Estimize and QuantopianCrowdsource Earnings Predictions and the

Research Platform

QuantopianSeong Lee (about)

What are we talking about?

● EPS, Wall Street Consensus, Earnings Surprise

● Estimize, the crowdsourced financials aggregator

● Cover Estimize’s whitepaper and replicate the results in the Quantopian Research Platform

● Develop a simple trading strategy

By the end of this talk you’ll have

● Tasted the potential for crowdsourced financial data

● A sense of how the Quantopian Research Platform works and how to run parameter optimization

What you need to know

● EPS: Earnings Per Share o (Net Income - Dividends)/(Shares Outstanding)o An important factor in predicting a company’s

performanceo I.E. Did it increase from last quarter?

What you need to know

● Wall Street Consensuso Aggregated consensus of Wall Street Analysts

(mostly sell-side)o Sell-side: Investment Banks, FAs, Brokerso Buy-side: Hedge funds, mutual funds, pension

funds, VCs, Prop firms, PEso IBES (Institutional Brokers’ Estimate System)

What you need to know

● Earnings Surpriseo When announcements do better than or worse than

earnings estimateso Look at Apple Q2:

Actual: 1.66 Consensus: 1.46 Surprise: 13.7%

Summary

● EPSo Earnings Per Share

● Wall Street Consensuso Average of all Wall Street Estimates

● Earnings Surpriseo When earnings announcements differ from

expectations

Who is Estimize?

● Estimize is a financial estimates aggregatoro “Independent, buy-side, and sell-side analysts as

well as private investors and students”o EPS and Revenue estimates side-by-side with Wall

Street Consensus estimates

Revisiting Apple Q2, 2014

Crowdsourced Estimates

● What makes them different?o Diversity of contributors

Only 7% of total participants are sell-side analysts

Looking at the whitepaper claims

● Released September 24, 2013● Claim #1: “More accurate 65% of the time

when there are 20 or more contributors”● Claim #2: “Average absolute error of the

Estimize consensus is smaller than the Wall Street Consensus by 12 bp when contributors are greater than 20”

The Toolbox

Claim #1: “65% more accurate”

● Did Estimize land higher than Wall Street when it was a positive surprise?

● Did Estimize land lower than Wall Street when it was a negative surprise?

Revisiting Apple Q2, 2014

Implementation

● Compare the number of times that Estimize correctly guessed direction

● Data was preprocessed so feel free to reach out if you want steps

Wrangling our DataFrame

● We have 13,000 rows, each with this data

● So for each row, see if estimize predicted direction correctly

Loading our Data

Prediction Direction

Plot the results

What about the number of participants?● Each announcement can have as little as 1

participants or more than 167

● Apple Q2, 2014:

Plotting against N participants

Plotting against N participants

Summary

● Positive correlation between number of participants and the accuracy rate of Estimize versus the Wall Street Consensus

● Accuracy > 65% when N > 20

Claim #2: Lower absolute error

● We’re going to look at the relative delta of each estimate instead

● Steps:1. Wrangle our DataFrame/spreadsheet (add a

column)2. Plot the results against N participants

Wrangling our data

Graphing the score: Code

Graphing the score: Plot

Conclusions

● Claim #1: Positive relationship between accuracy and number of participantso Matches up

● Claim #2: Lower relative error as number of participants increaseso Matches up

● So how do we use this data?

Implementing a trading strategy

● Goals:o Write a simple algorithm to backtest our strategyo Compare the Wall Street estimates versus Estimize

estimates in generating alphao Get a sneak-peak into the Quantopian Research

Platform

The strategy

● PEAD - Post Earnings Announcement Drifto “The tendency for a stock’s cumulative abnormal

returns to drift in the direction of an earnings surprise”

● Logic:o If there is a positive surprise (actual > estimate)

Buy and hold for 1 day o If there is a negative surprise (actual < estimate)

Sell and hold for 1 day

Implementation

● Same format as Quantopian IDE:

Steps

1. Create a universe of stocks from our dataa. Only where N >= 20

2. Setup our `initialize` and `handle_data` methods

3. Run the algorithm4. Optimize our parameters and choose the

best one

Creating a universe

Initialize

Handle_data: Main Logic

Run the algorithm

Optimize parameters: Brute Force

● Our parameters: ● Run a for loop over these parameters:

o ● Redefined initialize with new params:

Results

Results

● The strategy that held a position for 3 days using the Estimize estimates had cumulative returns of 4.97% from 10/12 - 1/14

● Try:o Long onlyo Short onlyo Longer holding periods

Final thoughts and Summary

● There are more efficient ways to optimizeo Gradient descent, Walk forward optimization,

Genetic algorithms● Easier plotting tools exist (Seaborn)● Crowdsourced estimate data can be

interesting new sources of alpha● Parameter optimization/research is possible

in the Quantopian Research Platform

Questions and Notes● Email us at research@quantopian.com

o Ask us about the iPython notebook these slides were based off!

● Visit us at Quantopiano www.quantopian.com

● Estimize Whitepaper: http://com.estimize.public.s3.amazonaws.com/papers/Estimize%20Whitepaper%20Executive%20Summary.pdf

● Deutsche Bank Paper: http://blog.estimize.com/post/80676086439/deutsche-bank-quant-research-estimize-more-timely-and