FNCE 4070: FINANCIAL MARKETS AND INSTITUTIONS Lecture 3: The Role of Expectations in Financial Markets How Expectations Shape Financial Asset Prices. The Efficient Market Hypothesis (Eugene Fama).

Where is this Financial Center?

Canary Wharf

The Role of Expectations Expectations play a very important role in all aspects of life. In financial markets expectations play an equally important role. The behavior of what we observe today in these markets is often driven by what we expect will happen in the future. An expected political upheaval or civil unrest in oil producing countries will increase the current (spot) price of crude oil. An expected increase in United States interest rates relative to the U.K. will raise the rate of return on U.S. dollars above the rate of return on British, leading to an appreciation of the (spot) U.S. dollar against the pound.

The Role of Expectations Additionally, many theoretical models in the financial economics literature assume a relationship between the current value of a financial instrument and expectations about the future. For example, the dividend discount model assumes that the current price of a certain common stock is the discounted value (i.e., present value) of all expected future dividend payments. Gordon “Growth Model” based on future series of dividends Market Price of a Stock (P) = D/(k – G), where: P = Present value of future dividends D = Expected dividend 1 year from now k = Required rate of return for an equity investor in the market G = Expected annual growth rate in dividends http://www.ultimatecalculators.com/constant_growth_model_calculator.html

Approximating Expectations However, one of the most critical aspects of expectations is that they are unobservable. One approach to this issue has been to model how market participants form expectations. Adaptive, Rational Expectations, Behavioral Finance (which looks at the emotions ,irrational decision-making, and biases that can come into play in setting asset prices; thus, integrating both economics and psychology). A second approach to “approximate” expectations is through the use of survey data. In a typical survey, representative market participants are questioned about their subjective forecasts about the future value of a particular economic (e.g., GDP, unemployment) or financial variable (corporate earnings). The assumption is that the aggregate measure of individual expectations should give a reasonable proxy for `the' market's expectation.

How are Market “Expectations” Formed? Adaptive Model Prior to the 1960s, most economists (and thus economic models) assumed that market participants formed adaptive expectations about the future, or that: Market expectations about a variable were based primarily on past values of that variable, and These expectations changed slowly over time. This approach undoubtedly reflected the “relatively stable” environment of the early post World War II period, 1945 – late 1950s. (see series of post WW II slides).

Post War (WWII) Interest Rate Environment: 1945 - 1960

Post War (WW II)Interest Rate Environment: 1945 - 1960

Post War (WWII) Exchange Rate Environment: GPB Against the USD

Post War (WWII) Exchange Rate Environment: JPY Against the USD

Problems with the Adaptive Model There were, however, potential problems with the post WWII adaptive model of expectations: (1) A particular variable could easily be affected by many other variables (not just the variable itself). Thus, financial market participants are likely use all relevant data in forming an expectation about a variable. Perhaps more importantly: (1) By the 1970s, the economic and financial environment began to experience sudden and dramatic swings. Change in U.S. monetary policy, demise of Bretton Woods (fixed exchange rates) and formation of OPEC. (2) As a result, we realized that expectations could change very quickly.

Abrupt Change in 1970s/1980s in the Environment Affecting Expectations

Inflation Environment in the 1970s

The 1970s -80s: A New Problem In the 1970’s, global inflation became the major economic issue for industrial countries. Two distinct inflation peaks: 1973/74 and 1980/81. The inflation of this period was attributed to cost push “supply shocks” to the global economy. Especially oil. As a result, many central banks turned their attention to inflation and some to the use of inflation targets as a macro economic goal. Beginning with New Zealand in March 1980. Inflation in Industrial Countries, % per year

The Result of the Changing Environment on U.S. Interest Rates

Volatility of Short Term Interest Rates in the Late 1970s Though the 1980s

Changing Exchange Rate Environment; The Japanese Yen: 1966 - 1979

Changing Exchange Rate Environment; The British Pound: 1966 - 1979

Rational Expectations Model A second approach to financial market expectations, called rational expectations, took hold in the 1960s. According to the rational expectations model, market participants form expectations using all available information (not just past information and not just the variable itself). Model also assumed that new information is constantly being introduced to the market. The rational expectations model, in turn, became a bridge to “efficient markets theory (hypothesis).” The efficient markets theory assumes that asset prices reflect all available information (events) that directly impact on the future cash flow of a security (i.e., a financial asset).

Eugene Fama and The Efficient Market Hypothesis According to Eugene Fama (see Appendix 1), who is regarded as the originator of the efficient market hypothesis: “In an efficient market, competition among many intelligent participants leads to a situation where, at any point in time, the actual prices of securities already reflects the effects of information based on events that have: (1) already occurred [i.e., in the past], and events, (2) as of now [i.e., in the present], and events (3) the market expects to take place in the future. [i.e., what it anticipates]” Source: Eugene F. Fama, "Random Walks in Stock Market Prices," Financial Analysts Journal, September/October 1965

Illustrating The Role of Expectation Thus, according to Fama in an efficient market, financial asset prices reflect the best knowledge of the past, the present and predictions (anticipations) of the future. Important Questions: What happens when something unanticipated occurs and how quickly do asset prices adjust? (1) How does the market react if the market is efficient? (2) How does the market react if the market is inefficient? What happens when something anticipated occurs? (1) How does an efficient market react to anticipated events? (2) How does an inefficient market react to anticipated events? Next four slides illustrate possible answers to these questions.

