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Relationship Of Sub Sector Indices And Crude Oil Finance Essay

The global crude oil price has been seen a sharp increase in recent years and has been widely reported in the daily newspaper or TV news. For example, popular business and financial US based website Bloomberg has been constantly providing breaking news headlines like “The crude oil hits to level of $ 120 per barrel” or “Crude Oil Increases to 25-Month High as Commodities Gain”. Besides, video clips are uploaded on the website with commentary by senior investment analyst on the last traded crude oil price with prominent TV host. It has been noted that rising crude oil prices has created jittery and uncertainty in the financial market. For example, any negative news on price increase or disruption to oil supply will cause stock market indices like Hang Seng Index, Nikkei 225, STI (Straits Times Index), Shanghai Composite, Seoul Composite, and others regional markets to fall sharply - knee jerk reaction from the investors on panic selling .

Theoretically, soaring of crude oil prices will cause inflation and inadvertently would cause interest rates to go up. Consequently, this would impact various segments of the financial market especially the stock market. It has been argued that continues rising on global oil price will eventually erode the company profit margin. Basher, Haug, and P. Sadorsky (2010) found that oil price can affect prices directly by impacting future cash flows or indirectly through an impact on the interest rate used to discount future cash flows along with in the absence of complete substitution effects between the factors of production and rising oil price. For example, there would be an increase in the cost of doing business as cost of capital will increase. In financial terms, discounting the free cash flow with the higher discount rate (cost of capital) will cause the fair value of stock price valuation to decrease significantly from previous valuation. J.Happonen (2009) also highlighted that spiking high prices on crude oil will affect greatly the poor as fuel costs are most significant in food production and transportation cost. High oil costs also hit various economies on a macro-level. Commodity analysts employ various types of methodology e.g. fundamental or technical analysis to forecast the future trend of the crude oil price meanwhile investment bankers’ start to develops and launches a new commodity mutual fund or unit trust products to attract attention on the public. As a precaution and in order to protect their investment, risk adverse investors are moving their assets into the safer assets like precious metal, e.g.; gold, silver and etc.

According to Basher & Sadorsky (2006), oil is the lifeblood of modern economies. When growth of Growth Domestic Product (GDP) of the countries are rapidly increasing like BRIC’s (Brazil, Russia, India, and China), total demand oil of the countries will increase significantly. There is a positive relationship between the crude oil price and global gold price trend in the market. The linkage of gold between the risings of crude oil price has been investigated and empirical studies show that the two commodities are correlated each others. P. Narayan, S. Narayan and Zheng (2010) examine the long-run relationship between gold and oil spot and futures markets at different levels of maturity and found a significant positive correlation between crude oil and gold price.

The most oil producer’s Organization of the Petroleum Exporting Countries (OPEC) members are from Islamic country such as Iran, Iraq, Saudi Arabia, Libya, and etc. Based on the Islamic historical studies, Islamic law is forbids the use of a promise of payment such as fiat money USD dollar acting as a medium of exchange. Thus, most of members try to diversify their vast US dollar revenue holding into precious metals e.g. trade in gold Dinar and Dirham. The concept of Gold Dinar System was mooted out by our former Prime Minister Malaysia Tun Dr. Datuk Seri Mahathir on year 2002 before. The purpose of adopted the gold Dinar and Dirham is to represent the solely currency for international trade and prevent the Asia currency crisis 1997 to happen again. Meanwhile, some of the members also refused to accept USD as currency trade on the crude oil like Iran and Venezuela have been pushing for a switch to the euro to protect the value from further losses. This caused by US government adopted the ease monetary policy on keep printing their money to curb the recession economy. Ultimately lead to USD dollar depreciated value relative with the Middle East oil producers’ currency.

1.2 Problem Statement

Oil has been an important commodity and influences the economic activities of the country. On the other hand, gold has been used as effective hedging tools to hedge against inflation which among others has been caused by rising oil prices. At present, with the present escalating oil prices, the world economy is grappling to contain inflation and ensure that the economic growth is not derailed. As a result, commodities like crude oil and gold has been a subject of studies by academics in various countries.

Gold has been used as a good indicator of expected inflation in the market while oil is a barometer for deflation. Thus, when inflation is expected, investors will divert their asset to the gold portfolio to protect their asset value. On the other hand, when deflation is expected investor will reallocate their funds and start to buy safer government bond. This reaction can partially be explained by behavioral finance whereby the investor is irrational and market is an imperfect.

