Introduction

Background

In the past 50 years, we have witnessed the rise of neoclassical finance with the beginning of the simple notion that risk must be incorporated into investment decisions, diversification is essential for successful investing, and the markets are hard to beat (Bernstein, 2007). Starting from 1960s, the Harry Markowitz’s idea on maximizing risk-return tradeoff, William Sharpe, John Lintner and Jan Mossin’s Capital Asset Pricing Model (CAPM), as well as Eugene Fama and Paul Samuelson’s concept of efficient financial markets, had exerted remarkable impacts in transforming the entire finance literature and industry. However, while researchers are arguing on the issues of systematic-unsystematic risk, alpha-beta, risk premium framework, or the extent to which markets are efficient; an important piece of the puzzle, namely, details on macroeconomics impacts and effects have received relatively less attentions before 1980s. This is pretty surprising, as common sense would inform us that economy conditions and business cycles will affect business entities, and thus the stock returns of these companies.

Macroeconomics comes into the picture when the beta as a measure of systematic risk in CAPM is found to be unsatisfactory estimates in equilibrium-based asset pricing models due to heroic assumptions demanded by the model. Furthermore, intuitively, it is obvious that the CAPM is not built upon a direct cause and effect relationships. Beta concept simply uses past prices to predict theoretical asset prices instead of ferreting out the root cause which could impact the future prices. As it is obvious that beta does not tell the whole story of risk. There seem to be risk factors that influence stock market returns beyond beta’s one-dimensional measurement of market sensitivity (Bodie, Kane & Marcus, 2007). For this, early studies on macroeconomics factors’ influence on stock market were mainly motivated by Arbitrage Pricing Theory (APT) by Ross (1976), where a number of macroeconomic factors, such as industrial production index, inflation, interest rates and oil prices have been used to represent risk in stock market. The ultimate objective of APT framework was to explain expected returns in the context of systematic risks. Since then, Chen, Roll & Ross (1986), Fama (1990), Chen (1991), Ferson & Harvey (1991), McQueen & Roley (1993) and many others started to investigate macroeconomics forces relationships to the stock market. However, one obvious weakness of these researches is the implicit assumption that there exist unidirectional impacts from macroeconomic variables to stock prices (Muradoglu, Taskin & Bigan, 2000). In this respect, two questions arise. First, do statistical significant relationships imply causality? Second, is the causality from stock markets to economic performance or from economic performance to stock markets – or does it work both ways?

On the other side of the coin, there are also many books written by practitioners on economic indicators and the profit opportunities to trade on these signals. Among of the best selling list include: The Secrets of Economic Indicators: Hidden Clues to Future Economic Trends and Investment Opportunities by Baumohl (2005), Using Economic Indicators to Improve Investment Analysis by Tainer (2006), and The Trader’s Guide to Key Economic Indicators by Yamarone (2007). Practitioners argue that economic indicators are the keys to unlocking invaluable information about financial market behavior, and provided it is interpreted correctly, indicators can lead to successful trading and speculating, profitable investing and proper policy making. Baumohl (2005) even ranked the importance of the economic indicators in US as shown in Table 1 in the following page. However, many of the interpretations of the economic indicators are based on the experience of the authors, and thus, could be subjective; suffer from hindsight bias and often, without confirmation from any scientifically-based statistical approaches.

Table 1: Economic Indicators Most Sensitive to Stocks

Source: Baumohl (2005)

Rationales for the Research

Generally, there are various reasons for us to get excited to study the relationships of macroeconomic variables and stock returns. Firstly, macroeconomic forces are construed as representative of systematic risk, a key concept of neoclassical finance which serve as the cornerstone for modern portfolio management, strategic asset allocation or even risk management (Maginn, Tuttle, Pinto & McLeavy, 2007). Thus, an understanding and research on the macroeconomic forces could be crucial. Secondly, by investigating the linkages between macroeconomic variables and stock returns, it would enhance our knowledge on macroeconomics roles in systematic movement of the stock market. Consistent with the ability of investors to diversify, as well as the rapid growth of various index funds, it is reasonable for us to perform research on this area, to discover potential profitable market timing strategy, to generate positive alpha by actively trading a well-diversified portfolio based on the releases of economic information (as contrast to the buy-and-hold strategy). Thirdly, perhaps should any of the empirical evidences found is against the theory on capital market efficiency, we could add extra literature to further complicate the debate on Efficient Market Hypothesis (EMH). Fourthly, while the macroeconomics and stock market is a topic popularly discussed by people, many of the arguments presented are based purely on personal observations, experience or subjective judgments. This further convince us that, a quantitative and scientifically-based research on this topic, is needed, to provide statistical proofs for ambitious investors to switch from a beta grazer to become an alpha hunter, to make a killing in the market.

Literature Review

Macroeconomics and Stock Market

Macroeconomics basically consists of three basic pillars: output, money, and expectations. To begin, the notion of output (i.e., value delivered to end users in goods or services form) is the heart of macroeconomics, where the total amount of output that a country can produce will determine the prosperity of the nations. Money is then created to facilitate transaction of these values. Typically, a central bank is appointed to take good care on the prices of the money and the amount of money in circulation in the economy system. For this, central bank can attempt to stimulate the economy through expansionary monetary policy or prevent an overheating economy with contractionary monetary policy. As the entire economic system ultimately concerns people, expectations or perceptions from people would also affect the macroeconomic system. To have a stable economic system, among others, people must have belief in the credibility on the money created by government and trust central bank’s ability in preventing a hyperinflation situation (Moss, 2007).

The stock market enters the scene as corporations wish to raise money from the public to undertake huge projects. Here, stock market serves as an intermediary which promotes efficiency in the allocation of funds and propels the economy growth of a nation. As stock ownership represent claims on the output and profitability of corporation, it also serves as a barometer of the economic health of a nation. As the economy booms, stock market becomes bullish; while when the economy is contracting, stock market becomes bearish. Several macroeconomic variables are essentially related to stock returns. Firstly, output (i.e., goods and services), which is commonly defined as real economic activities in literatures, represents the value delivered by corporations to the public. As more output is produced and sold, the profitability of corporation will increase. This in turn means that the future cash flow to corporations and shareholders would be increasing as well, and thus stock prices will rise.

