Bjarte Bogstad, Institute of Marine Research, Bergen, Norway [email protected]
Does Behavioral Finance add to our understanding of financial markets? by Per Bjarte Solibakke
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Transcript of Does Behavioral Finance add to our understanding of financial markets? by Per Bjarte Solibakke
Dissertation December 6th
2001 Side: 1
Does Behavioral Finance add to our
understanding of financial markets?
by
Per Bjarte Solibakke
Dissertation December 6th
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Overview
1. Behavioral Finance
2. Building Blocks of Behavioral Finance
i. Limits of Arbitrage
ii. Psychology Biases
3. Behavioral Finance and Financial Markets
i. Market Puzzles
ii. Cross Section of Average Asset Returns
iii. Individual Investor/Security analyst behavior
iv. Corporate Finance and Management Decision behavior
4. Summaries and Conclusions
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Behavioral Finance (BF)
Behavioral Finance (BF) argues that some financial phenomena can plausible be understood using
models in which some agents are not fully rational.
Hence, BF deals mostly with investor irrationality / bounded rationality / cognitive and decision biases.
Those biases create market ineffiencies in the shape of mispricings.
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Behavioral Finance (BF)
1. Agents’ beliefs are correct.
2. Given these beliefs, agents make choices that are normative acceptable (SEU, Savage, 1964)
The traditional finance paradigm, the Efficient Market
Hypothesis (EMH), use models in which agents are rational,
implying that
In broad terms, BF argues that some financial phenomena can be better understood using
models in which some agents are not fully rational.
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Behavioral Finance (BF)
Moreover, the efficient market hypothesis (EMH) appealingly simple, seems to show that its predictions
are not fully confirmed by available data.
Applying the EMH, basic facts are not easily understood in:
1. the aggregate stock market,
2. the cross section of average returns and
3. individual trading/security analyst behavior
4. classical corporate finance management decisions
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Behavioral Finance (BF)
1. Failure to apply Bayes’ law properly
2. Agent hold correct belief but makes choices that are normative questionable (incompatible by
SEU)
Specifically, BF analyses what happens when we relax one or both, of the two tenets that underlie the
finance view of rationality
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Behavioral Finance (BF)
Main Objection against BF (arbitrage argument):
Even if some agents are irrational, rational agents will prevent them from influencing security prices for
very long periods of time.
Recently a series of theoretical papers show that irrationality can have a substantial and long-lived
impact on security prices.
The literature on “limits of arbitrage” is the first of two building blocks of behavioral finance.
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Limits of Arbitrage
EMH suggests ”no free lunch” and security prices equal ”fundamental value”. The suggestion requires:
1. Deviation from fundamental value or simply mispricing, creates attractive investment opportunities
and that
2. Rational investors will immediately snap up the opportunity
BF disputes the first argument due to risk!
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Limits of Arbitrage
Sources of risk:
1. Fundamental Risk imperfect
substitute security
2. Noise Trader Risk mispricing
worsen in the short run
3. Implementation Costs difficult selling
securities short
4. Model Risk relying
100% on a model of fundamental value
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Limits of Arbitrage
Evidence: The ”joint hypothesis problems” make it difficult to
provide definite evidence of inefficiency. However, the
following financial market phenomena are almost certain
mispricing, and persistent ones:
Twin shares (Royal Dutch and Shell Transport)
ADR’s (New York price <> Home country price)
Index Inclusions (Yahoo increased by 24%)
Internet Carve-Outs (3Com 5% IPO of Palm Inc.)
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Arbitrage Risk (ex. US 1995-2000)
S&P 500 and NASDAQ indices seemed highly overvalued!
Few dared to act on their hunch, due to
Fundamental risk No effective substitute
security. Using Russel 2000 will make the position vulnerable to large stocks news.
Noise trader risk
Noise traders may push them up still further in the short run.
Model Risk
Is the index really mispriced?
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Scenario: Limited efficient Markets
Price and Value tend to converge, but markets can still move
far from reality (fundamental/intrinsic values) at times.
Hence, the raise and evolvement of market anomalies and deviations seem to suggest a need for
building behavioural models assuming specific form for irrationality.
