The Effect of Oil Price Dynamics on Equities in Emerging Markets: an Industry-level Analysis of Brazil and China

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The Effect of Oil Price Dynamics on Equities in Emerging

Markets: an Industry-level Analysis of Brazil and China

Thesis

MSc Business Administration – Finance

Rijksuniversiteit Groningen

Faculty of Economics and Business

Abstract

Past studies have documented an adverse relationship between oil price and stock returns, although they have concentrated primarily on developed nations. This study attempts to address the effect of oil price dynamics on equity returns in emerging markets, focusing on Brazil and China. Brazil represents a net oil-exporter, whereas China is one of the world’s largest net oil-importers. Regression analyses is conducted on returns from 10 FTSE industry indices for the period: 1999 – 2009, with the sample split into three subperiods for our analysis: 1999 – 2005, 2006 – 2008, 2009. Our results show that the returns of the Oil & Gas and Basic Materials sectors carry a positive relation with respect to oil price for both countries, whereas the opposite relation holds for the Consumer Services industry. Findings also show that in 2009, as a net oil-exporter, equity returns in Brazil were positively affected by oil price. There was little or no evidence to support the effect of OPEC decisions on stock returns in this case.

JEL classification: C22, G15, O57, Q43

Keywords: Emerging markets, Oil price, Equity returns, Brazil, China

Author: Khilan Sonigra

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Student No: 1942832

Supervisor(s): Dr. P. P. M. Smid / Dr. J. H. von Eije

Submitted on: 30 November 2010

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1. INTRODUCTION

“With rising demand for oil and other energy resources, these economies will significantly impact oil consumption

and prices in the coming decades. However, the impact of oil price volatility on an oil producing and a non-oil producing country varies to a large extent.”

– Euromonitor International [2010], Economic Growth and Oil dependence in Emerging Market Economies2

From its key purpose in fuel production to its uses to create a wide variety of by-products, stretching from gasoline and plastics to fertilizer, crude oil (petroleum) is widely regarded as the world’s premier energy source. The price of this vital commodity has a significant effect on the economic and financial environment, this includes worldwide stock markets.

Countries experiencing rapid economic growth, such as: Brazil, Russia, India and China (BRICs), are likely to significantly increase their demand for oil to help drive modernization and urbanization. According to a report by the Energy Information Administration(EIA) [2007], 18 percent of total annual oil demand in 2006 (84.6 million barrels per day) came from these emerging markets. A recent report by Goldman Sachs [2010], stated that these emerging economies contribute to over a third of world GDP growth and have expanded to account for almost a quarter of the world economy (in terms of Purchasing Power Parity).

Surplus oil demand leads to higher oil prices, which in turn act as an inflation tax on producers and consumers. Higher oil prices introduce an opportunity cost for consumers, reducing the disposable income that they have left to spend on other goods and services. Higher prices also increase the operating costs of companies that are not engaged in oil exploration or production, this can ultimately restrict profits and dividends which are key drivers of stock performance. In addition to global demand and supply, oil prices also respond to geopolitics, institutional arrangements, and changes in the futures market [Sadorsky, 2006].

Developed countries achieve energy efficiency through technological innovation and a diverse range of energy sources. On the contrary, emerging economies tend to be more energy intensive and therefore face greater exposure to higher oil prices. Consequently, oil price changes are likely to have a profound impact on profits, returns and stock prices in these markets.

China is the fastest growing major economy in the world, and the third largest after the US and Japan. In 2008, China consumed an estimated 8 million barrels of oil per day, making it the second largest consumer of oil after the US. In the same year, China was also the world’s third largest net oil importer. The Energy Information Administration(EIA) [2009] predict that in the coming years China’s consumption of oil will increase, however its production will remain stable, signalling a strong reliance (present and future) on imported oil. BBC News Asia-Pacific [20 July 2010] reported: “China has overtaken the United States to become the world's top energy consumer for the first time… The US has been the biggest consumer of energy for more than 100 years. But China's rapid economic growth in the past two decades has pushed it into top spot”; a clear indication of the prominence of this emerging nation. An imperative report by the International Energy Agency [2004] that looked at the impact of high oil prices on the global economy outlined that the adverse economic impact of higher oil prices on oil-importing

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developing countries is generally even more severe than for developed countries. This is because their economies are more dependent on imported oil and are more energy-intensive, as energy is used less efficiently. On average, oil-importing developing countries use more than twice as much oil to produce a unit of economic output as developed countries. They are also less able to weather the financial turmoil wrought by higher oil-import costs.A study by Park and Ratti [2008] also drew similar findings, showing that Norway as an oil exporter shows a statistically significantly positive response of real stock returns to an oil price increase.

The main industries in China include: metals, mining, production of electrical equipment, textiles, chemicals and consumer products. With export goods primarily made up of electrical goods and equipment, while oil and mineral fuels remains a major import good. China’s largest trade partner is the US, the destination of about 20% of all Chinese exports.

The eighth largest economy in the world and one of the fastest growing; Brazil’s prospects look a lot brighter since the appointment of President Lula Da Silva. Petrobras was a monopoly in oil-related activities until 1997, when the Brazilian government opened up the sector to competition. Brazil consumed on average around 2.5 million barrels of oil a day in 2008. The country was a net oil importer until 2006, however its ability to meet the majority of oil demand through local oil and ethanol production has made it less vulnerable to global oil price movements relative to net oil-importing countries. With the second highest oil reserves in South America, after OPEC-member Venezuela, and the constant discovery of new offshore supplies, Brazil achieved oil self-sufficiency in 2006 and has since become a net oil exporter. The Economist [2009] reported that the Brazilian government has imposed rule changes to try and bring more state control to huge oil and gas field discoveries off the coast of Brazil.

Main industries in Brazil include: metals, mining, petroleum, aircraft, consumer products and other locomotives. With export goods heavily consisting of transport equipment, soybeans, coffee and now petroleum, whereas electronics comprise the majority of import goods. Interestingly, the main trade partners of Brazil are China and the US, accounting for almost a quarter of all Brazilian exports together.

Existing literature regarding oil price changes and stock returns have displayed a negative relation between the two, although Faff and Nandha [2007] found that firms involved in: mining and materials, and oil and gas operations are more exempt from this relation. Oil & Gas and Basic Materials industries should carry a positive relation regarding oil price. As producers of oil, they benefit from a rise in price of their primary product, whereas higher oil prices shifts investment to alternative sources of energy which are usually accounted for by the mining and materials sector. We must also consider that crude oil has a huge array of by-products, which find applications from aviation fuel through to shampoo and shoes. Moreover, higher oil prices might have an impact on interest rates and depress consumer confidence, creating indirect channels for reflecting higher oil prices into equity prices. Although the latter relation between oil price and interest rate, is less-supported.

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and investments of all types. Given the oil intensity of these emerging economies, it is important for global portfolio investors to understand the susceptibility of equities in these markets to movements in global oil prices.

