Leveraging Temporal Patterns with LSTMs Networks for Financial Forecasting: A New Stastical Machine Learning Approach
Abstract
The intricate temporal patterns observed in financial markets might have an impact on market outlook and trading decisions. The challenge of effectively predicting these markets remains difficult due to the limitations inherent in conventional linear forecasting methods. The aim of this study is to assess the efficacy of Long Short-Terms Memory (LSTMS) networks, which are advanced recurrent neural network designs, in accurately forecasting financial time-series data. This evaluation will entail a comparison of the performance between LSTMS networks and classical models. The present study employs data derived from the S&P 500 index spanning a twenty-year timeframe for the purpose of analysis. The information comprises not just the daily closing prices but also incorporates trading volume and a range of technical indicators, including moving averages, relative strength index (RSI), and momentum. The information provided is extensive and serves as the foundation for the development and evaluation of the forecasting models. A sequence-to-sequence Long Short-Terms Memory (LSTMS) model was devised with the explicit objective of capturing the inherent temporal dynamics inherent in financial data. The research employed a rolling window approach, wherein a predetermined sequence of daily data was employed to predict the closing price of the subsequent day. The LSTMS architecture is composed of input layers, multiple LSTMS units, and output layers, with the incorporation of dropout layers in between to mitigate the problem of overfitting. In order to develop a benchmark, a comparison was conducted between the LSTMS approach and traditional models such as ARIMA and a basic RNN structure.