Using Social Network Sentiment Analysis and Genetic Algorithm to Improve the Stock Prediction Accuracy of the Deep Learning-Based Approach

Traditionally, most investment tools used to predict stocks are based on quantitative variables, such as finance and capital flow. With the widespread impact of the Internet, investors and investment institutions designing investment strategies are also referring to online comments and discussions. However, multiple information sources, along with uncertainties accompanying international political and economic events and the recent pandemic, have left investors concerned about information interpretation approaches that could aid investment decision-making. To this end, this study proposes a method that combines social media sentiment, genetic algorithm (GA), and deep learning to predict changes in stock prices. First, it employs a hybrid genetic algorithm (HGA) combined with machine learning to identify chip-based indicators closely related to fluctuations in stock prices and then uses them as input for long short-term memory (LSTM) to establish a prediction model. Next, this study proposes five sentiment variables to analyze PTT social media on TSMC's stock price and performs a grey relational analysis (GRA) to identify the sentiment variables most closely related to stock price fluctuations. The sentiment variables are then combined with the selected chip-based indicators as input to build the LSTM prediction model. To improve the efficiency of the LSTM analysis, this study applies the Taguchi method to optimize the hyper-parameters. The results show that the proposed method of using HGA-screened chip-based variables and social media sentiment variables as input to establish an LSTM prediction model can effectively improve the prediction accuracy of stock price fluctuations.

Mixed frequency composite indicators for measuring public sentiment in the EU.

Monitoring the state of the economy in a short time is a crucial aspect for designing appropriate and timely policy responses in the presence of shocks and crises. Short-term confidence indicators can help policymakers in evaluating both the effect of policies and the economic activity condition. The indicator commonly used in the EU to evaluate the public opinion orientation is the Economic Sentiment Indicator (ESI). Nevertheless, the ESI shows some drawbacks, particularly in the adopted weighting scheme that is static and not country-specific. This paper proposes an approach to construct novel composite confidence indicators, focusing on both the weights and the information set to use. We evaluate these indicators by studying their response to the policies introduced to contain the COVID-19 pandemic in some selected EU countries. Furthermore, we carry out an experimental study where the proposed indicators are used to forecast economic activity.

Assessing the impact of media sentiment on the returns of sukuks during the Covid-19 crisis

The wavelet approach covering simultaneously the time and frequency domains is employed to study the impact of the Covid-19 coverage in mass media on the performance of the Dow Jones Sukuk investment grade total return indices. The overall coherence level for the media-coverage – sukuk pairs is found to increase with the investment horizon. Multiple time-frequency regions with low level of coherence, observable along the Covid-19 systemic crisis, imply attractive diversification attributes of investing in Islamic fixed-income securities especially in times of financial stress and turmoil. We investigate coherence and phase difference patterns, which differ for distinct maturity buckets of the Sukuk indices, further highlighting their potentiality for the downside risk hedge, workable under economic and financial distress.

Does media sentiment affect stock prices? Evidence from China's STAR market.

Objective: This paper explores the impact of media sentiment on stock prices on the Shanghai Stock Exchange Science and Technology Innovation Board (hereinafter the STAR market) from a behavioral finance perspective. Methods: We collect Baidu News coverage of STAR-listed firms as the text, and measure text sentiment using a machine learning-based text analysis technique. We then empirically examine the impact of media sentiment on STAR market stock prices from two aspects: IPO pricing efficiency and IPO first-day stock performance. Results: (1) Media sentiment has no significant impact on IPO pricing efficiency, thus suggesting that institutional investors participating in such offerings are generally not affected by media sentiment. (2) Optimistic media sentiment has a positive impact on IPO first-day returns, which indicates that individual investors are more easily influenced by media sentiment and therefore likely to abandon their rational judgment. (3) Media sentiment had a greater impact on IPO first-day returns during the COVID-19 pandemic than those before it, which suggests that individual investors are more influenced by media sentiment during pandemics. Discussion: Our findings deepen the understanding of stock price formation on the STAR market, which provide a statistical basis for formulating policy directions and investment strategies.

Sentiment and hype of business media topics and stock market returns during the COVID-19 pandemic.

We examine COVID-19 related topics discussed in the printed edition of the Wall Street Journal. Using text analytics and topic modeling algorithms, we discover 15 distinct topics and present differences in their sentiment (polarity) and hype (intensity of coverage) trends throughout 2020. Importantly, the hype of the topic, not the sentiment, relates to stock market returns. In particular, the hype scores for Debt market and Financial markets have the strongest positive relation to the stock market performance.