A study by Basher and Sadorsky [2006] used risk analysis to examine the relation between oil price movements and stock returns in 21 emerging markets, concluding that oil price increases have a positive impact on stock market returns in emerging markets. Recent research by Bhar and Nikolova [2009] found the level of impact of oil price returns on equity returns and volatility in the BRIC countries depends on the extent to which these countries are net importers or net exporters of oil.

This study attempts to contribute to literature regarding stock markets and energy prices by studying the impact of oil price changes on equities in the emerging markets of: Brazil and China. Brazil represents an emerging net oil producer and exporter, with potential to become one of the world’s largest oil producers in the future. Whereas China represents an emerging economic powerhouse and an eventual global superpower. Proceeding, an industry-level analysis is performed to determine which industries are affected by oil price changes and to what magnitude. Observing the effect of oil price on the returns of 10 FTSE industry indexes for each, Brazil and China, as outlined by the FTSE Dow Jones Industry Classification Benchmark. Our aggregate timeframe covers the 10-year period: 1999 – 2009, split into three sub-periods for our analyses: 1999 – 2005, 2006 – 2008 and 2009. Other economic factors that literature suggest plays a role are also considered in our model, with a dummy variable accounting for OPEC decisions to control for the influence of any possible geopolitical effects. The preceding regression analysis, in which a simple Ordinary Least Squares approach is adopted, is performed using Eviews 7. All data is sourced from Thomson Reuters Datastream. Further in the analyses, a conscious effort is made to incorporate geopolitics, developments and economics in helping to explain the empirical results.

The main aim of this paper is to quantify a relationship (if any) between the price of oil and equity returns in Brazil and China. In order to do this we must consider many sub-topics. Firstly, which sectors in which country are affected and why? Also, whether the decisions of OPEC make a difference to equity returns, if so, which industries does this affect? Does the well-known positive relation hold for Oil & Gas, Basic Materials industries in these emerging markets as well? What are the effects across the three different sub-periods that we observe, did the spike in oil price and financial crisis (represented by period 2: 2006 – 2008) suffocate equity returns? Lastly, does it matter whether the country in question is a net-importer or exporter of oil?

This study is an extension of the aforementioned literature, as well as taking into consideration ideology and methodology put forth by various other studies in this field. All in attempt to address the issue:

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2. LITERATURE REVIEW

2.1 Existing Literature

There exists a fair amount of literature that attempts to establish an effect between oil prices and stock activity, though most of this research is directed towards application to developed nations; there is a slowly growing body of papers that concentrate on emerging markets.

The world’s most renowned investment bank and originator of the term ‘BRIC’: Goldman Sachs [2003], used demographic projections, a model of capital accumulation and productivity growth. Finding that China’s economy may eclipse that of the US by 2041. Nevertheless, the paper also stipulates that these projections are based on assumptions of: sound macroeconomic policy, strong political institutions, openness to trade and a high level of education. A set of assumptions that remain optimistic, given the political and ethical backgrounds of developing nations. Jim O’Neill, head of global economic research at Goldman Sachs, has since predicted growth rates between 2011 and 2050 of 4.3 percent a year for Brazil and 5.2 percent for China, surpassing the bank’s initial projections. Appendix.1 charts GDP for both countries since 1980, it is clear that both nations saw a steep rise in GDP after the turn of the millenium. Foreign Direct Investment (FDI) is graphed in Appendix.8, it is evident that both countries saw a rise in FDI over our sample period, especially China. Furthermore, a recent report in the Financial Times [3 October 2010]3 stated that: “Equity funds in emerging markets have seen inflows of $50bn, a striking contrast with the $80bn outlflows recorded by western funds up until the end of September”. Adding to this, as stipulated earlier in a report by the EIA, China has overtaken the US to become the world’s largest consumer of energy. These statistics are testament to the promise and global relevance of these two nations, deemed to stand amongst the largest economies in the world during the coming decades.

According to literature, there is a well-documented adverse linkage between oil prices and economic growth (Hamilton [1983], Gisser and Goodwin [1986], Mussa [2000]). Recent research (Faff and Nandha [2007]) which used regression analysis on 35 global equity indices concluded that oil price rises have a negative effect on equity returns. This is in support of previous research that found evidence that aggregate stock market returns in: the USA, Canada, Japan and the UK, carried this negative relation with respect to any adverse impact of oil price shocks (Jones and Kaul [1996]). An important study by Sadorsky [1999] also supports this well-cited notion, suggesting that changes in oil prices impact economic activity but, changes in economic activity have little impact on oil prices. Furthermore, a positive oil price shock has a negative impact on real stock returns. Park and Ratti [2008] found that for many European countries, but not for the USA, increased volatility of oil prices significantly depresses real stock returns. The contribution of oil price shocks to variability in real stock returns in the USA and most other countries is greater than that of the interest rate. A study that looked at the impact of oil price shocks and exchange rate volatility on economic growth in China, Japan and Russia (Jin [2008]), found that oil price increases have a negative impact of economic growth in China and Japan, and a positive effect for Russia. The same research also concluded that an appreciation of the exchange rate (to the US Dollar) had a positive effect on Russian GDP growth, whereas the opposite, negative relation held for China and Japan. The results of past studies suggest that oil price and its volatility both play important roles in affecting real stock returns, with evidence of increasing impact since 1986.

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Although the application of such knowledge to emerging markets is an extremely new approach, there exists some literature that finds results following on the findings of previously-mentioned studies.

An important recent study (Bhar and Nikolova [2009]) that observed oil price and equity returns in the BRICs, showed that the level of impact of oil price returns on equity returns and volatility depends on the extent to which these countries are net-importers or net-exporters of oil. They also found a negative relation between oil price and equity returns in China. A net oil-importer, China has to pay higher oil import prices when global oil prices increase. The higher import prices have a negative impact on cash flows of businesses and their ability to pay dividends to shareholders, which effectively translates into lower stock prices. These findings follow on from earlier inferences by Huang et al. [1996], whom opined that if oil plays an important role in an economy, one would expect changes in oil prices to be correlated with changes in stock prices. A similar paper by Basher and Sadorsky [2006], used both unconditional and conditional risk analysis to investigate the relationship between oil price movements and stock returns in 21 emerging stock markets, establishing that for weekly and monthly data, oil price decreases have positive and significant impacts on emerging market returns. However the study was open to limitations of an unobservable market portfolio as well as validity queries about applying the CAPM to such complex and developing economies. In a more up-to-date study, they observed the dynamics and linkages between oil prices, exchange rates and emerging stock markets (Basher, Haug and Sadorsky [2010]). Apart from the expected negative relation between oil price increases and emerging stock returns, they found that an increase in oil price leads to a drop in the real exchange rate. Making the domestic currency effectively ‘cheaper’, in a bid to attract further Dollar investment and stimulate exports. This finding is consistent with earlier literature observing oil prices and exchange rates (Golub [1983], Amano and van Norden [2008], Lizardo and Mollick [2010]).

