_{1}

^{*}

This paper renders new insights into the predictability of GCC stock returns using crude oil prices using the approach of [1] [2] that accounts for salient features of the predictor. The results show superior performance of the oil-based stock model over time-series models (namely, AR, MA, ARMA, and ARFIMA) for both in-sample and out-of-sample forecasts. The results are robust to different oil price series (Brent and WTI prices) and forecast horizons (30 and 60 days).

There is an increasing evidence of improved stock return predictability using oil price [

Thus, the present study contributes to the literature in the following ways. First, it evaluates both the in-sample and out-of-sample forecast performance of oil-based stock model relative to time series models. Second, it accounts for some important features of oil price which may have implications on its forecast performance. Consequently, the approach of [

Following this section, the rest of the paper is structured as follows. The next section presents the predictive model for estimation and the underlying forecasting procedures as well as data scope. Section three contains preliminary analyses of data features. Section four discusses the results while Section five concludes the paper.

The intention of the empirical analyses here is to isolate the contribution of oil price in the predictability of stock returns. Nonetheless, any endogeneity bias that may result from ignoring important covariates is captured implicitly in the estimation process following the approaches of Lewellen (2004) and Westerlund and Narayan (2015). We begin our methodology by specifying a bivariate single predictive model where crude oil price is hypothesized as a predictor of stock price:

s t = α + λ p t − 1 + ε t (1)

where s t is the log of stock price for each of the six GCC countries including Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirate; and p t is the log of crude oil prices where Brent price and WTI price are used separately in the estimation process. Thus, we have two predictive models for each of the oil price series across the six GCC countries considered (details about the data utilized are provided in the section that follows). The ε t is zero mean idiosyncratic error term on stock price and the coefficient λ measures the relative impact of crude oil prices on stock price and the underlying null hypothesis of no predictability is that λ = 0 .

In order to resolve any probable endogeneity bias resulting from the correlation between p t and ε t as well as any potential persistence effect, we follow the approach of [

s t = α + λ a d j p t − 1 + γ ( p t − ρ 0 p t − 1 ) + η t (2)

where the parameter λ a d j = λ − γ ( ρ − ρ 0 ) is the bias adjusted OLS estimator of [

λ a d j F Q G L S = ∑ t = q m + 2 T τ t 2 p t − 1 d s t d ∑ t = q m + 2 T τ t 2 ( p t − 1 d ) 2 (3)

where τ t = 1 / σ η , t is used in weighting all the data in Equation (2) and p t d = p t − ∑ s = 2 T p t / T .

In addition, three forecast measures are used to evaluate the in-sample and out-of-sample forecasts: the root mean square error (also called the mean square error), the [

We focus attention on the stock markets of the six GCC countries, namely, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirate so as to examine the sensitivity of their stock markets to changes in crude oil prices. We collect daily data on the two variables of interest, namely, stock prices of the six GCC countries and crude oil prices, comprising Brent price and West Texas Intermediate (WTI) price from various sources and over different time periods for most of the countries (see ^{th} January, 1999 to 15^{th} September, 2017. However, stock price data for Bahrain ranges between 11^{th} July, 2004 and 10^{th} September, 2017, and for the United Arab Emirate, stock price data are available between 6^{th} October, 2001 and 2^{nd} February, 2017. Thus, the analyses are conducted based on the available data for the individual countries.

We represent the trends of stock prices and crude oil prices (Brent and WTI) for the six GCC countries in

It has been widely argued that the impact of oil price changes on stock market indices depends on whether a country is an oil exporter or an oil importer (see, for instance, [

Variable | Start Date | End Date | No. of observations | 75% of full sample |
---|---|---|---|---|

Brent price | 1/8/1999 | 9/15/2017 | 976 | 732 |

WTI price | 1/8/1999 | 9/15/2017 | 976 | 732 |

Stock prices | ||||

Bahrain | 7/11/2004 | 9/10/2017 | 688 | 516 |

Kuwait | 1/8/1999 | 9/1/2017 | 974 | 730 |

Oman | 1/8/1999 | 9/15/2017 | 976 | 732 |

Qatar | 1/8/1999 | 9/15/2017 | 976 | 732 |

Saudi Arabia | 1/8/1999 | 9/1/2017 | 974 | 730 |

United Arab Emirate | 10/6/2001 | 9/2/2017 | 831 | 623 |

improving net foreign asset position. At the same time, increase in oil prices tends to increase private disposable income in oil exporting countries. This in turn increases corporate profitability, raises domestic demand and stock prices thereby causing exchange rate to appreciate. In oil importing countries, the process works broadly in reverse: trade deficits are offset by weaker growth and overtime, real exchange rate depreciates and stock prices decrease [

