An Analysis of Macroeconomic Indicators Effects on Stock Prices *

Behavioral vs. Fundamental Finance:

An Analysis of Macroeconomic Indicators Effects on Stock Prices[*]

Allison Keane

Fall 2008

1Introduction

An article in the Wall Street Journal on September 6, 2008 stated, “When the jobless report first came out Friday, the Dow Jones Industrial Average slid more than 150 points… The Dow’s nearly 345 point fall on Thursday was largely attributed to anticipation of grim job news.” The recent market turmoil and sensitivity to information would make one assume that macroeconomic new announcements, such as the non-farm payrolls report, when released, would cause fluctuations in the market. However, questions arise regarding the legitimacy of these assumptions. Does the market always react to the economic measures upon release each month, or does it do so currently only because the economy is suffering? If the economy is prospering, and the unemployment rate is released at a higher value than the marketexpected, would stock prices still suffer a shock?

The buying and selling of stocks depends on the individual investor and his or her reaction to outside factors. The state of the economy, current market fads, an individual’s personal wealth and willingness to take on risk are all factors affecting how one might invest. A risky technology stock might be appealing to an investor following the crowd, but passed up by a risk-averse individual. An investor with a family member who dies of lung cancer may decide to sell a cigarette company stock in their portfolio for personal reasons, while many people would choose to buy such a company’s stock during a recession. The release of a new cancer treatment may attract a rush of investors to a pharmaceutical company. Many factors play into market conditions and reactions to different events, especially media attention. The release of an economic indicator may be overshadowed in the media by a political scandal or natural disaster, causing investors to ignore the indicator. During times of economic prosperity, the indicators are not the “hot news” to report. The Dow Jones Industrial Index appeared to have fallen last September due to the jobless report as the Wall Street Journal described, but during the technology bubble in the late 1990s were investors even paying attention to the jobless rate?

Speculative bubbles occur when stock prices rise to an overvaluation according to the fundamental valuation technique. Malkiel’s “Castle-in-the-air theory” of investing described in his book, A Random Walk Down Wall Street, assumes people invest based on how they believe the crowd will behave in the future. From this viewpoint, a stock is worth as much as someone else is willing to pay, regardless of the company’s finances or strategy. As a result, investors buy at any price as long as they expect to be able to sell at a higher price. The increase in prices results in increased demand, which leads to even higher prices, and the cycle runs continually until the bubble pops. Shiller calls this repetitive process the feedback theory. “In the most popular version of feedback theory, one that relies on adaptive expectations, feedback takes place because past price increases generate expectations of further price increases” (69). The “castle-in-the-air theory” and “feedback theory” help explain the reoccurrence of market bubbles throughout history.

As described by Chancellor (14-20), the emergence of modern economic bubbles began with the Tulip Craze in the 17th century, when the DutchRepublic was introduced to the tulip. The flower quickly grew in popularity and the more colorful the flower was, the more valuable it became. Merchants cross-pollinated the colors to produce increasingly more expensive goods and create more market participation. The tulip became a sign of wealth and an assumed safe investment. Prices rose so high that people decided they would rather trade the flower for the money, and the tulip bubble crashed. In the end, the flower was worthless, and people who had traded goods for tulips were left with nothing but perishable flowers.

Such bubbles and investor actions are not only confined to the past. A recent example is the technology bubble in 1999. New technology was developed and considered a new business opportunity that caught on as a market obsession. Through the feedback loops described in Shiller’s theory, technology company stocks soared in prices. “Cisco was selling at triple-digit multiple of earnings… If Cisco returned 15% per year for the next twenty-five years and the national economy continued to grow at 6% over the same period, Cisco would have been bigger than the entire economy (Malkiel 80). Clearly, these prices and estimates were outrageous and unrealistic, which caused analysts to create “new metrics” for evaluating technology companies,such as the number of people visiting the websites. Following the fad, non-technology companies changed their names to include a web orientation, such as“.com”,to increase the stock prices. “Companies that changed their names enjoyed an increase in price… that was 125 percent great than that of their peers…even when the companies core business had nothing whatsoever to do with the Net” (Malkiel 82).The technology craze also increased the financing of ridiculous startup companies;for example Digiscents, which offered a computer plug-in to make web sites smell, gained financial support (Malkiel 83). As is common in all economic bubbles, an oversupply and the realization of the absurd ventures caused a burst.