Du, He and Wei [2010] used a multivariate autoregression (VAR) to investigate the relationship between the world oil price and China’s macro-economy based on a monthly time series from 1995 – 2008. The results show that the world oil price affects the economic growth and inflation of China significantly, and the impact is non-linear. The structural stability tests demonstrate that there is a structural break in the VAR model because of the reforms of China’s oil pricing mechanism, thus it is more appropriate to break the whole sample into different sub-samples for the estimation of the model. As we saw in the introduction, China is fast-becoming one of the largest economies in the world and is one of the largest oil consumers too, when taken into account, the findings of this study are unsurprising that oil price significantly effects China’s economy.

The paper by Faff and Nandha [2007] discussed earlier, also had some more profound findings. Indicating that oil price changes have a negative impact on equity returns of all industries, except those of; basic materials (which includes companies engaged in metals and mining), and oil and gas operations. As explained earlier in the introduction, firms in these two industries stand to benefit from a rise in oil price.

2.2 Oil Price Dynamics

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world’s oil reserves and a third of total global oil production. It goes without saying that such a powerful cartel in this respect can play a significant role in shaping oil prices and hence the wider economy and financial markets. Although scarce, there has been some research into the effects of OPEC announcements and the pricing power beheld by OPEC.

OPEC sets production quotas based on its assessment of the market’s call on its supply. Oil prices fluctuate in part according to how well OPEC performs this calculation. Through the process of adjusting its production quotas, OPEC can only hope to influence price movements towards a target level or target zone. In a supply– demand framework, the oil price is determined by OPEC and non-OPEC supplies as well as oil arriving to the market from OPEC members who do not abide by the assigned quotas. Since these supplies cannot be predicted with accuracy and are influenced by factors other than prices, OPEC can only hope that the resulting oil price is close to its preferred price. In this context, models that consider OPEC simply as a price setter to maximize the net present value (NPV) of oil receipts over time are of limited usefulness.

This leads us to the analysis of the price band mechanism which OPEC adopted in 2000 as a signaling mechanism. This mechanism set a target range for the OPEC basket price between US$22 and US$28 per barrel of oil. If prices are below the floor for ten consecutive days, OPEC will automatically cut production. If prices are above the upper band for twenty days, it will automatically increase production.

At present, worldwide oil sales are denominated in US Dollars, changes in the value of the Dollar against other world currencies affect OPEC's decisions on how much oil to produce. For example, when the dollar falls relative to the other currencies, OPEC-member states receive smaller revenues in other currencies for their oil, causing substantial cuts in their purchasing power. After the introduction of the Euro, pre-invasion Iraq decided it wanted to be paid for its oil in Euros instead of US Dollars. This caused OPEC to consider changing its oil exchange currency to Euros, although after Iraq's invasion, the interim government reversed this policy and the subsequent Iraqi government(s) stuck with the US Dollar.

A paper by Fattouh for the Oxford Institute for Energy Studies [2007] explained how oil futures prices had replaced spot prices as a measure of the price of oil, this shift to the futures markets for price determination has introduced a large number of players and the large variety of participants (floor traders, fund managers, refiners, producers, financial institutions and speculators) has certainly complicated the process of decision-making within OPEC. OPEC’s influence on prices has become dependent on the expectations of these participants and how they interpret OPEC signals. In a special report about oil prices for CBS News4, Dan Gilligan (president of the Petroleum Marketers Association of America), stated: “Approximately 60 – 70% of the oil contracts in the futures markets are now held by speculative entities. Not by companies that need oil, not by the airlines and not by the oil companies, but by investors who are looking to make money from their speculative positions”. Theorists have even adopted behavioural approaches to help address oil price dynamics (Ellen and Zwinkels [2010]), explaining that there exist two groups: fundamentalists who trade based on mean-reversion, and chartists whom follow price trends. Speculators then choose between these rules based on past profitability. Estimation results on Brent and WTI oil

reveal that both groups are active in the oil market, and that speculators often switch between the groups. The paper

discussed previously by Fattouh also claimed that OPEC’s pricing power has diminished as a result of new

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international oil pricing systems, although there is still room for OPEC to affect world oil prices even under this system.

Guidi et. al [2006] performed a study to look at the significance of OPEC meetings, but mainly from the point of view of the impact they have on stock markets, rather than on crude oil returns. Their approach involves division of the data into periods of ‘conflict’ and ‘non-conflict’. They then compare the reaction of the stock markets in the US and UK to OPEC quota decisions between conflict’ and ‘non-conflict’ periods. They concluded that these markets appear to be efficient and well able to anticipate and absorb changes in production quotas without any significant increase in volatility.

Lin and Tamvakis [2010] adopted an event study methodology to investigate the effect of OPEC announcements on oil prices, concluding that the effect of the announcements depends on the context in which they are made. They also figured that during times of high oil prices, OPEC meetings receive extensive coverage and attention, whereas the opposite is true during times of tumbling oil prices.

2.3 1973 Oil Crisis

A real-life example which encompasses the theory put forth by the preceding literature is The Oil Crisis [1973]. In which OPEC proclaimed an oil embargo, declaring it would limit or halt oil shipments to the US and any other nation supporting Israel in the Yom Kippur War, with the Netherlands facing a complete embargo. The price of oil quadrupled by 1974, to nearly 12 US Dollars/barrel. This increase in the price of oil had a dramatic effect on oil exporting nations, for the countries of the Middle East who had long been dominated by the industrial powers were seen to have acquired control of a vital commodity. The traditional flow of capital reversed as the oil exporting nations accumulated vast wealth. The 1973 oil crisis was a major factor in Japan's economy shifting from oil-intensive industries, and resulted in huge Japanese investments in industries such as electronics. The Western nations' central banks decided to sharply cut interest rates to encourage growth, deciding that inflation was a secondary concern. Although this was the orthodox macroeconomic prescription at the time, the resulting stagflation surprised economists and central bankers, and the policy is now considered by some to have deepened and lengthened the adverse effects of the embargo. The above example displays the many ways in reality that oil can affect macroeconomic measures, some of which are in line with the afore-mentioned literature.

2.3 Hypotheses

Given the existing literature on the topic as a whole: we would expect there to be a negative relationship between oil prices and stock prices in Brazil and China, however this will only hold for companies that operate in a sector reliant on oil consumption such as: transport, aviation, mass-production etc. For companies involved in oil, mining and materials, we expect a positive relation to prevail. Whether the country is a net importer or net exporter of oil will also determine to what extent oil price fluctuations will affect their equity prices, if the composition of the respective equity market is made up primarily of production-based firms and the nation in question is a net-importer then a negative relationship makes more sense.

