The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics

Trichilli, Yousra; Abbes, Mouna Boujelbène; Zouari, Sabrine Article The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics Journal of Capital Markets Studies (JCMS) Provided in Cooperation with: Turkish Capital Markets Association Suggested Citation: Trichilli, Yousra; Abbes, Mouna Boujelbène; Zouari, Sabrine (2020) : The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics, Journal of Capital Markets Studies (JCMS), ISSN 2514-4774, Emerald, Bingley, Vol. 4, Iss. 1, pp. 61-76, https://doi.org/10.1108/JCMS-04-2020-0005 This Version is available at: https://hdl.handle.net/10419/313277 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ The impact of political instability driven by the Tunisian revolution on the relationship between Google search queries index and financial market dynamics Yousra Trichilli, Mouna Boujelb ene Abbes and Sabrine Zouari Department of Economic and Management Laboratory (LEG), Faculty of Economics and Management of Sfax, University of Sfax, Sfax, Tunisia Abstract Purpose –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. Design/methodology/approach –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. Findings –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. Research limitations/implications –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. Originality/value –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. Keywords Political instability, Investor sentiment, Tunindex, DCC GARCH model, Wavelet coherence model, FMOL method Paper type Research paper Political instability and stock market dynamic 61 © Yousra Trichilli, Mouna Boujelb ene Abbes and Sabrine Zouari. Published in Journal of Capital Markets Studies. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http:// creativecommons.org/licences/by/4.0/legalcode The current issue and full text archive of this journal is available on Emerald Insight at: https://www.emerald.com/insight/2514-4774.htm Received 7 April 2020 Revised 10 June 2020 Accepted 26 June 2020 Journal of Capital Markets Studies Vol. 4 No. 1, 2020 pp. 61-76 Emerald Publishing Limited 2514-4774 DOI 10.1108/JCMS-04-2020-0005 1. Introduction Political instability is one of the most important impediments to economic development. It is related to decreased welfare (Dupas and Robinson, 2010), increased propensity to engage in violence (Blattman and Miguel, 2010) and increased risk-taking (Dupas and Robinson, 2012). The politically unstable events often lead to radical and sudden changes in property rights laws and rules governing the conduct of business (Gyimah-Brempong (1999)). As a result, the political risk remains valued by investors because this risk often has a strong impact on investor confidence, usually their feelings and emotions, and has a greater impact on the economy, performances and volumes of financial market transactions (Beaulieu et al. (2006), Bailey et al. (2005) and Frey and Waldenstrom (2004). Several studies have looked at the effects of political instability on financial market performance. However, Frieden et al. (2000) study the impact of political economy factors on exchange rate policy in Latin America. Arfan et al. (2012) concludes that the political instability is one of the main causes that affects the capital inflows in Pakistan. Investor sentiment is an important topic in behavioral finance that plays a fundamental role in predicting returns and volatilities of financial markets. As outlined in a literature review by Lee et al. (2002), investor sentiment is a significant factor that explains excess returns and conditional volatility shocks. Hence, a great amount of literature has approved the positive or negative impact of investors’sentiment on market dynamics (Brown and Cliff, 2005;Baker and Wurgler, 2007; Hengelbrock et al., 2009;Grigaliuniene and Cibulskiene, 2010). Investor sentiment also plays a crucial role in the transmission of economic, financial and political crises from one country to another, as well as the transmission of political shocks to financial markets. Perotti and Oijen (2001) have shown that political shocks have an effect on stock markets; their results show strong changes in excess returns with the fluctuation of political risk. Motivated by the critical period, which is characterized by the starting of the Tunisian revolution in 2011, the objective of this study is to empirically examine the impact of political instability on the return, volatility and sentiment of the Tunisian stock market. Meanwhile, it also analyses the transmission of shocks between the Tunisian stock market and investor’s sentiment during periods characterized by turmoil and political instability. It is of interest, therefore, to consider a recent measure of investor sentiment. This paper uses the volume of Google search to proxy