Impact of Industry-Specific Risk Factors on Stock Returns of the Malaysian Oil and Gas Industry in a Structural Break Environment

Abstract

This study examines the impact of industry-specific risk factors such as oil price, gas price, and exchange rate on stock returns of Malaysian oil and gas firms in a structural break environment by employing the break least square approach of Bai and Perron (1998, 2003). Existing studies fall short of providing such empirical evidence. The results document evidence of structural breaks in the relationship between industry risk factors and the stock returns of the oil and gas industry. Industry-specific risk factors are shown to significantly affect the stock returns of oil and gas industry sub-sectors alongside market-based risk factors. The results reveal that the beta values of oil price, gas price, and exchange rate vary across sub-periods hence confirming that exposure of oil and gas stocks to industry risk factors varies over time and across sub-periods. The effects of oil, gas, and exchange rate risk factors also differ across the sub-industry, with impacts and directions largely dependent on the core business activities of the oil and gas sub-industries. The empirical results offer implications for asset managers and investors.

1. Introduction

It is important for portfolio managers to grasp a good understanding of the influence of industry-specific risk factors on stock returns because changes in the risk–return trade-off go hand in hand with changes in industry-specific risk factors [1,2,3]. Such understanding increases the effectiveness of hedging and portfolio diversification. Furthermore, the importance of industry-specific risk factors in driving stock returns are highlighted in several empirical studies [1,4,5,6,7,8,9]. The empirical studies also postulated that industry-specific risk factors mostly dominate over domestic risk factors [10]. One possible explanation is that industry-specific risk factors are embedded in business activities and hence reflected in the stock price of the industry. Another possible explanation is that industry concentration and product structure are likely to influence the riskiness of a firm’s cash flows and hence its stock returns [11]. The oil and gas industry is an important sector of the stock market. The sector’s business activities, productions, services, and profitability are directly and indirectly related to oil and gas prices [6,12,13,14]. According to Hong and Sarkar [15], commodity price is an influential risk factor for companies that operate in natural resource industries, and therefore the risk factor affects stock returns. Oil and gas are essential commodities and have become the world’s most important energy sources. Hence, in line with Hong and Sarkar [15], oil and gas prices are considered significant risk factors for industry stock returns. Additionally, Scholtens and Wang [16] suggest that the oil price factor is an industry-specific risk factor in the oil and gas industry. Henceforth, the price of oil and gas can be considered as industry-specific risk factors in the oil and gas industry sector. In addition to oil and gas prices, the exchange rate is also a potential risk factor regardless of whether a firm operates in oil-importing and oil-exporting countries. This is because the currency factor affects firms’ contracts, investments, profits, and hence the stock price of oil and gas firms (see [13,17]). Based on these premises, this study considers oil price, gas price, and exchange rate as industry-specific risk factors that could affect oil and gas industry stock returns.

The literature relating to the impacts of risk factors on oil and gas stocks has been growing over the years. Sadorsky [3] finds that market excess returns, exchange rate, and oil price changes have significant positive impacts on the returns of Canadian oil and gas stocks. Lanza et al. [17] evidence that market, exchange rate, and oil price are important risk factors to the stock prices of six major oil companies. Jin and Jorion [18] evidence that oil and gas prices have a significant positive effect on the firm values of oil and gas producers. Boyer and Filion [19] report that crude oil prices and natural gas prices have significant positive impacts on the stock returns of Canadian oil and gas companies. Sadorsky [20] demonstrates that the impact of global oil market risk factors such as oil reserves and oil production on oil price risk and oil companies’ stock prices. Sadorsky [21] finds that the impacts of oil prices are size-dependent, with the strongest impacts observed for medium-sized companies. Kretzschmar and Kirchner [22] find that Fama and French risk factors, oil price, and reserve location have effects on oil and gas stock returns. Using an augmented four-factor asset pricing model, Mohanty and Nanda [12] find that various risk factors such as oil price changes, market, book-to-market, size, and momentum factor are significant return determinants of the oil and gas sector and that the exposures to oil risk vary across subsectors. Using a similar method, Sanusi and Ahmad [23] evidence that oil-related risk factors such as market, oil price, size, and book-to-market risk factors are significant determinants of stock returns for oil and gas companies in the UK. Ramos et al. [24] find that market, size, momentum, and oil price factors are systematic factors in the US oil industry stock returns. Similarly, Hoque et al. [13] demonstrate that market risk, oil price, gas price, and exchange rate are systematic risk factors of Malaysian oil and gas stock returns. Lv et al. [14] find that oil prices affect the stock returns of US and China, but the effects vary with the subsector category of an oil firm.

The Malaysian oil and gas industry includes thousands of small and major enterprises, with less than 40 listed on the main board of the stock exchange. These Malaysian oil and gas companies have been price takers of international crude oil and gas prices, and the volatility in oil and gas prices has affected their cash flows and stock values [13]. Uncertainty in the foreign exchange market also causes volatility in the earnings and stock performance of the oil and gas business. Furthermore, since Malaysia is a net oil and gas exporter, currency rates have a crucial influence in affecting energy inventory prices [13]. Moreover, Hoque et al. [13] discussed that market risk, oil price, gas price, and the exchange rate could influence Malaysian oil and gas stock returns. However, studies related to Malaysian oil and gas research have not looked at the influence of risk factors in a structural break framework environment.