Unanticipated “Favorable” Event Inefficient Market: Prices would drift upward for some time following the event Efficient Market: Prices would adjust up very quickly at the time of the announcement and stabilize P Event Time P Event Time

Example: Unanticipated “Favorable” Event Walmart announced profits and sales which exceeded analysts forecasts before the market opened on May 18, 2012

Unanticipated “Unfavorable” Event Efficient Market: Prices would adjust down very quickly at the time of the announcement and stabilize Inefficient Market: Prices would drift downward for some time following the event P Event Time P Event Time

Example: Unanticipated “Unfavorable” Event JPMorgan announced a $2 billion dollar trading loss before the market opened on Friday, May 11, 2012

Anticipated “Favorable” Event Inefficient Market: Prices would drift up for some time before the event and continue up after Efficient Market: Prices would drift up for some time before the event and then stabilize P Event Time P Event Time

Anticipated “Unfavorable” Event Inefficient Market: Prices would drift down for some time before the event and continue down after Efficient Market: Prices would drift down for some time before the event and then stabilize P Event Time P Event Time

Krispy Kreme and the Efficient Market Theory Founded in 1937 (in Winston-Salem, NC) , the company went public on April 5, 2000 and traded on NASDAQ (eventually listing on the NYSE on May 17, 2001). By 2004, the company was selling over 7.5 million doughnuts a day. Earnings announcement due on Monday, November 22, 2004 for the three months ending October 31, 2004 (Announcement prior to the opening on the NYSE). Stock had closed at $11.50 the previous Friday. Analysts anticipated earnings of 13 cents per share Instead, the company announced its first quarterly loss (of 5 cents a share) since going public in 2000. Since announced earnings were not in line with market expectations, what do you think happened to Krispy Kreme stock and how quickly did it react?

Krispy Kreme: November 22, 2004; Reaction to Unanticipated “Unfavorable” Event

Nike Reacts to an Unanticipated “Unfavorable” Event On Thursday, November 18, 2004, near the close of the market (just before 4:00) the company announced that the company’s co-founder Philip H. Knight was stepping down as president and chief executive officer of the company.

Conclusions from the Efficient Market Hypothesis If markets are efficient, anticipated events have already been discounted in asset prices. If markets are efficient, financial asset prices will adjust quickly to new and unanticipated events (including data, news, speeches). Any unexploited profit opportunities (i.e., a situation in which an investor can earn a higher than normal return) will quickly disappear as market participants adjust prices in accordance with the new event. Thus, it is impossible to beat (or do better than) the market with respect to any financial asset. Essentially your return will be no better than what the market, or, a particular security returns.

Issues Surrounding the Efficient Market Hypothesis How efficient are financial markets in terms or assimilating new information into asset prices? Industrial country financial markets (especially the large financial markets) appear to be very efficient. Developing country financial market prices react more slowly to information. Even in industrial country markets, are there situations when a market acts inefficiently? See Appendix 2 for possible examples. Additionally, what news appears to be most important in affecting asset prices?

How Efficient are Equity Markets? Studies suggest that equity prices adjust within 1 to 15 minutes upon receiving information. Dann, Mayers, and Raab (1977), Patell and Wolfson (1984), Jennings and Starks (1985) Conclusion: Most researchers generally agree that equity markets are reasonably efficient, however, debate is kept alive by the search for and discovery of market anomalies (see Appendix 2)

How Efficient are Foreign Exchange and Bond Markets? Studies suggest that foreign exchange markets (for major currency pairs) reacts very quickly to news. Ederington and Lee (1993, 1995): Found that exchange rates (U.S. Dollar/German Mark exchange rate study) reacted after about 10 seconds of scheduled macroeconomic news releases and are complete after another 30 seconds. This study also found similar reaction times in the U.S. Treasury bond markets

The Role of Expectations in Specific Financial Markets Kim, McKenzie, and Faff, Macroeconomic News Announcements and the Role of Expectations: Evidence for US Bond, Stock and Foreign Exchange Markets, Journal of Multinational Finance Management, 2004: These researchers found for the three markets tested that: (1) Balance of trade news had the greatest impact on the foreign exchange market. (2) In the bond market, news related to the internal economy (e.g., retail sales) was found to be important. (3) For the US stock market, consumer and producer price information was found to be important. They also concluded that: “it is not the act of releasing macroeconomic information which the market considers to be important, but rather the ‘news’ component of each release – i.e., the difference between the markets expectation and the actual figure.