the investor attention due to the reason that Google’s search data are easy accessible and free available to investors. It is a direct methodology of collecting trend data that has not been used in the prior literature for Tunisian stock market. In addition; this general and broad measure reflects the rapidly changing interests of millions of Internet users especially whom that access to huge amounts of economic and financial information online. In terms of methodology, varieties of models have been employed to explore the link between the stock market returns, volatility and investor’s sentiment. First, by using the DCC-GARCH model, we examine the effect of investor sentiment on the Tunisian stock market return. The DCC-GARCH models are comprehensive tools and provide intriguing insights to understand the interdependence between variables over time. The parameters of this model can easily be estimated, and the model can be evaluated and used in straightforward way. Second, we employ the fully modified dynamic ordinary least square method (FMOL) to estimate the long-term relationship between investor sentiment and Tunisian stock market return. More precisely, this particular model intent to identify the long-run cointegration among Google search queries index and return index. Finally, we use the wavelet coherence model to test the comovement between investor sentiment measured by Google Trends and Tunisian stock market return. The continuous wavelet decomposition technique has been proposed to improve the forecasting accuracy of financial time series. This method can JCMS 4,1 62 provide a matrix to accommodate correlation at each time and frequency point. This advantage makes it useful for observing the shift of coherency between Google search index and Tunindex. Meanwhile, it also empirically analyses the direction and intensity of comovement in the Tunisian stock market in different time-frequency domains. As compared to other standard methods, the advantage of wavelet coherence is that allows us to understand the lead–lag relationships between Google search index and Tunindex over the sample period. 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 remainder of this paper is organized as follows. Section 2 presents the literature review. Section 3 describes the methodology as well as the descriptive statistics. Section 4 provides the empirical findings and Section 4 concludes. 2. Literature review In the literature, we found a number of research studies that have studied the effect of investor sentiment on stock returns and the effect of investor sentiment on realized volatility, respectively. Brown and Cliff (2004) have found that investor sentiment has a negligible impact on subsequent monthly market returns, while Huang et al. (2014) have concluded that investor sentiment is a reliable contrarian predictor of subsequent monthly market. Naik and Padhi (2016) explored the relationship between investor sentiment and stock return volatility using monthly data from National Stock Exchange (NSE) of India from July 2001 to December 2013 period. They show that sentiment index has a significant effect on market excess returns and negative effect on the conditional volatility. Whereas, when the sentiment index is decomposed into positive sentiment and negative sentiment changes, the study reveals that positive and negative sentiments have asymmetric impacts on excess return volatility. Along these lines, Cheema and Nartea (2018) have found that investor sentiment indexes of both Baker and Wurgler (2006) and Huang et al. (2014) were contrarian predictor of aggregate stock market returns at all horizons but only during high sentiment states. Trichilli et al. (2018) have studied the impact of googling investor’s sentiment on the monthly Islamic and conventional index returns during the period 2004–2016. They have indicated that investors can use googling investor’s sentiment as an indicator to predict returns and volatility of MENA financial markets. As far as the impact of political instability on financial market dynamics is concerned, the financial literature has been recently enriched with several empirical studies that show divergent results. For example, Bittlingmayer (1998) concluded that political uncertainty has an impact on stock market volatility and output in post-WWI Germany. Aggarwal et al. (1999) showed that political shocks contribute to large fluctuations in the volatility of emerging stock market returns. Chesney et al. (2011) studied the effect of terrorism that occurs in 25 countries on the global economy over an 11-year time period. They demonstrate that a majority of the events have a negative effect on the financial markets. By way of example, Ikizleri and € Ulk€ u (2012) indicated that political risk influences stock market returns, net external flows and macroeconomic activity in Turkey. Abdelbaki (2013) used the recently developed techniques of time series data cointegration: vector error correction model (VECM) to investigate the impact of political instability, economic instability and external events associated with the Egyptian revolution that started on 25th January 2011 on thestock market performance. They find that political instability has a negative impact on the EGX30 index. In a more related study, Murtaza et al.