While the above studies examined the relationship between oil and gas risk factors and stock returns, the research focused mainly on developed economies, producing inconclusive findings. The mixed findings in the literature are quite expected due to the following reasons. Firstly, the relationship between risk factors and stock returns often changes with time (e.g., [25,26,27,28,29,30,31,32,33,34]). Additionally, structural shifts in the industry, stock market, and the economy could be sparked by domestic and global level economic shocks leading to changes in the relationship between risk factors and stock returns of the oil and gas industry. One possible explanation is that in response to dynamic economic and geopolitical development, OPEC can make changes to oil production capacities which impact oil supply levels and lead to a sudden shift in oil price levels. Another possible explanation is that the exchange rate follows some types of regimes, which can possibly change the relationship as well. Furthermore, gas prices generally follow oil prices. Gas price levels can change rapidly if something disrupts the supply of oil, refinery operations, or if there are changes in gas production policy. That said, this study differentiates itself from past studies of oil and gas stock performance by examining the effects of industry-specific risk factors on the returns of Malaysian oil and gas stocks in a structural break framework has been lacking. The industry-specific risk factors are oil price, gas price, and the exchange rate factor.

The current study extends the literature in the following ways. First, evidence of industry-specific risk factors effects on stock returns of oil and gas industry have been conducted mostly for developed countries and those studies separately examined the impacts of oil price, gas price, and exchange rate (e.g., [3,12,14,17,18,19,20,21,22,23,24]. The current study integrates three industry-specific risk factors all at once in the empirical model and confirms that these risk factors affect the stock returns of the oil and gas industry. Second, while the current study is similar to Lv et al. [14] for investigating oil risk factors’ effects on stock returns of oil and gas sub-sector, Lv et al. [14] did not consider time-varying effects of risk factors on oil and gas stock returns. Hence, the current study enhances the understanding of the relationship by investigating the time-varying effects of oil and gas industry-specific risk factors. Additionally, although Mohanty and Nandha [12] did examine the time-varying effects taking into consideration different sub-periods, their multi-factor market models were not estimated in a structural break environment. Henceforth, the empirical findings of this study improve on Mohanty and Nandha [12] and provide a more robust result. Third, the current study lends strong support to the findings of Hoque et al. [13] on the Malaysian oil and gas industry and enhances the understanding that the effects of industry-specific risk factors such as oil price, gas price, and the exchange rate on stock returns depend on industry subsectors and vary across different sub-period.

The rest of the paper is organized as follows. The next section presents the data and methodology of the study. Section 3 discusses the empirical findings, and the final section summarizes the results and offers study implications.

2. Materials and Methods

2.1. Dataset Description

As reported by the Energy Information Administration [35], “Malaysia is the second largest oil and natural gas producer in Southeast Asia, and the second-largest exporter of liquefied natural gas globally; in addition, it is strategically located amid important routes for seaborne energy trade” (Badeeb et al. [36], p. 156). Malaysia has been considered the second-largest LNG (Liquefied natural gas) exporter after Qatar. Thus, changes in LNG prices could have an impact on energy company’s stock performance. Malaysia has oil reserves of 4.0 billion barrels and natural gas reserves of 100.7 trillion cubic feet [35]. Thus, the oil and gas sector has been contributing directly to Malaysia’s gross domestic products (GDP) and indirectly to the government’s tax income. In addition, the oil and natural gas sector contributed 9.1% to gross domestic products (GDP) in 2014, but its contribution has dropped significantly in 2015 and 2016 due to a sustained period of low oil prices [37].

The employed dataset comprises weekly data from January 2010 to December 2017 of firm-level data for 33 oil and gas stocks extracted from Bursa Malaysia and Yahoo Finance websites. The data were then sorted into seven sub-industry portfolios ($R_p={{\displaystyle \sum }}_{i=1}^I{W}_i{R}_I$, where $I$ is equally weight and $I_i$ is individual weekly returns), according to their GSIC structure. The GSIC structure of oil and gas industry firms is presented in

Table 1. Sub-industry structure with GISC code.

Sub-Industry (Energy: 1010 and Gas Utilities: 551020)	GISC Code
Oil and Gas Drilling	10101010
Oil and Gas Equipment and Services	10101020
Oil and Gas Refining and Marketing	10102030
Oil and Gas Storage and Transportation	10102040
Oil and Gas Exploration and Production	10102020
Coal and Consumable Fuels	10102050
Gas Utilities	55102010

Note: A total of 33 companies are in the oil and gas industry based on GISC Code of 2016. Source: Osiris Database.

. This study employs a 90-day T-bill rate as a proxy for the risk-free rate, and data were extracted from the Central Bank of Malaysia (Bank Negara Malaysia). Brent crude oil price is used as a proxy for the oil price, LNG price as a proxy for gas price, and those data were compiled from the website of Energy International Administration and DataStream. The exchange rate factor is expressed as the value of Malaysian Ringgit for one US dollar with data compiled from the Central Bank of Malaysia website and DataStream. This study also constructs common market-based factors such as size, value, and momentum factors as recommended in Fama and French [38,39].

2.2. Multi-Factor Market Model

The main objective of this study is to assess the effects of industry-specific risk factors on oil and gas stock returns. Many researchers have used multi-factor asset pricing models for estimating the effects of oil price fluctuation on stock returns (e.g., [6,12,13,19,23,24,30]). Based on numerous empirical studies this study thus adopts multi-factor market models for estimating the effects of oil and gas risk factors on stock returns of the oil and gas industry. In estimating such effects, some researchers employed Fama–French’s [38] three risk factors (market, size, and value factors) together with oil and gas risk factors, while others have used Carhart’s [40] four risk factors (market, size, value, and momentum factors) alongside oil and gas risk factors (e.g., [12,23,24]). In fact, market, size, value, and momentum risk factors are popularly known as common market-based risk factors in stock returns.