This is the second building block in behavioral finance:
Psychology biases or
Investor irrationality /bounded rationality / cognitive and decision biases
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Psychology biases of particular interest
Two main groups of biases are found in the behavioral finance literature:
1. Beliefs
2. Preferences
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Psychology biases
1. Beliefs
Overconfidence Poor
calibrating, certain occurrences (80%) and impossible occurrences (20%).
Aggressive Trading.
Optimism and Mood effects, Wishful Thinking
Representativeness
Base Rate neglect and Sample size neglect Past performance
indicator for future performance
Conservatism
Base rate are over-emphasised relative to sample
evidence
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Psychology biases
1. Beliefs (cont.)
Confirmation Bias
Insufficient attention is paid to new data
Anchoring
Slow adjustment
Memory Biases
More recent events and more salient events will weight
more heavily and distort the estimate
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Psychology biases
2. Preferences
The vast majority of models of preference is represented by the expectation of a von Neumann-
Morgenstern utility function (EU).
Unfortunately, EU theory is systematically violated when choosing among risky gambles.
Several suggestions for improvements.
Prospect theory may be the most promising for financial applications (Kahneman and Tversky, 1979, 1992)
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Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
Allais Example “Fanning out” (1953) show inconsistence in expected utility theory!
Consider choosing between A1 and A2:
• A1 Sure gain of $1,000,000
$5,000,000 with probability 0.10
• A2 $1,000,000 with probability 0.89
$0 with probability 0.01
Now consider choosing between B1 and B2:
•B1 $5,000,000 with probability 0.10
$0 with probability 0.90
•B2 $1,000,000 with probability 0.11
$0 with probability 0.89
To be consistent with expected utility theory, A1 is preferred to A2, if and only if B2 is preferred to B1
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Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
Consider choosing between C1 and C2:
•C1 Sure gain of $240,000
•C2 $1,000,000 with probability 0.25
$0 with probability 0.75
Risk aversion makes most individuals to gravitate toward the sure gain.
Now consider choosing between D1 and D2:
•D1 Sure loss of $750,000
•D2 Loss $1,000,000 with probability 0.75
$0 with probability 0.25
Choosing D2, which most individuals would do, makes the utility function “abnormally” convex because of the “certain loss aversion effect”, showing risk preference.
Individuals focus more on “prospects” –gains and losses- than on total wealth
Investors are reluctant to sell at loss.
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Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
People are more sensitivity to differences in probabilities at higher probability levels:
Consider choosing between E1 and E2:
•E1 Sure gain of $3,000
•E2 Gain $4,000 with probability 0.8
$0 with probability 0.2
where E1 is preferred to E2, and consider choosing between F1 and F2:
•F1 Gain $4,000 with probability 0.2
$0 with probability 0.8
•F2 Gain $3,000 with probability 0.25
$0 with probability 0.75
where F1 is preferred to F2. Violate EU theory.
People place much more weight on certain outcomes than merely probable outcomes: the certainty effect.
Non-linear probability transformation
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Prospect Theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992)
Contribute to a higher understanding of:
1. Framing: the way a problem is posed for the decision manager
2. Mental accounting in prospect theory is accounted for by the fact that the reference point from
which gains and losses are calculated can change over time
3. Narrow framing is the tendency to treat individual gambles separately from other portions of wealth.
4. Regret theory is a tendency for people to feel the pain of regret at having made errors, not putting such errors into
a larger perspective.
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Ambiguity Aversion (Ellsberg, 1961)
Probabilities are rarely objectively known. Savage (1964) developed a counterpart to EU known as SEU. Ellsberg
(1961) shows a violation of SEU.
Two urns, 1 and 2. Urn 2 contains a total of 100 balls, 50 red and 50 blue. Urn 1 also contains 100 balls, again a mix of red and blue, but the subject does not
know the proportion of each.
