Study on the sentimental influence on Indian stock price

Varghese, Renju Rachel; Mohan, Biju R.

doi:10.1016/j.heliyon.2023.e22788

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…They confirmed that negative news affects stock Prices more than positive news. In [ 31 ], the authors studied information flow between the news sentiment and stock Price, emphasising the impact of news during the COVID-19 pandemic on the pharmaceutical sector. They applied Granger Causality, Shannon Transfer Entropy, and Rényi’s Transfer Entropy [ 32 ].…”

Section: Related Workmentioning

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: Related Workmentioning

confidence: 99%

Causality-driven multivariate stock movement forecasting

Díaz Berenguer,

Da,

Bossa

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Therefore, it is crucial for both companies and investors to be able to predict the movements in the stock markets, which are characterized by high volatility, dynamism, and complexity. On the other hand, the subject of the predictability of stock prices, which is exposed to various impacts such as macroeconomic factors, international phenomena, and human behavior, is a controversial issue in the literature [ 2 ]. In the “Efficient Markets Hypothesis” developed by Fama's (1970) it was stated that the price of a stock fully reflects available information, therefore, the price movements of stocks are independent of their past prices and predicting prices in advance is impossible [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

How effective is machine learning in stock market predictions?

Ayyildiz,

Iskenderoglu

2024

Heliyon

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Study on the sentimental influence on Indian stock price

Cited by 2 publications

References 38 publications

Causality-driven multivariate stock movement forecasting

Causality-driven multivariate stock movement forecasting

How effective is machine learning in stock market predictions?

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