Augmented Textual Features-Based Stock Market Prediction

Abstract

Due to its dynamics, non-linearity and complexity nature, stock market is inherently difficult to predict. One of the attractive objectives is to predict stock market movement direction by using public sentiments analysis. However, there is an active debate about the usefulness of this approach and the strength of causality between stock market trends and sentiments. The opinions of researchers range from rejecting the relationship to confirming a clear causality between sentiments and trading in stock markets. Nevertheless, many advanced computational methods have adopted sentiment-based features, yet did not attain maturity and performance. In this paper, we are contributing constructively in this debate by empirically investigating the predictability of stock market movement direction using an enhanced method of sentiments analysis. Precisely, we experiment on stock prices history, sentiments polarity, subjectivity, N-grams, customized text-based features in addition to features lags that are used for a finer-grained analysis. Five research questions have been investigated towards answering issues associated with stock market movement prediction using sentiment analysis. We have collected and studied the stocks of ten influential companies belonging to different stock domains in NASDAQ. Our analysis approach is complemented by a sophisticated causality analysis, an algorithmic feature selection and a variety of machine learning techniques including regularized models stacking. A comparison of our approach with other sentiment-based stock market prediction approaches including Deep learning, establishes that our proposed model is performing adequately and predicting stock movements with a higher accuracy of 60%.

Data fusion between time series and textual information for predictive tasks in the financial market: A systematic mapping

“…The following study used Twitter data to predict six NASDAQ companies' ups and downtrends [38]. First, a methodology was proposed that starts by extracting various text-based resources to enrich the representation of feelings.…”

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

“…Moreover, a few studies have used Latent Dirichlet Allocation (LDA) [82][83][84]., where words are viewed as a probabilistic collection of concepts, and the concepts are used as features [82][83][84]. Some works [44,76,79] used the N-grams technique. N-grams is the contagious collection of N words from a given sequence of text.…”

Predictive intelligence using ANFIS‐induced OWAWA for complex stock market prediction

“…Ruan et al 36 analysed the sensitivity of hyperparameters in the LSTM method and found that the approach yields an optimal result when the hidden size is set between 250 and 260. Bouktif et al 37 used sentiment‐based stock market prediction utilising the deep learning method. The suggested approach excelled sufficiently and forecasted stock movements with a greater accuracy of 60%.…”