A large body of empirical research has been conducted on the impact of oil prices and other macro variables with relation to the stock market. Wang, CP. Wang, and Huang (2010) attempt to establish the relationships among oil price, gold price, exchange rate and international stock market. They investigated the fluctuations in crude oil price, gold price, and exchange rates of the US dollar against other various currencies on the stock price indices of the United States, Germany, Japan, Taiwan and China respectively, as well as the long and short-term correlations among these variables. G. Sharma, A. Mahendru, (2010) studies on the impact of macro-economic variables on stock prices in India. In Malaysia, Shaharudin and Hon (2009) extended the research to investigate the stock return in relation with firm’s size and macroeconomic variables (Consumer Price Index, Industrial Production Index, Money Supply, Interbank Money Market Transaction, three months and six months Treasury Bills Discount Rate and crude oil prices) and found that stock return were significantly influenced by selected macroeconomic variables.

Based on the importance of two commodities prices and gold, this paper is attempt to investigate and address the significant level of relationship between the commodities and the selected 10 major sub-sector components indices in FBM Kuala Lumpur Composite Index (KLCI). There have been limited researches studies on the different degree of impact of the crude oil price, gold price, market return, and short-term interest rate against sub-sector components index. A small number of studies were mainly using stock index FBM Kuala Lumpur Composite Index (KLCI) as the general proxy for overall performance of stock market. However, the stock index consist a numbers of sub sector components index in FBM Kuala Lumpur Composite Index (KLCI) it may not be a true reflective of a particular contribution of a sector to the overall stock market index. Thus, in our research will studies on these and examine the degree of significant level for commodities impact to a particular sub sector composite index.

1.3 Objective of the Study

The main objective of the study is to examine the relationship of majors’ sub-sector indices between the crude oil prices, gold prices, market return, and short-term interest rate. The study will includes the examination of correlation between sub sector indices and 4 other variables as mentioned earlier. A sub-analysis on the gold oil ratio will also be conducted. Gold oil ratio is a barometer of economic vibrancy and when times are good; the ratio's indicator remains low and these reflect a relatively robust price—and demand—for crude oil. When fear is pervasive or the economy slumps, the ratio is high, as gold is chased by investors looking for a safe haven. In other words, this would infer that when the current ratio is below the benchmark, gold price is either too cheap or crude oil is too expensive. When the ratio is greater than benchmark, it will mean otherwise.

1.4 Significance of the Study

The economies of the world are now integrated in terms of trade and capital flows with formation of global network across different region. As such, when financial crisis occur, it will have systematic effect throughout the world. A clear example is the occurrence of U.S. Sub-prime crisis which happened in 2009 and present year Euro Zone Debt Crisis was created contagion effect to the global economy. With advancement of technology and innovation of financial product, risk adverse investors should be more alert on the important signals or indicators as a guide to monitor and time the market to avoid any unexpected risk.

The aim of this paper is to study the relationship of oil prices, gold price, market return, and short-term interest rate on majors selected sub-sector index. The results on this study will add to the body of knowledge and assist policymakers like Bank Negara Malaysia as well as pratictioners such as corporate managers and investors to participate in the stock market. It also enhance their understanding on the level of impact on the four (4) variables to the selected sub-sector indices.

The Arbitrage Pricing Theory (APT) postulates that every investor believes that the stochastic properties of returns of capital assets are consistent with a factor structure. For the purpose on this study, the APT model was adopted in evaluating the major sub-sector components indices relationship with various macroeconomic risk factors. The conclusion of the study shall enrich investor understanding on some sub-sector industries relationships to macroeconomic risk factors. Thus, smart investors still have a chance to explore it and gain return on that sub-sector industries.

1.5 Definition of Terms

KLSE (Kuala Lumpur Composite Index)

The FBM Kuala Lumpur Composite Index (KLCI) is used as a proxy for the performance of the Kuala Lumpur Stock Exchange and comprises the largest 30 companies listed on the Main Board by full market capitalisation that meet the eligibility requirements of the FTSE Bursa Malaysia Ground Rules. The two main eligibility requirements stated in the FTSE Bursa Malaysia Ground Rules are the free float and liquidity requirements.

London Bullion Market (LBM) (U$ Troy Ounce) price

Index shows the performance of gold prices over time per troy ounce. The troy ounce is a weight measure for precious metals, which is still used in the Anglo-American zone. It is named for the French city of Troyes.

Crude Oil WTI (West Texas Intermediate)

Known as Texas light sweet, is a type of crude oil used as a benchmark in oil pricing. It is a light (low density) and sweet (low sulfur) crude oil. It is the underlying commodity of New York Mercantile Exchange's oil futures contracts.

T-Bill band 4

T-Bill band 4 is type of money market instrument. The Malaysian Treasury Bills (MTB) issued by the Central Bank of Malaysia are tradable on yield basis (discounted rate) based on bands of remaining tenure (e.g., Band 4 = 68 to 91 days to maturity). The standard trading amount is RM5 million, and it is actively traded in the secondary market. This instrument represents the short-term interest rate in the Malaysia money market. The high or low interest rate will make bonds look more attractive than stock and consequently impact the stock price return.

