Validating the impact of accounting disclosures on stock market

Prajwal Eachempati, Praveen Ranjan Srivastava, Ajay Kumar (), Kim Hua Tan and Shivam Gupta
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Prajwal Eachempati: IIM-Rohtak - Indian Institute of Management Rohtak
Praveen Ranjan Srivastava: IIM-Rohtak - Indian Institute of Management Rohtak
Ajay Kumar: EM - EMLyon Business School
Kim Hua Tan: UON - University of Nottingham, UK
Shivam Gupta: NEOMA - Neoma Business School

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Abstract: Firms disclose information either voluntarily or due to the regulator's mandatory requirements, and such disclosures form good sources to know the prospects of a firm. Information in the disclosures and analysts' opinions influence investor-trading behavior, and consequently, affects the asset prices. Assentiments factored in disclosures are a source of market action, this study aims to capture the sentiments from disclosure information to assess asset prices' impact. The paper adopts a deep neural network-based prediction model for conducting sentiment analysis on heterogeneous datasets. We construct a sentiment simulation model of voluntary disclosures to know whether themanagers can use themarket sentiment as a strategic input to boost market performance by suitably drafting the tone and content of disclosures without compromising their quality and veracity. The Deep Neural Networks with LSTM algorithm is found to outperform the Deep Neural Networks with RNN and other baseline machine learning classifiers in terms of predictive accuracy of the NSE NIFTY50. The variable importance computed also validates that market news, combined with historical indicators, predicts the stock market trend closer to the actual trend.

Keywords: Data intelligence; Private decision-making; Forecasts; Stock market; Deep learning; Machine learning; finance; Analytics; Disclosures (search for similar items in EconPapers)
Date: 2021-09-01
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Published in Technological Forecasting and Social Change, 2021, 14 p. ⟨10.1016/j.techfore.2021.120903⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05626268

DOI: 10.1016/j.techfore.2021.120903

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