That said, the current study employs an augmented multi-factor model integrating oil and gas risk factors with Carhart’s [40] four risk factors. The use of the current multi-factor model is consistent with the multi-factor capital asset pricing model, and arbitrage pricing theory as those factors have been shown to influence oil and gas stock returns (see, [13,23]). Additionally, the model is also supported by financial economics theory which postulates that any factor that drives a firm’s cash flows could also influence stock returns. The said model can also be employed as a baseline model for reformulating a new multifactor market model with time-varying components of oil and gas risk factors.

$$\begin{array}{ll}(R_{jt}-r_{ft})={\alpha }_j& +{\beta }_{0,j}\left(R_{mt}-r_{ft}\right)+{\beta }_{1,j}SM{B}_t+{\beta }_{2,j}HM{L}_t+{\beta }_{3,j}{WML}_t\\ & +{\beta }_{4,j}OI{L}_t+{\beta }_{5,j} GA{S}_t+{\beta }_{6,j} E{X}_t+{\epsilon }_{jt } ; j=1, \dots , J; t\\ & =1, \dots , T. \end{array}$$

(1)

where, $R_{jt}$ are returns of each sub-industry j for each week t, respectively; $r_f$ stands for the risk-free rate for each week; $R_{mt}$ are returns of the market portfolio for each week; t, SMB, HML, and WML are the size factor and book to market value factor, and momentum factor, respectively. $OIL$, $GAS$, and $EX$ are returns of oil prices, LNG prices, and exchange rates, respectively. ${\epsilon }_{jt}$ symbolizes pricing error.

This study follows the procedures suggested by Fama and French [38,39] for constructing small-minus-big (SMB) and high-minus-low (HML) factors. The study uses a two-by-three sorting technique dependent on scale and book to market value to create SMB and HML factors. In order to define size groups, all stocks are ranked based on their market capitalization. Two types of portfolios are developed based on the median market capitalization value: small stock portfolio and big stock portfolio. Similarly, all stocks are ranked and sorted into three categories using the book-to-market ratio, with the top 30%, middle 40%, and bottom 30% of the book-to-market values being classified as high, medium, and low book-to-market portfolios, respectively (see [24,38,39]).

Six portfolios were constructed by sorting the intersections of the two size portfolios and the three book-to-market portfolios. The price-weighted portfolios are small–low (SL), small–medium (SM), small–high (SH), big–low (BL), big–medium (BM), and big–high (BH). For each week, the SMB factor is determined as the average of the difference between a small and a big portfolio. In contrast, the HML factor is defined as the weekly average of the high-low portfolio difference. The SMB and HML variables are calculated using the following approach:

SMB = 1/3 {(SL + SM + SH) − (BL + BM + BH)}

HML = 1/2 {(SH + BH) − (SL + BL)}

We adopted the approach of Fama and French [39] to construct the WML factor. Hence, this study classified all stocks into three groups based on the lag cumulative momentum returns of month-12 (in this study, week-52) and month-2 (in this study, week-5) during portfolio formation, where the top 30%, middle 40%, and bottom 30% of book-to-market values are defined as winners, losers, and neutral portfolios, respectively. After sorting, intersections of size and momentum portfolios produced six portfolios: small-loser (SL), small-neutral (SN), small-winner (SW), big-loser (BL), big-neutral (BN), and big-winner (BW). The difference between the average returns on winning and losing portfolios may now be used to determine the WML factor. The WML factor is calculated using the technique below.

WML = 1/2 {(SW + BW) − (SL + BL)}

2.3. Multi-Factor Market Model within Break Environment

It has been noted in the oil price-stock return literature that there have been structural shifts, changes, and time-varying patterns in the relationship between oil prices and stock returns over time (e.g., [25,26,27,28,29,30,31,32,33,34]). These studies have empirically documented that the oil risk factor’s effects on stock returns change over time. Moya-Martinez et al. [30] assess the time-varying impacts of oil price uncertainty on stock return by employing break-least-square regression. They document that the impacts of oil price uncertainty on sectoral stock returns vary across different periods. Zhu et al. [32] examine the time-varying effects of oil price changes on industry stock returns in China using multi-factor asset pricing with break-least-square and quantile regression models. Similarl to Moya-Martinez et al. [30], Zhu et al. [32] also find the time-varying effects of market, oil price, and exchange rate risk factors on industry stock returns.

The structural break approach has recently attracted researchers’ attention in examining the relationship between oil risk factors and stock returns in a structural break environment [29,30,32]. This approach allows for the testing of multiple structural breaks in a linear model and can detect breaks at a priori unknown date. Thus, structural changes in oil and financial markets have led to the reformulation of Equation (1) in the presence of structural breaks. Structural changes in the relationship between oil price and sub-sectoral equity returns can be estimated by employing the approach of Bai and Perron [41,42]. Hence, the following Equation (2) is modelled with m breaks (m + 1 regimes).

$$\begin{gathered} \begin{array}{ll}{(R_{jt}-r_{ft})}_k={\alpha }_{j,k }& +{\beta }_{0,j,k }{\left(R_{mt}-r_{ft}\right)}_t+{\beta }_{1,j,k } SM{B}_t+{\beta }_{2,j,k }HM{L}_t\\ & +{\beta }_{3,j,k }WM{L}_t+{\beta }_{4,j,k }OI{L}_t+{\beta }_{5,j,k } GA{S}_t+{\beta }_{6,j,k }E{X}_t+{\epsilon }_{j,k,t} \\ t=T_{k-1}+1\dots \dots T_k\end{array} \end{gathered}$$

(2)

where, k = 1, …, m + 1; k and T are the segment index and total sample size, respectively. The breakpoints ($T_1\dots T_m)$ are treated as unknown and by convention $T_0$= 0 $T_{m+1}=T$. All other specifications are the same as the specification of Equation (1).