Sub-sector Price Index

Major sub-sector prices index are the 10 majors sub-sector price index consist of Consumer, Plantation, Finance, Trading and Services, Industrial, Industrial Products, Construction, Mining, Properties, and Technology. Each index is representing overall performance instituted on sub-part of FBM Kuala Lumpur Composite Index (KLCI).

CHAPTER 2

THEORETICAL FRAMEWORK AND LITERATURE REVIEW

2.1 Introduction

This chapter provides a comprehensive review on the empirical evidences on four (4) variables and the theories on Arbitrage pricing Theory (APT) model and Efficient Market Hypothesis (EMH). It will provide a better understanding of the relationship between variables and sub-sector component indices performance.

2.2 Macroeconomic Factors

Choo, Lee and Ung (2011) investigates the behavior of Japanese stock market volatility with respect to a few macroeconomic variables including gold price, crude oil price and currency exchange rates (Yen/US$). The authors using the performance of GARCH models and Ad Hoc methods to carried out a comparison study.

Their results show that macroeconomic variables used in this study have no impact on the volatility of Japanese stock markets and the simplest GARCH (1, 1) model yields the best result. Maysami et al. (2004) study on relationship between macroeconomic variables and stock market indices: co-integration Evidence from Stock Exchange of Singapore’s All-S Sector Indices and based on the study concludes that the Singapore’s stock market and the property index form co-integrating relationship with changes in the short and long-term interest rates, industrial production, price levels, exchange rate and money supply.

2.3 Crude oil

Based on the past study from Huang et. Al, (1996), they found that oil future returns do not have much impact on S&P 500 Index. On the other hand, Al-Rjoub,Samer Am* (2005) investigated the effect of oil price shocks in the U.S. for 1985-2004 using VAR Mixed Dynamic and Granger Causality Approaches to study the whether the U.S. stock market react to the oil shocks, a big importer of crude oil. They found that from VAR suggests that oil shock affect the stock market returns in the U.S. oil price are important in explaining the stock market reactions. According to Basher & Sadorsky (2006), oil is the lifeblood of modern economies and can have significant impact on the growth of a country’s economy.

In addition, Driesprong, Jacobsen and Maat (2004) found that investors in stock markets under react to oil price changes in the short run. Recent study by Charles (2009) found that higher volatility in both gold price and oil price reduces volatility of stock price. Some studies directly tested the relationship between oil prices and stock values. Huang, Masulis and Stoll (1996) applied vector autocorrelation models to find the time-series relationship and concluded that crude oil futures lead stock prices of oil companies. However, they were unable to bring a conclusion for any significant relationship to other stock prices. In addition, the volatilities of crude oil futures lead the volatilities of oil industry stock index. A related study (Sadorsky, 1999) had different conclusion. It showed that oil prices as an important factor which predicts stock prices very well. Sadorsky (2003) used vector autocorrelation model to verify the importance of oil price, federal fund rate, CPI, foreign exchange as variables to describe the performance of technology stock prices.

Hamilton (2008) examines the factors responsible for changes in crude oil prices and the statistical behavior of oil prices. The study includes the role of commodity speculation, Organization of the Petroleum Exporting Countries (OPEC), and resource depletion and found that although scarcity rent made a negligible contribution to the price of oil in 1997, the situation at present would be different and crude oil prices might play an important role.

2.4 Gold

Melvin and Sultan (1990) consider a different approach of establishing the relationship between gold and oil markets. Their study was based on the implication of the gold prices through the export revenue channel. As gold is an integral part of the international reserve asset of several countries, including the oil producing countries, their finding reveal that stock shock will leads to expectations of official gold purchases and this in turn will make the expected future price of gold to soar higher. Sultan (1990) argue that when oil price rises, the oil exporters countries will benefit in terms of higher oil revenues. This in turn may have implications on the price of gold especially when the gold consists of a significant share of the asset portfolio of oil exporters (relative to other nations) and oil exporters purchase gold in proportion to their wealth. The impact on this will lead to an increase in demand for gold and subsequently rise in price of gold and ultimately an oil price rise leads to a rise in gold price.

Ismail et al. (2009) develop a forecasting model for gold prices using Multiple Linear Regression Method to predict gold prices based on economic factors such as inflation, currency price movements and others. They argue that investor starts to invest their asset in gold because of depreciation of US dollar currency and gold as an important stabilizing role for investment portfolios. based on their findings, they conclude that many factors determine the price of gold and several economic factors such as Commodity Research Bureau future index (CRB); USD/Euro Foreign Exchange Rate (EUROUSD); Inflation rate (INF); Money Supply (M1); New York Stock Exchange (NYSE); Standard and Poor 500 (SPX); Treasury Bill (T-BILL) and US Dollar index (USDX) were considered to have influence on the gold prices.

2.5 T-bill (short term interest Rate)

T-Bill rate is a benchmarking for short-term interest rate and is deemed as risk free. As such, T-Bill rate is normally taken into consideration for financial valuation purpose and widely used by financial institutions and academics especially to determine the fair value of stock pricing. Chan et al. (1992) reaffirmed that the short-term riskless interest rate is one of the most fundamental and important prices determined in financial markets.

