A Hybrid Framework Using PCA, EMD and LSTM Methods for Stock Market Price Prediction with Sentiment Analysis

Krittakom Srijiranon · Yoskorn Lertratanakham · Tanatorn Tanantong

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Time Series Analysis and Forecasting

Abstract

The aim of investors is to obtain the maximum return when buying or selling stocks in the market. However, stock price shows non-linearity and non-stationarity and is difficult to accurately predict. To address this issue, a hybrid prediction model was formulated combining principal component analysis (PCA), empirical mode decomposition (EMD) and long short-term memory (LSTM) called PCA-EMD-LSTM to predict one step ahead of the closing price of the stock market in Thailand. In this research, news sentiment analysis was also applied to improve the performance of the proposed framework, based on financial and economic news using FinBERT. Experiments with stock market price in Thailand collected from 2018–2022 were examined and various statistical indicators were used as evaluation criteria. The obtained results showed that the proposed framework yielded the best performance compared to baseline methods for predicting stock market price. In addition, an adoption of news sentiment analysis can help to enhance performance of the original LSTM model.

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References

2008Surveying stock market forecasting techniques – Part II: Soft computing methods 2017Can Twitter Help Predict Firm-Level Earnings and Stock Returns?2020Evaluating time series forecasting models: an empirical study on performance estimation methods 2016Forecasting daily stock market return using dimensionality reduction 2020Stock market prediction using machine learning classifiers and social media, news 2021A systematic review of stock market prediction using machine learning and statistical techniques 2017Can investor sentiment be a momentum time-series predictor? Evidence from China 2011A novel text mining approach to financial time series forecasting

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