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Crucial impact Crude Oil pricing has on economy

Crude Oil is the crucial input of modern economies. As countries urbanize and renovate their demand for oil raises drastically. Potential demand for oil is hard to forecast but is usually highly correlated with the growth in industrial production. Therefore, countries experiencing hasty economic growth are the ones probably to significantly amplify their demand for crude oil. Increases in oil demand without equalizing increases in supply lead to higher crude oil prices. Higher crude oil prices act like an inflation tax on consumers and producers by 1) plummeting the amount of disposable income consumers have left to spend on other goods and services and 2) increasing the costs of non-oil producing companies and, in the absence of fully passing these costs on to consumers, sinking profits and dividends which are key drivers of stock prices. In addition to worldwide demand and supply conditions, crude oil prices also respond to geopolitics, institutional arrangements (OPEC), and the dynamics of the futures market (Sadorsky, 2004). Unanticipated changes in any of these four factors can create volatility, and hence risk, in oil futures prices. Oil Price volatility increases risk and uncertainty which negatively impacts stock prices and reduces wealth and investment.

One macroeconomic factor that is receiving increasing empirical attention is crude oil. A key factor input, crude oil prices have the potential to dramatically alter the financial performance of national economies and the firms that operate therein. it is reasonable to expect that stock markets are profoundly influenced by oil price changes, remarkably little empirical evidence exists. Hammoudeh et al. (2004), for example, argues “…there has been a large volume of work investigating the links among international financial markets, and some work has also been devoted to the relationships among petroleum spot and futures prices. In contrast, little work has been done on the relationship between oil spot/futures prices and stock indices”.

Even the findings of the extant work are mixed. Chen et al. (1986) and Hamao (1988) found no evidence of an oil price factor in the U.S. and Japan, respectively. In contrast, Sardorsky (1999) and Kaneko and Lee (1995) concluded that oil prices were a significant factor in the U.S. and Japan, respectively. Jones and Kaul (1996), Faff and Brailsford (1999), Sardorsky and Henriques (2001), and Sardorksy (2001)have also examined the impact of oil price factors with disparate results. While these studies have provided at least some evidence that oil prices constitute a source of systematic asset price risk, and that the exposure to this risk varies across industries, no recent work is known in the Pakistani context.

Statement of Problem

At least since the development of the capital asset pricing model, a literature has sought to identify the determinants of asset prices and returns. Given the capital asset pricing model rests on the premise that assets are priced according to their covariance with the market portfolio, the increasing acceptance that other pricing factors, especially macroeconomic factors, should also be modeled has led to yet further refinements, most notably in the form of the arbitrage pricing theory. With this multifactor specification as a starting point, an increasing number of empirical studies have sought to investigate whether macroeconomic variables constitute a source of systematic asset price risk at the market and industry level [see, for instance, Poon and Taylor (1991), Antoniou et al. (1998), Faff and Chan (1998), Dinenis and Stailouras (1998), Elyasiani and Mansur (1998), Canova and Nicolo (2000), Choi et al. (2002), Apergis and Eleftherious (2002), Patro et al. (2002), Chaudhuri and Smiles (2004), Ryan and Worthington (2004), Erdem et al. (2005) and West and Worthington (2006)]. The fundamental endeavor of this analysis is to find out whether macroeconomic information, particularly crude oil prices, gives incremental information beyond the market portfolio about the behavior of industry stock returns


Crude oil being the core input of productions it has been assumed that changes in crude oil price significantly changes the cost of production. Therefore an increase in crude oil price leads to higher cost of production. Consequently higher cost of production leads to lower profit margins or it forces producer to increase the price of the goods. And increase in price of goods leads lower demand for the good resultantly sales of the firm goes down and overall profitability suffer. Further more firm making tiny or negative profit loses investors confidence and its stocks price go down which leads to negative stock returns and vice versa. Following hypotheses are suggested:

Ho: change in oil prices has significantly impact on the stock returns of oil and gas sector of Pakistan

H1: change in oil prices has significantly impact on the stock returns of automobile and parts sector of Pakistan

H2: change in oil prices has significantly impact on the stock returns energy sector of Pakistan

H3: change in oil prices has significantly impact on the stock returns Chemical and Pharmaceutical sector of Pakistan

H4: Change in oil price has different impact on the stocks return of different industrial sectors understudy


Asset prices are generally believed to respond sensitively to macroeconomic news. Every day experience gives the impression to support the observation that individual asset prices are influenced by a broad range of unpredicted events and that various events have a more persistent impact on asset prices than do others (Nai-Fu Chen, Richard Roll, Stephen A. Ross). Therefore Macroeconomic news is important factor in the explanation of stock returns at the industry level.