In referred to Damodaran (2002) published textbook - Investment Valuation: Tools and Techniques for Determining the Value of Any Asset, Choice of risk-free security - the returns on both Treasury bill (t-bills) and treasury bonds (t-bonds), and the risk premium for stocks can be estimated relative to each other. This was based on the yield curve in the US that has been on upward-sloping for most of the past seven decades. The risk premium is larger when estimated relative to short-term government securities (such as Treasury bills). Damodaran (2002) also stated that the risk risk-free rate chosen in computing the premium has to be consistent with the risk-free rate used to compute expected returns. So, if the Treasury bill rate is taken into consideration as a risk-free rate, the premium has to be earned by stock over that rate. This applies to the Treasury bond rate as well and premium has to be estimated relative to that rate. He also mentioned that for the most part, in corporate finance and valuation, the risk-free rate will be a long-term default free (government) bond rate and not a Treasury bill rate. Thus, the risk premium used should be the premium earned by stocks over Treasury bonds.

2.6 FBM Kuala Lumpur Composite Index (KLCI)

The FBM KLCI is taken as a proxy to represent the market growth optimal portfolio. This research paper attempt to construct and compare various total-return world stock indices based on daily data. The data was collected from DataStream Advance cover the period from 01 January 1973 to 31 August 2006. Due to the diversification, these indices are noticeably similar. This proposed method of constructing a proxy for the growth optimal portfolio has specific advantages over the methodologies of diversity weighting and market capitalization weighting. The diversified world stock index has applications to derivative pricing and investment management.

Petttengill et al. (1995) developed a conditional relationship between return and beta that depends on whether the excess return on the market index is positive or negative. When the excess return on the market index is positive (negative), there should be a positive (negative) relationship between beta and return. Their empirical results support the conclusion that there is a positive and statistically significant relationship between beta and realized returns. Furthermore, consistent with Hodoshima et al. (2000), the results are similar when the test is done on 20 beta sorted portfolios. However, it seems that the negative relationships during down market are steeper in Tokyo Stock Exchanges (TSE), which seems to have contributed to have negative rewards for holding beta risk in the long run. Consistent with the findings of Pettengill et al. (1995) in the USA and Hodoshima et al. (2000) in the Tokyo Stock Exchange (TSE), the result found that there is a significantly positive relationship between portfolio beta and portfolio return during up markets and the relationship is significantly negative during down markets. Moreover, the test of individual stock return shows that this conditional relationship can even be seen in individual stock returns. That is, there is a significantly positive (negative) relationship between individual stock beta and individual stock return up (down) markets. However, the results of the study suggest that the beta-return relation, in the Tokyo Stock Exchange (TSE), seems to be negatively steeper during down markets, which seems to have contributed to have a negative reward for holding beta risk even in periods where the average market excess return is positive. Therefore, in conclusion, the results suggest that, though the slopes during down markets seem to be steeper than up markets, there seems to have a conditional relationship between beta and return, which justifies the continued use of beta as a measure of market risk.

2.7 Arbitrage Pricing Theory (APT)

The Capital Asset Pricing Method (CAPM) is a single factor model - it specific risk as a function of only one factor, the security’s beta coefficient. CAPM has been considered as one of the main tools to study for the risk-return trade-off assets. CAPM has been widely referred and used in academic research and business financial studies. As long as the return for any asset is interrelated to one variable with its market beta, or the systematic risk, it is defined as the covariance of an asset’s return and the market return. CAPM implies that expected returns and market beta exists, and only market beta that efficiently exanimate the time series and cross-sectional tests for asset returns.

CAPM has its restrictions, assume investors are rational and based on several assumptions that were not practical in the real world. According to empirical studies by Fama and MacBeth (1973), there are several variables e.g. the market value of equity ratio (MVE), the earnings to stock price ratio (E/P), and the book-to-market equity ratio that having greater influence compare to market beta. Another study was carried out by Ross (1976) on the Arbitrage Pricing Theory (APT) which was considered a new modeling for CAPM. Ross refute through Arbitrage Pricing Theory (APT) that market beta is not the only variable to measure the systematic risk. There are multiple variables that have an effect on the stock returns beside market beta. The study tested on systematic, unconditional, and positive trade-off between average returns and beta.

Perhaps the risk-return relationship is more complex, with a stock’s required return a function more than one factor. For example, what if investors, because personal tax rate on capital gain are lower than those on dividends, value capital gains more highly than dividends. Then, if two stocks had the same market risk, the stock paying higher dividend would have the higher required rate of return. In that case, required returns would be a function of two factors, market risk and dividend policy. The Arbitrage Pricing Theory (APT) can include any number of risk factors. So the required rate of return could function of two, three, four or more factors. The Arbitrage Pricing Theory (APT) is based on complex mathematical and statistical theory that goes far beyond the scope for discussion in this paper.