Andrews [43,44] and Andrews and Ploberger [45] have designed an F-statistic for selecting a specific alternative and testing against the null hypothesis of one break with unknown timing. With breakpoint i, this study needs to compare OLS residuals ${\widehat{e}}_i$ of regression among each subsample and OLS residuals ${\widehat{e}}_i$ of each subsample with the whole e sample, which is presented below:

$$F_i=\frac{{\widehat{e}}^T\widehat{e}-{\widehat{e}}_i{}^T{\widehat{e}}_i}{\frac{{\widehat{e}}_i{}^T{\widehat{e}}_i}{n-2k}}\hspace{1em}\hspace{1em}i=n_h,\dots .,n_h\left(n_h\ge k\right)$$

(3)

Bai and Perron [41,42] have extended this given method to test 0 break against L break and L + 1 break. Before confirming the number of breaks, Bai and Perron [41,42] recommend that UDmax and WDmax tests should be performed to confirm that one break exists in the relationships. These are the two tests used for testing the hypothesis of “Global L Breaks Vs. None”. The equal-weighted version of the test, termed Udmax, chooses the alternative which maximizes the statistic across the number of breakpoints. An alternative approach applies WDmax weights to the individual statistics so that the implied marginal values are equal prior to taking the maximum. Henceforth, the sequential estimation of SUP $F_T=\frac{L+1}{L}$ statistics should be conducted to select appropriate numbers of breaks. The SupFT (L + 1|L) is a sequential test for the null of L breaks versus the alternative of L + 1 breaks.

3. Empirical Results

Table 2. Descriptive statistics, normality tests, and unit root.

	Mean	SD	Skewness	Kurtosis	Jarque–Bera Test	p-Value	PP	ADF
Panel A: Sub-Sectors
Oil and gas drilling	−0.009	0.483	−0.091	10.262	479.366	0	−15.98 ***	−6.56 ***
Oil and gas equipment services	0.007	0.229	0.949	6.965	335.74	0	−17.08 ***	−10.58 ***
Oil and gas refining marketing	0.002	0.36	1.233	13.028	1853.016	0	−18.48 ***	−18.53 ***
Oil and gas storage transportation	0.003	0.164	0.375	6.437	215.001	0	−19.62 ***	−19.02 ***
Oil and gas exploration production	0	0.347	1.195	8.356	597.669	0	−12.40 ***	−12.39 ***
Coal and consumable fuels	−0.001	0.424	0.556	4.45	25.038	0	−23.25 ***	−23.47 ***
Gas utilities	0.002	0.145	0.216	6.7	241.089	0	−15.98 ***	−6.56 ***
Panel B: Risk Factors
MKT	0	0.093	−0.165	4.964	68.899	0	−20.43 ***	−23.29 ***
SMB	0.009	1.295	−1.69	21.211	5960.437	0	−22.01 ***	−23.31 ***
HML	0.014	1.699	6.407	84.344	117821.5	0	−20.22 ***	−22.08 ***
WML	0.069	0.446	−1.072	8.455	596.94	0	−20.00 ***	−20.57 ***
OIL	0	0.3	−0.262	4.486	43.143	0	−12.09 ***	−20.55 ***
GAS	0.007	0.932	5.443	61.142	60794.96	0	−19.95 ***	−20.53 ***
EX	0.001	0.08	0.113	5.232	87.469	0	−20.43 ***	−23.29 ***

This table reports the results of the normality test, unit root tests, along with some summary statistics. The estimated p-value of the Jarque–Bera test lower than 0.05 indicates the rejection of the null hypothesis of the independent test at a 5% significance level. MKT, SMB, HML, OIL, GAS, and EX are market, size, value, oil price, gas price, and exchange rate risk factors, respectively. ADF and PP are the empirical statistics of the Augmented Dickey and Fuller (1979) unit root test and Phillips and Perron (1988) unit root test, respectively. The GICS Structure code was used for categorizing sub-industries in the oil and gas industry, as shown in Table 1. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

present descriptive statistics, normality tests, and unit root tests results. The normality test of the Jarque–Bera statistic highlights that the returns of all oil and gas sub-sectors and the market are not normally distributed. The distribution of risk factors’ returns is also not normally distributed. Thus, it can be conjectured that the relationship between risk and return could be intertemporal. Table 2 also present the results of the ADF and PP unit root tests which reveal that the data series is stationary at level form.

3.1. Breaks

Following the study of Moya-Martinez et al. [30], this study employs the method recommended by Bai and Perron [41,42] for detecting multiple structural breaks in the relationship between oil and gas risk factors and stock returns, which allows testing for multiple breaks at unknown dates. This study allows for a maximum of five breaks with a trimming parameter of 0.05, and the effective sample size is 390 for each series of the sub-industries. First, this study estimates the UDmax and WDmax tests at the 5% level to confirm that at least one break exists in the risk and returns relationship. Then, this study proceeds to estimate the sequential test supFT (L + 1|L) statistics for selecting the appropriate breaks.

Table 3. Results for the structural breaks test.

Sub-Industry	UDMax	WDMax	SupFT (0/1)	SupFT (1/2)	SuPFT (2/3)	Break	Break Date
Oil and gas drilling	72.58 **	83.42 **	68.65 **	20.64		1	22 May 2017
Oil and gas equipment services	65.41 **	65.41 **	65.41 **	13.51		1	4 April 2016
Oil and gas refining marketing	36.93 **	36.93 **	36.93 **	12.27		1	31 October 2016
Oil and gas storage transportation	19.98	17.063	14.726			0	0
Oil and gas exploration production	35.54 **	37.78 **	35.54 **	21.56		1	26 December 2011
Coal and consumable fuels	66.55 **	76.49 **	38.65 **	87.52 **	26.53	2	31 August 2015, 21 March 2016
Gas utilities	33.16 **	42.54 **	29.33 **	25.88 **	15.74	2	6 February 2012, 31 October 2016

Note: This table reports the results of the procedure developed by Bai and Perron [41,42] to search for structural breaks. The effective sample size is 390. A maximum of five breaks are allowed, and a trimming parameter (minimum size of a segment with respect to the sample size) of 0.05 is used. The double maximum (UDmax and WDmax test) tests the null of no structural breaks against the alternative of an unknown number of breaks. The SupFT (L + 1|L) is a sequential test of the null of L breaks versus the alternative of L + 1 breaks. Test statistics employ HAC covariances (Prewhitening with lags from AIC, Bartlett Kernel, Newey–West automatic bandwidth). The heterogeneous error distributions across breaks are allowed. *, ** and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

report the multiple breaks in the link between risk factors and oil and gas stock returns.