In recent years there have been numerous studies which argue that stock prices not only replicate changes in current and future cash flows and anticipated returns, but are also determined by speculative dynamics, that is, fads, investor attitude, and/or overreaction to news. Many researchers have claimed that the strong predictability of stock returns over various horizons is proof of such fads. In an endeavor to measure whether the predictability of stock returns is rational, several recent studies tested whether using Capital Asset Pricing Model (CAPM) or a more general asset pricing model like the Arbitrage Pricing Theory (APT) could eliminate or ex-plain their predictability. If factors and/or their coupled risks can explain the predictability of stock returns then the market is convincing, and vice versa (see, for example, Fama and French (1989), Ferson and Harvey (1991), Ferson and Korajczyk (1995), and Sentana and Wadhwani (1991)).

The approach taken in this paper uses an global multi-factor model that permits for both unconditional and conditional risk factors. This approach is related to the international capital asset pricing model (CAPM), the implications of which have been studied by Brealey & Myers, (2003). Whereas the focus of the CAPM is on market risk, the multi-factor model includes multiple sources of risk (Ross, 1976). The CAPM and multi-factor models are essential building blocks of contemporary portfolio theory. In both models, expected returns are linearly connected to risk factors and risk premiums. So far the CAPM has been broadly tested both domestically and internationally and the general agreement is that the CAPM shows no statistically meaningful relationship between systematic risk (beta) and returns (Fama & French 1992).

Modern economies are more energy efficient nowadays than they were 40 years ago with oil usage per dollar of GDP less than half of what it was in the 1970s. This increase in energy efficiency has happened because of cheap energy intensity through technological modernization and more dependence on a broadened range of energy sources (like a greater mix between non-renewable and renewable energy sources). Emerging and new economies tend to be more energy intensive than more developed economies and are therefore more exposed to high oil prices. Consequently, oil price changes are likely to have a larger impact on earnings and stock prices in emerging economies.

Past practice has shown that oil price shocks have a much bigger impact on the poorer countries in the world. The OPEC oil embargo of 1973, which raised the price of oil from $3 per barrel to $13 barrel in just over a few short months, created real economic and social destitution for developing countries by increasing their costs of imported crude oil. Worldwide lending institutes like the World Bank and the International Monetary fund (IMF) had to grant loans to developing countries so that they could keep on with their economic development projects (Rifkin, 2002, chapter 9)

If crude oil plays a vital role in an economy, one would anticipate changes in oil prices to be interrelated with changes in stock prices. Specifically, it can be argued that if oil influences real economic activity, it will impact earnings of businesses through which oil is a direct or indirect cost of operation. Thus, a swell in oil prices will cause expected earnings to change, and this would lead to an immediate change in stock prices if the stock market efficiently capitalizes the cash flow propositions of the oil price increases. If the stock market is inefficient, stock returns might be slowly changing (Aktham Maghyereh 2004).

Sadorsky (2003) exercised monthly data from July 1986 to April 1999 to examine the macroeconomic determinants of U.S. technology stock price conditional volatility. Sadorsky (1999) projected a vector auto regression model with monthly data to cram the affiliation between oil prices changes and stock returns in the United States. In his analysis, he found that oil price alteration and oil price volatility both play vital roles in affecting stock returns. The pragmatic results indicated that the conditional volatilities of industrial production, oil prices, the default premium, the federal funds rate, the foreign exchange rate, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices.

According to McSweeney and Worthington (2007) surplus returns in the retailing industry are negatively connected to the oil price factor. A latent explanation for the observed negative effect is the influence of oil price increases on consumer discretionary spending. Since the price of oil get higher relative to other goods and as a percentage of household expenditure, the nondiscretionary character of household petroleum expenses, at least in the short-run, restricts the amount of discretionary funds presented to consumers. This ought to lower the returns on retail firms.

S.A. Basher, P. Sadorsky (2006) studied the influence of oil price on 19 emerging equity markets including Pakistan. They found strong proof that oil price risk influences stock price returns in emerging equity markets while the precise relationship depends, to some extent, on the data frequency being used. The conditional association is not yet symmetrical. For daily and monthly data, positive oil price changes have a positive effect on surplus equity market returns in emerging economies. For weekly and monthly data, negative oil price changes have positive and significant effects on emerging equity market returns.

