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This paper examines the impact of political instability on the investors' behavior, measured by Google search queries, and on the dynamics of stock market returns.

First, by using the DCC-GARCH model, the authors examine the effect of investor sentiment on the Tunisian stock market return. Second, the authors employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. Finally, the authors use the wavelet coherence model to test the co-movement between investor sentiment measured by Google Trends and Tunisian stock market return.

Using the dynamic conditional correlation (DCC), the authors find that Google search queries index has the ability to reflect political events especially the Tunisian revolution. In addition, empirical results of fully modified ordinary least square (FMOLS) method reveal that Google search queries index has a slightly higher effect on Tunindex return after the Tunisian revolution than before this revolution. Furthermore, by employing wavelet coherence model, the authors find strong comovement between Google search queries index and return index during the period of the Tunisian revolution political instability. Moreover, in the frequency domain, strong coherence can be found in less than four months and in 16–32 months during the Tunisian revolution which show that the Google search queries measure was leading over Tunindex return. In fact, wavelet coherence analysis confirms the result of DCC that Google search queries index has the ability to detect the behavior of Tunisian investors especially during the period of political instability.

This study provides empirical evidence to portfolio managers that may use Google search queries index as a robust measure of investor's sentiment to select a suitable investment and to make an optimal investments decisions.

The important research question of how political instability affects stock market dynamics has been neglected by scholars. This paper attempts principally to fill this void by investigating the time-varying interactions between market returns, volatility and Google search based index, especially during Tunisian revolution.

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).

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.

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.

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.

Previously elaborated research works, dealing with the political uncertainty effect on stock market, have been primarily concerned with such political events as terrorist attacks, elections, wars, natural catastrophes and financial crashes. Such little research has been concerned with civil uprisings and revolutionary movements, as crucial sources of political uncertainty. The purpose of this paper is to study the impact of political uncertainty (resulting from the Tunisian Revolution) on the volatility of major sectorial stock indices in the Tunisian Stock Exchange (TSE).

The authors apply the fractionally integrated exponential generalized autoregressive conditional heteroscedasticity model (FIEGARCH), which helps maintain a direct shock-persistence as well as a shock asymmetric volatility measurement. This model is applied to the daily returns relevant to nine sectorial stock indices and to the Tunisian benchmark index (TUNINDEX) with respect to three sub-periods (before, during and follows the Tunisian Revolution).

The reached findings suggest that the shock impact throughout the Revolution period on construction, industries, consumer services, financial services, financial companies indices’ sectorial and the TUNINDEX return volatilities have proven to be permanent, while its persistence on the other indices has been discovered to be transitory. In addition, the achieved results appear to reveal a low leverage effect on all indices. This result seems to be very important since the Tunisian Revolution turns out to have a very important effect on the TSE.

The paper’s empirical contribution lies in using the FIEGARCH approach to model the Tunisian sectorial indices’ volatility dynamics, persistence degree and leverage effect. This contribution goes a long way in helping regulators and international investors to further recognize the extent to which political instability does participate in affecting the TSE.

This chapter examines the existence of dynamic herding behavior by Tunisian investors in the Tunisia stock market during the revolution period of 2011–2013. The sample covers all Tunindex daily returns as a proxy for the Tunisia stock exchange index over the period 2007–2018. The author modifies the cross-sectional absolute deviation model to include all market conditions (bull and bear markets) and the geopolitical crisis effect corresponding to the Tunisian Jasmine revolution during 2011–2013, and show that herding is indeed not present in the Tunisia stock market including during its turmoil periods. These findings imply that the Tunisian emerging financial market became more vulnerable to adverse herding behavior after the revolution. There is also a clear implication for capitalist firms and angel investors in Tunisia that adverse herding behavior tends to exist on days of higher uncertainty and information asymmetry.

This study aims to test the contagion effect of the Tunisian revolution on the Egyptian stock market. Thus, the purpose of this research is to distinguish the contagion effect from the simple interdependence between these markets.

This paper examines the contagion hypothesis between Tunisia and Egypt during the Arab Spring, using a DCC-MGARCH model to capture time-varying contagion effects and dynamic linkages in stock markets. Therefore, to identify the contagion effect from the simple interdependence, the authors apply the pure contagion test developed by Forbes and Rigobon (2002).

The findings indicate a contagion effect, as the EGX 30 index exhibited similar changes, positive or negative, as the Tunindex index during the period of the Tunisian revolution. Moreover, the analysis demonstrates the presence of an interdependence between the Tunisian revolution and the Egyptian market, emphasizing the interconnections between these two economies.

The findings provide investors with a better understanding of financial market dynamics in times of major political unrest, notably on the Tunisian and Egyptian markets. By understanding the contagion effect of the Tunisian revolution on the Egyptian stock market, investors can further explore the complexities of these markets in times of financial crises, which can help mitigate losses and identify strategic investment opportunities.