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3. DATA AND EMPIRICAL METHODOLOGY

3.1 Data Description

Fig.15

Quantifying a statistical relation between oil price and stock market activity will require panel data on various economical parameters and indicators. The data for this study was collected using the Thomson Reuters Datastream database. The dataset comprises of weekly data from the period January 1999 – December 2009, as figure 1 shows, this presents quite a volatile period for crude oil prices, especially from 2005 - 2009. As Bekaert et al [2002] explained, this represents a post-liberalization period for emerging markets, characterized by increased levels of industrialization and economic development. Reports show that GDP in China and India more than doubled since the turn of the millennium, with Brazil and Russia almost following suit. These are both good demographics given our study route, as times of intensive growth require investment in fuel, in this case taking the form of crude oil, so a cause and effect relation (if applicable) will be easier to distinguish. From January 1999 – December 2005, crude oil price displayed fairly steady growth. This growth was also evident in worldwide capital markets. Prior to this period, in January 1999, oil prices reached lows due to the Asian Financial Crisis coinciding with increased production from Iraq, ultimately reducing demand. It is also worth noting that two significant world events took place during this period: September 11th terrorist attacks in New York [2001] and the US-led invasion of Iraq [2003]. The following two-year period: January 2006 – December 2008, represents a time of turmoil and uncertainty in financial markets. Brought on by the emergence of the financial crisis, as well as the most volatile period for oil prices in our sample. Nevertheless, the price of crude oil saw a massive surge to record levels never achieved before; fuelled by investor sentiment and concerns whether supply would meet new unprecedented demand levels, especially by speculative investors. The subsequent period from January 2009 – December 2009 was a time of recovery, for worldwide oil prices after reaching lows at the end of 2008, also for worldwide financial markets and economies in wake of the financial crisis. For these reasons, our analysis will be split into 3 separate timeframes: 1999 – 2005, 2006 – 2008 and 2009.

The data for this study consists of weekly closing levels of the FTSE Brazil and China industry indices from Thomson Reuters Datastream, which covers each of the 10 industries as outlined by the FTSE Dow Jones Industry Classification Benchmark(ICB)6. From this we are able to compute basic log returns of each index, the majority of past studies have adopted log returns to model stock price, they have the favourable properties of being time-additive and mathematically convenient. The ICB is used globally to distinguish various industries and more

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Crude Oil Price is an average of weekly prices of: Brent Crude, West Texas Intermediate and Dubai Crude

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See appendix.8 for a full description of the Industry Classification Benchmark (ICB)

Crude Oil Price, 1999 - 2009

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specifically; sectors, facilitating easier comparison between industries. From the literature review, studies such as: Basher and Sadorsky [2006] and Park and Ratti [2008] also used indices as oppose to individual company data, suggesting that it still provides an accurate reflection of their respective market via a majority share in the country’s market capitalization. It also helps avoid certain data problems, new markets such as Brazil and China can often contain newly-formed companies with incomplete stock data, using index values helps to avoid issues regarding a lack of data. China is also served by two main stock markets: the Hang Seng Index in Hong Kong and the Shanghai Stock Exchange, using indices allows us to alleviate any bias as they incorporate companies from both stock markets. All indices used are quoted in US Dollar terms and are produced by the Financial Times Stock Exchange (FTSE).

The crude oil price used in our analysis is an average of: UK Brent, Dubai and West Texas Intermediate, these are by far the most widely-traded crude oils in the modern world in terms of volume7. Previous studies (Faff and Nandha [2006], Bhar and Nikolova [2009], Basher and Sadorsky [2006]) have usually adopted either UK Brent or West Texas Intermediate as their basis for oil price, however Dubai oil is a benchmark for Middle-Eastern production and is heavily exported to the Asia-Pacific region (which includes China).

As was explained earlier in this paper, oil prices can have profound effects on shaping many economic factors. Previous literature (Faff and Nandha [2007] ,Sadorsky [1999]) has shown that oil price changes contribute to fiscal policy dynamics, although this relation is a subject of scepticism by many scientists to this day. For this reason, information regarding domestic interest rates will also be included in our model. The interest rates used are official rates set by the respective central bank of the country under observation, deemed credible as they supposedly reflect economic conditions including the impact(s) of oil prices. For Brazil: The CDB (up to 30 days) Middle Rate, for China: The Relending rate (3 months) Middle Rate.

Official spot exchange rates are expressed as the US Dollar worth of the domestic currency, beneficial as crude oil prices are quoted in US Dollar/barrel and research from the literature review implies that oil prices contribute to the shaping of these exchange rates. At present, worldwide oil sales are denominated in US Dollars, changes in the value of the Dollar against other world currencies affect OPEC's decisions on how much oil to produce. For example, when the dollar falls relative to the other currencies, OPEC-member states receive smaller revenues in other currencies for their oil, causing substantial cuts in their purchasing power. After the introduction of the Euro, pre-invasion Iraq decided it wanted to be paid for its oil in Euros instead of US Dollars. This caused OPEC to consider changing its oil exchange currency to Euros, although after Iraq's invasion, the interim government reversed this policy and the subsequent Iraqi government(s) stuck with the US Dollar.

A dummy variable to control for the influence of OPEC decisions is also proposed, as was seen earlier in the introduction and literature review, OPEC announcements can impact oil prices. This is as far as the scope of this paper goes to include a variable to incorporate geopolitical effects. As discussed earlier in this paper, OPEC help facilitate the smooth functioning of the oil markets, without cooperation between OPEC and the global economy, there is the possibility of adverse supply shocks.

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3.2 Empirical Model

Proceeding, a industry-level analysis is performed to determine which industries are affected by oil price changes and to what magnitude. Many past studies have shown that it is important to differentiate among various sectors as oil price changes can have opposing influences; for example, aviation companies versus oil producing firms. The sectors defined for the analyses are outlined by the FTSE Dow Jones ICB: 0001 Oil & Gas, 1000 Basic Materials, 2000 Industrials, 3000 Consumer Goods, 4000 Healthcare, 5000 Consumer Services, 6000 Telecommunications, 7000 Utilities, 8000 Financials and 9000 Technology.

The standard market model used by Faff and Nandha [2007] is adopted in this case, with a few slight modifications to better suit the subject at hand. Variations of this model have been extensively used in the past in asset pricing literature, including many of the studies regarding the effects of oil price, such as: Chen et al. [1986], Al-Mudhaf and Goodwin [1993] and Hammoudeh and Li [2004]. Adding to this, Faff and Brailsford [2000], carried out formal tests to confirm the application of a two factor ‘market and oil’ pricing model, finding evidence in favour of it and in particular for an industry-level analysis. The Ordinary Least Squares regression process will be carried out using EVIEWS 7.1.

The model can be defined as:

Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D + εt

[1]

Riyt: log return of index y, in country i, at time t OIL, t: log return of crude oil futures contract at time t rit: interest rate of country i, at time t

FXit: exchange rate of US Dollar to domestic currency for country i, at time t RWMt: log return on MSCI world index at time t

D: dummy variable = 0, OPEC increase/hold oil production = 1, OPEC decrease oil production εt: error term

βx: parameter coefficients

This regression will be applied to each of the outlined industries in each country, so an industry-level, country-by-country analysis is able to be undertaken, this is the distinct innovation that this paper attempts to put forth.