While it is definite that a nation’s prosperity is dependent on the aggregate of output it is able to produce, the employment rate of the nation is also crucial in determining the stability of the nation. Employment rate measures the proportions of the populations that have jobs, and thus are receiving income for survival. It is one of the most closely tracked figures by economists, politicians and policy makers. After all, ensuring everyone has a job and thus ability to earn a living is the utmost critical responsibility of government policy makers.

To prevent high unemployment rate, Central Bank usually implements expansionary monetary policy to stimulate the economy. Expansionary policy is done by decreasing the interest rates in order to increase the money supply. Often, expansionary monetary policy will translate into a bull market. However, high employment rate comes with a cost. As expansionary monetary policy is implemented, sooner or later inflationary pressure will set in. Inflation can erode the value of money, and thus it will reduce the public confidence on the credibility of the money printed or created by government or Central Banks. It is not uncommon that hyperinflation situation could cause instability and economic collapse, as proven historically in several emerging countries. To prevent erosion of money value by inflation as well as to mitigate the overheating economy system, contractionary monetary policy is often implemented by Central Bank to slow the economy down. Contractionary monetary policy is implemented by raising interest rates to reduce money supply. Often, this means a bear market.

Besides that, if we do analyze a particular nation’s macroeconomic situation in the international context, exchange rates will come into the picture. Basically, exchange rates mean the prices of money for a particular country (i.e., currency) relative to the prices of money for other country’s currency. As one of the components in the macro economy, exchange rates are closely connected to all other macroeconomic forces such as real economic activities, employment rate, interest rates, inflation and money supply.

Figure 1 in the following page summarize the three pillars of macroeconomics as well as the corresponding macroeconomic variables. All of these macroeconomic forces are interrelated and interdependent. As the aggregate stock returns are often perceived as one of the leading indicator of the macroeconomic system, an understanding and study of these forces are crucial for us in establishing the stock returns expectations.

Figure 1: Stock Returns and the Three Pillars of Macroeconomics

Source: Adapted from Moss (2007)

Selecting the Macroeconomic Determinants of Stock Returns

Voluminous financial and economic theories have asserted relationships between macroeconomic variables and stock markets. Many frequently investigated macroeconomic variables in the context of estimating stock returns are: Gross Domestic Product (GDP), industrial production index, unemployment rate, interest rates, inflation, money supply, short-term and long-term interest rates, default spread, credit spread, term structure, oil prices, and exchange rates. Some researchers even separate these variables into the expected and unexpected component. Broadly speaking, there are two methods in which researchers select the macroeconomic determinants of stock returns in their models.

One of the widely used methods is utilizing factor analysis to ferret out the variables which have the highest explanatory power in explaining the stock returns. According to Connor (1995), generally speaking, multifactor models of security returns can be divided into three types, namely: macroeconomic, fundamental, and statistical factor models (with some blurring at the boundaries).

Among some of the literatures that employed factor analysis to find the relevant factors for explaining stock returns are: Kim and Wu (1987), McGowan & Dobson (1993), Bodurtha, Cho & Senbet (1989), Fifield, Power & Sinclair (2002), Panetta (2002), as well as Gerlach & Yiu (2005). In this context, some of the macroeconomic variables typically found as factors are: inflation, the percentage change in industrial production index, the excess return to long term government bonds, and the realized return premium of low grade corporate bonds relative to high grade bonds.

Obviously, to take all factors or variables as done by the previous literatures into consideration are not feasible due to non-availability of data. Fortunately, in a parsimonious manner, five of the macroeconomic variables able to depict the economy state of a nation, namely, real economy activities, employment rate, money supply, inflation and interest rates. These variables are chosen mainly due to their popularity and the important roles they play in economic system as discussed in section above. Thus, these macroeconomic variables are intuitively logical to be used in explaining the aggregate stock returns, and serve as the theoretical foundation of this research. The relationships between each and every macroeconomic variable and the stock returns will be discussed in the following sections.

Relationships between Stock Returns and Real Activity

The relationships between stock returns and real economy activities are most widely investigated. This is intuitively understandable as common sense would inform us that real economic activities signal economic strengths and weaknesses; thus must be closely related to the stock market, which is generally perceived as a barometer of the economic state of a nation. The economic variables most often chosen to proxy real activities are Gross Domestic Product (GDP) and Industrial Production Index (IPI).

To begin, Gross Domestic Product (GDP) is the comprehensive report on the health of the economy and it measures the speed of economic growth. Practically, it is the best overall reflection of the economy’ ups and downs and is tracked by forecasters, corporate CEO, money managers as well as policy makers. Many investors focus on the GDP figure to gauge the outlook for corporate profits. Generally, healthy economy generates more business earnings (i.e., higher expected stock returns); while sluggish economy depresses revenue and income (i.e., lower expected stock returns).

Statistical relationship between stock returns and real economic activities. Generally, regression based empirical results often found that there exists significant statistical relationships between stock returns and real economic activities. Some of these examples are: Chen, Roll & Ross (1986), Kim & Wu (1987), Bodurtha, Cho & Senbet (1989), McGowan & Dobson (1993), Hondroyiannis & Papapetrou (2001), Fifield, Power & Sinclair (2002) and Merikas & Merika (2006).

