Asymmetric relationship of investor sentiment with stock return and volatility: evidence from India

Chakraborty, M.; Subramaniam, Sowmya

doi:10.1108/rbf-07-2019-0094

Cited by 26 publications

(19 citation statements)

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“…Although the sentiment volatility relation is examined by several papers, the empirical studies on the asymmetric sentiment volatility relation are relatively limited. Chakraborty and Subramaniam (2020) find an asymmetric effect in MMI (market mood index) causing stock market volatility in Indian market. The sign of the causality is found to be negative, indicating that high sentiment lowers the future volatility.…”

Section: Introductionmentioning

confidence: 90%

Investor sentiment, realized volatility and stock returns

Abdelmalek

2021

RBF

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PurposeThis paper examines the relationship between volatility, sentiment and returns in terms of levels and changes for both lower and higher data frequencies using quantile regression (QR) method.Design/methodology/approachIn the first step, the study applies the Granger causality test to understand the causal relationship between realized volatility, returns and sentiment as levels and changes. In the second step, the study employs a QR method to investigate whether investor sentiment and returns can predict realized volatility. This regression method gives robust results irrespective of distributional assumptions and to outliers in the dependent variable.FindingsEmpirical results show that the VIX volatility index is a better fear gauge of market-wide investors' sentiments and has a predictive power for future realized volatility in terms of levels and changes for both higher and lower data frequencies. This study provides evidence that the relationship between realized volatility, investor sentiment and returns, respectively, is not symmetric for all quantiles of QR, as opposed to OLS regression. Furthermore, this work supports the behavioral theory beyond leverage hypothesis in explaining the asymmetric relation between returns and volatility at higher and lower data frequencies.Originality/valueThis paper adds to the limited understanding of investor sentiment’s impact on volatility by proposing a QR model which provides a more complete picture of the relationship at all parts of the volatility distribution for both higher and lower data frequencies and in terms of levels and changes. To the author knowledge, this is the first paper to study the volatility responses to positive and negative sentiment changes for developed market and to use both lower and higher data frequencies as well as data in terms of levels and changes.

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Section: Introductionmentioning

confidence: 90%

Investor sentiment, realized volatility and stock returns

Abdelmalek

2021

RBF

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“…Jana ( 2016 ) finds a weak relationship between IS and returns in the Indian equity market. Recently, Chakraborty and Subramaniam ( 2020 ) have used a market-based measure, Market Mood Index (MMI), and a survey-based measure Consumer Sentiment Index (CSI) as proxies for IS. Their results show that IS plays a vital role in predicting stock market return.…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

“…Figure 1 also demonstrates that fewer studies are conducted on emerging and frontier markets like India, Mainland China, and the UAE, which are relatively described as inefficient markets. The stock market's inefficiency indicates institutional investors' lack of systematic arbitrage opportunities, providing a platform to assess the relationship of sentiment on the stock returns (Chakraborty and Subramaniam, 2020 ). Schmeling ( 2009 ) uses Consumer Confidence Index as a proxy for IS and shows that attitudes significantly influence less-developed markets with a lack of market integrity and are prone to herd-like behavior.…”

Section: Literature Review and Conceptual Frameworkmentioning

confidence: 99%

Cross Country Determinants of Investors' Sentiments Prediction in Emerging Markets Using ANN

Rao¹,

Kumar²,

Moonesar³

et al. 2022

Front. Artif. Intell.