ß1 is expected to take on a negative value. From our literature review; there seems to be an intrinsic negative relation between oil price and stock price. However, this will only be applicable to all sectors other than: Oil and Gas and Basic Materials. For all other industries, a rise in oil price indicates higher costs of operating machinery, higher costs of production, increased logistics costs etc. Whereas for firms involved in Oil & Gas and Basic Materials, ß1 is expected to be positive. As increases in the price of oil are beneficiary to such companies, as was explained earlier.

We would expect the relation between stock prices and the world market index to be positive, rationality suggests that stock prices should follow the path set by the overall market. Hence a positive value for ß2 should prevail. However, it must be taken into consideration that different sectors may be differently affected at different times, a gain in the MSCI World Market Index does not necessarily imply a gain in the price of all of the stocks or industries under observation.

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are expected to cut interest rates at such times as an attempt to induce expenditure via lower costs of borrowing. Whether the country in question is a net importer or exporter of oil will also play a role in determining this relationship.

As was discussed in the literature review, the exchange rate to the US Dollar is an important mechanism in determining purchasing power. An appreciation of the spot exchange rate should in theory lead to increased stock returns. Although this depends on the sector in which the firm is based. In this case, the domestic currency is now worth more in US Dollar terms, so firms must pay a higher relative price per barrel of crude oil and in the case that the country is a net oil importer; this can have a substantial adverse effect. Again, a negative relation is therefore expected for ß4, apart from the case of companies in Oil & Gas and Basic Materials. For them, the opposite relation should hold, as an appreciation of the exchange rate against the US Dollar is likely to attract further investment due to the domestic currency becoming effectively ‘cheap’.

With respect to ß5, we would expect ß5<0 for all industries except for Oil & Gas and Basic Materials. As we saw from our literature review, returns of companies should suffer when OPEC decides to decrease the production of crude oil, implying more pressure on prices and demand for the commodity. Whereas Oil & Gas and Basic Materials companies should benefit more so when production is decreased, signalling a rise in value and convenience yield for the commodities they produce and hold. This effect will also be more severe for oil-importing nations such as China.

Fig.2 Descriptive Statistics

OIL RWM r (Brazil) r (China) FX (Brazil) FX (China)

Mean 0.001474 0.000014 0.172540 0.035460 0.023017 0.079288 Median 0.003620 0.001114 0.169200 0.035100 0.021762 0.082766 Maximum 0.074090 0.050536 0.436000 0.048600 0.039231 0.082800 Minimum -0.100370 -0.097200 0.083700 0.029700 0.012100 0.068183 Std. Dev. 0.021452 0.011389 0.054579 0.004438 0.005163 0.005330 Skewness -0.78251 -1.28312 1.415924 1.178344 0.797733 -1.25828 Kurtosis 5.080068 13.67005 7.177293 4.691751 2.994242 2.938237 Jarque-Bera 161.7768 2875.398 608.0756 200.9319 60.77488 151.2928 Probability 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 Sum 0.844750 0.008083 98.86560 20.31840 13.18879 45.43225 Sum Sq. Dev. 0.263240 0.074192 1.703911 0.011265 0.015250 0.016247 Observations 573 573 573 573 573 573

Note: “r” and “FX” refer to the interest rate and exchange rate, respectively.

Fig.2 displays some simple descriptive statistics of our independent variables, it is obvious that the skewness, kurtosis and Jarque-Bera scores imply non-normality for our sample. Although it is a well-known fact that financial assets rarely display normality in reality, however, log returns are adopted for our empirical model in attempt to lessen any adverse effects that may arise from this non-normality.

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Fig.3 presents correlation matrices for Brazil and China respectively. The greatest correlation in terms of magnitude is clearly between oil returns and return on world market, and also interest rate and exchange rate (positive for Brazil, negative for China); for both countries. However, this is somewhat expected. The interest rate and exchange rate are both tools of economic policy, hence some degree of correlation between them is acceptable. With regard to return on the world market and oil returns, as we have discussed considerably throughout this paper, oil prices affect financial markets worldwide and therefore the companies listed on them. The MSCI World Market Index incorporates the stocks of all developed markets in the world, some correlation between returns on this index and oil returns is normal.

Fig.3 Correlation Matrices Brazil OIL r FX RWM OIL 1.000000 0.031213 -0.040832 0.228876 r 0.031213 1.000000 0.387997 0.020288 FX -0.040832 0.387997 1.000000 -0.002835 RWM 0.228876 0.020288 -0.002835 1.000000 China ROIL r FX RWM ROIL 1.000000 -0.035756 0.047181 0.228876 r -0.035756 1.000000 -0.285645 -0.095697 FX 0.047181 -0.285645 1.000000 0.037698 RWM 0.228876 -0.095697 0.037698 1.000000

3.3 Import – Export Ratio

Finally, a second analysis is also performed, this will be a simple comparison of countries’ import/export levels and stock prices of firms in our sample. As we saw in the introduction and literature review (Bhar and Nikolova [2009], International Energy Agency [2004]), whether a country is a net importer or exporter of oil plays a largely

significant role in determining their reactions to oil price changes. This will be incorporated into the empirical analysis as an additional explanatory variable. Therefore, data on the level of imports and exports in each nation is also required. For this we define an import/export ratio (I/X ratio):

exportsit

I/X ratiot = ────── [2]8 importsit

• exportsit > importsit, I/X ratiot > 1 … country i is a net exporter at time t • exportsit = importsit, 0.95 ≤ I/X ratiot ≤ 1.05 … country i is neither at time t9 • exportsit < importsit, I/X ratiot < 1 … country I is a net importer at time t

8

Export and Import levels are measured as per thousand barrels/day, see appendix.3

9

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4. EMPIRICAL RESULTS

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4.1 Regression Results and Analysis

4.1.1 Period 1: 1999 – 2005

Table 1: Regression Results for Brazil in Period 1: 1999 - 2005

β1 β2 β3 β4 β5 Adj. R² DW

Industry Oil & Gas 0.1711*** 1.0679*** 0.0178 -0.1289 0.0007 0.2035 2.3404

(0.0521) (0.1189) (0.0232) (0.2011) (0.0068)

Basic Materials 0.0168*** 1.1187*** 0.0141 0.1771 -0.0070 0.1864 2.1025 (0.0545) (0.1244) (0.0243) (0.2105) (0.0072)

Industrials -0.0629 1.1910*** -0.0050 -0.1210 0.0034 0.1811 2.1851 (0.0589) (0.1345) (0.0262) (0.2276) (0.0077)

Consumer Goods N/A N/A N/A N/A N/A N/A N/A

Healthcare 0.0079 0.8328*** 0.0064 -0.1457 0.0033 0.1039 2.3854 (0.0548) (0.1252) (0.0244) (0.2118) (0.0072) Consumer Services -0.7007** 0.5638*** -0.0533 0.6716 0.0002 0.0006 1.9919 (0.3486) (0.7956) (0.1552) (1.3461) (0.0458) Telecommunications 0.0216 1.4352*** 0.0216 0.0337 -0.0074 0.2229 2.3487 (0.0619) (0.1413) (0.0276) (0.2391) (0.0081) Utilities 0.0232 1.2255*** 0.0168 -0.0313 -0.0035 0.1184 2.2398 (0.0748) (0.1708) (0.0333) (0.2889) (0.0098) Financials -0.0059 1.1770*** 0.0000 0.0376 -0.0012 0.1355 2.2928 (0.0675) (0.1540) (0.0301) (0.2606) (0.0089)

Technology N/A N/A N/A N/A N/A N/A N/A

Notes: The table above displays the results from the regression analysis for Brazil in Period 1 (1999 – 2005): Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D +

εt , Riyt: log return of index y, in country i, at time t, OIL, t: log return of crude oil futures contract at time t, rit: interest rate of country i, at time t

,FXit: exchange rate of US Dollar to domestic currency for country i, at time t, RWMt: log return on MSCI world index at time t, D: dummy variable = 1,

OPEC decrease oil production, εt: error term, βx: parameter coefficients.