We also take account of other statistical features including skweness, kurtosis and Jarque-Bera statistic. In terms of skewness, we observe that the two oil price series (Brent and WTI prices) are negatively skewed, whereas all stock price indices, except that for Bahrain, are also negatively skewed. In terms of kurtosis, both stock and crude oil prices are largely platykurtic (for kurtosis values being less than 3.0). In addition, Jarque-Bera statistics indicate that all series (stock prices and crude oil prices) do not follow normal distribution across the GCC countries.

Here, we conduct autocorrelation and heteroscedasticity tests using Ljung-Box test Q-statistics and Autoregressive conditional heteroscedasticity lagrangian multiplier (ARCH-LM) test F-statistics, respectively (see

Premised on the fact that the rejection of the null hypothesis of a unit root for the predictors, which are crude oil prices (Brent and WTI prices) in our own case, is not a sufficient condition to assume the absence of persistence, we further test for persistence and endogeneity in the predictors (see

Variable | Mean | Std. | Skw. | Kurt. | J-B stat. | Autocorrelation | Heteroscedasticity | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

k = 30 | k = 60 | k = 90 | k = 30 | k = 60 | k = 90 | |||||||||||||

p t b r | 3.974 | 0.587 | −0.353 | 2.19 | 46.9*** | 118.25*** | 154.44*** | 179.03*** | 1.381* | 1.352** | 1.791*** | |||||||

p t w t i | 3.96 | 0.533 | −0.377 | 2.234 | 46.98*** | 127.49*** | 157.15*** | 185.98*** | 1.591** | 1.194 | 1.332** | |||||||

s t | ||||||||||||||||||

Bahrain | 7.355 | 0.286 | 0.583 | 2.135 | 60.41*** | 259.13*** | 295.88*** | 365.74*** | 3.132*** | 1.727*** | 1.508*** | |||||||

Kuwait | 8.552 | 0.682 | −0.788 | 2.48 | 111.8*** | 103.24*** | 125.25*** | 138.7*** | 0.128 | 1.063 | 0.937 | |||||||

Oman | 8.391 | 0.513 | −0.626 | 2.177 | 91.23*** | 165.4*** | 204.52*** | 213.57*** | 0.508 | 0.274 | 0.301 | |||||||

Qatar | 8.633 | 0.779 | −0.973 | 2.434 | 167.2*** | 146.66*** | 176.25*** | 220.01*** | 1.542** | 0.777 | 0.725 | |||||||

Saudi Arabia | 8.632 | 0.598 | −0.631 | 2.595 | 71.29*** | 70.499*** | 101.24*** | 121.09** | 0.300 | 0.231 | 0.416 | |||||||

United Arab Emirate | 8.025 | 0.421 | −0.6 | 2.454 | 60.24*** | 27.802 | 38.097 | 55.577 | 0.031 | 0.032 | 0.109 | |||||||

Note: p t b r , p t w t i , and s t are respectively, the natural logs of Brent price, WTI price, and stock price. Std. is standard deviation, Skw. is skewness, Kurt. is Kurtosis, and J-B stands for Jarque-Bera. For autocorrelation and heteroscedasticity tests, the reported values are the Ljung-Box test Q-statistics for the former and the ARCH-LM test F-statistics in the case of the latter. We consider three different lag lengths (k) of 30, 60, and 90 for robustness. The null hypothesis for the autocorrelation test is that there is no serial correlation, while the null for the ARCH-LM test is that there is no conditional heteroscedasticity. ***, ** and * imply the rejection of the null hypothesis in both cases at 1%, 5% and 10% levels of significance, respectively.