In US, Chen, Roll & Ross (1986), Kim & Wu (1987) and McGowan & Dobson (1993) found that industrial production index innovations are statistically significantly priced in explaining stock returns. In the international context, Bodurtha, Cho & Senbet (1989) found that industrial production index is also positively and statistically significant in explaining stock returns in US, Canada, UK, France, Germany, Australia and Japan. The findings of Merikas & Merika (2006) also support this view in Germany, as they documented evidences that stock returns are positively related with GDP growth. Hondroyiannis & Papapetrou (2001) also found evidences (i.e., weaker evidences) that industrial production index is important in explaining stock prices movements in Greece. Similarly, Fifield, Power & Sinclair (2002) found that GDP is one of the economic factors able to explain stock returns in 13 emerging stock markets.

Lead-lag relationship between stock returns and real economic activities. Besides employing factor analysis and regression models to investigate statistical relationships between stock returns and real economic activities, many researchers had also tried to investigate the lead-lag relationships of these two variables. While there are some mixed results if industrial production index lead or lag stock returns, generally speaking, it is more widely documented that changes in stock prices predict the direction of the future changes in the level of economic activities, as measured by GDP (particularly in US). As a matter of fact, Mahdavi & Sohrabian (1991) even argued that as the stock index is a component of the US Index of Leading Economic Indicator, it is somehow implicitly suggesting that the notion that stock prices lead real economic activities is accepted at the official level. However, from the international context, stock returns as a predictor (i.e., leading) of real economic activities is less obvious, where evidences are mixed and unclear. Some of the literatures are: Fama (1990), Chen (1991), Lovatt & Parikh (2000), Hondroyiannis & Papapetrou (2001), Hamori, Anderson & Hamori (2002), and Flood & Marion (2006).

Fama (1990) found that stock returns are significant in explaining (i.e., highly correlated with) future real economic activities in US. Specifically, regression results show that past stock returns are significant in explaining current production growth rates while future production growth rates are significant in explaining current stock returns. One interesting finding is that the degree of correlation between stock returns and future production growth rates increases with the length of time where stock returns are measured. Apart from that, it is also found that variations in annual stock returns are being explained well by future production growth rates if compared to variations in monthly stock returns. For this, Fama (1990) argued that regressions of long horizon stock returns on future production growth rates give a better picture of the cumulative information about industrial production embodied in stock returns. Lovatt & Parikh (2000) found evidences that support Fama’s (1990) findings in the case of UK.

Causal relationship between stock returns and real economic activities. Causal relationships between growth of stock market prices and the rate of growth of GNP in US have also been performed by various researchers. Among them are Huang & Kracaw (1984), Gallinger (1994), Kwon & Shin (1999), Muradoglu, Taskin & Bigan (2000), as well as Hondroyiannis & Papapetrou (2001). Generally, it is found that stock returns Granger-cause real economy activities in both US as well as in the international context.

Huang & Kracaw (1984) found that variations in stock market returns did Granger-cause the changes in the log of real GNP in US. Mahdavi & Sohrabian (1991) found unidirectional Granger-causality from stock price index to the rate of growth of GNP in US. Gallinger (1994) performed Granger-causality test on real stock returns and real economic activities in US and found strong evidences of Granger-causation from real stock returns to real economic activities, while weaker evidences on bidirectional Granger-causality between real stock returns and real economic activities, as well as weaker evidences on Granger-causation from real economic activities to real stock returns after causation runs from real stock returns to real economic activities. Muradoglu, Taskin & Bigan (2000) found that stock returns Granger-cause industrial production index in India and Mexico.

Nonetheless, there are also views that the linkage between stock prices and economic performance reflects causation running in both directions. Specifically, the nation’s economic performance influences the nation’s stock market. People pay higher prices for common stocks as companies’ sales and profits increase. However, stock prices also influence macroeconomic activities. Economists suggested that two critical components of aggregate expenditures – consumption spending and investment spending – are influenced by stock prices (Thomas, 2006).

Summing it up. Overall, there are statistical and economically important relationships between real economy activities and stock returns. There are substantial theories to support the linkages between stock returns and real economy activities as well. Generally, stock prices are found to lead (i.e., Granger-cause) real economy activities. However, other factors could distort or impact this relationship. The most important one perhaps is the business cycle. At early recovery stage, high growth in the economy is bullish as investors realize increasing productivity and corporate profit. In the middle stage of the business cycle, rising interest rates (due to rising inflation) and rising corporate profits are the two opposing forces impacting the stock returns. Relationships between stock returns and real economic activities are ambiguous at this time. Around the peak of the business cycle (i.e., in speculation of the turning point), interest rates become highly important and dominant in affecting stock prices. Further high growth in real economy activities, along with high inflationary pressure, could cause rising interest rates and potential tightening of monetary policy, which is bearish for equity market. Other factors which could affect the stability of this relationship are: inefficient market where stock prices do not fully reflect economic situation, speculative bubble, persistent market anomalies, cultural differences, and country-specific effects. The impact of real economic activities announcement to stock returns is also dependent on business cycle and other factors. We could also observe that the viewpoints from practitioners and empirical evidences discovered by researchers are roughly consistent. Overall, the real economy activities are just one of the many elements which could impact and impacted by stock returns. More comprehensive set of data will be needed to reach a more accurate forecasting for the stock returns.

Relationships between Stock Returns and Employment Situation

Employment Situation report, which conveys the employment and unemployment rate to the stock market, is argued by practitioners such as Baumohl (2005) and Tainer (2006), as one of the market shaking economic indicators in US. There are various justifications for this phenomenon. Firstly, the question that whether jobs are being created exerts not only economic, but also political significance. The public looks more to this figure than to any other to judge the health of the economy, thus, exerting pressures to the president and the Congress and the Central bank, whenever unemployment rate increases. Secondly, the indicator is timely. It is among one of the earliest indicator released to the public. Thirdly, employment situation is essentially a measure of the workers well being, and as these workers earn more, they tend to spend more and propel the economy forward. Fourth, it is a matter of fact that household spending contributes for 66% of the US entire economy’s output (Siegel, 2002; Baumohl, 2005; Tainer, 2006).