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The paper models investor sentiments (IS) to attract investments for Health Sector and Growth in emerging markets, viz., India, Mainland China, and the UAE, by asking questions such as: What specific healthcare sector opportunities are available in the three markets? Are the USA-IS key IS predictors in the three economies? How important are macroeconomic and sociocultural factors in predicting IS in these markets? How important are economic crises and pandemic events in predicting IS in these markets? Is there contemporaneous relation in predicting IS across the three countries in terms of USA-IS, and, if yes, is the magnitude of the impact of USA-IS uniform across the three countries' IS? The artificial neural network (ANN) model is applied to weekly time-series data from January 2003 to December 2020 to capture behavioral elements in the investors' decision-making in these emerging economies. The empirical findings confirmed the superiority of the ANN framework over the traditional logistic model in capturing the cognitive behavior of investors. Health predictor—current health expenditure as a percentage of GDP, USA IS predictor—spread, and Macro-factor GDP—annual growth % are the common predictors across the 3 economies that positively impacted the emerging markets' IS behavior. USA (S&P 500) return is the only common predictor across the three economies that negatively impacted the emerging markets' IS behavior. However, the magnitude of both positive and negative impacts varies across the countries, signifying unique, diverse socioeconomic, cultural, and market features in each of the 3 economies. The results have four key implications: Firstly, US market sentiments are an essential factor influencing stock markets in these countries. Secondly, there is a need for developing a robust sentiment proxy on similar lines to the USA in the three countries. Thirdly, investment opportunities in the healthcare sector in these economies have been identified for potential investments by the investors. Fourthly, this study is the first study to investigate investors' sentiments in these three fast-emerging economies to attract investments in the Health Sector and Growth in the backdrop of UN's 2030 SDG 3 and SDG 8 targets to be achieved by these economies.

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“…Investors' irrationality has attracted the attention of academicians and practitioners across the globe. As a result, researchers have increasingly focused on investor sentiment and its relationship with stock returns (Aggarwal & Mohanty, 2018;Al-Nasseri et al, 2021;Baker & Wurgler, 2006;Brown & Cliff, 2004, 2005Canbas & Candir, 2009;Chakraborty & Subramaniam, 2020;Dash & Mahakud, 2012;Rashid et al, 2019;Sayim & Rahman, 2015). However, the studies focusing on the sentimentreturn relationship in the Indian context are limited (Aggarwal and Mohanty, 2018;Chakraborty and Subramaniam, 2020;Mahakud, 2012, 2013;Dash and Maitra, 2018;Pandey and Sehgal, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, researchers have increasingly focused on investor sentiment and its relationship with stock returns (Aggarwal & Mohanty, 2018;Al-Nasseri et al, 2021;Baker & Wurgler, 2006;Brown & Cliff, 2004, 2005Canbas & Candir, 2009;Chakraborty & Subramaniam, 2020;Dash & Mahakud, 2012;Rashid et al, 2019;Sayim & Rahman, 2015). However, the studies focusing on the sentimentreturn relationship in the Indian context are limited (Aggarwal and Mohanty, 2018;Chakraborty and Subramaniam, 2020;Mahakud, 2012, 2013;Dash and Maitra, 2018;Pandey and Sehgal, 2019). We extend the literature documenting the effect of investor sentiment on stock returns by constructing the investor sentiment index using seven market and firm-related implicit proxies, namely: advances to decline ratio, buy-sell imbalance ratio, equity issues to total issues, volatility premium, share turnover, price-earnings ratio, and turnover volatility ratio.…”

Section: Introductionmentioning

confidence: 99%

Investor Sentiment and Stock Market Returns: Evidence from the Indian Market

Yadav¹,

Chakraborty²

2022

purushartha

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Investor sentiment refers to investors' confidence in the market based on their subjective beliefs. Investor sentiment has become a topic of substantial interest among academicians as investor sentiment induces uninformed demand shocks, which can potentially drive away stock prices from their fundamental value. This study expands the literature on the effect of investor sentiment on stock market returns by constructing an Investor sentiment index for the Indian market. We use Nifty-Fifty returns and the sample period ranges from April 2012 to March 2022. The Principal Component Technique is employed to construct an investor sentiment index using seven market and firm implicit proxies: advances to decline ratio, buy-sell imbalance ratio, equity issues to total issues, volatility premium, share turnover, price-earnings ratio, and turnover volatility ratio. The regression analysis shows that the investor sentiment index has the predictive power to forecast Nifty-Fifty returns.

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Asymmetric relationship of investor sentiment with stock return and volatility: evidence from India

Cited by 26 publications

References 60 publications

Investor sentiment, realized volatility and stock returns

Investor sentiment, realized volatility and stock returns

Cross Country Determinants of Investors' Sentiments Prediction in Emerging Markets Using ANN

Investor Sentiment and Stock Market Returns: Evidence from the Indian Market

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