This study makes two significant contributions to the field. First, it addresses the scarcity of research specifically focused on the contagion effect during the Arab Spring, aiming to fill this gap by testing the contagion effect of the Tunisian revolution on a nearby market. Second, it extends the contagion test of Forbes and Rigobon (2002), which associates “pure” contagion with a significantly higher correlation between markets during a crisis.

The purpose of this study is to investigate the impact of terrorist attacks on the volatility and returns of the stock market in Tunisia.

The employed sample comprises 1250 trading day from the Tunisian stock index (Tunindex) and stock closing prices of 64 firms listed on the Tunisian stock market (TSM) from January 2011 to October 2015. The research opts for the general autoregressive conditional heteroscedasticity (GARCH) and exponential generalized conditional heteroscedasticity (EGARCH) models framework in addition to the event study method to further assess the effect of terrorism on the Tunisian equity market.

The baseline results document a substantive impact of terrorism on the returns and volatility of the TSM index. In more details, the findings of the event study method show negative significant effects on mean abnormal returns with different magnitudes over the events dates. The outcomes propose that terrorism profoundly altered the behavior of the stock market and must receive sufficient attention in order to protect the financial market in Tunisia.

Very few evidence is found on the financial effects of terrorism over transition to democracy cases. This paper determines the salient reaction of the stock market to terrorism during democratic transition. The findings of this study shall have relevant implications for stock market participants and policymakers.

The latest financial crisis marks a milestone in the development of financial markets. It was a period when it was possible to observe a booming development in the stock markets.

Faced with such a phenomenon, theorists have agreed on the need to resume the debate on the validity of the predictability of stock market returns, which is considered to be the cornerstone of all financial theories. The purpose of this article is to examine the predictability of the bearish stock market using a number of variables widely used in forecasting stock returns. In particular, we focus on variables related to imperfect credit markets.

We revisit the predictability of the bearish market using variables that measure the External Funding Premium (EFP), such as the Default Yield Spread.As the EFP is the key indicator of the extent of credit market imperfections, it should therefore be linked to stock market dynamics and provide useful predictive content.

This paper aims to quantify the volatility spillover impact and the directional predictability from stock market indexes to Bitcoin.

Daily data of 15 developed and 15 emerging stock markets are used for the period March 2017–December 2021.; The author uses vector autoregressive (VAR) model, Granger causality test and impulse response function (IRF) to estimate the results of the study.

Empirical results show a significant unidirectional volatility spillover impact from emerging markets to Bitcoin and only six stock markets are powerful predictors of Bitcoin return in the short term. Additionally, there is no a difference between developed and developing markets regarding the directional predictability however there is difference in the reaction of Bitcoin return to shocks in the emerging markets compared to developed ones.

The paper proposes different econometric techniques from prior research and presents a comparative analysis between developed and emerging markets.

The specific criteria to the microstructure of emerging markets such as low liquidity, very pronounced asymmetric information, and high volatility affect the risk market. Previous researchers have concluded that the calculation methods of the Value‐at‐Risk (VaR) adopted in developed markets are poorly adapted to the specific structure of emerging markets. The purpose of this paper is to see what these specific criteria of emerging markets are and whether these criteria have any impact on market risk and hedging capital. A second purpose it to see if practitioners should adjust the tools of risk measurement to the specifications of emerging markets and how the Value‐at‐Risk (VaR) should be adjusted.

The paper asks what are the specific criteria to the microstructure of emerging markets? Should we adjust the tools of risk measurement to these specifications? How do we adjust the Value‐at‐Risk (VaR)?

The paper demonstrated a market improvement in the performance of adjusted VaR. Indeed, models for measuring the VaR adjusted to liquidity and to asymmetry of information are accepted by the tests of backtesting. The term of average error has decreased.

This improvement of adjusted VaR in the performance of measuring risk implies a better estimation of the capital allocated to cover market risk.

The results from this empirical study offer an alternative approach adapted to the specific structure of emerging markets and a better estimation of the capital allocated to cover market risk.

The purpose of this paper is to investigate the effect of forecast earnings’ revision on the evolution of securities prices in the Tunisian stock market.

A portfolio study of investor reaction and stock prices following revisions is first conducted to highlight the existence of abnormal return related to analysts’ earnings revisions. Analysis is then supplemented by a second empirical investigation based on the panel data to quantify the effect of revision on the abnormal profitability of securities.

The evidence found in this paper validates the fundamental theoretical hypothesis according to which the psychological bias resulting from the effect of the forecast earnings revision is related to the abnormal profitability of the securities. The authors conclude the importance of the revision impact on investors’ behavior on one hand, and the informational content of the analysts’ forecasts and the biases which they lead on the other hand.

Globally, the empirical illustrations largely validate the findings of behavioral models particularly that of Kormendi and Lippe (1987), Cornell and Letsman (1989), Beaver et al. (2008) which states that investors under psychological bias, react to the effect of forecast earnings revision by an abnormal variation in stock prices.