key: cord-1010154-2s8u5sfk
authors: Alqahtani, Abdullah; Bouri, Elie; Vo, Xuan Vinh
title: Predictability of GCC stock returns: The role of geopolitical risk and crude oil returns
date: 2020-09-29
journal: Econ Anal Policy
DOI: 10.1016/j.eap.2020.09.017
sha: 6899a6a6dac66f0d3bcc2ca6d975bf7943c9fbef
doc_id: 1010154
cord_uid: 2s8u5sfk

Stock return predictability has always been one of the central themes of finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking. This paper evaluates the in-sample and out-of-sample stock return predictive power of the global and Saudi geopolitical risk indices and crude oil returns in the context of six Gulf Cooperation Council (GCC) countries. Monthly data from February 2007 to December 2019 and the feasible generalized least square (FGLS) estimator for predictive modelling by Westerlund and Narayan (2012, 2015) are used. Global and Saudi GPR indices show weak evidence of in-sample predictability of excess stock returns. However, the out-of-sample forecasts show that only the global geopolitical risk index provides superior prediction in the context of Kuwaiti and Omani stock markets, compared to the historical average benchmark model. Crude oil prices are shown to be a better predictor in most cases, in both in-sample and out-of-sample forecast models The results imply that crude oil returns can be used for active prediction of GCC stock market returns, once econometric issues are accounted for. The findings remain mostly unaffected when excess risk adjusted returns are used.

Stock price or stock return forecasting has always been a central theme of the finance literature, given its crucial implications for investment decisions, risk management, and financial and monetary policymaking (Apergis et al., 2018) . The central questions of the literature on stock return prediction are, firstly, whether stock returns are predictable. If the answer to this question is "yes", then another question follows immediately, "how?" There is a vast amount of past evidence that attempts to answer at least one of these questions. Regarding whether stock returns are predictable, a number of empirical studies yield positive results for the out-of-sample predictability of stock returns (see, inter alia, Campbell and Thomson, 2008; Gupta and Modise, 2013; Narayan and Gupta, 2015; Phan et al., 2018; Tissaoui and Azibi, 2019) . On the other hand, several other studies argue that in-sample evidence tends to be supportive of predictability while out-of-sample evidence tests tend to reject the hypothesis of significant prediction (Inoue and Kilian, 2005; Rapach and Wohar, 2006; Welch and Goyal, 2008; Apergis et al., 2018) . Regarding the question of how, scholars employ numerous variables as potential predictors of future stock returns. The types of predictors commonly adopted in predictive modelling include financial variables and ratios (see, among others, Cakici et al., 2013; Campbel and Shiller, 1988; Fama and French, 1993; Hanauer and Linhart, 2015) , economic factors (see, among others, Rapach et al., 2005; Gupta and Modise, 2013; Phan et al., 2015) , and crude oil prices (Narayan and Gupta, 2015; Ponka, 2016; Chiang & Hughen, 2017; Yin et al., 2019) . To date, studies have yet to provide a general consensus on which economic predictors, if any, consistently offer predictive power in forecasting stock returns.

There is a potential predictor, geopolitical risk, which continues to attract the attention of academicians, investors and the financial media given its ability to shape economic cycles and investment decisions (Caldara and Iacoviello, 2016) . Geopolitical risk covers geopolitical tensions such as Middle East tension, the risk of war, military threats and terror attacks. It can drive financial returns via its effects on corporate profits and investor sentiment (Bouri et al., 2019) . Events related to insecurity, terrorism, or political unrest can increase uncertainty in the financial markets, which ultimately leads investors to postpone or divest their stock investments, and the demand for stocks to decrease. Such events lead to a drop in investor demand for risky funds and in aggregate equity fund flows (Wang and Young, 2020) . Geopolitical risk can also affect corporate financing (Khoo and Cheung, 2020) .