Bubbles have possibly occurred many times within the US stock market as a result of the imperfections of human behavior which cause investors to act irrationally and make errors in judgment. Overconfidence in their own abilities, bias towards certain stocks, aversion to loss, and herding are four systematic ways people deviate from rational investment behavior (Malkiel). The study of stock prices as a result of human psychology and estimates of future actions is known as Behavioral Finance. Historic bubbles and market deviations from intrinsic values lend support to this branch of economics. The major underlying assumption of behavioral finance is that investors do not act rationally, and as a result, their decisions can only be determined by accounting for the underlying human psychology.

On the contrary, the market efficiency theory assumes all investors act rationally and the intrinsic value of a company is reflected as the price. The market may deviate from this value at times, possibly for an extended period as in economic bubbles, but will return to the “correct” price in the long-run. If the price is too high, investors will sell, and if the price is too low, investors will buy. The market gravitates towards the intrinsic value. The market efficiency theory assumes that prices reflect and correctly interpret available public information.

Malkiel discusses the “firm-foundation theory” to oppose his “castle-in-the-air theory” in which the value of a company is evaluated based on the present conditions and future company prospects. The dividends and earnings are the basis for the valuation, and the investments should be based on comparison of the actual price and the computed price. If it is trading below the fundamental value, the stock is a buy, but if trading above, the stock is a sell. The prices are affected by announcements and news that provides information regarding future returns and growth. The “firm-foundation theory” bases investments on estimating the values of the rate of return and the growth rate, two variables in the fundamental value calculation, as opposed to predicting the crowd behavior.

This paper investigates the fundamental viewpoint and behavioral finance viewpoint by analyzing the impact macroeconomic news announcements have on stock returns. A clear relationship would support the fundamentalist theory that investors react rationally to available news. Lack of a relationship would support that investors are acting on their own accord and ignoring the economic indicators, the behavioral view. The availability of high-frequency data has enabled the use of more precise measurements in the investigation. The overnight return can be calculated using prices from a specific time in the morning and the closing price of the previous day. The intraday returns can be determined and used to calculate variance. The data allows the development and implementation of the local variance, a new standardization technique. Therefore, the returns can be standardized by a value more accurately reflecting the volatility of the time when the return occurs.

This paper is organized as follows; section 2 describes the contrasting pricing models; section 3 describes the return models; the regressions for analysis are provided in section 4; the data is explained in section 5; section 6 describes the results, followed by the conclusion in section 7.

2Pricing Models

The fundamental pricing and behavioral finance viewpoints utilize different valuation models as support their theories. Both techniques are explained in the following section.

2.1Fundamental Stock Valuation

Market efficiency theory assumes rational behavior from investors and that stock prices accurately incorporate all available, public information. By combining the public information, such as macroeconomic announcements and company financials, the stock will be correctly priced via a mathematical model. Two different models of the fundamental value of a stock are discussed here, the Dividend Discount Model and Company Comparables. The latter is used when the firm being valued does not pay a dividend or pays a very small dividend.

2.1.1Dividend Discount Model (DDM)

The DDM is a mathematical model used to calculate the price of a stock based on the dividend a company pays to its shareholders and the discount rate. The discount rate, r, is the expected rate of return an investor requires in order to buy the stock. The riskier the stock is, the higher the rate of return will be; the higher return is the payoff for taking on more risk. The discount rate is calculated via the Capital Asset Price Model (CAPM),

.(1)

Here,rriskfreeis the return of a riskless asset, commonly the 10 year Treasury bond return, rmarketis the market return, and βis a measure of the riskiness of the stock. The discount rate is affected by economic activity via rriskfree and rmarket, and thus responds to macroeconomic announcements. As shown in the equation, the higher the β value (the riskier the stock), the higher the rate of return r.

The DDM takes the rate of return from the CAPM and the company’s dividend, which can be found on the company balance sheet, to calculate the current price. A balance sheet lists the assets and liabilities a company holds and is the record of the company’s financial position at a specific point in time. The pricing model,

,(2)

assumes that a stock is worth the sum of the future dividends, discounted to today’s value. P0is the price today,Dtis the dividend at time t, and r is the rate of return calculated from CAPM.

