Abstract

Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

Highlights

  • Forecasting future the trend and direction of stock price movement is an essential task which helps investors to take prudent financial decisions in the stock market

  • We compare the effectiveness of tree-based ensemble Machine learning (ML) models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement

  • The effectiveness of tree-based ML ensemble models (Random Forest classifier, XGBoost classifier, AdaBoost classifier, Bagging classifier, Extra Trees classifier, and Voting classifier) in forecasting the direction of stock price movement is examined in the study

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Summary

Introduction

Forecasting future the trend and direction of stock price movement is an essential task which helps investors to take prudent financial decisions in the stock (equity) market. A precise forecast of equity market movement is essential in order to maximize capital gain and minimize loss, since investors are likely to buy or desist from stock whose future value is expected to rise or fall respectively. Methods such as technical analysis, fundamental analysis, time series forecasting, and machine learning (ML) exist to forecast the behavior of stock prices. We examine and compare the effectiveness of the following tree-based ensemble ML models in forecasting the direction of stock price movement: (i) Random Forest Classifier (RF), (ii) XGBoost Classifier (XG), (iii) Bagging Classifier (BC), (iv) AdaBoost Classifier (Ada), (v) Extra Trees classifier (ET), and (vi) Voting Classifier (VC)

Experimental Design
Data and Features
Data Normalization
Feature Extraction
Machine Learning Algorithms
Base Classifier
Random Forest Classifier
AdaBoost Classifier
XGBoost Classifier
Bagging Classifier
Extra Trees Classifier
Voting Classifier
Evaluation Metric
Results and Analysis
Recallof measure of the tree-based
Conclusions

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