Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange

Rupande, Lorraine; Muguto, Hilary Tinotenda; Muzindutsi, Paul-François

doi:10.1080/23322039.2019.1600233

Cited by 55 publications

(33 citation statements)

References 70 publications

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“…The TGARCH-M mean equation shaped as an ARMA (1.1) resolves any potential issues related to autocorrelation in the mean equation. The outcome shows that the ARMA (1.1) coefficient is statistically significant (P < 0.001, t-value = 3.67, which implies that STOXX600 returns are explained by their historical values, indicative of momentum and mean reversion in share trading as well as past unanticipated shocks, confirming the analyses by Rupande et al (2019). This result may indicate that the effect of historical returns and historical shocks on the conditional mean may disappear afterward a brief period.…”

Section: Resultssupporting

confidence: 61%

A dynamic factor model applied to investor sentiment in the European context

Reis

Pinho

2021

Investment Management and Financial Innovations

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This paper proposes an Investor Sentiment Index for the European market and tests its predictability power over returns and volatility. The constructed Investor Sentiment Index for Europe draws upon three well-established and two recent individual sentiment proxies through a novel dynamic factor modeling addressed to behavioral finance. The index is obtained through an extended period of analysis and validated with other sentiment index measures. The work relies on individual sentiment proxies based on a dynamic factor model and tests it using a TGARCH model for volatility and returns. It carries out an in-sample and out-of-sample analysis to examine this sentiment index’s forecasting power over returns sustained on a recursive rolling window prediction against Fama and French’s three-factor model. The findings demonstrate that the proposed index closely predicts STOXX600 variance and returns and confirms a strong spillover effect between European and US stock markets. This study also concludes that the proposed European Sentiment Index is a valid alternative method for investors to monitor and predict market behaviors. The developed sentiment measure is a vital market prediction movement tool for financial information providers, investors, bankers, and financial analysts. The research combines the sentiment index with a TGARCH approach over the extended period of analysis and validates the method with other sentiment index measures. An in-sample and out-of-sample study confirms the predictive power of this work’s sentiment over returns compared to Fama and French’s three-factor model. AcknowledgmentThis work is funded by National Funds through the FCT – Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Polytechnic of Viseu for their support.

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Section: Resultssupporting

confidence: 61%

A dynamic factor model applied to investor sentiment in the European context

Reis

Pinho

2021

Investment Management and Financial Innovations

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“…By using residuals of weekly trading volumes regressed on a set of macroeconomic variables as a proxy for investor sentiment, in a period of high sentiment, the authors suggested that return shocks are highly significant in conditional volatility. Numerous other studies also demonstrated the significant relationship between market sentiment and stock return volatility (Lee, 2019; Rupande et al , 2019).…”

Section: Introductionmentioning

confidence: 83%

Islamic index market sentiment: evidence from the ASEAN market

Danila

Kamaludin

Sundarasen

et al. 2021

JIABR

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Purpose The purpose of this paper is to examine investor sentiment by measuring the impact of market sentiment shocks on the volatility of the Islamic stock index of five ASEAN countries, with noise traders as a proxy for market sentiment. Design/methodology/approach The GJR-GARCH model is used to capture the empirically observed fact that negative shocks in the past period have a stronger impact on variance than positive shocks in the present. Findings All five ASEAN Islamic stock indices show clustering volatility. However, only three countries, namely, Malaysia, Thailand and Singapore, demonstrate leverage effects. In addition, the effect of market sentiment on Islamic stock index returns is observed in the Indonesian and Malaysian markets, which are the two largest Islamic markets with a dominant Muslim population in the ASEAN. This finding implies that the trading behaviours of Muslim investors in the Shariah market are the same as their behaviours in the conventional market, that is, nonadherence to the Sunnah. Practical implications Whilst establishing investment strategies, creating portfolios and providing client-advisory services, investors and fund managers should factor in the presence of market sentiment and its impact on stock performance and volatility. In addition, a capital market system preventing rumour-based transactions is compelling. Social implications In some markets, the Islamic financial products awareness should be increased through education to attract increased domestic investors with the potential to boost growth in the Islamic stock market. Originality/value Investigation market sentiment impacts on the Islamic stock index using noise traders as a proxy.

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“…The opposite stands for the case when investors are pessimistic, but with less significance. Results obtained by the GARCH method in the paper by Rupande et al (2019) show a significant relationship between investors’ sentiment and stock return volatility on the Johannesburg Stock Exchange. Rahman et al (2013) show that changes in investor sentiment affects the stock return and volatility on the Bangladesh stock market.…”

Section: Introductionmentioning

confidence: 92%

Empirical analysis of dynamic spillovers between exchange rate return, return volatility and investor sentiment

Škrinjarić

Golubić

Orlović

2020

SEF

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Purpose This paper aims to analyze the effects of investors’ sentiment, return and risk series on one to another of selected exchange rates. The empirical analysis consists of a time-varying inter-dependence between the observed variables, with the focus on spillovers between the variables. Design/methodology/approach Monthly data on the index Sentix, exchange rates EUR–USD, EUR–CHF and EUR–JPY are analyzed from February 2003 to December 2019. The applied methodology consists of vector autoregression models (VAR) with Diebold and Yilmaz (2009, 2011) spillover indices. Findings The results of the empirical research indicate that using static analysis could result in misleading conclusions, with dynamic analysis indicating that the financial of 2007-2008 and specific negative events increase the spillovers of shock between the observed variables for all three exchange rates. The sources of shocks in the model change over time because of variables changing their positions being net emitters and net receivers of shocks. Research limitations/implications The shortfalls of this study include using the monthly data frequency, as this was available for the authors, namely, investors are interested to obtain new information on a weekly and daily basis, not only monthly. However, at the time of writing this research, we could obtain only monthly data. Practical implications As the obtained results are in line with previous literature and were found to be robust, there exists the potential to use such analysis in the future when forecasting risk and return series for portfolio management purposes. Thus, a basic comparison was made regarding the investment strategies, which were based on the results from the estimation. It was shown that using information about shock spillovers could result in strategies that can obtain better portfolio value over time compared to basic benchmark strategies. Originality/value First, this paper allows for the spillovers of shocks in variables within the VAR models in all directions. Second, a dynamic analysis is included in the study. Third, the mentioned spillover indices are included in the study as well.

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Investor sentiment and stock return volatility: Evidence from the Johannesburg Stock Exchange

Cited by 55 publications

References 70 publications

A dynamic factor model applied to investor sentiment in the European context

A dynamic factor model applied to investor sentiment in the European context

Islamic index market sentiment: evidence from the ASEAN market

Empirical analysis of dynamic spillovers between exchange rate return, return volatility and investor sentiment

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