Employment variables as priced factor in explaining stock returns. There are some literatures found that the employment (i.e., labor market related) indicators are one of the macroeconomic factors being priced in explaining stock returns. For instance, Gertler & Grinols (1982) found that the addition of unemployment rate to the standard two factor model of security returns able to increase the explanatory power of the regression significantly in US. Kim & Wu (1987) found that the labor market variables are one of the very significant priced risk factors in explaining stock returns in US.

Statistical relationships between employment and stock returns. Typically, it is found that employment growth is negatively related to stock returns from regression analysis. On the surface, this is indeed surprising because common sense would tell us that employment growth is somewhat a signal of economic growth and should be positively related to stock returns. However, over the long run, employment growth is one of the root cause contributing to inflation, which would cause interest rates to rise when Central Bank implement a contractionary monetary policy, and ending up with a bear strike in the stock market. Realizing this, it is then not unreasonable for us to observe negative relationships between employment growth and stock returns from empirical studies. Some literatures in this context are as follow. Park (1997) documented that employment growth rate show the strongest negative effect on stock returns among some of the famous macroeconomic variables. He gave two reasons to justify his observation. Firstly, compared with other variables, employment growth is related more negatively with future corporate cash flows and more positively with future inflation. Secondly, these effects are most pronounced in annual data, which reflect long term relationships. Besides US, Marikas & Merika (2006) found evidences in Germany that employment growth has a negative effect on stock returns and influences positively on inflation. They also suggest that this is due to the employment growth forecasts inflation which is expected to erode firm’s profits and this is expressed through falling stock returns.

Grange-causality between employment and stock returns. There are relatively few literatures investigating the causality relationships between employment and stock returns. Nonetheless, it is reasonable for us to expect that as a leading indicator, stock returns should Granger-cause employment growth, which is one of the indicators of economic growth. Example of the literature is: Huang & Kracaw (1984) found that unidirectional Granger-causality exists from stock returns variation to changes of unemployment rate in US.

Summing it up. Employment situation as conveyed by Employment Report is crucial in affecting stock returns. Many practitioners asserted that this particular macroeconomic announcement is the most influential piece of news to the stock market. This claim is indeed supported by some empirical evidences in US. This is reasonable as employment situation is not only a concern by economists or investors, but also being watched carefully by politicians, Central Bank and the normal public. Overall, it is found that employment growth is one of the priced factors in explaining stock returns. Generally, from linear regression perspective, employment growth is found to be negatively related to stock returns. However, a more comprehensive and accurate view on the relationships of employment growth to stock returns can only be formed when we take the business cycle into consideration. At the recovery stage, employment growth is positively related to stock return as it signal prosperity and future profit outlook for businesses. However, as the economy becoming overheating, employment growth will in turn cause inflationary pressure which would lead to rising interest rates. At this moment, employment growth is highly negative to stock returns. This time-varying relationships between stock returns and employment growth are recorded in some literatures. Interested readers may refer to Table 2.2 below, which summarized some of the literatures in this context.

Relationships between Stock Returns and Money Supply

Many academicians and professional observers hypothesize close relationships between stock prices and various monetary variables that are impacted by monetary policy. One of the most important indicators regarding monetary policy is the money supply (Reilly & Brown, 2003). There are really some fundamental and technical analysts who rely on monetary indicators, such as the federal funds rate, the discount rate, and the money supplies to forecast future stock returns (Siegel, 2002). For example, Martin Zweig (1997), one of the successful practitioners, stated that: ‘In the stock market, as with the horse racing, money makes the mare go. Monetary conditions exert an enormous influence on stock prices. Indeed, the monetary climate – primarily the trend in interest rates and Federal Reserve policy – is the dominant factor in determining the stock market’s major direction. Once established, the trend typically lasts from one to three years.’

This is consistent with some empirical evidences from various researchers. Typically, it is found that expansionary monetary policy is bullish for stock market, and vice versa. Friedman (2005) asserted that monetary policy played a supporting role in various booms in US and Japan in the past (Note: technological change played the major role). Conover, Jensen, Johnson & Mercer (2005) continue to affirm that US monetary policy continues to have strong relationships with security returns. They found that: periods of expansive monetary policy are often associated with a strong stock performance (i.e., higher than average stock returns and lower than average risk), whereas periods of restrictive monetary policy generally coincide with weak stock performance (i.e., lower than average stock returns and higher than average risk). Apart from that, it is also commented that there are highly consistent relationships between monetary conditions and stock returns over time. Also, small-cap companies are found to be more sensitive than large-cap companies to changes in monetary conditions. Likewise, cyclical stocks have a much higher sensitivity to changes in monetary conditions than defensive stocks. On the contrary, some evidences that tightening monetary policy is bearish for stock market are as follow. Thorbecke & Coppock (1996) found that the news of contractionary monetary policy triggers a large and statistically significant decline in stock returns. The study also found evidences that monetary policy changes have a large effect on small firms in bad times than in good times, consistent with the fact that credit constraints bind a larger number of small firms in a downturn. In addition, they also documented evidences that monetary policy shocks exert a larger effect on large firms during good times. Besides, Park & Ratti (2000) also found that shocks from monetary tightening generate statistical significant negative movements in inflation and expected stock returns.

Central banking and money supply. Central banks’ actions are often influential towards both stock returns and macroeconomic variables as central banks have the ability to expand or contract the money supply. Due to their monopolistic control over the monetary base, they are able to move interest rates. In implementation of monetary policy, although money is ultimately the key to be influenced element, central bankers generally focus on the short term interest rates as their main policy instrument, rather than the money supply itself. Of course, they would also manipulate the money supply as needed to support the level of interest rates they desire. Central banks can utilize monetary policy to achieve a variety of goals. For example, they can reduce interest rates to stimulate the economy when unemployment rate is high. In contrast, they can raise interest rates when the inflation is too high or the economy is overheating. They can also try to stabilize the exchange rates, by raising interest rates when the currency falls in value relative to other currencies, and vice versa. Today, central bank typically manipulates the money supply through open market operations. The purpose of open market operations is generally to move a particular short term interest rates (i.e., the federal fund rate in US) to the desired level. Essentially, when the central bank wants to expand the money supply (i.e., expansionary monetary policy); it buys government bonds or other assets from private financial institutions, to inject cash into the economy system. The opposite is performed when they implement contractionary monetary policy.