This study observes that all sub-industries in the oil and gas sector have at least one break, with the exception of the oil and gas storage transportation sub-industry. More specifically, two breaks are estimated for coal and consumable fuels and the gas utility sub-industry. Oil and gas equipment services, oil and gas refining marketing, oil and gas storage transportation, and the oil and gas exploration production sub-industry exhibit one break in the relationship between risk factors and stock returns. The possible explanation for detecting different structural breaks among sub-industries is that the oil and gas sub-industry’s stock performance also depends on the sub-industry’s core business activities, the country’s economic structure, political situation, stock market performance, and investors sentiment. Therefore, the oil and gas sub-industry’s stocks respond differently to the changes in risk factors’ value. The divergent responses have caused variations in the number of breaks in the relationship between risk factors and stock returns among oil and gas sub-industries.

Table 3 show that there is a common structural break for several oil and gas sub-industries in the period between 2011M12 and 2012M06. The break occurred possibly due to substantial increases in oil price in 2011 and 2012 when it reached a peak of just over $125 per barrel in early March 2012. However, by the end of June 2012, oil prices declined nearly 30% from the peak level to around $91 per barrel (Energy Information Administration, 2012). According to Energy Information Administration, some of the major drivers of oil prices during the first half of 2012 include positive changes in growth expectations of the global economy, oil supply disruptions in Syria, Sudan and Yemen, US and European sanctions on Iranian oil imports, and rising US oil production in early 2012. During this period, the Malaysian ringgit depreciated by 3.8% against the US dollar alongside other regional currencies. Additionally, the European sovereign debt crisis had somewhat contributed to the structural break given its impacts on global economic growth and financial market, which resulted in investors reducing their holding of financial assets in emerging markets. The structural break could also be potentially due to inflationary effects on the Malaysian economy.

A second common break occurred between 2015M09 and 2016M04. The period saw some major events take place, such as the Chinese stock market crash after a year-long run-up in stocks, OPEC oil production cut policy due to a supply glut, and weakening demand prospects. Thus, this break has increased the risk–return trade-off of oil and gas stocks. Additionally, the break could probably be the result of the oil and gas prices plunge that was accompanied by a slowdown in 2015 and 2016. The breaks could also be related to the continued weakening value of the Malaysian ringgit since it started falling in late 2014 due to lower oil prices. As Malaysia is a net oil exporter, the declining oil price has resulted in the huge depreciation of the Malaysian ringgit against other major currencies of the world.

The third common break took place between 2016M05 and 2016M10. This period was a crucial period as oil and gas prices started to increase gradually, and at the same time, the ringgit value was depreciating. Therefore, during this period, a break appeared in the relationships. Additionally, the impact of the Brexit referendum in June 2016 was reflected in the European financial market and economy. The outcome of Brexit caused a structural break in the risk and returns relationship. Additionally, during this period, the US presidential election was just about to be held, and it was the world’s most closely watched election ever. In times of US election uncertainty facing the world, some investors had reduced their asset holdings in emerging market economies. This could be the reason for a structural break in the risk and return relationship.

The fourth break point appeared between 2017M03 and 2017M05. During this period, even though oil and gas prices were stable for some time, the Malaysian ringgit continued to devalue, which led to the structural break in the risk and return relationship.

3.2. Discussions of Empirical Results

Table 4. Results of risk factors effects.

