.011) (-0.012)

SAVS

0.033*** 168 0.08 -0.002 166 0.516 0.003 163 0.711 -0.02 157 0.82

(-0.008) (-0.012) (-0.014) (-0.014)

0.013** 167 0.025 -0.004 165 0.514 -0.029*** 162 0.727 -0.038*** 156 0.838

(-0.006) (-0.008) (-0.009) (-0.009)

0.010** 164 0.026 -0.019*** 162 0.534 -0.029*** 159 0.751 -0.032*** 153 0.841

(-0.005) (-0.006) (-0.006) (-0.007)

India SANT

-0.001 135 -0.005 0.002 133 0.478 0.005 130 0.703 0.013** 124 0.84

(-0.003) (-0.004) (-0.005) (-0.006)

-0.0004 134 -0.005 -0.0001 132 0.477 0.001 129 0.705 0.003 123 0.835

(-0.001) (-0.002) (-0.002) (-0.003)

-0.0002 131 -0.006 <0.0001 129 0.5 0.0005 126 0.706 0.002 120 0.833

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

0.003 135 -0.005 -0.003 133 0.477 0.018 130 0.701 0.038 124 0.836

(-0.017) (-0.025) (-0.029) (-0.028)

-0.008 134 -0.001 -0.004 132 0.477 0.005 129 0.705 0.024 123 0.836

(-0.01) (-0.015) (-0.017) (-0.017)

-0.005 131 -0.003 -0.001 129 0.5 0.007 126 0.706 0.022* 120 0.836

(-0.008) (-0.011) (-0.013) (-0.012)

SAVS

0.002 135 -0.006 0.033 133 0.483 0.038 130 0.703 0.072** 124 0.84

(-0.019) (-0.027) (-0.032) (-0.032)

0.004 134 -0.005 0.01 132 0.478 0.014 129 0.706 0.03 123 0.837

(-0.01) (-0.015) (-0.018) (-0.018)

0.001 131 -0.007 0.006 129 0.501 0.01 126 0.707 0.018 120 0.834


(-0.007) (-0.01) (-0.013) (-0.013)

S. Korea SANT

0.0003 158 -0.013 0.005 155 0.456 0.005 150 0.685 0.004 143 0.838

(-0.003) (-0.004) (-0.004) (-0.004)

-0.0002 157 -0.012 0.0003 154 0.454 -0.0001 149 0.682 -0.001 142 0.832

(-0.001) (-0.001) (-0.002) (-0.002)

-0.0002 154 -0.012 -0.0001 151 0.468 -0.0002 146 0.688 -0.0004 139 0.829

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

0.006 158 -0.012 0.073* 155 0.463 0.078* 150 0.688 0.057 143 0.839

(-0.03) (-0.039) (-0.047) (-0.048)

0.018 157 -0.003 0.026 154 0.459 0.017 149 0.683 -0.023 142 0.832

(-0.015) (-0.022) (-0.026) (-0.027)

0.012 154 -0.005 0.012 151 0.471 0.009 146 0.688 -0.046** 139 0.836

(-0.011) (-0.015) (-0.018) (-0.018)

SAVS

0.004 158 -0.012 0.047* 155 0.462 0.04 150 0.686 0.018 143 0.838

(-0.02) (-0.026) (-0.031) (-0.032)

0.012 157 -0.003 0.019 154 0.46 0.004 149 0.682 -0.012 142 0.832

(-0.01) (-0.014) (-0.017) (-0.017)

0.008 154 -0.004 0.006 151 0.47 -0.004 146 0.688 -0.025** 139 0.835

(-0.007) (-0.009) (-0.011) (-0.011)

Australia SANT

0.003 171 0.009 -0.001 169 0.553 -0.021 166 0.742 -0.007 160 0.892

(-0.011) (-0.013) (-0.015) (-0.015)

-0.005 170 0.015 -0.01 168 0.56 -0.020*** 165 0.752 -0.004 159 0.893

(-0.005) (-0.006) (-0.007) (-0.008)

-0.008** 167 0.045 -0.013*** 165 0.577 -0.014** 162 0.75 0.007 156 0.894

(-0.003) (-0.004) (-0.006) (-0.006)

SANS


0.002 171 0.01 -0.014* 169 0.561 -0.013 166 0.742 0.002 160 0.892

(-0.007) (-0.008) (-0.01) (-0.009)

