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Future stock performance presentation
- 1. STOCK ANALYSIS - Financial Indicators of a stock price - 1
- 2. GOAL Can a novice investor buy a stock using machine learning algorithms? 2
- 3. AGENDA What is a Stock? What does it mean to own stock in a company? Basics 01 What sample? What features? What size? Dataset Zero dominance Null dominance Outlier detection Preprocessing Feature selection Modeling Comparison Modelling Important variables Future work Conclusion 02 03 04 05 3
- 4. BASICS What is a stock? what does it mean to own stock in a company? 4
- 5. Each company is part of a sector that classifies it in a macro-area US BASED STOCKS 01 02 03 • 4392 companies • 225 financial indicators • 2018 year Sample size • Binary Class • 1 Class • 0 Class Target variable These are commonly found in the 10-K filings each publicly traded company releases yearly From Kaggle DATASET
- 7. DATASET – TARGET • Binary Class • 1 indicates should Buy • 0 indicates should NOT Buy • Year 2019 1 – 70% 0 - 30% Target Variable 7
- 8. NULL DOMINANCE • Removed feature wise • 13 features have above 40% missing values ZERO DOMINANCE • Removed Feature wise • 10 features have above 65% zero values Data Preprocessing 8
- 9. IMPUTING NULLS Replacing remaining missing values with their means according to each sector INORGANIC GROWTH • Price variation above 300% • 19 companies Data Preprocessing Cont’d: OUTLIER DETECTION • Z-scores • IQR • Coerce 9 NAN
- 10. 200+ financial indicators at RANDOM! 10
- 11. Feature Selection methods Univariate Feature Selection - Chi-squared Wrapper Select via model Mutual info Classification Stepwise Recursive Backwards Feature removal L2 Regularization Exhaustive search Genetic search 11 Decision tree Classifier Random Forest Classifier Gradient Decent Classifier Ada Boost Classifier Neural Network Classifier Logistic Regression Support Vector Machine Modeling methods
- 12. Modeling – Decision Tree, Random Forest Selection method Accuracy AUC # Features selected Low Variance filter < 20% 0.64 (+/- 0.06) 0.59 (+/- 0.03) 10 Stepwise Backward Removal 0.62 (+/- 0.07) 0.57 (+/- 0.03) 5 Mutual Info Classification 0.63 (+/- 0.05 0.57 (+/- 0.04) 10 Wrapper Select 0.65 (+/- 0.04) 0.60 (+/- 0.04) 68 Selection method Accuracy AUC # Features selected Low Variance filter < 20% 0.71 (+/- 0.06) 0.77 (+/-0.02) 151 Stepwise Backward Removal 0.69 (+/- 0.06) 0.70 (+/0.03) 5 Mutual Info Classification 0.72 (+/-0.06) 0.77 (+/0.01) 10 Wrapper Select Random Forest 0.72 (+/- 0.06) 0.77 (+/-0.01) 62 Decision tree Random Forest 12
- 13. Decision Boundaries– Random Forest vs Decision Tree 13 Decision boundary technique helps to develop an intuition of how a model work These boundaries separate the data-points (companies) into regions signifying different classes (1,0) Actual 0 Class Actual 1 Class
- 14. Modeling – Boosting Methods 14 71 71 70 68 66 76 76 72 70 69 3 5 10 15 20 InPercentages Depth of a tree Max Depth vs CV Performance Accuracy AUC Selection method Accuracy AUC # Features selected Low Variance filter < 20% 0.72 (+/- 0.05) 0.77 (+/- 0.03) 151 Stepwise Backwards Removal 0.71 (+/- 0.05) 0.75 (+/- 0.03) 5 Mutual Info Classification 0.72 (+/- 0.02) 0.73 (+/- 0.02) 10 Wrapper Select 0.72 (+/- 0.04) 0.78 (+/- 0.03) 51 Selection method Accuracy AUC # Features selected Low Variance filter < 20% 0.72 (+/- 0.05) 0.76 (+/- 0.02) 151 Stepwise Backwards Removal 0.71 (+/- 0.06) 0.73 (+/- 0.04) 5 Mutual Info Classification 0.72 (+/- 0.03) 0.73 (+/- 0.02) 10 Wrapper Select 0.72 (+/- 0.05) 0.77 (+/- 0.02) 51 Gradient Boost Ada Boosting
- 15. Decision Boundaries– Gradient Boost vs AdaBoost 15 Blue region classifies to “NOT Buy” class while orange classifies to “Buy” class. Actual 0 Class Actual 1 Class
- 16. Modeling – Neural Network 16 Selection method Accuracy AUC # Features selected Low Variance filter < 20% 0.68 (+/- 0.05) 0.64 (+/- 0.05) 151 Stepwise Backwards Removal 0.69 (+/- 0.01) 0.52 (+/- 0.07) 5 Mutual Info Classification 0.71 (+/- 0.02) 0.69 (+/- 0.03) 10 Wrapper Select 0.70 (+/- 0.03) 0.59 (+/- 0.07) 51
- 17. Modeling – SVM 17 Selection methcod Accuracy AUC # Features selected Low Variance filter < 20% 0.72 (+/- 0.02) 0.72 (+/- 0.03) 88 Stepwise Backwards Removal 0.69 (+/- 0.00) 0.67 (+/- 0.03) 5 Mutual Info Classification 0.70 (+/- 0.00) 0.60 (+/- 0.03) 5 Wrapper Select 0.71 (+/- 0.02) 0.73 (+/- 0.03) 62