Sub-Industry	Break	Sub-Sample	Const	MKT	SMB	HML	WML	OIL	GAS	EX	Adj-R2	F-Stats	LM Test	BPG
Oil and gas drilling	1	04/11/2013–15/5/2017	−0.007 (−1.99) **	2.274 (6.42) ***	0.008 (0.77)	−0.021 (−1.86) *	−0.010 (−1.84) *	0.013 (2.04) **	0.044 (1.96) **	0.36 (0.33)	0.407	9.20	1.167	1.13
		22/52017–01/01/2018	−0.053 (−1.84)	4.210 (2.72) ***	0.192 (4.68) ***	0.072 (3.52) ***	−0.010 (−0.71)	0.198 (2.56) **	−0.745 (−1.84) *	−0.405 (−1.99) **
Oil and gas equipment services	1	11/1/2010–28/3/2016	0.0005 (0.44)	1.159 (8.70) ***	0.077 (1.53)	0.101 (2.85) ***	−0.002 (−0.96)	0.073 (1.96) **	0.004 (1.98) **	0.84 (0.54)	0.5865	18.31	3.58 *	1.18
		04/04/2016–01/01/2018	0.029 (7.76) ***	0.99 (2.94) **	0.012 (2.43) **	0.004 (0.77)	−0.05 (−1.61)	0.059 (2.06) **	−0.003 (−1.52)	0.24 (2.59) **
Oil and gas refining marketing	1	11/01/2010–24/10/2016	0.003 (0.34)	0.362 (5.44) ***	−0.014 (−0.82)	−0.007 (1.98) **	0.009 (1.58)	−0.016 (−0.98)	−0.003 (−1.01)	0.167 (1.69) *	0.3841	10.63	3.15 *	1.69
		31/10//2016–01/01/2018	0.017 (4.132) ***	1.311 (2.26) **	−0.003 (−0.53)	0.004 (1.05)	0.003 (0.23)	−0.161 (−2.05) **	0.009 (1.30)	0.824 (2.07) **
Oil and gas storage transportation	0	---	0.000 (0.34)	0.425 (2.30) ***	0.003 (0.23)	0.010 (0.96)	−0.009 (−1.78) *	−0.001 (−0.019)	−0.024 (−1.35)	0.119 (0.52)	0.0267	1.60	2.30	0.09
Oil and gas exploration production	1	11/01/2010–19/12/2011	−0.007 (−0.23)	0.51 (1.99) **	0.108 (1.00)	0.601 (0.99)	−0.022 (−1.10)	−0.007 (0.65)	0.004 (−0.09)	0.232 (0.39)	0.3823	9.34	2.183	1.99 *
		26/12/2011–01/01/2018	0.05 (0.51)	0.99 (4.22) ***	0.012 (1.43)	0.001 (0.01)	0.005 (0.97)	0.139 (2.27) **	−0.014 (−1.41)	−0.09 (−0.30)
Coal and consumable fuels	2	28/7/2014–24/8/2015	0.016 (2.42) **	2.183 (4.87) ***	−0.422 (−1.35)	−0.175 (−0.68)	−0.004 (−0.25)	0.21 (1.45)	0.034 (0.35)	1.444 (2.82) ***	0.578	8.81	0.426	2.29
		31/8/2015–14/3/2016	0.02 (1.38)	−0.36 (−2.29) **	2.726 (3.64) ***	0.528 (1.69) *	−0.051 (−1.49)	−0.232 (−0.15)	−0.076 (−6.40) ***	1.31 (1.29)
		21/3/2016–01/01/2018	−0.004 (−0.97)	0.894 (2.02) **	−0.0001 (−0.07)	−0.004 (−0.39)	0.011 (0.62)	0.023 (2.15)**	−0.047 (−0.61)	0.57 (2.51) **
Gas utilities	2	11/01/2010–30/01/2012	0.003 (1.301)	0.533 (3.22) ***	0.18 (3.34) ***	−0.105 (−2.21) **	0.007 (1.73) *	−0.113 (−2.65) ***	−0.014 (−2.81) ***	−0.277 (−1.68) *	0.4012	12.52	2.97 *	1.13 *
		06/02/2012–24/10/2016	0.002 (1.36)	0.442 (4.48) ***	0.003 (0.044)	−0.016 (−3.61) ***	−0.002 (−0.06)	−0.003 (−1.88) *	−0.002 (−0.31)	−0.135 (−1.04)
		31/10/2016–01/01/2018	−0.001 (−0.76)	0.829 (2.93) ***	−0.003 (−0.69) *	0.001 (0.17)	0.002 (0.46)	−0.11 (−1.98) **	−0.04 (−1.31)	−0.339 (−2.54) **

Note: This table reports OLS regression results of multi-factor linear model in Equation (1) for sub-sectors based on the breakpoints identified employing the test of Bai and Perron [41,42]. Standard errors of the estimated coefficients are corrected for autocorrelation and heteroscedasticity with the Newey–West procedure. Breaks denote the number of breaks selected by the sequential procedure of Bai and Perron at the 5% significance level. MKT, SMB, HML, WML OIL, GAS, and EX are mark0et, size, value, momentum, oil price, gas price, and exchange rate risk factors, respectively. *, **, and *** denote statistical significance at the 10%, 5%, and 1% levels, respectively. All F-statistics are significant at 5% except for the oil and gas storage transportation. The LM test of Breusch–Godfrey (BG) and Breusch–Pagan–Godfrey (BPG) was employed to check for serial correlation and heteroskedasticity problems in the model.

report the estimated results of Equation (2) with different sub-samples representing the oil and gas sub-industries. Table 4 show that the adjusted R-square of each sub-industry varies from 0.38 to 0.59, indicating acceptable goodness-of-fit values with significant F-statistics. The post-estimation test with ARCH and BPG tests reveals that the coefficients in the estimated models follow the stability hypotheses. Therefore, the estimated model in the structural break environment is stable, and the results could be generalized for the Malaysian oil and gas industry stocks.

The market risk factor is significantly and positively related to stock returns of all oil and gas sub-industries, but its beta value varies across sub-periods. These findings are as expected because all stocks listed in the stock market, including oil and gas stocks, tend to follow market movements. Furthermore, the coefficients of SMB, HML, and WML risk factors shows that SMB, HML, and WML factors have significant effects on stock returns at least in one sub-period. Thus, the models, to a large extent, have captured the time-varying behavior of oil and stocks.

3.2.1. Oil and Gas Drilling Sub-Industry

The empirical results show that oil price had a positive and significant effect on stock returns in both sub-periods (11 April 2013–15 May 2017 and 22 May 2017–1 January 2018), but the coefficients are different in each period, suggesting that the strength of the relationship changes with time. Hence, these results confirm that the relationship between oil price and the stock returns of oil and gas drilling sub-industry changes significantly over time. The positive effects of oil price changes on oil and gas stock price are expected as oil and gas stock price movements are projected to move in the same direction as oil price fluctuations. Additionally, when oil price increases (decreases), firms in the oil and gas drilling sub-industries get more (less) contracts from exploration and production-related firms. Thus, the firm’s profitability and cash flow also increase (decrease) and are hence reflected in stock price and returns. That is, the findings imply that stock returns of the oil and gas drilling sub-industry increase with an oil price increase, and vice-versa.

The empirical results also find that gas price changes have significant positive effects on stock returns but only during the period of 11 April 2013–15 May 2017 Weak negative effects were observed over the period from 22 May 2017–1 January 2018. These findings highlight direction differences in the influence of gas price on stock returns of the oil and gas drilling sub-industry as the effects of gas price tend to be positive and negative in different sub-periods. Such findings suggest that there is a time-varying relationship between gas price and stock returns of the oil and gas drilling sub-industry. That said, the intricate relationship between gas price and stock price needs to be cautiously observed and can serve as a guide to investing in oil and gas drilling sub-industry stocks.

