A new approach for trading based on Long Short Term Memory technique

TL;DR

This paper introduces an ensemble LSTM model incorporating annual and daily data to enhance next-day stock price prediction, demonstrating improved accuracy on NYSE data.

Contribution

It presents a novel ensemble LSTM approach combining different time frequencies for stock prediction, which is a new methodology in this domain.

Findings

Improved prediction accuracy over traditional methods.

Effective use of multi-frequency data in LSTM models.

Validated on 417 NYSE companies.

Abstract

The stock market prediction has always been crucial for stakeholders, traders and investors. We developed an ensemble Long Short Term Memory (LSTM) model that includes two-time frequencies (annual and daily parameters) in order to predict the next-day Closing price (one step ahead). Based on a four-step approach, this methodology is a serial combination of two LSTM algorithms. The empirical experiment is applied to 417 NY stock exchange companies. Based on Open High Low Close metrics and other financial ratios, this approach proves that the stock market prediction can be improved.