Previous studies provide evidence of the sensitivity of stock prices to terrorism (see, among others, Nikkinen et al., 2008; Barros and Gil-Alana, 2009; Chesney et al., 2011; Kollias et al., 2011) , war and conflict (see, among others, Choudhry, 2010; Guidolin and Ferrara, 2010) , and geopolitical risk (Antonakakis et al., 2017 , Balcilar et al., 2018 Bouras et al., 2018; Bouri et al., 2019; Clance et al., 2018) . However, the related literature mostly considers developed economies (Choudhry, 2010; Essaddam and Karagianis, 2014; Laborda and Olmo, 2019) , and to lesser extent large emerging economies such as China and India (e.g., Balcilar et al., 2018; Bouras et al., 2019) as well as smaller emerging economies in the Asia-Pacific region such as Indonesia, Malaysia, Thailand, and Pakistan (e.g., Kannadhasan and Das, 2020; Hoque and Zaidi, 2020) . Fewer studies (Mnasri and Nechi, 2016; Charfeddine and Al Refai, 2019) 1 focus on the Gulf Cooperation Council (GCC) region, which includes some of the nations that have been particularly exposed to geopolitical tension (e.g. Arab uprisings, wars) 2 . In turbulent regions such as the GCC, political instability, security concerns, and military tensions are quite common. For example, cities in Saudi Arabia have come under missile and drone attacks from Yemeni rebels several times since the onset of the Saudi Arabian-led intervention in Yemen in 2015 3 . Stocks in the GCC region are issued by corporations that are particularly exposed to geopolitical tensions, which makes geopolitical risk a systematic risk element that can shape economic fundamentals in GCC stock markets. For example, on June 5, 2017, a diplomatic rift between Qatar and its Gulf neighbours (Saudi Arabia, UAE, and Bahrain) led the Qatari stock market index to plunge more than 7% on a single day 4 . At the same time, stock indices in GCC countries are highly sensitive to crude oil prices (Arouri and Rault, 2012; Alqahtani et al., 2019) . In fact, the GCC region encompasses leading oil-producers and oil-exporters and the largest concentration of crude oil producing and exporting countries as well as reserves of around 497 billion barrels of crude oil, which represents almost 34% of the world's estimated proven crude reserves 5 . Furthermore, crude oil prices and geopolitical risk are 1 Other studies (e.g., Tissaoui and Azibi, 2019) consider Saudi stock return-volatility predictabilities but their focus is on the role of stock volatility risk. 2 A related strand of litertaure considers the effect of the Israeli-Hezbollah War (Bouri, 2014) and the Arab uprisings (Bouri et al., 2016) . 3 https://www.bbc.com/news/world-middle-east-48608213 4 https://www.reuters.com/article/markets-qatar/update-1-qatar-stock-market-tumbles-on-diplomatic-rift-with-saudigcc-states-idUSL8N1J20SW 5 https://www.kapsarc.org/research/publications/crude-oil-reserves-metrics-of-gcc-members/ J o u r n a l P r e -p r o o f Journal Pre-proof correlated (Kollias et al., 2013; Cunado et al., 2019) .

Given the above discussion, we contribute to the literature on stock return predictability for emerging markets by examining GCC stock markets. Specifically, we examine the predictive power of geopolitical risk for GCC stock indices and compare it to that of crude oil returns. This is important as it allows us to determine whether geopolitical risk index or crude oil returns perform better in predicting GCC stock returns, an unexplored research subject.