Even though the Arbitrage Pricing Theory (APT) model is widely discussed in academic literature, the practical usage to date has been limited. The concepts of Arbitrage Pricing Theory (APT) which assume that all stocks’ return depend on only three factors: Inflation, industrial productions, and the aggregate degree of risk aversion (the cost of bearing risk, it was assume that this will be reflected in the spread between the yields on Treasury and low-grade bonds). The primarily theoretical advantage of the Arbitrage Pricing Theory (APT) is that it permits several economic factors to influence individual stock returns, whereas the CAPM assumes that the effect of all factors, except those unique to the firm, can be captured in a single measure fewer assumptions than the CAPM and hence is more general.

Efficient Market Hypothesis (EMH)

The Efficient Market Hypothesis (EMH) was developed by Professor Eugene Fama. He said the efficient capital market theory is one in which security prices adjust rapidly to the arrival of new information and, therefore, the current prices of securities should be reflected all information about the security. In simple terms, it means that no investor should be able to employ readily available information in order to predict stock price movements quickly enough so as to make a profit through trading shares. If markets are efficient, stock price will rapidly reflected all available information. There are different types of information available to incorporate into stock prices. Financial theorist have been developed the three form of market efficiency. There are three common forms in which the efficient-market hypothesis is commonly stated—weak-form efficiency, semi-strong-form efficiency and strong-form efficiency, each of forms has different implications for how markets work.

In weak-form efficiency, future prices cannot be predicted by analyzing prices from the past. The abnormal return cannot be earned in the long run by using investment strategies solely depend on historical data share prices. Moreover, technical analysis techniques will not be able to consistently produce an abnormal profit, though some forms of fundamental analysis may still provide excess returns.

In semi-strong-form efficiency, it is implied that share prices adjust to publicly available new information very rapidly and in an unbiased fashion, such that no excess returns can be earned by trading on that information. Semi-strong-form efficiency implies that neither fundamental analysis nor technical analysis techniques will be able to reliably produce abnormal return. However, in strong-form efficiency, share prices reflect all information, public and private, and no one can earn excess returns. If there are legal barriers to private information becoming public, as with insider trading laws, strong-form efficiency is impossible, except in the case where the laws are universally ignored.

CHAPTER 3

RESEARCH METHODOLOGY

This chapter provides an outline of the research process designed to investigate the relationship between economic variables and Sub-sector price index.

3.1 The Data

In this section, we will summarize our model’s data and present the methodology of our model. The daily data for interdependent and dependable variables e.g. FBM KLSE (Kuala Lumpur Composite Index), T-Bill band 4, Crude oil WTI (West Texas Intermediate) price, London Bullion Market (LBM) (U$ Troy Ounce) price, and Sub-sector Price Index are collected from the DataStream and cover from period 17/04/2000 to 18/04/2011. There are 2610 daily observations obtained from DataStream. The data set is given in the Appendix of this paper. In relation on this, dependable variable are consists of ten (10) major’s price index e.g., Consumer Product, Plantation, Finance, Trading and Services, Industrial, Industrial Products, Construction, Mining, Properties, and Technology.

As can be seen from figure 1, there is an increasing trend on global gold price and reached it’s the highest point, $ 1,492.06, on 18th April, 2011. The gold price was tending to increased since year October, 2008. We believe this trend will continues increasing due to strong demand and short supply gold in the commodities market. Moreover, some expertise research firms like GFMS, a leading global precious metals consultancy, released its "2011 Gold Survey" and GFMS expects that gold will reach $1,600 by the end of 2011.

Another independent variable, Crude oil WTI (West Texas Intermediate) price known as Texas light sweet, is a type of crude oil used as a benchmark in oil pricing. As refer to figure 2, the oil price increase significantly during year 2007 and the reasons behind can be explained by the Asian growing demand on oil to sustain their economy growth. The past researchers also been reported, that oil consumption in India was increased approximately 8.7% according 1998 and 6.5% according to 2006. Mehmet Eryigit (2009) has studied and found that in year 2007, USA has been consumed the 23.9% of the total oil, however total share of the world oil consumption for China, India and Turkey in 2009 is only accounted 13.4% (China consumed 9.3%, India consumed 3.3%, and Turkey consumed 0.8%). Meanwhile, back to middle of year 2008 Sub-prime crisis was happened in U.S financial system and the crude oil price has reached to a minimum price $31, that is a minimum last trader price was reported since year 2004. After decreasing trend along the year 2008, early of 2009 crude oil price are at the recovery stages and maintained a reasonable price between $ 65 -$ 100 per barrels. We expect the bullish of crude oil price will continue increasing.