Theories linking money supply to stock returns. There are two conflicting theories available to explain the impact of money supply growth to stock returns. The first theory argues that an increase in money supply will cause inflation, assuming that the demand for money is relatively constant and the increase in inflation exceeded the growth rate of the economy. In this context, growth in money supply will negatively impact stock returns. Firstly, increase in money supply will raise inflation, and inflation had been shown historically negatively related to stock returns. Secondly, if the increase in money supply causes inflationary expectation, nominal interest rates will rise, and this is again detrimental to stock returns. Thirdly, should the interest rates rise; investors will be induced to rebalance their portfolio by substituting equity with bonds. This theory is intuitively appealing especially during the peak time of the economy, where inflationary pressure is widespread. Apart from that, this theory is also logical for unexpectedly high money growth, where a too high money growth is often associated with higher interest rates and lower stock prices.

However, on the other hand, the opposite theory, commonly referred as the “liquidity effect” predicts that the growth in money supply will improve stock returns. From this perspective, an increase in money supply may lower interest rates and cause excess balances of money. The excess balances of money create demand for various financial assets, and thus, could drive the stock prices up. In practice, this could be better clarified as follow. As a tool of monetary policy, money supply is controlled by the Fed through open market operations. Fed engages in buying and selling Treasury bonds to adjust bank reserves, and thus the money supply. As the Fed deals in government bonds, the initial liquidity impact when the Fed buys bonds affects the government bond market, creating excess liquidity for those who sold bonds to the Fed. As a result, bond prices increase and interest rates decrease. Sooner or later, the movements in government bond prices subsequently filter down to corporate bonds, common stocks as well as the real good market. The jargon describing the impact of money supply growth (resulting from a change in monetary policy) on the financial markets and then later on the aggregate economy is named transmission process (Reilly & Brown, 2003). This theory is again examined by Friedman (2005) with empirical data, confirming again that the quantity of money has a determinative effect on stock prices as well as national income. It is also observed that this second theory is more widely accepted as a more accurate depiction of the linkage between stock returns and money supply, in the context of expansionary monetary policy to revive the economy.

Moss (2007) offers a more neutral view, in which he asserted that the money growth and interest rates relationships are ambiguous. This indicates that the relationships between stock returns and money growth are indeed ambiguous as well. Two counteracting forces are in place. Initially, when the central banking increases the money supply, we could expect short term interest rates to fall. However, the growth in money supply (particularly if it is substantial) may invoke inflationary expectations, which could push long term nominal interest rates upward. Should the inflationary expectation is realized; short term nominal interest rates will rise as well. As a result, we cannot really tell if increase in money supply will increase or decrease nominal interest rates (i.e., the end result will be dependent on the future; if the inflation really kicks in). This is shown in Figure 2 below.

Figure 2: Ambiguous Effects on Stock Returns by Money Growth.

Source: Adapted from Moss (2007)

Statistical relationships between money supply and stock returns. Typically, it is widely documented that there exists statistical positive relationships between money supply and stock returns (unless in the case of unexpected high money growth). Kraft & Kraft (1977) and Abdullah & Hayworth (1993) documented statistical significant relationships between money supply and stock prices in US In the similar vein, Flannery & Protopapadakis (2002) also found that Monetary Aggregate announcement is one of the six candidates for priced factors. Thorbecke & Coppock (1996) found that 32% of the variation in stock returns is explained by monetary policy. Apart from that, researchers have also found evidences that money supply factor is significantly priced in explaining the stock returns. In US, Kim & Wu (1987) found that the money supply is one of the very significant priced factors in explaining stock returns. Similarly, Kwon, Shin & Bacon (1997) also found that money supply is one of the significant factors being priced in stock returns in Korean stock market. For 13 emerging market, Fifield, Power & Sinclair (2002) also found that money supply is one of the economic factors crucial in explaining stock returns.

Causal relationships between money supply and stock returns. However, controversies enter when discussions on the issue if money supply lead or lag stock returns. Empirical evidences are mixed (Reilly & Brown, 2003). For example, Kraft and Kraft (1977) found that past and current movements in the money supply do not influence (i.e., Granger-cause) stock prices, and suggest the inability of the money supply to forecast stock prices in US Conversely, Abdullah & Hayworth (1993) found that money growth Granger-cause stock prices in US In Korea however, Kwon, Shin & Bacon (1999) found that stock price indices are not a leading indicator for stock returns.

Summing it up. Generally, there are many empirical evidences on existence of positive statistical significant relationships between stock returns and money supply. However, the lead-lag relationships or issue concerning Granger-causality of these two variables is empirically controversial. Three are also two competing theories to suggest how stock prices should behave in response to growth of money supply. Both theories are relevant in its own sense, depending on the state of business cycle and investors’ expectations (on inflation). The first theory suggests that money supply growth (particularly large unexpected money growth which is perceived to be inflationary at the booming period of the economy) is bearish, because money growth will cause inflation, and consequently rising interest rates. Rising interest rates is detrimental to equity market. The second theory suggests that money growth will cause interest rates to fall, and thus causing equity market to prosper. This is particularly relevant when the money growth could be associated with the implementation of expansionary monetary policy to revive the economy.

To conclude, perhaps we should consider words from Siegel (2002, page 235): ‘Easing monetary policy, by definition, involve lowering short term interest rates. This is almost always extremely positive for stock prices. Stocks thrive on liquidity provided by central bank. When the central bank eases credit, it lowers the rate at which stocks’ future cash flows are discounted and provides a monetary stimulus to future earnings. Only if the central bank eases excessively, so that the market fears it might spark inflation, will stocks react poorly.’