Persistence | Endogeneity | |||
---|---|---|---|---|

p t b r | p t w t i | p t b r | p t w t i | |

s t | ||||

Bahrain | 0.993*** | 0.991*** | −0.283 | −0.257 |

Kuwait | 0.995*** | 0.994*** | −0.155 | −0.249 |

Oman | 0.994*** | 0.994*** | 0.043 | −0.064 |

Qatar | 0.994*** | 0.994*** | 0.116 | −0.027 |

Saudi Arabia | 0.995*** | 0.994*** | −0.096 | −0.194 |

United Arab Emirate | 0.996*** | 0.995*** | 0.014 | −0.026 |

Note: This table reports the endogeneity and persistence test results. Starting with the former, the test follows a three-step procedure: First, we run the following predictive regression model: s t = α + β x t − 1 + ε s , t where s t represents stock price and x t − 1 is the predictor variable (which are crude oil prices, in this case). In the second step, we follow [

effect in the predictors. The coefficient of the AR (1) process was estimated for each predictor using OLS estimator and the results were found to be close or equal to one which is often the features of series with higher order of integration, thus, suggesting that the predictors (crude oil prices) contain persistent effects. We, however, observe that our predictors are largely exogenous.

In line with [^{1} of [

^{1} [

The predictability power of a potential economic predictor hinges on the statistical significance of the first-order autoregressive coefficient in the theoretical (predictive) model at the conventional levels of significance, namely, 1%, 5%, and 10%. It can be observed that irrespective of measures of oil price series (Brent and WTI prices), the null hypothesis of no predictability is rejected at 1% level of significance (see

s t | ||
---|---|---|

p t b r | p t w t i | |

Bahrain | 0.772*** (0.024) | 0.744*** (0.022) |

Kuwait | 0.524*** (0.103) | 0.636*** (0.095) |

Oman | 0.431*** (0.045) | 0.253*** (0.041) |

Qatar | 0.503*** (0.096) | 0.656*** (0.079) |

Saudi Arabia | 2.451*** (0.134) | 2.333*** (0.115) |

United Arab Emirate | 1.982*** (0.067) | 1.885*** (0.069) |

Note: The in-sample predictability in a bivariate model case is obtained by estimating the equation s t = μ + δ z t − 1 + η ( z t − ρ z t − 1 ) + ε t where δ denotes the coefficient on the predictor z, which in this case stands for crude oil prices. We employ both Brent and WTI prices as proxies for crude oil prices. ***implies the rejection of the null hypothesis of no predictability at 1% level of significance. The values in parentheses are the standard errors associated with the first-order autoregressive coefficients in our predictive model. Here, we consider 75% of the full sample data.

Fluctuations (see

We further compare the in-sample and out-of-sample forecast performance of our oil-based stock model with four time-series models including AR, MA, ARMA, and ARFIMA using the RMSE, the C-T and the D-M statistics (see Tables 5-10). Generally, we observe that our predictive model, which in this case is the oil-based stock model, significantly outperforms all the four time series models both in-sample and out-of-sample. The result is also robust to the choice of oil price series (Brent and WTI prices) and the choice of time-series models used as benchmark. This result supports the previous findings^{2} of [

^{2} [

From the second to fourth columns of

s t | ||||||
---|---|---|---|---|---|---|

p t b r | p t w t i | |||||

In-sample | Out-of-sample | In-sample | Out-of-sample | |||

h = 30 | h = 60 | h = 30 | h = 60 | |||

Bahrain | 0.186 | 0.231 | 0.326 | 0.173 | 0.212 | 0.306 |

Kuwait | 0.468 | 0.467 | 0.463 | 0.444 | 0.445 | 0.446 |

Oman | 0.261 | 0.258 | 0.255 | 0.272 | 0.270 | 0.268 |

Qatar | 0.506 | 0.540 | 0.567 | 0.585 | 0.629 | 0.666 |

Saudi Arabia | 0.538 | 0.529 | 0.519 | 0.513 | 0.504 | 0.495 |

United Arab Emirate | 0.363 | 0.416 | 0.505 | 0.357 | 0.392 | 0.452 |

Note: Capturing 75% of the full sample, we evaluate the in-sample and out-of-sample forecast performance (using 30 and 60 days as the forecast horizons) of our predictive model, which in this case is the oil-based stock model (using Brent and WTI prices) with the aid of root mean square error (RMSE). The smaller the root mean square error (RMSE), the greater the predictive power of a model and vice versa.