Our analysis is related to a strand of literature that examines the economic linkages between stock markets and geopolitical risk, which mostly concentrates on large developed economies and large emerging economies (Antonakakis et al., 2017; Balcilar et al., 2018; Bouras et al., 2018; Clance et al., 2018; Bouri et al., 2019) . In the understudied context of GCC stock indices, our paper is closet to Charfeddine and Al Refai (2019) who apply connectedness measures among the stock returns of GCC countries and examine how the blockade on Qatar shaped the system of connectedness, and Mnasri and Nechi (2016) who take an event study approach and examine the effect of terrorist attacks on the stock market volatility of several emerging markets, including GCC markets. However, both Mnasri and Nechi (2016) and Charfeddine and Al Refai (2019) ignore stock return predictability and overlook the predictive ability of geopolitical risk comparative to crude oil for GCC stock returns. In terms of stock return predictability based on geopolitical risk, our paper is related to that of Apergis et al. (2018) who consider 23 global defence companies. Apergis et al. (2018) use both a Granger causality test and a nonparametric approach, and show that the geopolitical risk index exerts only mild predictive power over stock returns as shown by the Granger causality test, and find no evidence of stock return predictability from the nonparametric models. However, our paper differs in three aspects. Firstly, it focuses on the predictability of GCC stock market returns based on the Saudi Arabia geopolitical risk index and a global geopolitical risk index. Secondly, it includes a comparative analysis between the predictive power of geopolitical risk indices and that of crude oil prices. Thirdly, it conducts both in-sample and out-of-sample predictability analysis using the Westerlund and Narayan (2012; feasible generalized least square (FGLS) model. On this note, the academic literature offers various explanations for the common disagreement between in-sample tests and out-of-sample forecasts. Stambaugh (1999) , Lanne (2002) , Lewellen (2004) , and Westerlund and Narayan (2012 believe that the insignificant out-of-sample evidence and generally mixed findings in the literature J o u r n a l P r e -p r o o f Journal Pre-proof are mainly due to the data attributes of financial variables. For example, the majority of financial and economic variables tend to be persistent in that past innovations of the variable itself are correlated with the current value of the variable (Lanne, 2002) . One possible way to tackle the issue of data attributes is to employ a proper estimator for the predictive regression model (Westerlund and Narayan, 2012) .

The current paper has several novelties. Firstly, it is the first of its kind to examine the ability of two geopolitical risk indices, the global geopolitical risk index and the Saudi geopolitical risk index, to predict the stock returns of GCC countries that represent a significant part of the emerging economies which remain largely understudied in the stock-geopolitical risk nexus. The main motivation for using two geopolitical risk indices is to examine whether the GCC stock markets are more responsive to international-induced risk or regional-induced risk. Our empirical findings provide valuable insight for stock market participants in the GCC markets by showing whether current levels of geopolitical risk, and either global or Saudi geopolitical risk, can improve predictions of future stock returns in the Gulf region. Secondly, this paper comparatively examines the stock return predictive power of geopolitical risk versus crude oil price, which is important for investment decisions, asset pricing, and policymaking. The motivation for considering crude oil prices as a stock price predictor in GCC stock markets is that the economies and the stock markets of oil rich GCC countries are shaped by the level of oil prices, suggesting the ability of crude oil prices to predict GCC stock returns. Thirdly, this paper uses the newly developed Westerlund and Narayan (2012; FGLS estimator to construct a predictive regression. The FGLS estimator is robust to the presence of an endogenous and persistent predictor, as well as conditional heteroscedasticity. The FGLS estimator is expected to overcome the common econometric challenges faced when a least squares class of estimator is used. Furthermore, this paper performs out-of-sample forecasts across various forecast horizons, which effectively rules out the possibility of data mining. Fourthly, it confirms the robustness of the main findings by re-estimating the models based on excess returns adjusted for market risk factors and macroeconomic factors.

The remainder of this paper is structured as follows. Section 2 describes the dataset and the stock return predictive models. Section 3 reports and discusses the in-sample and out-of-sample forecast results. Finally, Section 4 concludes. observations. It uses three sets of data, stock indices, geopolitical risk indices, and crude oil prices.

The first set consists of monthly excess stock index returns of six GCC countries 6 , Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the UAE. The monthly stock index excess return is computed as the difference between the nominal (raw) stock index return 7 and the risk-free rate (i.e., threemonth rate in each GCC country) (see, among others, Narayan and Bannigidadmath, 2015) . The second dataset consists of two geopolitical risk indices, the global GPR and Saudi GPR indices.