Relationships between Stock Returns and Inflation

Aggregate price level is one of the issues closely tracked by central bank in implementing the monetary policy. Over the long term, the price level has almost never declined. As mentioned before, the amount of money in circulation (i.e., money supply) is of paramount important in determining the price level. Specifically, if the supply of dollars increases when there is no equivalent increases in output, inflation will happen. In layman term, this is exactly the situation where too much money chasing after too little goods or services (Siegel, 2002).

Inflationary-related indicators are among those highly influential macroeconomic variables towards stock market. This is not uncommon sense. Firstly, inflation will determine the prices for consumer goods and services, and it affects everyone. Secondly, inflation affects the cost do doing business, where high inflation will affect negatively on personal and corporate investments. Thirdly, inflation could exert political implications, such as having detrimental effects towards the quality of life of the public or particularly the retires. Fourth, there are also arguments that market participants are believed to be responsive to consumer goods prices when assessing real returns on equities. Fifth, inflationary pressures will lead to higher bond rates, raising the interest rates and invoke substitution effect from stocks to bonds. Sixth, inflationary pressure could raise the cost of corporate borrowing when interest rates increase. Seventh, while revenues or even profits might increase with inflationary pressures, these incomes are worth much less to shareholders, who prefer earning improvement comes from greater sales volume, instead of price hikes. Eight, often, the remedy of inflation will almost certainly force the Fed to raise interest rates, which is a disaster for stock returns (Abdullah & Hayworth, 1993; Baumohl, 2005; Tainer, 2006).

Statistical relationships between stock returns and inflation. In US, there are many literatures documenting that inflation is priced in explaining stock returns. Inflation is found to be a priced factor in explaining the aggregate stock index returns. For example, Gertler & Grinols (1982) discover that the addition of inflation to the standard two factor model of security returns able to increase the explanatory power of the regression significantly in US Apart from that, a more detail analysis also discover that inflation is also priced in explaining industry returns. McGowan & Dobson (1993) asserted that inflation rate is statistically and economically significant in explaining industry returns.

Inflation as a priced factor in explaining stock returns is not limited in US alone. There are literatures showing that inflation is a priced factor both in developed and emerging countries. Bodurtha, Cho & Senbet (1989) found evidences that the international unanticipated inflation is significant in explaining the cross-section of average stock returns in US, Canada, United Kingdom, France, Germany, Australia, and Japan. Patro, Wald & Wu (2002) found that country-specific macroeconomic variables such as inflation able to explain the beta (i.e., risk) of a country equity index returns (in 16 OECD countries). Fifield, Power, Sinclair (2002) found that inflation is one of the economic factors able to explain returns in 13 emerging stock markets.

Besides investigating if inflation is priced as a risk factor in explaining stock returns, there are also literatures documenting negative relationships between stock returns and inflation. Some of the evidences show a weak negative relationship while some other suggests a strong negative relationship between stock returns and inflation. According to Chen, Roll & Ross (1986), the unanticipated inflation as well as changes in expected inflation are found to be statistically weakly priced in explaining expected stock returns, during period when inflation is highly volatile. Outside US, Davis & Kutan (2003) found that macroeconomic volatility, as measured by movements in inflation, has a weak predictive power for stock market volatility and returns. The finding also suggests that there is no strong support for the existence of Fisher effect in the international context.

Those literatures which recorded empirical evidences that inflation is significantly negatively related to stock returns are as follow. In US, Kaul & Seyhun (1990) documented evidences that negative relations exists between stock returns with expected and unexpected inflation. Specifically, the negative stock returns and inflation relationships proxy for the adverse effects of relative price variability on economic activities, particularly during the seventies, when the US experienced oil supply shocks. Outside US, Amihud (1996) found a negative and strongly significant relationship between unexpected inflation and stock prices by employing a market-based measure of unexpected inflation in Israel. Also, Groenewold, O’Rourke & Thomas (1997) found negative relationships between (expected) inflation and stock returns in Australia. They documented empirical proof that the negative relationships puzzle is found in the macroeconomic interactions: an increase in the expected inflation rate will increase equilibrium real output that has a negative impact on stock returns. Adragi, Chatrath & Raffiee (1999a and 1999b) documented negative relationships between real stock returns and unexpected inflation, which persists even after purging inflation of the effects of the real economic activities in Korea, Mexico, Peru and Chile. Last but not least, Udegbunam & Eriki (2001) found empirical evidences strongly support the proposition that inflation exerts a significant negative impact on the behavior of stock prices in Nigeria.

As usual, there are some exception and few empirical evidences that contradict the major findings on macroeconomic variables relation to inflation. For example, while many empirical evidences show that inflation is a priced factor in explaining stock returns, Kwon, Shin & Bacon (1997) found that inflation related variables are not priced in stock return in Korean stock market. Not only that, while largely it is found that inflation is negatively related to stock returns, Abdullah & Hayworth (1993) found that Stock returns are positively related to inflation in US

Granger-causality and cointegration between stock returns and inflation. Similar to the other macroeconomic variables, empirical evidences on the direction of causality between stock returns and inflation is mixed. According to Abdullah & Hayworth (1993), inflation is found to Granger-cause stock returns and explains a substantial proportion of the forecast error variance of stock returns in US Another example in the international context is: Muradoglu, Taskin, & Bigan (2000) found that out of 19 emerging countries, there is only evidences showing stock returns Granger-cause inflation in Jordan and Zimbabwe; bidirectional Granger-causality between stock returns and inflation in Argentina.