s t | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

AR* | MA** | ARMA*** | ARFIMA**** | |||||||||

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | |||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | |||||

Bahrain | 0.313 | 0.305 | 0.298 | 0.299 | 0.293 | 0.288 | 0.313 | 0.305 | 0.298 | 0.304 | 0.296 | 0.289 |

Kuwait | 1.263 | 1.267 | 1.274 | 0.768 | 0.757 | 0.748 | 1.215 | 1.216 | 1.221 | 0.810 | 0.804 | 0.802 |

Oman | 0.636 | 0.641 | 0.649 | 0.546 | 0.543 | 0.543 | 0.582 | 0.586 | 0.593 | 0.548 | 0.546 | 0.548 |

Qatar | 1.279 | 1.293 | 1.314 | 0.786 | 0.782 | 0.784 | 1.186 | 1.198 | 1.214 | 0.799 | 0.800 | 0.808 |

Saudi Arabia | 1.206 | 1.208 | 1.215 | 0.648 | 0.639 | 0.634 | 1.099 | 1.099 | 1.103 | 0.695 | 0.692 | 0.694 |

United Arab Emirate | 0.793 | 0.803 | 0.822 | 0.409 | 0.412 | 0.423 | 0.695 | 0.703 | 0.718 | 0.426 | 0.435 | 0.453 |

*AR stands for autoregressive process/model; **MA for moving average process/model; ***ARMA for autoregressive moving average process/model, and ****ARFIMA for fractionally integrated autoregressive moving average process/model. Capturing 75% of the full sample, we evaluate the predictive power of the ARFIMA model both for the in-sample data and out-of-sample data cutting across the forecast horizons of 30 and 60 days using the root mean square error (RMSE). The smaller the root mean square error (RMSE), the greater the predictive power of a model and vice versa.

statistic is generally positive for the GCC countries except for Bahrain where the C-T stat is negative for the forecast horizon of 60 days. Since we are able to establish the predominance of positive C-T statistic, it can be concluded that our oil-based stock model is preferred to the AR model in predicting stock prices in the GCC countries. This result is generated from the fact that the RMSE associated with our predictive model is predominantly smaller than the RMSE associated with the AR model (compare

OSM versus AR | OSM versus MA | OSM versus ARMA | OSM versus ARFIMA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | |||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | |||||

Bahrain | 0.406 | 0.242 | −0.097 | 0.379 | 0.212 | −0.132 | 0.407 | 0.242 | −0.096 | 0.388 | 0.219 | −0.127 |

Kuwait | 0.629 | 0.632 | 0.636 | 0.391 | 0.383 | 0.381 | 0.615 | 0.616 | 0.621 | 0.422 | 0.420 | 0.423 |

Oman | 0.589 | 0.597 | 0.608 | 0.522 | 0.524 | 0.531 | 0.552 | 0.559 | 0.570 | 0.523 | 0.526 | 0.535 |

Qatar | 0.604 | 0.583 | 0.568 | 0.357 | 0.309 | 0.277 | 0.574 | 0.549 | 0.533 | 0.367 | 0.325 | 0.298 |

Saudi Arabia | 0.554 | 0.562 | 0.572 | 0.169 | 0.172 | 0.180 | 0.510 | 0.519 | 0.529 | 0.225 | 0.235 | 0.251 |

United Arab Emirate | 0.542 | 0.483 | 0.386 | 0.113 | −0.009 | −0.193 | 0.478 | 0.408 | 0.297 | 0.149 | 0.045 | −0.115 |

Note: The Campbell-Thompson (C-T) test statistics as used here compares the unrestricted model, which in this case is the oil-based stock model (using Brent price) with the time-series models (AR, MA, ARMA, and ARFIMA), which constitute the class of restricted models. Positive C-T stat implies that the oil-based stock model (using Brent price) is preferred to AR, MA, ARMA, and ARFIMA models in predicting stock prices using the in-sample data covering 75% of the full sample and the out-of-sample forecast horizons of 30 and 60 days. On the other hand, negative C-T stat implies that AR, MA, ARMA, and ARFIMA models are preferred to the oil-based stock model (using Brent price) in predicting stock prices using the in-sample data covering 75% of the full sample the out-of-sample forecast horizons of 30 and 60 days.