Subjects are then asked to choose one of following two gambles, each of which involves a possible payment of $10,000, depending on the colour of a ball drawn
at random from the relevant urn
g1 : a ball is drawn from Urn 1, $10.000 if red, $0 if blue
g2 : a ball is drawn from Urn 2, $10.000 if red, $0 if blue
Subjects are then asked to choose between following two gambles:
h1 : a ball is drawn from Urn 1, $10.000 if blue, $0 if red
h2 : a ball is drawn from Urn 2, $10.000 if blue, $0 if red
Typically, g2 is preferred to g1 and h2 is chosen over h1. Inconsistent with SEU.
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Ambiguity Aversion (Ellsberg, 1961)
People dislike subjective, or vague uncertainty more than they dislike objective uncertainty (see Camerer and Weber, 1992
for a review.)
“ambiguity aversion”.
Ambiguity can be defined as a situation where information that could be known, is not. Possibly, strengthen where
people feel their competence in assessing relevant probabilities is low (Heath and Tversky, 1991).
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Does BF add to our Understanding of Financial Markets?
Disclaimer:
Note that the conventional efficient market view is not abandoned. I could have, if it was the goal of this
presentation, found very many cases/results that suggest that the markets are impressively efficient.
Hence,
This is a behavioral oriented presentation attempting to understand phenomena applying psychological biases in
financial markets.
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BF: The Aggregate Stock Market
Three puzzles from the aggregate stock market:
1. The equity premium (Mehra & Prescott (1985))
2. Volatility (Name:Campbell (2000))
3. Predictability
The reference to as puzzles, is that they are hard to rationalize in a simple consumption based model.
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BF: The Equity Premium Puzzle
i) Prospect Theory (Benartzi and Thaler, 1995)
Suppose
where and are the probability weighting function and the value function from prospect theory, respectively. Rf,t and Rt+1 are gross returns on T-Bills and
the stock market from t to t+1, respectively. is the fraction of his financial wealth allocated to stocks.
, 1(1 ) 1f t tE R R
Additional Assumptions: Gains and Losses of prospect theory refer to changes in financial wealth and the relevant time interval is [t, t+1] for gains and
losses.
To make Rf and R equally attractive:
From prospect theory portfolio evaluation: Once a year.
More frequent evaluation myopic loss aversion.
The essence is that the 3.9% excess return for stocks cannot easily be explained by risk.
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BF: The Equity Premium Puzzle
i) Prospect Theory (Barberis, Huang and Santos, 2001)
Suppose preferences
Investors get utility from consumption + the holding of risky assets (X).
1
0 0 10
( )1
t tt t
t
CE b C X
They show that loss aversion can indeed provide a partial rationalization of the high Sharpe ratio on the
aggregate stock market.
The utility is determined by
where 2.25 is from Tversky and Kahneman (1992).
0ˆ( )
2.25 0
X for XX
X for X
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BF: The Equity Premium Puzzle
ii) Ambiguity Aversion
When faced with an ambiguity, people entertain a range of possible probability distributions and act to
maximize the minimum expected utility under any candidate distribution.
Epstein and Wang (1994) showed how such a approach could be incorporated into a dynamic asset
pricing model.
Maenhout (1999) applies state equations and non-linear objective functions to the equity premium.
However, to explain the whole 3,9% equity premium requires an unreasonable high concern about
misspecifications.
Only a partial explanation for the equity premium.
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BF: The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
The essence is that the empirical volatility of the price-dividend ratio cannot easily be explained by
variation in expected dividend growth rate.
i) Beliefs (changing forecasts of future cash flows)
1. Investors believe that the mean dividend growth rate is more variable than it is.
The version of Representativeness known as the law of small numbers.
2. Overconfidence about private information.
Positive private information will push prices up too high relative to current dividend.
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BF: The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
i) Beliefs (cont.)
3. Investors extrapolate past returns too far into the future.
Again representativeness known as the law of small numbers.
4. Investors confuse real and nominal quantities when forecasting future cash flows (Ritter and Warr, 2000)
Incompetence
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BF: The Volatility Puzzle (Schiller, 1981 and LeRoy and Porter, 1981)
ii) Preferences
A straightforward extension of the model presented under the equity premium puzzle can explain the
volatility puzzle
where zt is a state variable tracking past losses and gains.