Values in the cells succeeding each industry are parameter values for each of: β1 – β5, with those below them in brackets representing the standard errors of

the respective βi. Results for the industries: Consumer Goods and Technology, are unobservable during this period due to a serious lack of data. Indication of

statistical significance is symbolised by the asterisk(s) that superscript certain values: 1% level***, 5% level** and at the 10% level*

Table 1 displays the regression results for Brazil during the period: 1999 – 2005. In accordance with findings from our literature review (Faff and Nandha [2007], Bhar and Nikolova [2009]) and theory, the regression displays a positive relation between oil returns and the returns of the industries: oil & gas and basic materials, these results are significant at the 1% and 5% levels respectively. So we can reject the null hypothesis, the returns of the oil & gas, basic materials industries are positively affected by oil price for this period. The oil & gas industry has the highest positive β1, as it is they who benefit most from a rise in value of their primary product. These results are

10

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strengthened by the Import/Export (I/X) Ratio (appendix.3), The I/X Ratio for Brazil went from a mere 0.00207 in 1999 to a massive 0.7124 by 2005; with exports displaying a colossal rise from just 1000 barrels/day to 270,000. With steadily rising oil prices and a surge in export levels of the commodity, Brazilian firms operating in these sectors benefited greatly.

A negative relation was hypothesized to have held for all other industries. The consumer services industry showed the strongest negative relation which was significant at the 5% level, this makes sense given that the three supersectors that constitute this industry are: Retail, Media and Travel & Leisure. As previously explained, a rise in oil price signals increasing costs for households (eg. increased fuel costs), this decreases the disposable income that households have to spend on the goods that are provided by the consumer services industry. Although this result is marred by a very low adj-R² of 0.0006.

The remainder of industries did not display any significant results, making it hard for us to draw suitable inferences based on them. However we can make some deductions based on the signs they showed.

In this sense, a negative relation was exhibited by all other industries for this period, adhering with past research (Sadorsky [1999], Basher and Sadorsky [2006]). Alarmingly, as the results above show, this was not the case for the industries: healthcare, utilities and telecommunications. Each of the aforementioned displayed a positive (albeit small) sign with regard to oil price. One reason for this unexpected positive relation may be due to the well-documented, rapid growth in Brazil during this period and the appointment of President Lula Da Silva. Figures from the World Bank Database of World Development Indicators [2010] show that GNI per capita11 in Brazil rose from $6460 in 1999, to $8300 in 2005, appendix.1 also illustrates that within this period, a steep rise in Brazilian GDP started to commence. This rise in economic and social standards can help usher an increased demand for modern human needs such as those served by the utilities, healthcare and telecommunications industries, investment in these is also paramount for any emerging nation undergoing modernization. It was also within this period, in 2003, that President Lula Da Silva was newly-elected into the presidency of Brazil, with his primary objective(s) of increasing social conditions and infrastructure in Brazil.

The values for β5 showed no significance and were very small in magnitude, providing little evidence for the effect of OPEC decisions on company returns in Brazil for period 1.

Table 2: Regression Results for China in Period 1: 1999 – 2005

β1 β2 β3 β4 β5 Adj. R² DW

Sector

Oil & Gas 0.1497*** 0.5090*** -0.4091 -0.1517 0.0277 0.0640 2.4124 (0.0567) (0.1297) (0.4278) (2.3312) (0.1972) Basic Materials 0.0126 0.6740*** 0.0716 0.3218 -0.0279 0.0422 1.8483 (0.0681) (0.1553) (0.3279) (3.0696) (0.2549) Industrials -0.0184 0.3915*** 0.2784 0.6216 -0.0612 0.0183 1.5268 (0.0578) (0.1319) (0.2785) (2.6072) (0.2165) Consumer Goods -0.0032 0.2955** 0.1186 1.7037 -0.1470 0.0151 1.8914 (0.0559) (0.1276) (0.2695) (2.5232) (0.2095) 11

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Healthcare 0.0226 0.4028*** 0.2355 0.1720 -0.0225 0.0238 1.8087 (0.0538) (0.1228) (0.2593) (2.4269) (0.2015) Consumer Services -0.1087** 0.3628*** -0.0671 1.4322 -0.1160 0.0258 2.2029 (0.0565) (0.1289) (0.2723) (2.5486) (0.2116) Telecommunications 0.0486 1.0909*** -0.4572 0.1652 0.0028 0.1262 2.6073 (0.0777) (0.2166) (0.4989) (2.3277) (0.1986) Utilities -0.0388 0.3307*** 0.0026 1.9867 -0.1637 0.0191 2.1889 (0.0491) (0.1121) (0.2368) (2.2164) (0.1840) Financials -0.0877*** 0.3567*** 0.3329 -0.6279 0.0416 0.0168 1.9952 (0.0595) (0.1358) (0.2867) (2.6836) (0.2228) Technology -0.0305 0.4810*** 0.2982 0.3158 -0.0365 0.0239 2.1458 (0.0597) (0.1362) (0.2876) (2.6921) (0.2235)

Notes: The table above displays the results from the regression analysis for China in Period 1 (1999 – 2005): Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D +

the respective βi. Indication of statistical significance is symbolised by the asterisk(s) that superscript certain values: 1% level***, 5% level** and at the 10%

level*

The regression results for China in period 1 are summarized in Table 2. As can be seen, the expected positive relation held for the oil & gas and basic materials industries with respect to oil price, with the largest positive sign of 0.1497 attributing to the oil & gas industry. This result was also significant at the 1% level. From this we can reject the null hypothesis and deduct that companies in the oil & gas industry were positively affected by oil price during period 1 in China. The only drawback of this result is the very low adj-R² value of 0.0640.

All other industries (apart from healthcare and telecommunications) displayed a negative sign with regard to oil price, represented by a negative β1 , with those of the consumer services and financial industries significant at the 5 and 10% levels respectively. These results are in line with previously discussed literature by Bhar and Nikolova [2009]. Again, we can reject the null hypothesis, the returns of the consumer services and financials industries were negatively influenced by oil prices during this period in China.