Moreover, the empirical results also discover that exchange rate changes had a significant negative effect on stock returns during the period of 22 May 2017–1 January 2018. The results provide new insights, which is contradictory to the initial hypothesis of a positive effect given that exchange rate increase is considered as a value-creating factor for oil and gas firms’ cash flow and profit. However, the new finding is not all that surprising given that the oil and gas drilling sub-industry is characterized by high investment capital and technologically intensive firms that need to import their drilling equipment and machinery. Hence, when the exchange rate depreciates, it becomes costly for firms to own and import equipment and machinery. Thus, the oil and gas drilling sub-industry’s stock returns were negatively affected by exchange rate depreciation.

3.2.2. Oil and Gas Equipment and Services Sub-Industry

The coefficients of the oil price risk factor’s effects show that oil price changes positively affected stock returns during the periods of 11 January 2010–3 March 2016 and 4 April 2016–1 January 2018. Therefore, the findings suggest that regardless of the time period, returns of the equipment and services sub-industry increased with the increase in oil prices. However, the extent of the impact is not the same throughout the sub-sample period, possibly owing to the positive and negative oil price shocks evolutions. The findings evidently point to the time-varying positive effects of oil price on stock returns of the oil and gas equipment services sub-industry.

The empirical results relating to the effects of gas price changes showed positive effects on stock returns over the period of 11 January 2010–3 March 2016 and no significant influence in other subsample periods. This implies the presence of the varying effect of gas price on the stock returns of the oil and gas equipment services sub-industry.

The coefficients of exchange rate changes show that exchange rate depreciation had a positive effect during the period of 4 April 2016- 1 January 2018, indicating time-specific effects of an exchange rate change on stock returns of the oil and gas equipment services sub-industry. While the exchange rate risk factor did not have a significant influence in other sub-period samples, the observed effect during the period of 11 January 2010–3 March 2016 is in line with the full sample analysis. The positive effect of exchange rate depreciation during this period suggests that exchange rate depreciation is beneficial for the stock price of companies in the oil and gas equipment services sub-industry. This is because there are much Malaysian oil and gas equipment and services companies such as Bumi Armada and SapuraKencana, which provide oil and gas services to overseas markets such as Australia, Brazil, Nigeria, and West Africa. Their business presence in foreign countries results in foreign currency earnings which translate into additional profits in times of exchange rate depreciation.

3.2.3. Oil and Gas Refining and Marketing Sub-Industry

The coefficients related to the effects of oil price changes show that oil price changes had a significant negative effect during the period of 31 October 2016–1 January 2018. This period was characterized by a gradual increase in oil prices which pushed up the sub-industry’s production and refining costs. Oil price movements affect the operational profitability of this sub-industry because profit depends on the spread between the cost of oil and the selling price of the end products. Therefore, increase in cost resulted in lower net profit and translated into lower stock price, which explained why an increase in oil price affected stock price negatively.

The coefficients pertaining to the gas price factor demonstrate that the gas price factor has insignificant effects on the stock returns of the oil and gas refining marketing sub-industry. This is possibly because refining, distribution, and marketing costs and profits have already been reflected in the retail price of gasoline. The exchange rate coefficients show that it had a positive effect on the stock returns of the oil and gas refining marketing sub-industry during the period of 31 October 2016–1 January 2018. This suggests the presence of time-varying effects since the oil price-exchange effects are insignificant in other sub-periods. A possible explanation for the finding is that during this period, gains from currency depreciation balanced some of the losses incurred from an oil price decline.

3.2.4. Oil and Gas Storage and Transportation Sub-Industry

For this sub-industry, the estimated model did not detect any structural break in the risk–return relationship, suggesting that the risk–return relationship is time-invariant. To be consistent with other sub-industry models, the model for the oil and gas storage transportation sub-industry is also estimated within the standard asset pricing framework. The model also does not capture variance in stock returns and does not have an acceptable goodness-of-fit value. Accordingly, this study opines that perhaps a higher-order or more complex model is required to capture the time-varying effects of risk factors on stock returns for the oil and gas storage transportation sub-industry. This sub-industry belongs to the midstream segment of the oil and gas industry involved in storing and transporting oil and gas-related commodities and is a vital link along the oil and gas supply chain that connects producers and consumers.

3.2.5. Oil and Gas Exploration and Production Sub-Industry

The empirical results exhibit that the oil price risk factor had a significant and positive effects during the period of 26 December 2011–1 January 2018, suggesting that the effects of the oil price are time-varying. The oil price positive effect is expected given that stock price moves in tandem with oil price changes. These effects of oil price on stock returns of the oil and gas exploration production sub-industry are not only time-varying but also asymmetric because of differences in the magnitude of the effects and significance levels.

The coefficients pertaining to gas price and exchange rate risk factors exhibit insignificant influence throughout the sub-period samples. These findings imply that oil price risk factors dominate over other factors in influencing stock returns of this sub-industry. One possible explanation for the insignificant effect of the gas (LNG) price factor is that gas price movement always follows that of oil price, hence the dominant role of oil price in explaining stock returns. Furthermore, investors with commodity-based stock portfolios usually give more weight to oil price than gas price, and thus the stock price of this sub-industry tends to respond to oil price changes rather than the gas price.

3.2.6. Coal and Consumable Fuels Sub-Industry

The coefficients of oil price indicate that oil price changes had a positive and significant effect on stock returns during the period of 21 March 2016–1 January 2018. The stocks of this sub-industry performed well in accordance with the increasing trend of oil prices. During this period of 21 March 2016–1 January 2018, the oil price had started to increase after a prolonged period of negative oil price shocks, and the price increases were expected to have positive effects on the returns of any oil and gas stocks. The increase in oil price during this period had boosted investors’ confidence to buy the stock of this sub-industry and hence resulted in a positive influence on oil price on stock returns.

The coefficients on the time-varying effects of gas price risk factor show that while the gas price had a negative effect on stock returns during the period of 31 August 2015–14 March 2016, it does not significantly influence stock returns during other sub-period samples. Hence, the different effects of gas price over different periods imply that the effects of gas price risk factors on a stock return are time-varying. The negative effects of gas prices on stock returns were possibly driven by investors’ negative perceptions of oil and gas stocks owing to the decline in oil and gas prices.