Judging from the equilibrium perspective, there are empirical evidences suggesting that stock returns and inflation is cointegrated. For instance, Ahmed & Cardinale (2005) investigated both the long-term and short-term aspect of the correlation between equity returns and changes in consumer prices (in US, Japan, UK & Germany). They found mixed support on the hypothesis regarding stable long run equilibrium relationships between inflation and stock returns, while strong evidences that indicate asymmetric behavior during different inflationary regimes in the short-term. Besides, Patra & Poshakwale (2006) found empirical results indicating that short run and long run equilibrium relationships exists between inflation and stock prices in Athens stock exchange.

Business cycle, inflation and stock returns. Inflation relationships with stock returns are largely time-varying. As an important component of business cycle, this relationship does differ depending on the state of business cycle. Besides, the relationship also varies accordingly depending on the common perception of the investment fraternity. For instance, Pearce (1984) asserted that the relationships of expected stock price changes and expected inflation seem to have changed over time. Specifically, prior to 1972, the survey respondents apparently believed common stock to be a good hedge against inflation since they expected stock prices to rise at about the same rate as the general price level, holding expected real growth constant. However, in the period of high inflation since 1972, empirical evidences suggests that agents expected real stock returns to be adversely affected by inflation in US Another example is: Ferson & Harvey (1991a) found that compensation for bearing inflation risk is highest near business cycle peaks.

Summing it up. Inflation is one of the most influential macroeconomic indicators to the stock markets. This is supported empirically and agreed by practitioners. Generally, inflation is found statistically significant in explaining stock returns in both US and in the international context. Most of the empirical evidences documented suggested a negative relationship between stock returns and inflation. A positive relationship between the two variables is less common. Besides, inflation and stock returns are also found to be cointegrated. However, the directions of Granger-causality for these two variables are mixed. Studies on inflation announcement news impact to stock returns found that inflation is indeed exerting significant influence on the stock market. However, the relationships between stock returns and inflation vary dependent on the state of business cycle. The most conventional theoretical framework on inflation and stock returns is Fisher hypothesis, which stated that inflation should be positively related to the stock returns. This is logical as stocks ownership represent claims on the real output, and should the output prices increase, the profit of the company should also be increasing. As empirical evidences typically discover that the opposite is true, Fisher hypothesis is considered as misleading in various literatures. For this, several hypotheses had been proposed to explain the negative relationships between stock returns and inflation. First, the observed relationships between stock returns and inflation reflect relationships of other economic factors. Second, investors are simply irrational. Third, the negative relationships are based on nominal contracting effects. Nevertheless, while it is not clear why stocks fail as a short term inflation hedge, stocks do indeed turn out to be a good hedge against inflation in the long run. This is in fact the key ideas in Siegel’s (2002) best selling investment book titled Stock for the Long Run.

Relationships between Stock Returns and Interest Rates

Daily observations suggest that interest rates are vital to stock price movements. This is not surprising and could be explained by a number of economic rationales. Firstly, commonly used discounted cash flow model for securities valuation take interest rates as discount rates in the calculation, and thus affecting the estimated present value of the expected future cash flow. Secondly, long term bonds will compete with stocks for investors’ dollar, and thus, any increases in long term interest rates will caused stock prices to fall due to portfolio rebalancing in the process of asset allocation. Thirdly, rising interest rates could also hurt the profitability of companies by raising financing costs, and possibly trigger the onset of a recession. Fourthly, rising interest rates could also adversely impact mergers and acquisitions activities, and thus could adversely affect the stock market sentiment (Abdullah & Hayworth, 1993; Siegel, 2002). Apart from that, as mentioned in the previous section, interest rates (especially short term interest rates) are closely related to the implementation of monetary policy by central banks. Together with various money supplies related variables (such as the loan demand in economy and liquidity in the banking system), aggregate price level (e.g., inflation or deflation) and policy decisions by the central bank, interest rates produce the monetary climate which are critical in predicting stock returns (Zweig, 1997). Last but not least, there are also findings that interest rates related variables are the only statistically and economically meaningful macroeconomic figure which could predict stock returns out-of-sample. According to Chan, Karceski & Lakonishok (1998), most macroeconomic variables (except term structure and default premium) fail miserably in the out of sample test. They asserted that fundamental factors are more statistically significant in explaining the stock returns in the out-of-sample test.

A glance through the past history. According to Siegel (2002), in the short and intermediate term, interest rates are the single most crucial influencer on stock prices. From 1950s to 1990s, the changes in the fed fund rates have been an excellent predictor of future stock returns. It is observed and calculated that, after increases in the fed funds rate, the subsequent returns on stocks are significantly less than average; while after decreases in the fed fund rate, the subsequent stock returns are significantly higher than average. The implied message is: should the results persist in the future, investors could beat the market by increasing their stock holdings when the Fed is easing credit conditions, and reducing their stock holdings when the Fed is tightening credit. Unfortunately, this strategy, which had performed remarkably well in the past, failed to perform in the 1990s to 2000s. Siegel (20002) suggested that one possible explanation for this is due to the boom and burst of technology stocks in the early 2000. Due to the diminishing effects on accuracy of fed fund rate in forecasting stock returns, Siegel (2002) argued that the best strategy for stock investors is not to count on the Fed in their investment decisions. Nonetheless, he maintains the view that central bank policies are still essential to the financial markets. Low interest rates and liquidity will still feed the stock market. Cautious investors should simply aware that the monetary policy actions, since 1990, have not evoked a consistent interpretation by players in the equity market.

