A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

Mohammad Kamel Daradkeh

doi:10.3390/electronics11020250

Abstract

Stock market analysis plays an indispensable role in gaining knowledge about the stock market, developing trading strategies, and determining the intrinsic value of stocks. Nevertheless, predicting stock trends remains extremely difficult due to a variety of influencing factors, volatile market news, and sentiments. In this study, we present a hybrid data analytics framework that integrates convolutional neural networks and bidirectional long short-term memory (CNN-BiLSTM) to evaluate the impact of convergence of news events and sentiment trends with quantitative financial data on predicting stock trends. We evaluated the proposed framework using two case studies from the real estate and communications sectors based on data collected from the Dubai Financial Market (DFM) between 1 January 2020 and 1 December 2021. The results show that combining news events and sentiment trends with quantitative financial data improves the accuracy of predicting stock trends. Compared to benchmarked machine learning models, CNN-BiLSTM offers an improvement of 11.6% in real estate and 25.6% in communications when news events and sentiment trends are combined. This study provides several theoretical and practical implications for further research on contextual factors that influence the prediction and analysis of stock trends.

Highlights

Financial stock markets have an immense impact on the world economy as well as on financial and social organizations
It includes five main steps: (1) the stock-related financial data and news are separately sifted and preprocessed to create the stock database and the news database; (2) the stock news are divided into stock events, and each news event is labeled with an event type; (3) a conventional neural networks (CNNs) classifier is developed and trained to classify the event type; (4) the news events are labeled with a sentiment, and a news sentiment classifier is developed using BiLSTM; and (5) the stock news features and stock price features trained in steps (3) and (4) are fed into the long short-term memory (LSTM) network to evaluate their fusion fitness for predicting the rise and fall of stock trends
For the dataset analyzed in this study, the CNN-based news classifier achieved an accuracy of 93.0% in the training dataset and 87.7% in the testing dataset, outperforming the support vector machine (SVM)-based and Maxent-based news classifiers

Summary

Introduction

Financial stock markets have an immense impact on the world economy as well as on financial and social organizations. While financial markets are associated with colossal gains, big gains carry risks that can lead to misfortune. This makes stock market prediction an interesting but difficult endeavor, as it is extremely difficult to predict stock markets with high accuracy due to high instability, random fluctuations, anomalies, and turbulence. Verifiable time series data from financial stock exchanges provide detailed information about a particular stock during given stock market cycle [2]. This temporal data includes opening and closing prices, highs and lows, and the volume of stocks traded during a particular time period. Fundamental and technical analysis techniques typically rely on quantitative stock data such as stock costs, volumes, and portfolios, as well as subjective data about the companies involved, their profiles, and their trading strategies [3]

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