The next independent variable is Market returns FBM Kuala Lumpur Composite Index (KLCI). The Kuala Lumpur Composite Index (KLCI) is used as a proxy for the performance of the Kuala Lumpur Stock Exchange and comprises the largest 30 companies listed on the Main Board by full market capitalization.

The last independent variable is T-Bill band 4. T-Bill band 4 is type of money market instrument. The Malaysian Treasury Bills (MTB) issued by the Central Bank of Malaysia Are tradable on yield basis (discounted rate) based on bands of remaining tenure (e.g., Band 4 = 68 to 91 days to maturity). This instrument are represents the short-term interest rate in the Malaysia money market. The high or low interest rate will make bonds look more attractive than stock and consequently impact the stock price return.

Figure 1: London Bullion Market (LBM) (U$ Troy Ounce) Price

Figure 2: Crude Oil WTI (West Texas Intermediate) Price

3.2 Conceptual Framework

1. Crude Oil WTI

2. London Bullion Market (LBM) (U$Troy Ounces)

3. KLSE (Kuala Lumpur Composite Index)

4. T-Bill Band 4

Sub Sector Price Index

Consumer Product,

Plantation,

Finance

Trading and Services,

Industrial,

Industrial Products,

Construction,

Mining,

Properties, and

Technology.The conceptual framework of this study was derived from literature review where proven macroeconomic variables like FBM Kuala Lumpur Composite Index (KLCI) are used as independent variables. The Crude oil WTI (West Texas Intermediate) future contract price, London Bullion Market (LBM) (U$ Troy Ounce) price, and T-bill band 4 had been widely used in evaluating a significant statistical relationship between dependent variables example on this research is sub-sector price index. Further to that, crude oil price is also proven to be a macroeconomic variable that direct impact to the conditional of the stock market. In fact, oil price can affect prices directly by impacting future cash flows or indirectly through an impact on the interest rate used to discount future cash flows. On other hands, Gold is a long run effective hedging tool for hedge against inflation and political uncertainty. Fluctuation of gold price will send a signal to investors and they’re start to expecting the stock market will going down in future.

3.3. Design of Study

The Arbitrage Pricing Theory (APT) is an expansion model of Capital Asset Pricing Model (CAPM) Single –factor model. That is, it specifies risk as a function of only one factor, the security’s beta coefficient. In a reality, the risk / return relationship is more complex, with a stock’s required return a function of more than one factor. For example, CAPM method is not suitable on this research because there are a various interdependent variables effect the dependent variables. Thus, we should adopt the APT (Arbitrage Pricing Theory) model to define and analyses these factors.

In additional, the statistical technique that simultaneously develop a mathematical relationship between a single depend variable and two or more independent variables. With the four independent variables the prediction of Y is expressed by the following equation:

Regression equation is;

Multi-factor Regression Model:

Rit = ∝+ BetaMRtMRt + Betaoil Oilt + BetaGoldGoldt+ BetaT-billT-billt (1)

Indicators

∝ = Intercept / Alpha

Rit = Return on major sub sector

MRt = Market Returns

Oilt = Oil Returns

Goldt = Gold Returns

T-billt= T-bill Returns

Where the Sub sector price index is a dependent variable and it shows the return on the Sub sector price index. Beta is constant term and we have four (4) independent variables; Gold price, Oil price, Market returns, and short-term interest rate respectively. We used Ordinary Least Squares (OLS) method to evaluate the relationships between the Gold price, Oil price, Market returns, and short-term interest rate against the ten (10) sub sector price index. The market return was benchmark to the FBM Kuala Lumpur Composite Index (KLCI) composite share price index. Time series of T-bill band 4 taken considerations as a short-term interest rate and same time this instrument consider as a risk-free interest rate.

The first steps, we required to find out the return of each independent and dependent variables using below formula:

Daily return formula is calculated using as per below:

Ri,t = (Pi,t - Pi, t-1)/ (Pi, t-1) (2)

Where;

Ri,t is the price return of ith variable on time t

Pi, t is the closing price of day t for variable i.

Pi, t-1 is the closing price previous of day t for variable i.

Then daily returns are aggregated that are our preliminary input to run regression analysis. The sample period for our study extends from periods 17/04/2000 to 18/04/2011.

Then after, used the input and the multi-factor regression model to run the regression analysis on each interdependent and dependent variable to examine whether each of them have any significant relationship.

In additional, the sub-part of the analysis section will examine the Gold Oil ratio analysis, the purpose is to determining whether the current Gold Oil ratio is below the benchmark ratio is either too cheap, or crude oil is too expensive otherwise when ratio is greater than benchmark, oil is either too cheap or gold. The analysis on Gold ratio trend will cover from period 17/04/2000 to 18/04/2011. The mean of gold oil ratio as an indicator for investor to decide whether the gold price is expensive, crude oil prices is cheap or the gold price is cheap, crude oil price is expensive.

