Predictability of stock returns of financial companies and the role of investor sentiment: A multi-country analysis

Kadilli, Anjeza

doi:10.1016/j.jfs.2015.09.004

Cited by 33 publications

(22 citation statements)

References 75 publications

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“…Related strand of literature provide compelling empirical evidence towards the application of consumer confidence index (CCI) data as a suitable proxy for investor sentiment. For instance, Fisher and Statman (2002), Lemmon and Portniaguina (2006), Chung, Hung, and Yeh (2012) for U.S. stock market, Schmeling (2009) for 18 European countries, Kadilli (2015) for 20 developed countries show that CCI can be considered as a potential measure of investor optimism. Nevertheless, Schmeling (2009) suggest that it seems natural to use CCI metric as a sentiment proxy for an international analysis, because consistent data availability across different countries for reasonable periods of time and perhaps the only consistent way to obtain a sentiment proxy that is largely comparable across countries.…”

Section: Variables and Descriptive Statisticsmentioning

confidence: 99%

“…We use four proxies to capture the global investor sentiment. For instance, Fisher and Statman (2002), Lemmon and Portniaguina (2006), Chung, Hung, and Yeh (2012) for U.S. stock market, Schmeling (2009) for 18 European countries, Kadilli (2015) for 20 developed countries show that CCI can be considered as a potential measure of investor optimism.…”

Section: Sentiment Variablesmentioning

confidence: 99%

“…For our EMSI construction we follow two steps. In the first step, similar to the approach of Lemmon and Portniaguina (2006) and Kadilli (2015), we regress each country CCI data with one month lag value of macroeconomic variables of the respective country. Specifically, we consider inflation, industrial production growth rate, term spread, and change in money supply.…”

Section: Sentiment Variablesmentioning

confidence: 99%

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Stock market liquidity: Implication of local and global investor sentiment

Debata

Dash

Mahakud

2020

Journal of Public Affairs

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This article examines the impact of local and global investor sentiment on stock market liquidity using data from an order‐driven emerging market. Along with four different liquidity proxies, the empirical analysis uses one domestic investor sentiment index and four global sentiment proxies that represent investor sentiment of United States, Europe, and aggregate emerging market sentiment. Granger‐causality test results suggest that investor sentiment significantly Granger‐causes stock market liquidity. We also find that investor sentiment is an essential determinant of stock market liquidity and the impact of global investor sentiment persists even after controlling local sentiment. The empirical findings are robust across different sample periods and liquidity measures.

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Section: Variables and Descriptive Statisticsmentioning

confidence: 99%

Section: Sentiment Variablesmentioning

confidence: 99%

Section: Sentiment Variablesmentioning

confidence: 99%

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Stock market liquidity: Implication of local and global investor sentiment

Debata

Dash

Mahakud

2020

Journal of Public Affairs

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“…Some authors believe that investor sentiment is a subjective part of expectations (Baker and Stein, 2004;Shefrin, 2008;Ramiah et al, 2015), whereas Baker and Wurgler (2006), Black (1986) and Karakatsani and Salmon (2008) define sentiment as excessive and unjustified optimism or pessimism by individuals. Johnson et al (2006) and, more recently, Kadili (2015) present this concept as a deformation in market beliefs reflecting a gap between what the asset price is and what it should be.…”

Section: Introductionmentioning

confidence: 99%

“…Indirect measures include essentially financial and economic indicators that contain expectations not justified by fundamentals. In this way, many researchers have attempted to construct composite index measures, combining direct and indirect measures, of investor sentiment by taking into account market specificities (Beer and Zouaoui, 2013;Finter et al, 2012;Hudson and Green, 2015;Kadili, 2015;Lemmon and Portniaguina, 2006).…”

Section: Introductionmentioning

confidence: 99%

Some evidence from a principal component approach to measure a new investor sentiment index in the Tunisian stock market

Bouteska

2019

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Purpose The purpose of this paper is to study a novel and direct measurement of investor sentiment index in the Tunisian stock market that overcomes the weaknesses of a well-known investor sentiment index by Baker and Wurgler (2006, 2007). Design/methodology/approach Based on the data of 43 firms of the Tunisian stock market index (Tunindex) over the period 2004–2016, the author constructs a monthly investor sentiment that reflects both the economic fundamentals and the investor sentiment components. Seven indirect indicators collected from investor sentiment literature and Tunisian stock exchange were analyzed. Specifically, after accounting to remove the sentiment component for macroeconomic factors, the author estimates each sentiment proxy with a number of controlling variables. The residual from the estimation is used to define the author’s measure of excessive investor sentiment. To determine the best timing of sentiment indicators, the author employs a factor sentiment series as the first principal component of these total seven sentiment proxies and their lags of a month. Furthermore, by capturing the highest saturations with the first factor analysis, the author regressed each selected indicator’s lead or one-month lag in a second linear principal component analysis to reach the author’s Tunisian market’s total sentiment index. Findings The results show that all employed indicators may reflect the investor sentiment on the Tunisian stock market. The findings also indicate significant evidence that the author’s sentiment index takes into consideration the political and economic events such as the Jasmine Revolution experienced by Tunisia during the period from January 2, 2004 to December 30, 2016. Moreover, investor sentiment index flow appears to be one leading mechanism for the performance of Tunindex. Originality/value Results found have clearly shown that the author’s seven indirect indicators can reflect investor sentiment in the Tunisian context. The various sentiment proxies are bullish indicators of investor sentiment. Brown and Cliff (2004) argue that the higher bull/bear ratio, the more investor sentiment is bullish. An important value of price–earnings ratio implies that the level of investor confidence as for change in market is also important. Liquidity measured by trading volume, market turnover ratio and liquidity ratio reflects individual investor sentiment. Otherwise, it seems that investors only invest when they are optimistic and reduce market liquidity once they became pessimistic. The monthly response rate to initial public offerings (IPOs) represents a bullish sentiment indicator. Indeed, the more optimistic investors are, the higher the response rate to IPOs. Investor satisfaction also reflects investor sentiment. In other words, a high level of satisfaction translates an important level of optimism. In addition, the author also recognizes that the authors’ Tunisian sentiment index follow general trend of stock market prices and appears to be an important determinant of Tunindex returns during the period of study, from January, 2004 to December, 2016. The author suggests investor sentiment can help predict Tunindex returns, distinguishing between turbulent and tranquil periods in the financial market. The graphical illustration of monthly investor sentiment index shows that it captures extreme events such as the Tunisian revolution of January, 2011, also known as the Jasmine revolution which marked the start of the Arab Spring and the consequences of economic and political turmoil in Tunisia that have disrupted economic activity in the next few years. Like all research work, the current research paper has certain limitations. The choice of control variables allowing the author to separate sentiment component of that fundamental might be criticized. Moreover, there is no unanimous number of control variables but they are chosen according to data availability. The author also believes that one of the study’s weaknesses is that the author has not examined the impact of investor sentiment on the Tunisian stock market. For future interesting avenues of research, the author proposes, first, to study the effect of investor sentiment on financial asset returns and check, second, if sentiment factor constitutes an additional source of business risk valued by the marketplace.

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