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![Traditional approach
© Copyright 2016 MUTIS. All rights reserved 2016.
• 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)](https://image.slidesharecdn.com/mutisforecast-160706140204/85/Equity-forecast-Predicting-long-term-stock-market-prices-using-machine-learning-3-320.jpg)
Uploaded byNikola Milosevic
Equity forecast: Predicting long term stock market prices using machine learning
The document discusses using machine learning to predict long-term equity price movements over a one year period and classify which equities will grow by at least 10%. Traditional Graham criteria are discussed but found too strict. Experiments used machine learning algorithms on financial indicator data from 1739 stocks to predict price growth. Random forests performed best, able to predict price movements from only 11 key financial indicators.
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Equity forecast: Predicting long term stock market prices using machine learning
- 1. Forecasting equity usingMachine Learning Nikola Milošević
- 2. Goal © Copyright 2016MUTIS. 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
- 3. Traditional approach © Copyright2016 MUTIS. All rights reserved 2016. • 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)
- 4. Other approaches © Copyright2016 MUTIS. All rights reserved 2016. • Models inspired by Graham’s • Following news and trends
- 5. Problems with Grahammodel © Copyright 2016 MUTIS. All rights reserved 2016. • It was developed in 1940s • It is hard to find a stock that satisfies criteria • Too strict • Too defensive
- 6. Help from technology ©Copyright 2016 MUTIS. All rights reserved 2016. • 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
- 7. Machine learning intro ©Copyright 2016 MUTIS. All rights reserved 2016. • Field of study that gives computers the ability to learn without being explicitly programmed
- 8. Experiment (1) © Copyright2016 MUTIS. All rights reserved 2016. • 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…
- 9. Experiment (2) © Copyright2016 MUTIS. All rights reserved 2016. • 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
- 10. Results (1) © Copyright2016 MUTIS. All rights reserved 2016. • Trial with all financial indicators as a features
- 11. Results (2) © Copyright2016 MUTIS. All rights reserved 2016. • We performed feature selection among the indicators • Experiment with only 11 indicators
- 12. 11 indicators thatwere good © Copyright 2016 MUTIS. All rights reserved 2016. • The performance turned out not to be significantly different, but it showed that only 11 indicators are enough
- 13. Best performer © Copyright2016 MUTIS. All rights reserved 2016.
- 14. Decision trees (1) ©Copyright 2016 MUTIS. All rights reserved 2016. • Tries to understand the data and build a decision tree based on data
- 15. Decision trees (2) ©Copyright 2016 MUTIS. All rights reserved 2016. Outlook Sunny Overcast Rain
- 16. Decision trees (3) Outlook SunnyOvercast Rain Humidity High Normal Don’t play Play Wind Weak Strong Play Don’t play Play
- 17. Random forests © Copyright2016 MUTIS. All rights reserved 2016. • 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
- 18. Bagging © Copyright 2016MUTIS. All rights reserved 2016. • 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
- 19. Example © Copyright 2016MUTIS. All rights reserved 2016.
- 20. Reference © Copyright 2016MUTIS. All rights reserved 2016. • 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
- 21. Thank you andquestions © Copyright 2016 MUTIS. All rights reserved 2016. nikola.milosevic@mutis.com