Predicting the direction of stock market prices using tree-based classifiers

By Suryoday Basak, Saibal Kar, Snehanshu Saha, Luckyson Khaidem, Sudeepa Roy Dey. The North American journal of economics and finance, 2019

321 citations recorded by the source corpus.

Predicting returns in the stock market is usually posed as a forecasting problem where prices are predicted. Intrinsic volatility in the stock market across the globe makes the task of prediction challenging. Consequently, forecasting and diffusion modeling undermines a diverse range of problems encountered in predicting trends in the stock market. Minimizing forecasting error would minimize investment risk. In the current work, we pose the problem as a direction-predicting exercise signifying gains and losses. We develop an experimental framework for the classification problem which predicts whether stock prices will increase or decrease with respect to the price prevailing n days earlier. Two algorithms, random forests, and gradient boosted decisio‘n trees (using XGBoost) facilitate this connection by using ensembles of decision trees. We test our approach and report the accuracies for a variety of companies as improvement over existing predictions. A novelty of the current work is about the selection of technical indicators and their use as features, with high accuracy for medium to long-run prediction of stock price direction.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f64fbcabcc5e00ee127bf9c6bd855d1b518b9507

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Abhirup Datta
2. Amir Mosavi
3. Sumanta Basu

In Finance

1. Abhirup Datta
2. Arkajyoti Saha
3. Sumanta Basu

In Management Science and Operations Research

1. Pooyan Nayyeri
2. Hamed Jabani
3. Amir Mosavi
4. Sumanta Basu
5. Mojtaba Nabipour

Citations in the corpus

• Forecasting gold price with the XGBoost algorithm and SHAP interaction values (2021, unknown): “1 They have already been used to analyze financial data sets (Basak et al., 2019; Ma et al., 2018; Huck, 2019) but never to predict commodity price fluctuations.”
• A Random Forests Approach to Predicting Clean Energy Stock Prices (2021, unknown): “Popular modelling approaches include multifactor models (Henriques and Sadorsky 2008; Gupta 2017; Reboredo et al. 2017b; Bohl et al. 2013; Sadorsky 2012), vector autoregressions (Kumar et al. 2012; Dutta et al. 2018), or other approaches like wavelets (Maghyereh et al. 2019; Reboredo et al. 2017a),…”
• Deep Learning for Stock Market Prediction (2020, background): “The predictions are evaluated for 1,2,5,10,15,20 and 30 days in advance. It has been noted from the research background, the most of them focused on classification problems instead of regression ones [26, 27, 28]. By considering literature review, this research work examines the prediction performan”
• Random Forests for dependent data (2020, methodology): “[6] considered an exponential smoothing of the time series response to give more weights to recent data.”
• Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020, methodology): “Basak et al. [15] employed XGBoost and Random Forest methods for the classification problem to forecast the stock increase or decrease based on previous values.”