(2015) investigated the association of stock market returns with political instability in Pakistan stock market during the period of 2007–2012. Political instability and stock market dynamic 63 They have showed that political events that cause change in government policy have significant effect on stock market returns that proves our hypothesis. These results give an insight into the price behavior of KSE in response to various political events of different natures. As for Jeribi et al. (2015), they have concluded that the 2011 Tunisian revolution has a substantial impact on the volatility of sector index returns. Suleman et al. (2017) have found that global country risk and its economic, financial and political components play an important role in predicting the movements of returns and financial market volatility in about half of all cases. Recently, using EGARCH (1.1) model, Zaiane (2018) has investigated the impact of the political uncertainty on return and volatility of major sectorial stock indices in the Tunisian Stock Exchange. She has concluded that both of good and bad news have increased the volatility of major selected indices, including the TUNINDEX. However, the political news hasno impact on return of all indices. Ben Moussa and Talbi (2019) investigate the impact of political instability and terrorist attacks on the Tunisian stock market dynamics. The results of the estimation of EGARCH model showed that these two events have a negative impact on the performance of the Tunisian stock market and positive impact on the volatility of the Tunindex market especially after the revolution. However, the impact of these events on the behavior of the stock market was lower for the period before the revolution. Only one prior study specifically examines the relationship between investor sentiment and the Tunisian stock return (Soltani et al., 2017). These authors have employed simultaneous equations and GMM 2S method to study the impact of political instability on stock market dynamics by comparing the interaction between market returns, volatility and investor sentiments before and after the Tunisian revolution. They show that during the period of political stability, investor sentiment did not affect market return and volatility. Whereas, they show a significant bidirectional relationship between investor sentiment, market volatility and return during the period of the Tunisian revolution. Political instability has recently struck all continents around the world. In fact, it has an effect on the psychology of investors and any change in their sentiment is reflected in stock market performance and volatility. Overall, compared to the previous research studies, given the growing importance of the Tunisia’s picture worldwide and in the Arab world, in particular, this study is the first that aims at exploring the impact of political instability on the investors’behavior, measured by Google search queries and on the dynamics of stock market returns. 3. Methodology Our objective in this paper is to study the effect of investor sentiment on the return of the Tunindex by analyzing the correlation between these two variables during the period of the Tunisian revolution. In this respect, we use a methodology based on four steps: in a first place, we explain the methodology for constructing Google search queries index in details. In a second place, we use the DCC-GARCH model to study the impact of investor sentiment on the returns of Tunisian stock market. In a third place, we attempt to estimate the long-term relationship between investor sentiment and Tunisian stock market e using the modified dynamic ordinary least square (FMOL) method. Finally, we apply the wavelet coherence model to examine the comovements between these two variables. 