-0.002 170 0.012 0.003 168 0.555 0.009* 165 0.746 -0.0001 159 0.893

(-0.004) (-0.004) (-0.005) (-0.005)

0.001 167 0.01 0.006* 165 0.565 0.010*** 162 0.751 -0.004 156 0.894

(-0.003) (-0.003) (-0.004) (-0.004)

SAVS

0.002 171 0.009 -0.015* 169 0.561 -0.013 166 0.742 0.002 160 0.892

(-0.007) (-0.008) (-0.01) (-0.01)

-0.002 170 0.012 0.003 168 0.555 0.010* 165 0.746 -0.0001 159 0.893

(-0.004) (-0.004) (-0.005) (-0.005)

0.001 167 0.01 0.006* 165 0.565 0.010** 162 0.751 -0.004 156 0.894

(-0.003) (-0.003) (-0.004) (-0.004)

New Zealand SANT

-0.018 146 0.026 0.002 144 0.517 0.080** 141 0.747 0.058 135 0.89

(-0.024) (-0.032) (-0.036) (-0.036)

-0.012 145 0.034 -0.004 143 0.513 0.036** 140 0.747 0.02 134 0.888

(-0.012) (-0.016) (-0.018) (-0.018)

0.001 142 0.025 0.011 140 0.529 0.021* 137 0.751 0.01 131 0.897

(-0.007) (-0.01) (-0.011) (-0.011)

SANS

-0.162 146 0.036 -0.008 144 0.517 -0.069 141 0.739 0.043 135 0.887

(-0.113) (-0.15) (-0.171) (-0.168)

-0.059 145 0.033 0.037 143 0.513 0.07 140 0.741 0.087 134 0.888

(-0.066) (-0.088) (-0.1) (-0.098)

-0.035 142 0.029 0.025 140 0.525 0.027 137 0.744 0.047 131 0.896

(-0.046) (-0.061) (-0.071) (-0.067)

SAVS

-0.148 146 0.034 0.044 144 0.517 -0.106 141 0.739 0.023 135 0.887

(-0.111) (-0.148) (-0.169) (-0.166)


-0.022 145 0.028 0.016 143 0.513 -0.017 140 0.74 0.022 134 0.887

(-0.065) (-0.087) (-0.099) (-0.097)

-0.033 142 0.028 -0.012 140 0.524 -0.004 137 0.744 0.026 131 0.896

(-0.046) (-0.061) (-0.07) (-0.067)

Malaysia SANT

0.002 156 0.023 0.001 154 0.619 0.008* 151 0.822 0.012** 145 0.908

(-0.003) (-0.004) (-0.005) (-0.005)

-0.001 155 0.027 -0.00004 153 0.619 0.002 150 0.819 0.004* 144 0.907

(-0.001) (-0.002) (-0.002) (-0.002)

-0.001 152 0.024 0.0003 150 0.616 0.001 147 0.819 0.002* 141 0.906

(-0.001) (-0.001) (-0.001) (-0.001)

SANS

-0.001 156 0.021 -0.003 154 0.619 0.009 151 0.819 0.01 145 0.905

(-0.009) (-0.012) (-0.014) (-0.016)

-0.007 155 0.03 0.005 153 0.62 0.008 150 0.819 0.01 144 0.905

(-0.006) (-0.008) (-0.009) (-0.011)

-0.003 152 0.024 0.001 150 0.615 0.001 147 0.818 0.002 141 0.904

(-0.004) (-0.006) (-0.007) (-0.008)

SAVS

0.001 156 0.021 0.001 154 0.619 0.006 151 0.819 -0.005 145 0.905

(-0.005) (-0.007) (-0.008) (-0.009)

-0.002 155 0.024 0.006 153 0.625 0.004 150 0.819 -0.001 144 0.905

(-0.003) (-0.004) (-0.005) (-0.006)

0.0001 152 0.02 0.001 150 0.616 -0.002 147 0.819 -0.008** 141 0.907

(-0.002) (-0.003) (-0.004) (-0.004)

Singapore SANT

-0.00004 166 0.015 0.008 164 0.54 0.019** 161 0.749 0.032*** 155 0.882

(-0.006) (-0.008) (-0.009) (-0.009)

-0.002 165 0.02 0.003 163 0.534 0.003 160 0.74 0.014*** 154 0.882

(-0.002) (-0.003) (-0.004) (-0.004)