The Import/Export Ratio from China in this period also saw a substantial fall, from 0.19329 to just 0.06195 in 2005. Implying a heavier reliance on imported oil and a greater susceptibility to import prices, this can be potentially dangerous when coupled with a fixed exchange rate to the US Dollar, as was the case for China in this period. As we saw in the literature review (Bhar and Nikolova [2009], Park and Ratti [2008]), this can have an adverse effect on stock returns of firms operating outside the oil & gas or basic materials industries.

Again, the remainder of results were statistically insignificant, meaning that we cannot reject the null hypothesis based on them. Although this is the case, some insights can still be made from the magnitude and sign that they displayed.

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experiencing rapid growth, especially for a country with the largest population in the world and an ever-existent poverty gap.

Perhaps the most surprising result was that for the telecommunications industry, a positive β1 of 0.0486, a result that can be explained by the emergence of two of China’s largest corporations12: China Mobile and China Telecom. China Mobile had already existed since 1997, however in the period; 1999 – 2005: it acquired over 30 Chinese telecommunications companies, entered a strategic partnership with global telecommunications giant Vodafone, raised billions of US Dollars through the issue of corporate bonds and saw itself become the first overseas-listed Chinese telecommunications firm to operate in all 31 provinces of Mainland China. China Telecom followed suit, commencing business in 2002, by 2005 it had already acquired and expanded to cover 20 provinces. China Mobile and China Telecom are ranked 4th and 16th largest companies in China13, both companies underwent extensive growth and expansion during this period which aids in the explanation of the positive, statistically insignificant β1 value for the telecommunications industry in this period.

The results for β4 are statistically insignificant and present very high standard errors, ranging from 2.2164 – 3.0696, this can be due to the fact that up until 2005, China maintained a fixed exchange rate system against the US Dollar, of around 8.27 Chinese Renminbi Yuan per US Dollar. Another sub-optimal observation from this regression arises from the low adj-R² values, with the highest being 0.1262 for the telecommunications industry and the lowest for the consumer goods industry at 0.0151. A plausible argument to justify these low adj-R² and statistical insignificance stems from the fact that a substantial number of the largest companies in China that constitute the observed indices, were formed within the period: 1999 – 2005. In fact, six of the country’s top 20 companies14 were founded in this time, this includes China’s largest company: Sinopec; an oil & gas operations firm. This discontinuous data may have exhibited the low adj- R² and statistical insignificance that prevailed.

The results for β5 are statistically insignificant and mostly small in value, for this reason we cannot reject the null hypothesis. There is no incriminating evidence that OPEC decisions had an effect on stock returns in China during this period. This supports some of the findings from our literature review, stating that OPEC pricing power has diminished due to new international oil pricing systems (Fattouh [2007]). Guidi et. al [2006] also support this notion, finding that international stock markets appear to be efficient and well able to anticipate and absorb changes

in production without any significant increase in volatility.

4.1.2 Period 2: 2006 – 2008

Table 3: Regression Results for Brazil in Period 2: 2006 - 2008

β1 β2 β3 β4 β5 Adj. R² DW Industry Oil & Gas 0.6941*** 1.8543*** -0.0256 -0.6240 -0.0174 0.5624 2.0455

(0.0914) (0.1703) (0.1101) (1.0663) (0.0146)

Basic Materials 0.2525*** 2.0476*** -0.0227 -0.5502 -0.0173 0.5925 2.3189 (0.0800) (0.1490) (0.0963) (0.9327) (0.0128)

12

According to Fortune Magazine’s Global 500 list of the world’s largest corporations, July 26 issue, 2010

13

According to Fortune Magazine’s Global 500 list of the world’s largest corporations, July 26 issue, 2010

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Industrials -0.0151 1.4968*** 0.0185 -0.1548 0.0015 0.5108 2.2507 (0.0673) (0.1253) (0.0810) (0.7847) (0.0108)

Consumer Goods -0.1287** 1.7964*** 0.0334 -0.9369 0.0146 0.5949 2.2356 (0.0686) (0.1277) (0.0825) (0.7996) (0.0110)

Healthcare N/A N/A N/A N/A N/A N/A N/A Consumer Services -0.1559** 2.0094*** 0.1383 -2.0357** 0.0223* 0.5606 2.3198 (0.0825) (0.1537) (0.0993) (0.9621) (0.0132) Telecommunications -0.0006 1.8579*** -0.0370 -0.7627 0.0197 0.5191 1.9870 (0.0817) (0.1522) (0.0983) (0.9527) (0.0131) Utilities 0.0001 1.5105*** 0.1276 -2.0354** 0.0252** 0.4410 2.1936 (0.0777) (0.1448) (0.0935) (0.9063) (0.0124) Financials -0.1366* 2.2568*** 0.1150 -2.0125** 0.0269** 0.6137 2.0850 (0.0828) (0.1542) (0.0996) (0.9652) (0.0132)

Notes: The table above displays the results from the regression analysis for Brazil in Period 2 (2006 - 2008): Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D +

εt, Riyt: log return of index y, in country i, at time t, OIL, t: log return of crude oil futures contract at time t, rit: interest rate of country i, at time t

the respective βi. Results for the industries: Healthcare and Technology, are unobservable during this period due to a serious lack of data. Indication of

statistical significance is symbolised by the asterisk(s) that superscript certain values: 1% level***, 5% level** and at the 10% level*

Period 2: 2006 – 2008, is by far the most interesting time period in the greater context of this study, it signifies the period in which the financial crisis hit the world and more importantly in this case; it saw one of the largest spikes in worldwide oil prices which eventually led to the price of oil almost reaching $150 per barrel in 2008. However, this phase also saw emerging markets experience significant growth in GDP (see appendix.1).

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From these findings, we can reject the null hypothesis. The returns of Brazilian companies engaged in oil & gas, basic materials were positively affected by oil price from 2006 – 2008.

The Consumer Goods and Consumer Services industries had β1 values of -0.1287 and -0.1559 respectively, both of these results were significant at the 5% level. As was briefly explained before, these results become clear when we look at the make-up of these industries: consumer goods consists of the supersectors; automobiles & parts, food & beverage, personal & household goods, whereas consumer services consists of; retail, media, travel & leisure. It becomes apparent that both industries consist mainly of production-based sectors (see appendix.8). Despite the financial crisis at the time, as reported by Bloomberg15 [2008], domestic demand in Brazil rose due to increased incomes, consumer spending and the emergence of the “middle-class”. From a rational and economical perspective; companies operating in these industries would have had to increase production and distribution to match this increased demand and expenditure on their goods, this would have involved greater investment into fuel sources and hence oil. With oil prices displaying record levels in period 2, it is no wonder that returns of industries involved in consumer goods and services showed the largest statistically significant, negative relation with respect to the price of oil.

The returns of the financials industry also displayed an adverse relationship with oil price, statistically significant at the 10% level. It is common knowledge that financial firms were worst-affected by the financial crisis, due to overexposure to the US markets.

The remaining industries displayed a negative sign regarding oil price (apart from that of Utilities which had a β1 of 0.0001), this relationship is well-documented by past research (Hamilton [1983], Gisser and Goodwin [1986], Mussa [2000]). However, these results were statistically insignificant.