The global GPR index measures geopolitical tensions that have potential worldwide impact. The Saudi GPR index is a country-specific GRP index that measures regional geopolitical tensions originating from Saudi Arabia. Both GPR indices are used in their natural log-transformations. The GPR is a newly developed indicator for measuring the cross-border geopolitical tension of a given region or country. According to its constructors, Caldara and Iacoviello (2018) 

To evaluate the predictive power of geopolitical risk for GCC stock returns, a simple predictive regression for + ℎ period is constructed as follows:

where RET represents the excess stock returns of GCC markets, is either the global GPR index, the Saudi GPR index, or crude oil returns, is the intercept term, and is the forecast error at h lead periods. The in-sample stock return predictive power of the GPR (crude oil returns) can be tested using a simple t-test with the null hypothesis of = 0 , in which rejection of the null hypothesis indicates that GPR (crude oil returns) carries a significant in-sample stock return predictive power in the context of GCC countries.

Since the predictive model (1) involves financial data (RET) and a macroeconomic indicator (GPR or crude oil returns), using the ordinary least square (OLS) method to estimate the parameters in

(1) is likely to suffer from the presence of a persistent predictor, endogeneity, and heteroscedasticity (Westerlund and Narayan, 2012; . Consequently, the OLS estimates are biased and inefficient. To resolve the issues of endogeneity and a persistent predictor, Stambaugh (1999) developed the bias-adjusted least square (ALS) estimator for predictive regression using financial data, which was later extended by Lewellen (2004) . The algebraic representation of the ALS model is:

where ∆ is the difference operator. Since equation (2) captures both persistency and endogeneity of the predictor in the predictive model, is it robust to the presence of persistency and endogeneity issues (Lewellen, 2004; Westerlund and Narayan, 2012) .

Finally, while the ALS estimator adopted in equation (2) is free from the problems of persistency and endogeneity, it may still suffer from ARCH effects as the estimator ignores the possible presence of heteroscedasticity. Therefore, Westerlund and Narayan (2012) propose weighting the ALS estimators by 1/ 2 , which is obtained by estimating the error variance as in equation (3) Subsequently, equation (2) is modified to:

RET +ℎ * = * + * X + * (∆X +ℎ ) + +ℎ *

where RET +ℎ * = RET +ℎ / 2 , * = / 2 , * = / 2 , * = / 2 , and +ℎ * = +ℎ / 2 . The feasible generalized least square (FGLS) estimators * , * and * are therefore robust to the presence of persistency, endogeneity, and heteroscedasticity in the model 9 .

As mentioned, the literature on stock return predictions lacks consensus about the evidence from in-sample and out-of-sample tests. The question of whether an the in-sample or out-of-sample approach is superior is beyond the scope of this study. Still, this study takes both in-sample and out-of-sample approaches to forecasting GCC stock returns and hence the findings contribute to that debate. Specifically, this study uses half the sample, February 2007 to June 2013, to generate the forecast (Narayan et al., 2014) and applies three measures for the out-of-sample forecasting evaluation, the relative Theil U, the out-of-sample R-squared (OOSR 2 ) statistic of Campbell and Thompson (2008) , and the MSE-adjusted statistic of Clark and West (2007) .

This section provides the estimated predictive regression models for all GCC countries, using the OLS, ALS, and FGLS estimators. The results are summarized in Tables 1-3. In each of the three tables, Panels A, B, and C report the estimated coefficients of the first predictor (global GPR), 9 Notably, in all the predictive models that we apply in this study, our dependent variable is the excess returns computed using the 3-month rates.

J o u r n a l P r e -p r o o f Journal Pre-proof second predictor (Saudi GPR), and third predictor (crude oil returns), respectively. In each panel,

we present the results of the serial correlation LM test and ARCH test for each model up to 12 lags to indicate whether the applied model is free from serial correlation and heteroscedasticity. Table 2 shows the estimated predictive regression using the ALS estimator by Lewellen (2004) .

