Previs\~ao dos pre\c{c}os de abertura, m\'inima e m\'axima de \'indices de mercados financeiros usando a associa\c{c}\~ao de redes neurais LSTM

TL;DR

This paper proposes a novel model combining three parallel LSTM neural networks to predict opening, minimum, and maximum stock index prices, demonstrating reasonable accuracy on a diverse global dataset.

Contribution

It introduces an improved multi-LSTM model for stock index prediction, leveraging parallel networks for different price points, enhancing forecast accuracy over traditional methods.

Findings

Model predicts stock prices with reasonable accuracy.

Uses data from over 10 global stock indices.

Employs a novel combination of three parallel LSTM networks.

Abstract

In order to make good investment decisions, it is vitally important for an investor to know how to make good analysis of financial time series. Within this context, studies on the forecast of the values and trends of stock prices have become more relevant. Currently, there are different approaches to dealing with the task. The two main ones are the historical analysis of stock prices and technical indicators and the analysis of sentiments in news, blogs and tweets about the market. Some of the most used statistical and artificial intelligence techniques are genetic algorithms, Support Vector Machines (SVM) and different architectures of artificial neural networks. This work proposes the improvement of a model based on the association of three distinct LSTM neural networks, each acting in parallel to predict the opening, minimum and maximum prices of stock exchange indices on the day following the analysis. The dataset is composed of historical data from more than 10 indices from the world's largest stock exchanges. The results demonstrate that the model is able to predict trends and stock prices with reasonable accuracy.