3.1 Constructing the Google search queries index To understand the key to the construction of a search queries index, we followed Da et al. (2015). First, among 743 words with the “Econ @”or “ECON”markers, we gathered only words related to a positive and negative sentiment, and we removed noneconomic words that not revealed sentiment toward economic conditions. Second, using Google Translate, we JCMS 4,1 64 translate the set of 149 primitive terms, which are “economic”words that are specified by the widely used dictionaries in the literature on finance (“Harvard IV-4 Dictionary”and «Lasswell Dictionary ») into Tunisian’s corresponding language. We have employed the Arabic and French languages. Third, we put these 149 translated words into Google Trends product, and we identified the top ten terms associated with each word. Indeed, after removing terms that produced insufficient data and that were not semantically related to economics or finance, we have considered only search terms with at least 100 monthly SVI files. Fourth, we download the monthly search volume index (SVI) for each of the search terms during our sampling period from January 2004 to April 2018. Fifth, we calculate the monthly change in SVI (ΔSVI) for each search term. Then we optimize the extreme observations, eliminate the seasonality and normalize the time series to make them comparable to finally get the change weekly adjusted search volume (ΔASVI) (Trichilli et al., 2018;Trichilli et al., 2019;Trichilli et al., 2020a;Trichilli et al., 2020b). Next, we identify search terms that are the most important for the Tunisian stock returns. Hence, we determine the historical correlation between each term and contemporaneous Tunisian market returns. The final step in the construction of the Google search queries index was calculating the ΔASVI average of the top 30 positively correlated and the top 30 negatively correlated search terms for each month. Then the formula is presented as follows: Google search queries index ¼X 30 i¼1 Ri þðΔASVIiÞX 30 i¼1 Ri −ðΔASVIiÞ(1) With P30 i¼1Ri ±ðΔASVIiÞis the t-statistic-weighted average of the top 30 positively (negatively) correlated search items. 3.2 Fully modified ordinary least square (FMOLS) approach This method corresponds to the Engel–Granger approach for which estimation can be done by OLS when there is a cointegration relationship between the dependent variable and its fundamentals. For nonstationary panels, Pedroni (2000) demonstrates that MCO estimators are asymptotically biased. Indeed, the Group-Mean Fully Modified OLS (GM-FMOLS) panel technique proposed by Pedroni (1996,2000) solves this problem in the sense that it allows the use of heterogeneous cointegrating vectors. For Maeso-Fernandez et al. (2004), the FMOLS estimator takes into account the presence of the constant term and the possible existence of correlation between the error term and the differences of the regressors. Adjustments are made for this purpose on the dependent variable and the long-term coefficients obtained by regressing the adjusted dependent variable. In the case of panels, the long-term coefficients resulting from the technique GMFMOLS are obtained by the “group mean”of the estimators relative to the sample size (N). Thus, the GM-FMOLS estimator is written as follows: bβGM−FMOLS ¼N−1X NT i¼1"X T t¼1x0 itxit−1 XT t¼1x0 itY* it Tc +i!# (2) where Y* it represents the regressands adjusted for the covariance between the error term and the vector xit . Tc +irepresents the adjustment due to the presence of the constant term. The term in the brackets is the individual FMOLS estimator for the Kfundamentals. Political instability and stock market dynamic 65 3.3 DCC-GARCH model Engle (2002) proposed the DCC-GARCH, model to estimate the dynamic conditional correlations between series. This model is a generalization of Bollerslev’s (1990) constant conditional correlation model (CCC), where volatilities vary over time, but conditional correlations are assumed to be constant. In this context, the estimated time-varying correlation coefficient helps us in analyzing the correlation between Google search queries index and Tunindex return. In the DCC-GARCH (1.1) model, the variance covariance matrix, H, is giving by AðLÞYt¼ ε t(3) where Adenotes the matrix, Ldenotes the lag polynomial matrix, ε idenotes the innovations vector with ε tjΩt−1∼Nð0;HtÞand t¼1;T. Accordingly, the conditional covariance matrix of the vector ε iis specified as Ht¼DtRtDt(4) and hi;t¼ ω iX pi p¼1 α ip ε 2 it−pþX Qi q¼1 βiqhit−qwith i¼1;2 (5) where Dt¼diag ffiffiffiffiffi hit pdenotes the 2 32 matrix including the time-varying standard deviations from estimating GARCH model. Rt¼ ρ ijt denotes the 2 32 matrix of conditional correlations with i;j¼1;2. Therefore, we obtain the DCC structure which is defined as follows: Rt¼Q*−1 tQtQ*−1 t(6) where Qt¼ 1X K k¼1 α kX L l¼1 bl!QþX K k¼1 α kð ε t−k ε t−kÞþX L l¼1 blQt−1(7) where Qdenotes the matrix of unconditional covariances of the standardized errors, Q* t denotes the 2 32 diagonal matrix consisting of the square root of the diagonal elements of Qt. The resulting time-varying correlation can be written as follows: ρ ij ¼qij;t ffiffiffiffiffiffiffiffiffiffiffiffiffi qii;tqjj;t pwith i;t¼1;2 (8) 3.4 Wavelet coherence model By using signal processing, the wavelet coherence model is a powerful tool that offers a single chance to assess simultaneously the comovement between two series at different