Using below formula:

Gold Ratio = Gold Price0, t / Crude oil Price0, t (3)

Where;

Gold Price0, t is the closing price of day t for Gold.

Crude oil Price0, t is the closing price of day t for Crude oil.

CHAPTER 4

FINDINGS AND DISCUSSION

This chapter presents the findings of the study and provide a through discussion and analysis of the findings.

4.1 Data Analysis

To observe the effect of crude oil price, gold price, market returns, and short-term interest rates, the regression is calculated by using Ordinary Least Square (OLS) estimation procedure. Results are presented in Table 1.

Referring to the result obtained from Ordinary Least Square (OLS) analysis, the result found that gold and market return have a positive significant statistical relationship with consumer price index at 5% significant level. These also make a same result for plantation price index. The result implies that gold and market return have a positive significant statistical relationship with plantation price index. Between, the trading and services index shows positive significant relationship with market returns but negative statistical relationship with gold. It implies, when the trading and service price index increase 1% the gold price will decrease 0.011%. Moreover, the regression model is statistically useful in explaining the variation in the Finance, mining, and technology price index with 95% confidence level. The result shows positive significant relationships with market returns.

In additional, the industrial, industrial product, and properties price index regression model analysis results shows that market returns have a positive significant statistical relationship with three sub-sector price index. On the other hands, the industrial price index show negative significant statistical relationship with crude oil price at 5% significant level but the industrial product and properties have a positive significant statistical relationship with crude oil price. Finally, from the regression model analysis result found that, only the construction price index has a negative significant statistical relationship with t-bill at 5% significant level and others sub sector price index don’t have any statistical relationship with the T-bill.

4.2 Subpart Analysis

Gold Oil Ratio

Gold Oil Ratio is an expressed mathematically as the per-ounce price of gold divided by the cost of a barrel of crude oil, the ratio was telling us how many barrels of oil can be bought with an ounce of gold.

Even though oil and gold are thought to be hedging on inflation, their price movements aren't in lockstep. Since the 2001 launch of the current bull cycle, the correlation between U.S. benchmark West Texas Intermediate (WTI) crude oil and the London morning gold fix is only 23 percent. In fact, it's the lack of a tight correlation that makes the gold/oil ratio meaningful.

The ratio can fluctuated over time; since 2002, one ounce of gold could have bought between 11 and 16 barrels of oil. In midyear 2008, as oil prices surged, gold scraped a historic low at a 6x multiple (a 6-to-1 ratio). After half year later, the ratio had shoot to the 23x level after massive de-leveraging sent oil prices down $100 a barrel. The increasing trend of ratio kept continues from year 2009 until recent year.

An article was written by Brad Zigler said; it’s fair to ask if the 15:1 ratio is still an indicator of economic equilibrium. In his articles highlighted that the breakaway point for the gold multiple in 2008 was around 12x. Fear has pushed oil prices violently higher and, consequently, the ratio lower.

Table 1: Minimum and Maximum Gold Oil Ratio Record during the Period.

 

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

Max

15,18

15,74

13,85

11,67

8,94

11,08

12,54

23,11

28,05

17,55

16,29

Min

9,14

10,56

9,35

11,67

6,96

8,22

8,33

6,52

13,15

13,69

13,98

 

 

 

 

 

 

 

 

 

 

 

 

Table 2: Summary Table

 

Consumer Product

Plantation

Finance

Trading & services

Industrial

Industrial Product

Construction

Mining

Properties

Technology

Market return

Oil

×

×

×

×

×

×

×

Gold

×

×

×

×

×

×

×

T-Bill

×

×

×

×

×

×

×

×

×

√ = Significant at 5%

× = No significant at 5%

Table 3: ANOVA Table:

Components

Consumer Product

Plantation

Finance

Trading & services

Industrial

Coefficient

Intercept

0,0002420

0,0003258

0,0000982

(0,0000444)

0,0000733

KLCI - PRICE INDEX

0,5623974

0,9784759

1,0645010

0,9994746

0,7729328

Crude oil

(0,0054988)

(0,0018498)

0,0025010

0,0026129

(0,0109819)

Gold

0,0158866

0,0441310

(0,0037211)

(0,0112407)

0,0098721

T-bil

(0,0034387)

(0,0048518)

(0,0059951)

0,0023219

(0,0004433)

Adjusted R Square

0,5606859

0,5041736

0,8081372

0,8927299

0,6798043

 

P-value

Intercept

0,00310016

0,04191723

0,24917804

0,43452323

0,39876364

KLCI - PRICE INDEX

-

-

-

-

-

Crude oil

0,0931

0,7729

0,4634

0,2512

0,0016

Gold

0,0258

0,0016

0,6160

0,0233

0,1922

T-bil

0,4479

0,5845

0,2042

0,4613

0,9267

Components

Industrial Product

Construction

Mining

Properties

Technology

Coefficient

Intercept

(0,0000330)