There are some improvements observed in terms of the significance of the three predictors' coefficients compared to the OLS estimates, in particular in the models involving crude oil returns as a predictor. This may be due to the capacity of the ALS estimator to capture the endogeneity and persistency of the predictor. Still, there is not much sign of improvement in terms of the diagnostic checks, in which most predictive models have the same issues as the OLS models. The presence of serial correlation and heteroscedasticity in the ALS models are unexpected yet reasonable, as the design of the bias-adjusted least squares estimator is intended only for capturing the persistency and endogeneity of the predictor. The model estimated using the ALS approach is far from forecasting-fitted, as the presence of the ARCH effect and the autocorrelation are not accounted for. J o u r n a l P r e -p r o o f Table 3 reports the estimated predictive regression using the FGLS estimator by Westerlund and Narayan (2012 . One can observe significant improvement in terms of the results of the two diagnostic checks, autocorrelation and heteroscedasticity. This result is not surprising given that the FGLS estimator is expected to be both unbiased and efficient (Westerlund and Narayan, 2012) .

Panel A shows that the results using the Global GPR as a predictor are quite comparable to those previously reported in Tables returns. According to the adjusted R 2 s reported, and except for Bahrain, the forecasting power of crude oil returns is stronger than either of the two GPR indices. This supportive evidence from insample tests is consistent with numerous earlier studies (see, among others, Campbell and Shiller, 1988; Chiang and Hughen, 2017; Narayan et al., 2014; Phan et al., 2015) .

Having analysed the in-sample performance of the predictive models, this section assesses the outof-sample predictability of each model. As evident from the earlier section, the OLS and ALS estimators are not well suited for prediction analysis, therefore the out-of-sample tests only consider the FGLS models. We assess the out-of-sample forecasting by comparing the forecasting accuracy of two models, the competition (unrestricted) model and the benchmark (restricted) model (Narayan et al., 2014; Phan et al., 2019) . Using 50% of the sample as an in-sample period (February 2007 to June 2013), we produce forecasts of excess returns for the rest of the sample (June 2013, December 2019). We employ three measures for the out-of-sample forecasting evaluation, the relative Theil U, the out-of-sample R-squared (OOSR 2 ) statistic of Campbell and Thompson (2008) , and the forecasting mean squared error (MSE)-adjusted statistic of Clark and West (2007) . The relative Theil U statistic is the ratio of the Theil U from the unrestricted model (also called the competition model) to the restricted, i.e. constant return, model (also called the J o u r n a l P r e -p r o o f benchmark model). When the relative Theil U is less than one, it implies that forecasts based on the unrestricted model outperform the forecasts based on the restricted model (Narayan et al., 2014) . OOSR 2 is used to compare the accuracy of the forecasting MSE from the unrestricted and restricted models. Specifically, OOSR 2 represents the percentage reduction of the mean squared predictive error (MSPE) of the unrestricted model to the MSPE of the restricted model. A positive OOSR 2 value indicates that the forecasting of the unrestricted model is more accurate than that of the restricted model (Phan et al., 2018) . The MSE-adjusted statistic of Clark and West (2007) is used to test the null hypothesis OOSR 2 < 0, against the alternative hypothesis OOSR 2 > 0. (2007) is used to test the null hypothesis OOSR 2 < 0, against the alternative hypothesis OOSR 2 > 0. *, **, and *** imply rejection of the null hypothesis at the 10%, 5%, and 1% levels of significance, respectively. the Saudi GPR, and in three cases (Kuwait, Qatar, and Saudi Arabia) for crude oil returns. Thirdly, the null hypothesis OOSR 2 < 0 is rejected in at least three cases for Global GPR and crude oil returns.

The most appropriate forecasting model uses crude oil returns, since the null is rejected for all six indices, followed by Global GPR, for which the null is rejected for three of the six indices.