1
0 0 10
( , )1
t tt t t
t
CE b C X z
The stock market is pushed up assuming good cashflow news. This create a cushion of prior gains (z) lower
risk aversion.
Discounting at a lower rate push prices further relative to current dividends.
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BF: The Cross-section of Average Returns
Anomalies:
The size premium (Fama and French, 1992)
Long-term Reversals (DeBondt and Thaler, 1985)
The Predictive power of Scaled-price Ratios (Fama and French, 1992)
Momentum (Jegadeesh and Titman, 1993)
Event studies of (e.g. Baker and Wurgler, 2000)
•Earnings announcements
•Dividend Announcements and Omissions
•Stock Repurchases
•Secondary Offerings
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BF: The Cross-section of Average Returns - Anomalies
i) Belief Based Models
1. The anomalies is the result of systematic errors of investors using public information (Barberis et al.
(1998))
Representativeness bias and the law of small numbers
Conservatism suggest that investors put too little weight on the latest piece of earnings news
relative to their prior beliefs.
The model generates post-earnings announcement drift, momentum, long-term reversal and cross-sectional
forecasting power for scaled-price ratios.
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BF: The Cross-section of Average Returns - Anomalies
i) Belief Based Models (cont.)
2. The anomalies is the result of systematic errors of investors using private information (Daniel et al.
(1998))
Overconfidence If
private information is positive the investor will push prices too far relative to fundamentals.
To generate momentum and post-earnings announcement effect, model is extended so that public
information change the private information asymmetrically (self-attribution bias).
Initial overconfidence is on average followed by even greater overconfidence, generating momentum.
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BF: The Cross-section of Average Returns - Anomalies
i) Belief Based Models (cont.)
Chopra et al. (1992) and La Porta et al. (1997) provide compelling evidence that supports the idea that
investors make irrational forecasts of future cash flows.
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BF: The Cross-section of Average Returns - Anomalies
i) Belief Based Models (cont.)
3. Momentum and reversals may also be positive feedback trading, when one group of investors buy more
of an asset which has recently gone up in value. (e.g. model in De Long et al. (1990).
Extrapolative expectations based on past returns due to representativeness and to the law
of small numbers.
4. Hong and Stein (1999) build a model where two boundedly rational groups of investors interacts (subset
of available information).
Hong et al. (1999) present supportive evidence for the view of Hong and Stein: the momentum
effect is high in small firms.
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BF: The Cross-section of Average Returns - Anomalies
ii) Belief Based Models with institutional frictions
1. A large class of investors, mutual funds, are not allowed to short stocks. Miller (1977) shows that
short sales constraints explain why high price-earnings ratio stocks earn lower returns. Scherbina
(2000) and Cheng et al. (2000) confirms.
2. The implications of short sales constraints and differences of opinion for higher order moments, lead to
skewness (Hong and Seng (1999))
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BF: The Cross-section of Average Returns - Anomalies
iii) Preferences
Barbereis and Huang (2000) show that applying prospect theory, narrow framing and a dynamic model of
loss aversion, individual stocks can generate evidence on long term reversals and on scaled-price
ratios.
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BF: Closed-end Funds (CeF)
The view predicts that closed-end funds should comove strongly, which is confirmed by Lee et al. (1991).
Noise trader risk must be systematic. Another group of assets primary owned by individuals are small
stocks. Consistent with the noise trader risk being systematic, Lee et al. (1991) find strong positive
correlation.
Why doesn’t CeF trade at the price of Net Asset Value (NAV)?
Lee et al. (1991) argue that some of the individual investors who are primary the primary owners are noise
traders, exhibiting irrational swings in their expectations about future fund returns (noise traders).
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BF: Co-movements / Cross-Correlations
Lee et al. (1991) assume a “habitat” view of comovement.
Many investors choose to trade only subset of available assets. As these investors’ risk or sentiment
changes, they alter exposure inducing a common factor in the returns.
Barberis and Scheifer (2000) argues categorizing as a co- movement factor
Many investors group stocks into categories, and then allocate funds across these various categories. An
asset added to a category should therefore begin comovement with the category.