OSM versus AR | OSM versus MA | OSM versus ARMA | OSM versus ARFIMA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | |||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | |||||

Bahrain | 0.449 | 0.304 | −0.028 | 0.425 | 0.277 | −0.062 | 0.449 | 0.305 | −0.027 | 0.432 | 0.283 | −0.057 |

Kuwait | 0.649 | 0.648 | 0.650 | 0.423 | 0.412 | 0.404 | 0.635 | 0.634 | 0.635 | 0.453 | 0.447 | 0.444 |

Oman | 0.573 | 0.578 | 0.588 | 0.502 | 0.502 | 0.507 | 0.533 | 0.538 | 0.548 | 0.504 | 0.504 | 0.511 |

Qatar | 0.543 | 0.514 | 0.493 | 0.256 | 0.196 | 0.151 | 0.507 | 0.475 | 0.452 | 0.268 | 0.214 | 0.176 |

Saudi Arabia | 0.575 | 0.583 | 0.592 | 0.209 | 0.211 | 0.218 | 0.534 | 0.541 | 0.551 | 0.262 | 0.272 | 0.286 |

United Arab Emirate | 0.549 | 0.512 | 0.450 | 0.127 | 0.049 | −0.067 | 0.486 | 0.442 | 0.371 | 0.162 | 0.099 | 0.003 |

Note: The Campbell-Thompson (C-T) test statistics as used here compares the unrestricted model, which in this case is the oil-based stock model (using WTI price) with the time-series models (AR, MA, ARMA, and ARFIMA), which constitute the class of restricted models. Positive C-T stat implies that the oil-based stock model (using WTI price) is preferred to AR, MA, ARMA, and ARFIMA models in predicting stock prices using the in-sample data covering 75% of the full sample and the out-of-sample forecast horizons of 30 and 60 days. On the other hand, negative C-T stat implies that AR, MA, ARMA, and ARFIMA models are preferred to the oil-based stock model (using WTI price) in predicting stock prices using the in-sample data covering 75% of the full sample the out-of-sample forecast horizons of 30 and 60 days.

in predicting stock prices across the GCC countries. Irrespective of choice of oil price series (Brent and WTI prices), we therefore establish the superior forecast performance of our predictive model over the AR model both in-sample and out-of-sample.

From the fifth to seventh columns of

OSM versus AR | OSM versus MA | OSM versus ARMA | OSM versus ARFIMA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | |||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | |||||

Bahrain | −11.68*** | −5.71*** | 1.47 | −12.14*** | −5.31*** | 2.01 | −11.79*** | −5.76*** | 1.46 | −11.66*** | −5.26*** | 1.90 |

Kuwait | −29.43*** | −30.78*** | −32.34*** | −18.76*** | −18.47*** | −18.59*** | −28.66*** | −29.91*** | −31.39*** | −29.06*** | −29.39*** | −30.51*** |

Oman | −22.34*** | −23.66*** | −25.17*** | −30.46*** | −31.29*** | −32.76*** | −21.43*** | −22.65*** | −24.11*** | −31.51*** | −32.59*** | −34.24*** |

Qatar | −31.89*** | −33.21*** | −34.84*** | −14.34*** | −12.59*** | −11.76*** | −30.04*** | −30.99*** | −32.43*** | −19.66*** | −17.70*** | −17.17*** |

Saudi Arabia | −22.87*** | −24.07*** | −25.45*** | −6.32*** | −6.52*** | −6.97*** | −19.94*** | −20.94*** | −22.15*** | −8.58*** | −9.19*** | −10.11*** |

United Arab Emirate | −19.83*** | −19.47*** | −16.52*** | −3.41*** | 0.34 | 4.28 | −15.59*** | −14.48*** | −10.78*** | −4.81*** | −1.41 | 2.94 |

Note: The Diebold-Mariano (D-M) test statistic as used here compares the forecast errors of the unrestricted model, which in this case is the oil-based stock model (using Brent price) and the restricted model comprising the time-series models (AR, MA, ARMA, and ARFIMA). The negative and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the oil-based stock model (using Brent price) significantly outperforms the AR, MA, ARMA, and ARFIMA models using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days. However, the positive and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the AR, MA, ARMA, and ARFIMA models significantly outperform the oil-based stock model (using Brent price) using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days.

