Forecasting stock prices of fintech companies of India using random forest with high-frequency data

Abstract

The fintech segment is currently one of the most rapidly growing industries, attracting numerous investors who anticipate substantial returns in the future. Notably, not only individual retail investors but also mutual fund agencies are actively engaged in predicting stock prices within this sector to maximize their trading gains. The purpose of the study is to formulate stock forecasting models for top three Fintech Companies of India i.e., Policy Bazar, One 97 Communications Paytm Ltd., and Niyogin Ltd. Using Random Forest model with high-frequency data in Python. The literature review section also proves that this study is a novel piece of work as none of the existing research study focused on predicting stock prices of Fintech Companies of India using Random Forest model. The data is extracted from www.moneycontrol.com and www.kotaksecurities.com, for the period from 1st October, 2022–30 th September, 2023. The study deals about 293,280 data points i.e., 3 companies @ 97,760 each. It has been found that the forecasting model of random forest provides very successful results for prediction as the co-efficient of determination of all the selected companies is more than 95%.

Keywords

• Forecasting
• Random Forest
• Fintech
• High-frequency data
• Python