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BF: Investor Behaviour
Particular success in:
Explaining how groups of investors behave and
What kinds of portfolios investors choose to hold and trading
Growing Importance as:
Cost of entering the market has fallen dramatically
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BF: Investor Behaviour
Insufficient diversification
Investors diversify their portfolio holdings much less than recommended by normative models of portfolio
choice. French and Poterba (1991) show that investor are domestically biased (>90%). Grinblatt and
Keloharju (1999) show geographical local preferences in Finland.
Ambiguity and Familiarity offers a simple explanation; the degree of confidence in the probability distribution
is important.
Naive diversification
Investors diversify applying the 1/n heuristic, whatever option that exist.
Investor incompetence
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BF: Investor Behaviour
Excessive Trading
Overconfidence; investors believe they have information strong enough to justify a trade, while in fact it’s
too weak. Moreover, Odean (1999) suggest a worse situation: Misinterpreted valid information.
Evidence: Barber and Odean (2000)
2. Prospect Theory and Narrow Framing1. Irrational belief in mean reversion
The Selling Decision
Disposition effect suggest that investors are reluctant to sell assets trading at a loss relative to purchase
price. Odean (1998) show that investors are more willing to sell stocks that have gone up relative to
buying price than down.
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BF: Investor Behaviour
The Buying Decision
Odean (1999) shows that “Buys” are evenly split between prior winners and losers. Conditioning on the
stock being a prior winner (loser) though, the stock is a big winner (loser).
- Attention effect
- Good past performance (momentum)
- Poor prior performance (undervalued and will rebound)
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BF: Security Analysts biases
Analysts forecasts and recommendations are biased
•Stock recommendations are predominantly buys over sells, by a seven to one ratio (e.g. Womack
(1996))
•Optimistic forecasts at 12-month and longer horizon (e.g. Brown 2001)
Analysts forecast errors are predictable based upon past accruals, past forecast revisions and other
accounting value indicators.
•Past accounting accruals predict forecast errors (Teoh and Wong (2001))
•Analysts seem to underreact to unfavorable information and overreact to favorable information
(Easterwood and Nutt (1999))
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BF: Corporate Finance
1. Security Issuance, Capital structure and Investment
Results from actions taken by rational managers faced with irrational investors. Market timing suggest:
- security issuance and repurchase due to mis-pricing
Results from actions taken by managers that does not find mispricing irrational. Assuming Pecking-Order
financing:
- if stock prices go up, more attractive new projects eventually requiring new equity
Baker and Wurgler (2000) find supportive evidence for the market timing hypothesis.
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BF: Corporate Finance
2. Why pay firms dividends?
Notion of self-control Consume the
dividend but don’t the portfolio capital (Shefrin and Statman, 1984)
Mental Accounting Firms
make it easier for investors to segregate gains from losses to increase their utility:
Gains:
Losses:
Avoiding Regret
Stronger for action they took than action they failed to take
(10) (2) (8) ( 10) (2) ( 12)
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BF: Corporate Finance
3. Managerial Irrationality
Overconfidence
i. Hubris Hypothesis (Roll, 1986)
ii. Future performance is positive:
Can explain pecking order financing
Correlation in Cash flow and investments
Free cash flow should be minimized
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Summaries and Conclusions
persuasive evidence that investors make major systematic errors
persuasive evidence that psychological biases affect market prices
indications that there is substantial misallocation of resources
However, much of the BF work is narrow and partial. As progress is made, more than one or two strands are
incorporated into models.
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Summaries and Conclusions
Two predictions for the understanding of financial markets:
2. There will be better theories.
1. We will find that most of our current theories, rational and behavioural, are wrong.
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BF Fund in Operation
Fund -- Objective The Fund aims to provide long term capital appreciation through investments in listed Japanese
equities. The Fund's investments are based on insights from behavioural finance. In selecting companies for
investment, the Fund will focus on stocks that are currently undervalued because of emotional and behavioural patterns
present in stock markets. The selection of stocks is a systematic way.
Why Japan ?
Attractive valuation level: Nikkei 225 at its lowest in 15 years
Increased focus on shareholder value - beneficial for investors
Structural reforms heading in the right direction
Increased foreign investments in the Japanese equity market