Statistical relationships between stock returns and interest rates. Consistent with the theoretical and rationales outlined above, virtually all studies that have examined the effects of interest rates on stock returns found a very significant statistical negative relationship. Some examples are: Kim & Wu (1987), Bodurtha, Cho & Senbet (1989), Chen (1991), Abdullah & Hayworth (1993), Kwon, Shin & Bacon (1997), Hondroyiannis & Papapetrou (2001) and Fifield, Power & Sinclair (2002). Kim & Wu (1987) found that the interest rates is one of the very significant priced risk factor in explaining stock returns in US Chen (1991) documented that the 1-month T-bill rate is one of the important determinants of future stock market returns. Similarly, Abdullah & Hayworth (1993) found that stock returns are negatively related to both short and long term interest rates. In the international context, Bodurtha, Cho & Senbet (1989) found evidences that interest rates (calculated from international bond returns) is significantly priced in explaining the cross-section of average stock returns in US, Canada, UK, France, Germany, Australia and Japan. This is consistent with Hondroyiannis & Papapetrou (2001) findings in Greece and Fifield, Power & Sinclair (2002) findings in 13 emerging stock markets. The only exception is documented by Kwon, Shin & Bacon (1997). They found that interest rates related variables are not priced in stock return in Korean stock market.

Causal relationships between stock returns and interest rates. The directions of causality in this context, however, are mixed. Kraft & Kraft found unidirectional causality exists from the stock price measures to interest rates (as proxied by Moody’s AAA corporate bond rate) in US. Abdullah & Hayworth (1993) found that short and long term interest rates Granger-cause stock returns and explain a substantial proportion of the forecast error variance of stock returns. Muradoglu, Taskin & Bigan (2000) however, documented empirical evidences as follow. Out of 19 emerging countries, there are only evidences that interest rates Granger-cause stock returns in Brazil, Pakistan and Zimbabwe; stock returns Granger-cause interest rates in Korea and Mexico; and bidirectional Granger-causality between stock returns and interest rates in Argentina.

Term structure of interest rates and stock returns. Besides using a particular single interest rates figure to explain or predict stock returns, many researchers also employ various interest rates spread figures (i.e., the difference of two interest rates) or the yield curve (i.e., graphical analysis). Some of the examples are: Chen, Roll & Ross (1986), Chen (1991), McGowan & Dobson (1993) and Resnick & Shoesmith (2002). Chen, Roll & Ross (1986) found that changes in risk premia (i.e., the spread between high grade and low grade bond) and twist on the yield curve (i.e., spread between long-term and short-term interest rates) innovations are found to be significantly priced in explaining stock returns. Similarly, Chen (1991) documented that the default spread (i.e., the difference between composite bond yield and high grade bond yield) as well as the term spread (i.e., difference between long-term T-bond and short-term T-bill) are two of the important determinants of future stock market returns. Apart from that, McGowan & Dobson (1993) also asserted that the term structure of interest rates and the default premium is statistically and economically significant in explaining industry returns. Resnick & Shoesmith (2002) also found that the value of yield spread between 10-year T-bond and 3-month T-bill possesses crucial information about the probability of a bear market. Contradicting the concept of efficient market, at a 50% probability screen, their simulations show that, for the period studied, a market timer switching out of stocks into T-bills (or vice versa) one month before a bear (bull) stock market could have realized a compounded annual return of 16.46% versus 14.17% from a stock-only buy and hold strategy.

Business cycle impact on interest rates. Although it is typically documented that interest rates are negatively related to stock returns, there are also reasons for us to believe that the degree of strength for this relationships would vary across business cycle. First, changes in interest rates (especially the short term interest rates) may be an act of counter-cyclical monetary policy which could exert a greater adverse effect to stock prices when the economy is either at the peak or trough. Second, the substitution effect between debt securities and equity securities could be stronger during the peak or through (relative to other state of the business cycle). To elaborate, when economic activities is booming (and possibly peaking), bonds become a better alternative to stocks as the yields from bonds is high and the expected returns from stocks could be low. Any sign of the overheating economy and rising inflationary pressure could cause interest rates to increase, and may result in a huge decrease in stock returns as it is reasonable to expect many investors would switch their assets to bonds. The opposite would happen when the economy is roughly around the trough. Some literatures in this context are: Ferson & Harvey (1991), Bolten & Weigand (1998) and Funke & Matsuda (2006). Ferson & Harvey (1991) found that compensation for bearing real interest rates risk is highest near business cycle troughs. Bolten & Weigand (1998) asserted that changes in interest rates throughout the economic cycle are shown to cause changes in the level of stock prices, thus suggesting that monitoring and forecasting interest rates could help us to predict stock prices behavior over time. Funke & Matsuda (2006) documented evidences in US that there exist asymmetric reactions of stock prices to macroeconomic news. In a booming time, lower than expected interest rates may be good news for stock returns, and vice versa. However, this phenomenon is less clear in German.

Summing it up. There seemed to have unanimously agreement among researchers and practitioners on the accuracy and explaining power of interest rates in forecasting stock returns. In the short and intermediate term, interest rates are the single most important influencer on stock prices. Together with money supply, interest rates form the monetary climate and exert huge impact to the market sentiment. Literature wise, empirical studies have shown that the relationships of interest rates to stock returns is negative, although the causal relationships is less clear (i.e., the directions of causality are mixed). There are also empirical evidences which documented that interest rates relation to stock returns are time-varying, dependent on the state of business cycle. In addition, many researchers and practitioners also found that the spread of interest rates, calculated from different maturity or quality of various fixed income securities, does possess good forecasting power in predicting stock returns.

Nonetheless, although in the past 40 years, changes in the fed fund rates have been a very good predictor of future stock prices, the efficiency and practicality of this strategy is diminishing in the recent years. One possible reason for this is due to the technology stock bubble in 2000. As such, although expansionary monetary policy, by lowering short term interest rates, will still likely to feed the stock markets; alert investors should aware that basing their investment decision solely on the movement of central bank’s policy might not be the best strategy.

Conclusion

To conclude, we have seen many empirical evidences on the subject of relationships between macroeconomic variables and stock returns. Generally speaking, most of the empirical studies typically found that there are valid relationships between stock returns and GDP growth, industrial production index growth, unemployment or employment rate, money supply, inflation, interest rates, differences between long term and short term interest rates and differences between corporate bond yield and government bond yield. However, these relationships are not an exact science, as there are also some empirical evidences which documented controversial results against the mainstream findings.

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