-0.001 162 0.018 0.001 160 0.538 0.003 157 0.75 0.009*** 151 0.888

(-0.002) (-0.002) (-0.002) (-0.002)

SANS

0.022 166 0.025 0.011 164 0.538 0.009 161 0.742 -0.021 155 0.871

(-0.016) (-0.024) (-0.027) (-0.028)

0.01 165 0.021 -0.005 163 0.533 -0.008 160 0.74 -0.033** 154 0.876

(-0.01) (-0.014) (-0.016) (-0.016)

0.003 162 0.018 -0.005 160 0.538 -0.011 157 0.75 -0.019* 151 0.879

(-0.007) (-0.01) (-0.011) (-0.011)

SAVS

-0.0002 166 0.015 -0.003 164 0.537 -0.011 161 0.742 -0.023 155 0.871

(-0.018) (-0.026) (-0.03) (-0.03)

0.003 165 0.015 0.011 163 0.534 0.007 160 0.739 -0.008 154 0.873

(-0.011) (-0.015) (-0.018) (-0.018)

0.001 162 0.017 0.003 160 0.538 -0.006 157 0.748 -0.012 151 0.878

(-0.007) (-0.01) (-0.012) (-0.012)

Thailand SANT

-0.002 165 0.007 0.023*** 163 0.543 0.041*** 160 0.739 0.054*** 154 0.877

(-0.006) (-0.007) (-0.009) (-0.008)

-0.001 164 0.011 0.005* 162 0.524 0.010*** 159 0.714 0.015*** 153 0.858

(-0.002) (-0.003) (-0.004) (-0.004)

0.001 161 0.007 0.003* 159 0.532 0.006** 156 0.713 0.009*** 150 0.857

(-0.001) (-0.002) (-0.002) (-0.002)

SANS

-0.067** 165 0.034 -0.047 163 0.518 -0.087* 160 0.707 -0.121** 154 0.848

(-0.031) (-0.041) (-0.051) (-0.052)

-0.01 164 0.013 -0.021 162 0.517 0.003 159 0.701 -0.017 153 0.843

(-0.018) (-0.024) (-0.03) (-0.031)

-0.013 161 0.011 -0.0003 159 0.522 0.007 156 0.701 -0.015 150 0.844

(-0.013) (-0.017) (-0.022) (-0.023)


SAVS

-0.052* 165 0.027 -0.002 163 0.515 -0.008 160 0.701 -0.014 154 0.843

(-0.027) (-0.037) (-0.046) (-0.047)

-0.007 164 0.012 0.023 162 0.519 0.032 159 0.704 0.021 153 0.844

(-0.015) (-0.02) (-0.025) (-0.026)

-0.003 161 0.005 0.012 159 0.524 0.016 156 0.703 -0.0001 150 0.843

(-0.01) (-0.014) (-0.017) (-0.018)

The Philippines SANT

-0.004 145 -0.008 0.035 143 0.559 0.058** 140 0.764 0.104*** 134 0.877

(-0.018) (-0.024) (-0.029) (-0.031)

-0.009 144 -0.003 0.017 142 0.558 0.027** 139 0.764 0.040*** 133 0.875

(-0.009) (-0.012) (-0.014) (-0.015)

-0.003 141 -0.007 0.007 139 0.554 0.011 136 0.764 0.024** 130 0.873

(-0.006) (-0.007) (-0.008) (-0.009)

SANS

-0.022 145 -0.007 0.018 143 0.552 0.067 140 0.759 -0.008 134 0.867

(-0.044) (-0.056) (-0.067) (-0.076)

-0.002 144 -0.009 0.024 142 0.553 0.044 139 0.76 0.04 133 0.869

(-0.026) (-0.033) (-0.039) (-0.044)

0.007 141 -0.008 0.018 139 0.553 0.009 136 0.761 0.045 130 0.869

(-0.019) (-0.025) (-0.029) (-0.032)

SAVS

-0.047 145 0.0002 0.019 143 0.552 0.109* 140 0.762 -0.015 134 0.867

(-0.042) (-0.054) (-0.063) (-0.071)

-0.005 144 -0.009 0.055* 142 0.563 0.075** 139 0.766 0.013 133 0.868

(-0.022) (-0.028) (-0.034) (-0.038)

0.02 141 0.001 0.044** 139 0.564 0.013 136 0.761 -0.002 130 0.867

(-0.017) (-0.022) (-0.027) (-0.029)


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