A comparative study of the performance of Stock trading strategies based on LGBM and CatBoost algorithms.
Publish Year: 1401
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJFMA-7-26_004
تاریخ نمایه سازی: 11 اسفند 1400
Abstract:
Today, investment in the stock market requires novel and efficient methods along with effective trading strategies for more accurate prediction of stock price future movements. This paper compares the performance of implementing LGBM and CatBoost trading strategies on a portfolio, which is formed, based on fundamental analysis and future study. First with use of future study and expert’s opinion, stock market scenarios designed and a portfolio consist of ۶ fundamental stocks is built. In next step for each selected stocks a model is developed by means of LGMB and CatBoost algorithms and related stocks data from ۲۰۱۴ to ۲۰۱۹ to predict stock price movement. Model inputs includes, technical indicators, stocks trading data and some market and fundamental index. Bayesian hyper parameter was used to optimize the model’s key parameters. Results show that models optimized with Bayesian hyper parameter are more accurate than models, which optimized with grid search and implementing short-term trading strategies based on gradient boosting machine (LGBM) prediction signals cause better performance in comparison with CatBoost based strategies and Tehran Stock Exchange Index.
Keywords:
Trading Strategies , Future study , Light Gradient Boosting Machine (LGBM) , Categorical boosting (CatBoost) , Bayesian hyper parameter
Authors
Seyed Majid Mousavi Anzahaei
Ph.D. Candidate, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Hashem Nikoomaram
Professor of financial management, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.