The coefficient pertaining to time-varying effects of exchange rate risk factor demonstrates that positive change in exchange rate return had a positive effect on the stock returns of the coal and consumable fuels sub-industry during the period of 21 March 2016–1 January 2018, whereas it did not have a significant effect on stock returns during other sub-period samples. The positive effect during the period of 21 March 2016–1 January 2018 is not surprising because of the sustained exchange rate depreciation over that period. Additionally, since the business activities of this sub-industry are directly involved with exports of oil and LNG products, exchange rate depreciation brings in additional cash flow and profit, which translates into a higher stock price. Hence, oil and gas firms benefited from the prolonged period of exchange depreciation, which resulted in positive stock returns.

3.2.7. Gas Utilities Sub-Industry

The coefficients relating to time-varying effects of oil price factor indicate that the oil price risk factor had a negative effect on stock returns during the period of 11 January 2010–6 February 2012 and 31 October 2016–1 January 2018. However, the extent of the impact is not the same. Thus, it could be said that the effect of oil prices on stock returns of the gas utility sub-industry is time-varying. Additionally, as mentioned earlier, this sub-industry stock tends to exhibit a negative reaction to oil price movement due to the nature of its business activities.

The coefficient of time-varying effects of gas price risk factor shows that the gas price risk factor had a negative effect on stock returns during the period of 11 January 2010–6 February 2012. Since the effects are only significant in one period but not the others, this implies that the effect of gas price on stock returns of the gas utility sub-industry is time-varying. In addition, the negative effects of the gas price factor on this sub-industry stock return are not surprising because the use of fuel-related products tend to decrease when oil and gas prices increase. During this period, oil and gas prices had started to increase after a prolonged period of low prices. When oil and gas prices increase, firms in the gas utilities sub-sector earn lower profits which eventually pushes down the stock price. Therefore, an increase in oil and gas prices negatively affect the stock returns of this sub-industry.

The coefficients relating to time-varying effects on the exchange rate risk factor demonstrates that the exchange rate risk factor had a negative effect on stock returns during the period of 11 January 2010–6 February 2012 and 31 October 2016–1 January 2018. This suggests that the effect of the exchange rate factor on stock returns of the gas utility sub-industry is time-varying. In addition, as mentioned previously, this sub-industry stock tends to react negatively to exchange rate changes owing to the nature of its business activities.

3.3. Robustness Test

To check for the robustness of the estimated results in Table 4, this study employed rolling regression for the oil and gas industry portfolio using the same risk factors. In the rolling regression, this study rolls a window of 20-week returns initially and forward one week at a time. The first observation of the study sample started on 4 January 2010. Henceforth, the beta coefficients for each week were estimated from 4 June 2010 to 28 December 2017. The estimated time-varying betas of risk factors are presented in Figure 1. It is revealed the beta coefficients of risk factors vary with time, suggesting that the relationship between risk factors and stock returns tends to be time-varying and intertemporal. Such evidence also suggests that the relationship could be asymmetric and heterogeneous. Given the test results, it can be affirmed that the estimated results in Table 4 are qualitatively robust.

4. Concluding Summary

The study uses multi-factor market models to examine the impact of industry-specific risk factors, namely oil price, gas price, and exchange rate, on oil and gas sub-industry stock returns after controlling for the effects of Fama-French-Carhart risk factors in the models. The models are estimated in structural break environments using the break-least-square of Bai and Perion [42]. This study found that the effects of industry-specific risk factors on the stock returns of the oil and gas industry exhibit time period varying patterns as the effects vary across different subperiods. We found the following results. When we compared the coefficient of the oil price factor among sub-industries, we observed that the oil and gas refinery and gas utilities sub-industries have negative exposures to oil price increase, while other sub-industries have positive exposures. Regarding the gas price factor, we observed that the coal and consumable fuels and gas utilities sub-industry are negatively affected by the increase in gas prices while other sub-industries have positive exposures to gas price changes. These results suggest that the stocks of the gas utilities sub-industry perform better when oil and gas prices decline. Such findings were expected since this industry sells oil and gas related services and products; increases in the prices of oil and gas are usually passed down to consumers. A surge in oil and gas prices cause demand to drop due to the lower usage of oil and gas-related utilities. Hence, the business performance of firms in the gas utilities sub-industry falls with an increase in oil and gas prices and hence the negative effects on stock returns. Regarding the exchange rate factor, while it exerts a positive influence on the stock returns of the oil and gas equipment and services, oil and gas refining and marketing, and coal and consumable fuels sub-industries, it has a negative impact on the stock returns of the oil and gas drilling industry. In summary, the findings of this study on the time-varying risk exposure to oil and gas risk factors are supported by previous studies (e.g., [25,34]). Moreover, the results also lend strong support to those of Lv et al. [14], Mohanty and Nanndha [12], and Ramos et al. [24] that the effects of oil price on oil and gas stock returns vary across sub-industries and hence confirms the presence of heterogeneity effects within the industry. The empirical findings offer policy and practical implications. As a net oil-exporting nation, Malaysia has constant exposure to currency or exchange rate risk. The evidence of the changing effects of industry risk factors on the stock return relationship implicitly means that investors should closely monitor major policy development in the global oil market and gas market to be able to respond promptly to the time-varying effects. Hence, investors need to cautiously manage their portfolio formation and risk hedging if they do trade in the Malaysian stock market. Additionally, the time-varying and sub-industry effects provide insights that can serve as a guide to investing in the oil and gas market. The findings of subsector dependent effects between oil and gas industry-specific risk factors and stock returns imply an opportunity for diversification strategies across the oil and gas sub-industry. Industry allocation can be an important consideration for investors in constructing portfolios that maximize the risk–return trade-off among industry-specific risk factors.