A simplified version of the DDM is the Gordon Growth Model which assumes that a company’s earnings will grow at a constant rate g and the company will retain a constant dividend payout ratio d. The expected dividend at time t then becomes

.(3)

N0and D0 are the current earnings and dividend values. The DDM simplifies to

.(4)

The value of the constant growth rate g can be estimated based on historical growth, analyst estimates, or an accounting method

.(5)

ROE is the company’s Return on Equity, a statistic found on the company’s balance sheet. The return on equity is a ratio defining how efficiently a company is using its equity. It is calculated by dividing the net income by the total equity. The estimate for g used will alter the stock price determined by the fundamental valuation.

Based on the market efficiency value and the assumption that investors act rationally, the r and g values used in the DDM should be affected by economic indicators, and consequently, the price of the stock P0 should be affected. For example, the theory assumes that if a high unemployment rate is released representing the onset of a recession, the values of r and gwill decrease in the future. If r decreases by more than g, then the price P will increase, but if g decreases by more than r, then the price P will decrease. The DDM assumes the investors incorporate the information such as announcements into this valuation.

2.1.2Company Comparables

The DDM does not provide a valuation technique for companies that do not issue dividends. A company may decrease dividends to fund a new project or reinvest the money into company operations to add value as opposed to issuing a dividend. According to the DDM, such companies would be worthless, which is misleading. An alternative method, using comparable companies to value a stock, can be used in such situations.

This model uses ratios to value comparable assets, but begins by first determining companies that are considered to be comparable to the company in question. The choices are based on comparing size, industry, market share, and structure. Once suitable companies are found, statistics of these companies, excluding the company in question, are analyzed. For example, the price P0of each company, the Book Value B0of each company, and the ROE of each company are noted. These three values can be found on each company’s balance sheet. Using the statistics, a regression,

,(6)

is performed to determine a value for . Once is found, it is multiplied by the ROE of the company J in question and the Book Value BJ of company J to determine the price PJ,

.(7)

The model can be applied using other statistic values instead of ROE and the Book Value, but those are chosen at the discretion of the analyst.

A short-coming of this valuation technique is its performance during bubbles or times of poor performance. If companies are overvalued and prices are being inflated, then the statistics of the similar companies being used would be inflated as well. For example, during the technology boom, the tech stocks were overvalued and the prices high. Using such prices in a valuation will skew the result towards the higher end. On the other hand, if on average a sector is poorly performing, then using these prices and statistics to value a prospering company within the sector is misleading. For these reasons, careful consideration of the companies used in the valuation must be exercised.

2.1.3Microstructure Noise

The observed market price of a stock may differ from the fundamental price determined from the Gordon Growth Model or comparable companies’model; this difference is known as microstructure noise

.(8)

is the fundamental price, and is the observed price. The fundamental model does not hold for every minute of trading and the price will fluctuate. Due to the bid-ask bounce throughout trading days, the price will not remain at a constant value. When using high-frequency data, such as that used in this paper, the microstructure noise can not be ignored. The noise affects the return volatility calculations and is accounted for by using five-minute intraday returns. Microstructure noise affects the overnight returns in this paper as well. The price at a specific time, such as 10:00am, is used to calculate overnight returns. If the price at this time is affected by microstructure noise, it could skew the return value.

2.2Behavioral Finance

The equations above calculate the fundamental value of an asset, but sometimes the prices that stocks are traded at differ greatly from these values. The valuations above are based upon the impression that investors act rationally to optimize their returns, but a contrary view has begun to be investigated in behavioral finance. Based on common human errors in investment decisions, stocks may not be priced according to their fundamental value, but subject to whatever price someone will pay. “Such price behavior is consistent with common models of an irrational market in which stock prices take long temporary swings away from fundamental values” (Fama, French 1988).

Economic bubbles can be explained through behavioral finance theories and are thoroughly discussed in Shiller’s Irrational Exuberance. The formation and propagation of bubbles is explained by the“feedback loop theory”. An increase in prices will create greater investor demand, which in turn causes a furtherrisein prices. Feedback that occurs because past increases generate expectations of even more price increases is a result of adaptive expectations. Past price increases can also boost investor confidence, thus causing growingdemand and further price increases. There are two specific types of feedback that can cause price inflation experienced in bubbles, price-to-price and price-to-GDP-to-price. In the first, an increase in the price creates feedback that directly increases the same price again. The latter is a process in which the stock prices increase, which raises personal wealth and optimism. This wealth effect causes people to invest more. The feedback theory propagates assuming the price will continue to rise and others will buy, but when investors begin to think the prices will stop rising, everyone wants to sell and the bubble bursts. Often such large increases in prices during bubbles are followed by equal or larger decreases during the price declines (Shiller 68-71).