Therefore, crude oil returns provide solid evidence of out-of-sample predictability, since the null is rejected in all cases. Conversely, there is no acceptable out-of-sample forecasting model for the Saudi GPR. Overall, the MSE-adjusted statistic of Clark and West (2007) provides the greatest evidence in favour of the unrestricted model. Furthermore, most of the out-of-sample forecasting evaluation measures indicate the superiority of the prediction model involving crude oil returns.

In this section, we conduct two sensitivity exercises. In the first, given that the choice of out-ofsample forecasting period is arbitrary, we assess the out-of-sample forecasting evaluation using a different length of training set. Specifically, we use 60% of the sample as an in-sample period (February 2007 to October 2014 . The results from the out-of-sample forecasting evaluation of the FGLS estimator are reported in Table 5 . They show that the forecasts from the unrestricted model are better than those obtained from the restricted model in two cases for each predictor, mainly in 11 Notably, a positive OOSR 2 implies that a forecasting model using the predictor to forecast excess returns outperforms the constant returns model.

Panels B and C, which is comparable to the results reported in Table 4 12 . In the second exercise, we assess the sensitivity of our predictability results to the use of excess risk adjusted returns that account for risk free rates, market risk factors and macroeconomic factors 13 . Following the existing literature, we compute excess risk adjusted returns using risk free 12 We assess the robustness of the out-of-sample forecasting evaluation using 40% of the sample period as an insample period (February 2007 to April 2012). Unreported results indicate somewhat similar results to those reported in Table 5 . 13 We thank an anonymous reviewer for this suggestion. Table 6 show strong predictive content of crude oil returns for GCC stock market returns, which is comparable to the results reported in Table 3 . In fact, these in-sample estimation results, including the adjusted R 2 s, indicate that the forecasting power of crude oil returns is stronger than either of the two GPR indices. We also evaluate the out-of-sample forecasting of the excess risk adjusted returns using the relative Theil U, out-of-sample R-squared (OOSR 2 ) statistic, and MSE-adjusted statistic. The results in Table 7 show a more prominently significant out-of-sample predictive power from crude oil returns to most GCC stock returns than from the GPR indices. 

This paper evaluates the in-sample and out-of-sample performance of two GPR indices (global GPR and Saudi GPR) and crude oil returns in predicting GCC stock returns using various J o u r n a l P r e -p r o o f estimators, specifically the ordinary least squares (OLS) estimator, the bias-adjusted least squares (ALS) estimator (Stamburg, 1999; Lewellen, 2004) , and the feasible generalized least squares (FGLS) estimator (Westerlund and Narayan, 2012) . The in-sample and out-of-sample tests under various forecasting windows reveal some important findings. Firstly, the reported predictive models with OLS estimates and ALS estimates fail to address various econometric issues and are therefore unfit for GCC stock return forecasting. Likewise, the in-sample results show that the For example, there is no acceptable out-of-sample forecasting model for the Saudi GPR, whereas some significant evidence is shown for the global GPR. Importantly, we note the superiority of the out-of-sample prediction model involving crude oil returns.

It is recommended that crude oil returns be considered for active prediction of GCC stock market returns, once econometric issues are accounted for. The predictability of crude oil returns is especially effective in the Kuwaiti, Omani, Qatari, Saudi, and UAE stock markets. Likewise, investment banks and forecasters could consider incorporating crude oil returns along with traditional predictors in their forecasts of GCC stock market returns. The empirical results shed light on the methodological issues of related studies. Using various estimators reveals that the choice of estimator matters for stock return prediction. Thus, researchers and practitioners should properly consider the characteristics of their predictors prior to making any stock return forecasts.

Given that stock markets have been strongly affected by the COVID-19 outbreak, predictability might be disturbed by this unprecedented risk. This is based on the rationale that predictability is time-varying and can be altered by the presence of structural breaks, such as those due to the COVID-19 outbreak. Accordingly, as part of the agenda for future research, two research J o u r n a l P r e -p r o o f possibilities emerge. Firstly, the issue of time-varying predictability as documented by Devpura et al. (2018) , and secondly, the structural break predictability indicated by Devpura et al. (2019) .

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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