Robert W. Faff, and Timothy J. Brailsford in their study found that the degree of pervasiveness of an oil price factor, beyond the influence of the market, is detected across some Australian industries, positive oil price sensitivity in the Oil and Gas and Diversified Resources industries and similarly they found significant negative oil price sensitivity in the Paper and Packaging, and Transport industries. Generally, they revealed that long-term effects persist, although they hypothesize that some firms have been able to pass on oil price changes to customers or hedge the risk.

Mohan Nandha, a, and Robert Faffa analyzed 35 DataStream global industry indices for the period from April 1983 to September 2005.They indicated that oil price rises have a negative impact on stock returns for all sectors except mining, and oil and gas industries. Moreover in United Kingdom Idris El-Sharif, Dick Brown, Bruce Burton, Bill Nixon and Alex Russell analyzed that the oil and gas sector, using data relating to the United Kingdom, the largest oil producer in the European Union. Their findings indicated that the relationship is always positive, often highly significant and reflects the direct impact of volatility in the price of crude oil on share values within the sector.

Additionally, Evan J. Mcsweeney and Andrew Worthington (2007) examined the impact of macroeconomic risk factors on Australian industry returns. Their research indicated that the macroeconomic factor specifically oil prices are important determinants of excess returns for many industries. Of the nine industries considered, the energy industry exhibited a strong positive association with oil price increases, while the banking, retailing and


Methods of data collection:

Secondary data has been collected to scrutinize the relationship between macroeconomic variables and industry stock returns, monthly data over the period July 2003 to June 2008 has been employed. The choice of a monthly frequency is consistent with previous work which examines macroeconomic variables in relation to equity returns (Faff and Brailsford (1999), Sadorsky (2001)).

Sampling technique

Sample size

Monthly data of industrial returns, crude oil prices, KSE 100 and foreign exchange rate has been collected for the period of 5 years since july 2003 to june 2008. Data has been gathered from State Bank of Pakistan (SBP), Karachi Stock Exchange (KSE) and OGRA. Automobile and allied, Oil and Gas, Chemical and Pharmaceuticals sectors have been considered to examine the impact of crude oil price on their stock returns.

Research model

The fundamental endeavor of this analysis is to conclude whether macroeconomic information, specifically crude oil prices, gives incremental information beyond the market portfolio concerning the behavior of industry stock returns. While at least some work has been conducted at the market level [see, for example, Chen et al. (1986), Hamao (1988) and Cheung and Ng (1998)] reasonably few studies have attempted to scrutinize the relationship between macroeconomic factors and stock returns at the industry level. To model the relationship between the macroeconomic factors and industry returns, a multifactor model following Mcsweeney and Worthington (2007), Faff and Brailsford (1999), and Sadorsky (2001) is employed.

r it = βi0+ βi1 mkt +βi2 oil +βi3 fx + eit

where r it denotes the return on the stock index of the ith industry at time t, mkt is the return on the market portfolio, oil is the change in oil prices, fx is the change in the exchange rate, βi are parameters to be estimated that are expected to vary by industry, and eit is the error term.

Industrial stock Returns:

The excess return in each industry is calculated as:

rit =ln (indi;t/indi;t-1)-rfrt

where, rit is the continuously compounded monthly return for industry i at time t, indit and indi, t-1 are the index prices for industry i at time t and t -1, respectively, and rfr denotes the risk-free rate of interest.

Oil price

The oil price factor is constructed as:

oilt = ln(wtxt/wtxt-1)

Where, oilt is the log monthly change in the oil price at time t, and wtxt and wtxt-1 is the respective price of oil at time t.

Exchange rate

The exchange rate factor is constructed as:

fxt = {ln (PKRt/USDt)/(PKRt-1/USDt-1)}

where, fxt is the log monthly change in the PKR/USDt exchange rate at time t, and

PKR/USDt-1 is the respective PKR/USD exchange rate at time t and time t – 1

Market returns

The excess return on the market portfolio is calculated as:

mktt =ln (aoiit/aoiit-1)-rfrt

Where, mktt is the continuously compounded monthly return for the aggregate market Index at time t, aoiit and aoit-1 are the values for the market index at time t and t -1, Respectively, and rfrt is the risk-free rate of interest.

Results and Finding

To study the impact of crude oil price changes on the stock return of different industries, Multiple Linear Regression a statistical technique has been employed.

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