frequencies and over many time scales. In tune with Tiwari et al. (2013) and Torrence and Webster (1999), wavelet coherence coefficient is defined as: R2 nðsÞ¼ Ss−1WXY nðsÞ2 SS−1WX nðsÞ2Ss−1WY nðsÞ2(9) with Srepresents a smoothing operator . WXand WYrepresent the wavelet transforms for the time series Xand Y, respectively. WXY represents the cross wavelet transform. s -1 is employed to convert to energy density (ndenotes time position and sdenotes scale) . JCMS 4,1 66 Interestingly, the wavelet coherence coefficient R2 nðSÞ, a localized correlation coefficient in time frequency space, ranges from 0 (weak dependence) to 1 (strong dependence) (Grinsted et al., 2004). Monte Carlo simulations are used to determine the 5% statistical significance level of the wavelet coherence (Torrence and Compo, 1998). 4. Data and variables analysis This section describes the data used throughout the analysis. From the Morgan Stanley Capital International (MSCI) database, we obtain monthly close price of Tunindex. Moreover, we construct a monthly Google search queries proxy measured by Google Trends which is the main source of data that provides a search volume index (SVI) for search items in different countries in different languages since 2004. 4.1 Tunindex return The closing prices of the Tunindex data cover the period between January 2004 and June 2018. Tunisian monthly index return is measured as follows: Rt¼InPt Pt−1(10) with Ptpresents the closing index price on month t, and Pt−1presents the closing index price on month t−1. Figure 1 plots the dynamics of Tunindex return in the period 2004–2018. Graphical analysis reveals that there has been an enhanced decrease in the return of the Tunindex from 2008 which correspond to the global financial crisis. This result is in favor of the phenomenon of contagion between markets in periods of crisis and, it identifies the transmission of the effects of the global financial crisis on the Tunisian stock market. This chart also shows a remarkable fall in Tunindex return in 2011. There are major possible explanations justifying this fall. First, the starting of the Tunisian revolution since the Bouazizi’s desperate self-immolation in Sidi Bouzid at this year, Tunisia still suffers from sudden events and serious political turbulence that continuously and severely affect the economy on all sides. Second, the lack of investor’s confidence sustained by the information regarding the new provisions of the Finance Bill, 2011 related to direct taxes which stipulates for the capital gains tax on the sale of shares. Third, Tunisian and Arab investors’confidence was shaken by the political uncertainty that emanating from Arab spring revolution. 0.2 0.1 0.0 – 0.1 – 0.2 – 0.3 – 0.4 2006 2008 2010 2012 2014 2016 2018 Figure 1. Dynamics of Tunindex return in the period 2004–2018 Political instability and stock market dynamic 67 In addition, there is a sharp fall in return in 2014. This result is not due to the impact of oil crisis on the Tunisian stock market; it is rather due to the Tunisian legislative elections of the month of October 2014, which resulted in a new political landscape. This sharp also can be explained by the political turmoil that manifests after the killing of 15 Tunisian soldiers on Mount Chaambi by jihadists. Nonetheless, we can see a sharp fall in return in 2017. We explain this result by the fact that the state of emergency was extended by one month throughout Tunis. Also, this result may be due to the death of a police officer and two of his colleagues were injured in a terrorist attack in Jenoura. 4.2 Google search queries index Figure 2 presents a plot of the evolution of Google search queries index covering period starting from January 2004 to June 2018. Referring to Figure 2, it can easily be noticed that during the year 2009, the Google search queries index dropped until it reached the lowest level of a negative value of 40%. This finding can be explained by the transmission of the US shock, generated by the stock market crashes of the Suprimes, by the phenomenon of contagion between the markets in periods of crisis. Moreover, Google search queries index has sharply decreased during the Tunisian revolution of 2011. This finding shows that sentiment is bearish. Therefore, we can conclude that pessimism dominates the Tunisian stock market. Following to this sharp decline, Google search queries index fluctuates slightly around the interval of 10 and 10. This result can demonstrate that the investor becomes very indifferent toward all events after the revolution. 4.3 Volatility of the tunindex Figure 3 illustrates Tunindex volatility during 2004–2018. 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Corresponding author Yousra Trichilli can be contacted at: [email protected] For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected] JCMS 4,1 76