- 19. Important Features 19 A calculation used to gauge the quality of a company's earnings per share (EPS). EPS Diluted: If a company has been buying back shares, this number will be negative. Weighted Average Shares: The amount of money that would be returned to shareholders if all of the assets were liquidated. Shareholders Equity: The portion of a company's profit that is allocated to each outstanding share of its common stock. Net Income per share: Earnings per share divided by the share price. Earnings Yield: Long-term assets that have a useful life of more than one year. Total non-current assets:
- 20. Compare model results to 2020 financial year. Compute gains, losses and ROI for each stock. Future work Plotting decision boundaries using 2 components. 20
Editor's Notes
- What financial indicators should one look into before buying a stock?
- The objective of this project is to find out whether a person without any prior financial experience will be able to invest in stocks that are trustworthy by applying machine learning algorithms. Moreover, is it possible to accomplish this solely by analyzing the financial indicators of a company?
- The word “stock” refers to a share of ownership in a particular company If you own a stock, you are an owner of a very small fraction of that company.
- The dataset is obtained from Kaggle and sample contains 4392 companies with 225 features. This information is commonly found in the 10-k filings which are released yearly.
- In the sample each company is a part of a sector in macro-area. There are diverse sectors like Healthcare, Real estate, technology. And the companies include amazon, roku, Infosys, nexa,
- From a trading perspective, class 1 identifies those stocks that a trader should BUY at the start of the year and sell at the end of the year for a profit. The class 0 identifies those stocks that a trader should NOT BUY, since their value will decrease, meaning a loss
- One of the biggest challenges was dealing with missing values and values which are zero, the features which have more than 40% missing values and 65% zero values were omitted from the analysis. So totally 25 features
- There are 20 companies with price variation above 300% due to miss typing's which are also been omitted. Some financial indicators show huge discrepancy between max value and 75% quantile so I have droped bottom 3% and the top 3% of the data. The remaining nulls have been replaced wrt their sectors
- 25 features were removed I believe, feature selection is going to play a crucial role since this can be overwhelming to a novice investor. So part of the goal is to have less number of features as possible
- I was experimenting with various algorithms and feature selection methods. Also I tried different parameter combinations and compared the model performances
- Random forest exhibited better performance. AUC are higher than Accuracy in random forest whereas that’s opposite in decision tree.
- these scatter plots show classification of the companies in Feature Space using two of the most important features. Net income per share and earnings yield. Blue region classifies to “NOT Buy” class while orange classifies to “Buy” class. Random forest better classified than decision tree
- As you can see in the graph, I’ve limited to 3 nodes in the tree since the performance seems to decrease as the max depth increases. Both the boosting method performance was comparable.
- Specific to net income per share and earning yield features
- The neural network model consists of 2 Hidden layers with 10 nodes each. Compared to other models neural network had least performance. Neural network misclassified many of the companies
- Same is the case with SVM. The performance was comparable to each other. However, the variation in scores is less in svm compared to other models
- These are the common features selected by various feature selection methods. I believe these financial indicators will contribute towards buying a stock or not. From my analysis, financial indicators like, Net income pers hare, shareholders equity, total non-current assets should be kept in mind while buying a stock.
- I would like to compare my classification results to this year, also I would try to plot decision boundaries with reduced dimentionality using PCA or LDA.