OSM versus AR | OSM versus MA | OSM versus ARMA | OSM versus ARFIMA | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|

In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | In-sample | Out-of-sample | |||||

h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | h = 30 | h = 60 | |||||

Bahrain | −12.56*** | −7.39*** | 0.45 | −12.95*** | −7.15*** | 0.97 | −12.67*** | −7.46*** | 0.44 | −12.54*** | −7.02*** | 0.88 |

Kuwait | −29.43*** | −30.73*** | −32.24*** | −19.11*** | −18.68*** | −18.58*** | −28.64*** | −29.84*** | −31.26*** | −28.06*** | −28.21*** | −29.01*** |

Oman | −22.63*** | −23.95*** | −25.47*** | −31.49*** | −32.22*** | −33.67*** | −21.81*** | −23.03*** | −24.49*** | −32.93*** | −33.98*** | −35.64*** |

Qatar | −30.14*** | −30.87*** | −32.11*** | −9.45*** | −7.18*** | −5.69*** | −27.63*** | −27.81*** | −28.65*** | −12.64*** | −10.02*** | −8.56*** |

Saudi Arabia | −24.02*** | −25.26*** | −26.67*** | −7.44*** | −7.63*** | −8.05*** | −21.09*** | −22.12*** | −23.35*** | −9.95*** | −10.57*** | −11.49*** |

United Arab Emirate | −20.23*** | −20.79*** | −20.37*** | −4.24*** | −1.55 | 2.09 | −16.07*** | −15.99*** | −14.72*** | −5.65*** | −3.65*** | −0.11 |

Note: The Diebold-Mariano (D-M) test statistic as used here compares the forecast errors of the unrestricted model, which in this case is the oil-based stock model (using WTI price) and the restricted model comprising the time-series models (AR, MA, ARMA, and ARFIMA). The negative and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the oil-based stock model (using WTI price) significantly outperforms the AR, MA, ARMA, and ARFIMA models using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days. However, the positive and statistical significance at 1% (***), 5% (**) and 10% (*) implies that the AR, MA, ARMA, and ARFIMA models significantly outperform the oil-based stock model (using WTI price) using in-sample data covering 75% of the full sample and out-of-sample forecast horizons of 30 and 60 days.

horizon of 60 days. Since we are able to establish the predominance of positive C-T statistic, it can be concluded that our oil-based stock model is preferred to the MA model in predicting stock prices in the GCC countries. This result obtains from the fact that the RMSE associated with our predictive model is predominantly smaller than the RMSE associated with the MA model (compare

With regards to the eighth to tenth columns of

With respect to the eleventh to thirteenth columns of

We offer new evidence of the predictability of stock prices using crude oil prices for the six GCC countries as a direct confrontation to the assertion of [^{3} are robust to the choice of oil price series (Brent and WTI prices), the choice of benchmark time-series models (AR, MA, ARMA, and ARFIMA), and the choice of forecast horizons (30 and 60 days).

Meanwhile, quite a number of policy implications can be discerned from the various research findings in this paper. First, the information provided in this study as regards the ability of oil price to produce more accurate forecast for stock returns will be useful to financial analysts and investors who rely on such information for investment decisions. Secondly, policy makers will also find the results useful in terms of how much of information contained in the movements of oil price can be exploited by the stock market. This is particularly important during oil price crisis where policy makers are expected to implement policies to mitigate the negative spillover effects from oil to the stock market and other macroeconomic fundamentals. Notwithstanding the usefulness of the research findings of the study, a number of areas can still be explored to improve the paper and are therefore suggested for future research. The first area relates to the choice of countries; future research can conduct same for other countries particularly net oil importers and non-OPEC net oil exporters. The latter is also important to see if the results of the giant members of OPEC can be generalized for the non-members in terms of the predictive power of oil price in forecasting stock returns. The second area relates to other statistical properties underlying stock returns which are not captured in the current study. These properties include structural breaks and asymmetries. It will be an interesting exercise to see how the consideration of these properties will enhance the predictability of stock returns.

The author declares no conflicts of interest regarding the publication of this paper.

Author 1, Author 2 and Author 3 (2018) Paper Title. Theoretical Economics Letters, 8, 3073-3091. https://doi.org/10.4236/tel.2018.814191