Equity forecast using Machine Learning - MUTIS

Forecasting equity using Machine Learning
Nikola Milošević

Goal
© Copyright 2016 MUTIS. All rights reserved 2016.
• Predict long term equity price movement
• One year period
• Classify which equities will grow by 10%
• Past data are known
• Focus on technical analysis

Traditional approach
• Graham criteria
Stock Selection for the Defensive Investor:
1. Not less than $100 million of annual sales.
[Note: This works out to $500 million today based on the difference in
CPI/Inflation from 1971]
2-A. Current assets should be at least twice current liabilities.
2-B. Long-term debt should not exceed the net current assets.
3. Some earnings for the common stock in each of the past 10 years.
4. Uninterrupted [dividend] payments for at least the past 20 years.
5. A minimum increase of at least one-third in per-share earnings in the past 10
years.
6. Current price should not be more than 15 times average earnings.
7. Current price should not be more than 1-1⁄2 times the book value.
• Graham number = sqrt(22.5*EPS*BV)

Other approaches
• Models inspired by Graham’s
• Following news and trends

Problems with Graham model
• It was developed in 1940s
• It is hard to find a stock that satisfies criteria
• Too strict
• Too defensive

Help from technology
• In past decade were developed approaches
based on technology
• Algorithms based on statistics, heuristics,
probability and machine learning
• They mainly focused in the past on short
term trading

Machine learning intro
• Field of study that gives computers the ability
to learn without being explicitly programmed

Experiment (1)
• Use machine learning on past 2-3 year data
• Data obtained using Bloomberg terminal
• Data include 28 indicators
• Book value, Market capitalization, Change of stock Net price over the one
month period, Percentage change of Net price over the one month period,
Dividend yield, Earnings per share, Earnings per share growth, Sales revenue
turnover, Net revenue, Net revenue growth, Sales growth, Price to earnings
ratio, Price to earnings ratio -five years average, Price to book ratio, Price to
sales ratio, Dividend per share, Current ratio, Quick ratio, Total debt to equity,
margins, asset turnover…

Experiment (2)
• Selected 1739 stocks from different indexes (S&P
1000, FTSE 100 and S&P Europe 350…)
• Calculated which ones price grew more than 10%
• Used different Machine learning algorithms and
10 fold cross validation for evaluation
• Used Python for scripting and Weka toolkit for
machine learning

Results (1)
• Trial with all financial indicators as a features

Results (2)
• We performed feature selection among the
indicators
• Experiment with only 11 indicators

11 indicators that were good
• The performance turned out not to be significantly
different, but it showed that only 11 indicators are
enough

Best performer

Decision trees (1)
• Tries to understand the data and build a decision
tree based on data

Decision trees (2)
Outlook
Sunny Overcast Rain

Decision trees (3)
Outlook
Sunny Overcast Rain
Humidity
High Normal
Don’t play Play
Wind
Weak Strong
Play Don’t play
Play

Random forests
• Algorithm that creates a forest of decision trees
• Designed to improve the stability and accuracy of
machine learning algorithms
• Reduces variance and helps to avoid overfitting
• Uses technique called bagging

Bagging
• From a set of elements, creates n sets of
elements (in our case randomly)
• Builds n models using subsets for each model
• In order to get final class uses voting strategy
• Class with majority of votes wins

Example

Reference
• Milosevic, Nikola. "Equity forecast: Predicting long
term stock price movement using machine
learning." arXiv preprint arXiv:1603.00751 (2016).
• https://arxiv.org/ftp/arxiv/papers/1603/1603.00751.pdf

Thank you and questions
nikola.milosevic@mutis.com

Equity forecast using Machine Learning - MUTIS

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