Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak

Das, Nabanita; Sadhukhan, Bikash; Chatterjee, Tanusree; Chakrabarti, Satyajit

doi:10.1007/s13278-022-00919-3

Cited by 32 publications

(8 citation statements)

References 43 publications

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“…A significant aspect to consider in stock market forecasting concerns the co-occurring or associate covariates. Multiple factors, including the opinions of investors, the emotions of traders, the views of the public, and the news can influence the stock market [ 55 ]. Accordingly, this work focuses on the study of sentiment analysis and information flow underlying causality between news, sentiment, and the past stock market.…”

Section: Stock Market Forecastingmentioning

confidence: 99%

Causality-driven multivariate stock movement forecasting

Díaz Berenguer,

Da,

Bossa

et al. 2024

PLoS ONE

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Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

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Section: Stock Market Forecastingmentioning

confidence: 99%

Causality-driven multivariate stock movement forecasting

Díaz Berenguer,

Da,

Bossa

et al. 2024

PLoS ONE

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“…and modeled the relationship between sentiment indices and stock price movements through multiple regression analysis. Das et al (2022) employed a logistic regression algorithmic approach for sentiment analysis and stock price prediction based on 1,452 comments retrieved from the official website of the Oromo Democratic Party, achieving a 98.32% prediction accuracy. Meanwhile, Weng, Lin & Zhao (2022) utilised a finely-tuned BERT model to compute investor sentiment indices of stock pre-openings and merged them with the underlying stock ticker data.…”

Section: Related Workmentioning

confidence: 99%

LEET: stock market forecast with long-term emotional change enhanced temporal model

Liao,

Huang,

Tang

2024

PeerJ Computer Science

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The stock market serves as a macroeconomic indicator, and stock price forecasting aids investors in analysing market trends and industry dynamics. Several deep learning network models have been proposed and extensively applied for stock price prediction and trading scenarios in recent times. Although numerous studies have indicated a significant correlation between market sentiment and stock prices, the majority of stock price predictions rely solely on historical indicator data, with minimal effort to incorporate sentiment analysis into stock price forecasting. Additionally, many deep learning models struggle with handling the long-distance dependencies of large datasets. This can cause them to overlook unexpected stock price fluctuations that may arise from long-term market sentiment, making it challenging to effectively utilise long-term market sentiment information. To address the aforementioned issues, this investigation suggests implementing a new technique called Long-term Sentiment Change Enhanced Temporal Analysis (LEET) which effectively incorporates long-term market sentiment and enhances the precision of stock price forecasts. The LEET method proposes two market sentiment index estimation methods: Exponential Weighted Sentiment Analysis (EWSA) and Weighted Average Sentiment Analysis (WASA). These methods are utilized to extract the market sentiment index. Additionally, the study proposes a Transformer architecture based on ProbAttention with rotational position encoding for enhanced positional information capture of long-term emotions. The LEET methodology underwent validation using the Standard & Poor’s 500 (SP500) and FTSE 100 indices. These indices accurately reflect the state of the US and UK equity markets, respectively. The experimental results obtained from a genuine dataset demonstrate that this method is superior to the majority of deep learning network architectures when it comes to predicting stock prices.

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“…Moreover, the influence of sentiment analysis is considered to be able to influence stock price movements as seen from several previous studies [22] [23] .…”

Section: A Motivationmentioning

confidence: 99%

Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT

Girsang,

Stanley

2023

IEEE Access

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Cryptocurrencies are digital assets that are widely used for trading and investing. One of the characteristics that traders take advantage of for profit is the high volatility of the price. Its volatile and rapidly changing prices have made cryptocurrency price predictions a challenging and highly sought-after research topic. Cryptocurrency price predictions usually only use historical prices on the dataset, while price movements are also influenced by other aspects such as sentiment contained in social med ia. This study proposes a new machine learning method to predict Ethereum and Solana cryptocurrency price, which integrates cryptocurrency historical price data and social media sentiment as inputs of the prediction model. FinBERT, a pre-trained sentiment analysis model is used to extract the sentiment implied in social network tweets into daily sentiment score, which are then combined with the historical market price data. The hybrid model of LSTM-GRU model is used to train the dataset and perform cryptocu rrency price prediction. The experiment results show that the presented method can successfully predict the Ethereum and Solana price movement and has superior performance than all the benchmark models.

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Effect of public sentiment on stock market movement prediction during the COVID-19 outbreak

Cited by 32 publications

References 43 publications

Causality-driven multivariate stock movement forecasting

Causality-driven multivariate stock movement forecasting

LEET: stock market forecast with long-term emotional change enhanced temporal model

Hybrid LSTM and GRU for Cryptocurrency Price Forecasting Based on Social Network Sentiment Analysis Using FinBERT

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