(0,0001192)

0,0006207

(0,0000617)

(0,0006292)

KLCI - PRICE INDEX

0,7928138

1,1884864

0,8354046

0,9573352

0,9419663

Crude oil

0,0092151

0,0029884

(0,0115321)

0,0112074

0,0049796

Gold

0,0145574

0,0093185

(0,0493598)

0,0059426

0,0372188

T-bil

(0,0030311)

(0,0202644)

0,0016072

(0,0024516)

(0,0004453)

Adjusted R Square

0,6357414

0,6109031

0,0462843

0,5468360

0,3246577

 

P-value

Intercept

0,73923273

0,44480430

0,30905151

0,66749650

0,00515217

KLCI - PRICE INDEX

-

-

0,000000

-

0,000000

Crude oil

0,0203

0,6323

0,6369

0,0515

0,5801

Gold

0,0918

0,4929

0,3531

0,6349

0,0574

T-bil

0,5810

0,0191

0,9621

0,7582

0,9715

CHAPTER 5

CONCLUSION AND RECOMMENDATION

5.1 Conclusion

The crude oil is an important energy and lifeblood of modern economies. Rising of crude oil prices will create jittery and uncertainty in the global financial market. This situation can be express when soaring of crude oil prices create inflation and inadvertently would cause interest rates to go up. Consequently, this would impact various segments of the financial market especially the stock market. Beside on this, rising on global oil price will erode the company profit margin.

On the other hands, a second commodity is gold and the gold is an important effective hedging tool for hedge inflation. When inflation is expected, investors will divert their asset to the gold portfolio to protect their asset value. However, crude oil commodity is an indicator for deflation when deflation is expected investor will reallocate their funds and start to buy safer government bond. There is a positive relationship happened between the crude oil price and global gold price in the market. The reaction of investor against the negative news of the market can partially be explained by behavioral finance whereby the investor is an irrational and market is an imperfect. So, risk adverse investors should be more alert on the important signals or indicators as a guide to monitor and time the market to avoid any risk.

The aim of this paper is to study the relationship of oil prices, gold price, market return, and short-term interest rate on majors selected sub-sector index. The results on this study will add to the body of knowledge and assist policymakers like Bank Negara Malaysia as well as practitioners such as corporate managers and investors to participate in the stock market. It also enhances their understanding on the level of impact on the four (4) variables against the selected sub-sector indices.

The Arbitrage Pricing Theory (APT) was adopted in evaluating the major sub- sector components indices relationship with various macroeconomic risk factors. The conclusion of the study shall enrich investor understanding on some sub-sector industries relationships to macroeconomic risk factors. Thus, smart investors still have a chance to explore it and gain return on that sub-sector industries. The multi-factor regression model can be expressed as

Rit = ∝+ BetaMRtMRt + Betaoil Oilt + BetaGoldGoldt+ BetaT-billT-billt

Referring to the result obtained from Ordinary Least Square (OLS) analysis, the result found that gold and market return have a positive significant statistical relationship with consumer price index and same result for plantation price index whereby gold and market return have a positive significant statistical relationship with plantation price index. Between, the trading and services index shows positive significant relationship with market returns but negative statistical relationship with gold. It implies, when the trading and service price index increase 1% the gold price will decrease 0.011%. Moreover, the regression model is statistically useful in explaining the variation in the Finance, mining, and technology price index with 95% confidence level. The result shows positive significant relationships with market returns.

In additional, the industrial, industrial product, and properties price index regression model analysis results shows that market returns have a positive significant statistical relationship with three sub-sector price index. On the other hands, the industrial price index show negative significant statistical relationship with crude oil price at 5% significant level but the industrial product and properties have a positive significant statistical relationship with crude oil price. Finally, from the regression model analysis result found that, only the construction price index has a negative significant statistical relationship with t-bill at 5% significant level and others sub sector price index don’t have any statistical relationship with the T-bill.

5.2 Assumptions and Limitation of the Study

There are five important variables involved in this study which include four (4) independent variables and one dependent variable. In obtaining data for each variable, this study had outlined the research framework with several assumptions and limitations to enable data collection to be done.

T-Bill band 4 considered to be the short-term interest rate (risk-free interest rate).

KLSE (Kuala Lumpur Composite Index) represent as a benchmark for the market return.

Using Crude oil WTI (West Texas Intermediate) future contract price, is a type of crude oil used as a benchmark in oil pricing and underlying commodity of New York Mercantile Exchange’s oil futures contracts.

London Bullion Market (LBM) (U$ Troy Ounce) price. This index shows the performance of gold prices over time per troy ounce. The troy ounce is a weight measure for precious metals.

Consists of 10 majors sub-sector price index e.g. Consumer, Plantation, Finance, Trading and Services, Industrial, Industrial Products, Construction, Mining, Properties, and

All research data are limited from period 17/04/2000 – 18/04/2011.

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