There existed a well-established negative relation between industry returns and the exchange rate to the US Dollar, with all values of β4 being negative and those of: consumer services, utilities and financials industries being significant at the 10% level. In line with earlier discussion, returns of companies in Brazil will increase if the exchange rate drops, as it means their products and any proceedings arising from them are worth more in US Dollar terms.

The results for β5 in period 2 for Brazil imply that apart from the industries of: oil & gas, basic materials, all other industries experience a positive effect (indicated by their sign) when OPEC has decided to reduce production of oil. Adhering with earlier research we looked at in our literature review. The results for: consumer services, utilities and financials industries were also statistically significant at least at the 10% level, so we can say that these industries benefited most from a reduction in oil production by OPEC.

Table 4: Regression Results for China in Period 2: 2006 – 2008

β1 β2 β3 β4 β5 Adj. R² DW Industry

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Industrials 0.0136 1.1963*** -1.3330 -0.5598 0.0949 0.2081 2.1209 (0.1119) (0.2094) (0.9753) (0.8845) (0.1004) Consumer Goods -0.0073 0.7083*** -0.5832 0.1943 0.0061 0.1271 1.9482 (0.1024) (0.1915) (0.8920) (0.8090) (0.0918) Healthcare 0.0537** 1.1408*** 0.2698 0.7536 -0.0686 0.0794 2.2566 (0.1657) (0.3100) (1.4438) (1.3094) (0.1486) Consumer Services -0.3324*** 0.9757*** -2.5603*** -1.1514 0.1858* 0.1943 1.8983 (0.1279) (0.2392) (1.1139) (1.0102) (0.1146) Telecommunications -0.0411 1.0589*** -1.3806 -0.9017 0.1210 0.1256 1.9861 (0.1260) (0.2357) (1.0977) (0.9955) (0.1129) Utilities -0.1000 1.0618*** -1.0362 -0.4095 0.0707 0.1346 2.2052 (0.1241) (0.2322) (1.0812) (0.9805) (0.1112) Financials -0.0362 1.1026*** -0.2378 0.1631 -0.0005 0.1813 2.1266 (0.1057) (0.1978) (0.9209) (0.8352) (0.0948) Technology -0.0608 0.8708*** -0.9688 -0.6922 0.0898 0.1429 2.1955 (0.0975) (0.1824) (0.8493) (0.7703) (0.0874)

Notes: The table above displays the results from the regression analysis for China in Period 2 (2006 – 2008): Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D +

the respective βi. Indication of statistical significance is symbolised by the asterisk(s) that superscript certain values: 1% level***, 5% level** and at the 10%

level*

The regression results for China for the period: 2006 – 2008 are summarized in Table 4 above. As appendix.1 shows, China actually experienced its most steep rise in GDP in this period; almost doubling from just over 2 trillion US Dollars to around 4 trillion US Dollars. According to figures from the World Bank Database of World Development Indicators [2010], China experienced a record growth rate in 2007 of over 13%.

As hypothesized and supported by previous studies, positive β1 values are obtained for the oil & gas and basic materials industries, being statistically significant at the 5% level for the oil & gas industry. A well-expected relation given that oil prices reached record levels during this period as earlier discussed.

The Healthcare industry also presented a positive effect in relation to oil price, signified by a β1 value of 0.0537 which was significant at the 5% level. The Chinese government still faces a mammoth task in trying to provide medical and welfare services adequate to meet the basic needs of the immense number of citizens spread over a vast area. Although China's overall affluence has grown dramatically since the 1980s, a great proportion of its people live at socioeconomic levels far below the national average. This is perhaps the most useful reason as to why Chinese firms in the Healthcare industry maintained statistically significant, positive returns throughout our sample period, in spite of a global financial crisis and soaring oil prices.

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Industrial Engineering, Industrial Transportation, Support Services etc. Recall that this period represented substantial growth for China, with most sectors in the economy still in their infancy on a global scale. Whether it was new businesses being created, existing businesses expanding (evident in the example of the telecommunications industry used earlier) or bust businesses being repossessed; Industrials offered services that apparently stood to benefit overall from this period.

Regarding the adverse effect of oil price on stock returns as reported by previous study (Jones and Kaul [1996], Faff and Nandha [2007], Basher and Sadorsky [2006]), the Consumer Services industry showed the largest negative relation. With a β1 of -0.3324 which was significant at the 1% level, carrying an adj-R² of 0.1943. Although the remainder of the results provided no statistical significance, they did display the expected negative sign in relation to oil prices. These facts are further amplified by the drop in Import/Export ratio for China during this time (see appendix.3), to a level of only 0.01878 by the end of 2008.

For this period, only the consumer services industry provided a statistically significant relation for β5, benefiting from a reduction in oil production by OPEC. For the remaining industries we cannot draw any solid inferences.

4.1.3 Period 3: 2009

Table 5: Regression Results for Brazil in Period 3: 2009

β1 β2 β3 β4 β5 Adj. R² DW Industry

Oil & Gas 0.2251** 1.1182*** 1.0704*** -5.2761*** 0.0028 0.7371 1.9598 (0.1029) (0.1475) (0.3340) (2.2422) (0.0198) Basic Materials 0.0768 1.3720*** 1.4893*** -8.3905*** 0.0265 0.7035 2.4266 (0.1218) (0.1745) (0.3953) (2.6533) (0.0234) Industrials 0.1418 1.0607*** 0.3954 -1.9195 0.0040 0.6506 2.3895 (0.1097) (0.1572) (0.3561) (2.3901) (0.0211) Consumer Goods 0.0770 0.8445*** 0.1130 0.3829 -0.0132 0.6891 2.3514 (0.0757) (0.1085) (0.2457) (1.6491) (0.0146)

Healthcare N/A N/A N/A N/A N/A N/A N/A

Consumer Services 0.1095 0.7268*** 0.0615 0.2454 -0.0029 0.4753 2.3984 (0.1071) (0.1535) (0.3476) (2.3335) (0.0206) Telecommunications 0.0066 0.7797*** 0.2752 -1.3436 0.0033 0.4349 1.5901 (0.1065) (0.1527) (0.3459) (2.3219) (0.0205) Utilities 0.0466 0.6543*** 0.5242* -2.8138 0.0084 0.4851 2.2426 (0.0886) (0.1270) (0.2876) (1.9304) (0.0171) Financials 0.1836* 1.2360*** 0.1715 0.0468 -0.0141 0.7080 2.1908 (0.1110) (0.1592) (0.3605) (2.4198) (0.0214)

Notes: The table above displays the results from the regression analysis for Brazil in Period 3 (2009): Riyt = β1OIL, t + β2RWMt + β3rit + β4FXit + β5D + εt, Riyt:

log return of index y, in country i, at time t, OIL, t: log return of crude oil futures contract at time t, rit: interest rate of country i, at time t

The Effect of Oil Price Dynamics on Equities in Emerging Markets: an Industry-level Analysis of Brazil and China