Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest
Stock-market-forecasting
Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest
https://arxiv.org/abs/2004.10178
Pushpendu Ghosh, Ariel Neufeld, Jajati K Sahoo
We design a highly profitable trading stratergy and employ random forests and LSTM networks (more precisely CuDNNLSTM) to analyze their effectiveness in forecasting out-of-sample directional movements of constituent stocks of the S&P 500, for intraday trading, from January 1993 till December 2018.
Bibtex
@article{ghosh2021forecasting,
title={Forecasting directional movements of stock prices for intraday trading using LSTM and random forests},
author={Ghosh, Pushpendu and Neufeld, Ariel and Sahoo, Jajati Keshari},
journal={Finance Research Letters},
pages={102280},
year={2021},
publisher={Elsevier}
}
Requirements
pip install scikit-learn==0.20.4
pip install tensorflow==1.14.0
Plots
We plot three important metrics to quantify the effectiveness of our model: Intraday-240,3-LSTM.py and Intraday-240,3-RF.py, in the period January 1993