Using Survey Data to “Proxy” Market Expectations The following two sources provide us with survey data which we can use to “approximate” financial market expectations. We can then compare these expectations to the actual event (e.g., when the data is released) to assess market moves. Bloomberg: http://www.bloomberg.com/markets/economic-calendar/ FX Street: http://www.fxstreet.com/fundamental/economic-calendar/

Eugene Fama, the Efficient Market Hypothesis and Stock Prices Appendix 1 Eugene Fama, the Efficient Market Hypothesis and Stock Prices

Short Bio on Eugene Fama Eugene Fama (born February 14, 1939), an American economist, best known for his work on portfolio theory and asset pricing, both theoretical and empirical. He earned his undergraduate degree in French from Tufts University in 1960 and his MBA and Ph.D. from the Graduate School of Business at the University of Chicago in economics and finance. Fama is most often thought of as the father of the efficient market hypothesis, beginning with his Ph.D. thesis (1964) which concluded that stock price movements are unpredictable and follow a random walk. In 1963, he joined the faculty at University of Chicago Booth School of Business. For more information on Fama see: http://www.chicagobooth.edu/faculty/bio.aspx?&min_year=20084&max_year=20093&person_id=12824813568

Fama: The Efficient Market Hypothesis and Stock Prices Application of Efficient Market Theory to common stocks can be traced to the work of Eugene Fama (see: 1965, Financial Analyst Journal). There are two critical elements in his work: (1) Efficient market theory applied to Stock Prices: Stocks are always “correctly priced” given that everything that is publicly known about a stock is reflected in its market price. (2) Random walk theory: Since new information is random, all future price changes are independent from previous price changes; thus, future stock prices cannot be predicted. For a more complete discussion see: Burton Malkiel, A Random Walk Down Wall Street, (Norton Publishing 1973).

Testing the Efficient Market Hypothesis Appendix 2 Testing the Efficient Market Hypothesis

Testing the Efficient Market Hypothesis The EMH provided the theoretical basis for much of the financial market research during the 1970s and 1980s. During that time, most of the evidence seems to have been consistent with the EMH. Prices were seen to follow a random walk model and the predictable variations in equity returns, if any, were found to be statistically insignificant. So, most of the studies in the 1970s focused on the inability to predict prices from past prices. However, beginning in the 1980s, the EMH became somewhat controversial, especially after the detection of certain anomalies in the capital markets (i.e., situations which provided “abnormal returns”).

Testing for Financial Market Anomalies Some of the main financial market anomalies that have been identified are as follows: 1. The January Effect: Rozeff and Kinney (1976) were the first to document evidence of higher mean stock returns in January as compared to other months. The January effect has also been documented for bonds by Chang and Pinegar (1986). Maxwell (1998) showed that the bond market effect is strong for non-investment grade bonds, but not for investment grade bonds.

The Weekend (or Monday) Effect 2. The Weekend Effect (or Monday Effect): French (1980) analyzed daily returns of U.S. stocks for the period 1953-1977 and found that there was a tendency for returns to be negative on Mondays whereas they were positive on the other days of the week. Agrawal and Tandon (1994) found significantly negative returns on Monday in nine countries and on Tuesday in eight countries, yet large and positive returns on Friday in 17 of the 18 countries studied. Steeley (2001) found that the weekend effect in the UK disappeared in the 1990s.

Seasonal Effects 3. Seasonal Effects: Holiday and turn of the month effects have been documented over time and across countries. Lakonishok and Smidt (1988) showed that U.S. stock returns were significantly higher at the turn of the month, defined as the last and first three trading days of the month. Ziemba (1991) found evidence of a turn of month effect for Japan when turn of month was defined as the last five and first two trading days of the month. Cadsby and Ratner (1992) provided evidence to show that returns were, on average, higher the day before a holiday, than on other trading days.

Small Firm Effects 4. Small Firm Effect: Banz (1981) published one of the earliest articles on the 'small-firm effect' which is also known as the 'size-effect'. His analysis of the 1936-1975 period in the U.S. revealed that excess returns would have been earned by holding stocks of low capitalization companies.

Over/Under Reaction Effect 5. Over/Under Reaction of Stock Prices to Earnings Announcements: DeBondt and Thaler (1985, 1987) presented evidence that is consistent with stock prices overreacting to current changes in earnings. They reported positive (negative) estimated abnormal stock returns for portfolios that previously generated inferior (superior) stock price and earning performance. This was construed as the prior period stock price behavior overreacting to earnings announcements.

Standard and Poor’s Effect 6. Standard & Poor’s (S&P) Index effect: Harris and Gurel (1986) and Shleifer (1986) found an increase in share prices (up to 3 percent) on the announcement of a stock's inclusion into the S&P 500 index. Since in an efficient market only new information should change prices, the positive stock price reaction appears to be contrary to the EMH because there is no new information about the firm other than its inclusion in the index.

Weather Effect 7. The Weather: Saunders (1993) showed that the New York Stock Exchange index tended to fall when it was cloudy. Hirshleifer and Shumway (2001) analyzed data for 26 countries from 1982-1997 and found that stock market returns were positively correlated with sunshine in almost all of the countries studied.

Human Behavior in Markets If we assume that markets are not totally rational (i.e., they don’t react as a rational expectations model would suggest), it might be possible to explain some of the anomaly findings on the basis of human and social psychology. John Maynard Keynes once described the stock market as a "casino" guided by "animal spirit" (1939). Shiller (2000) describes the rise in the U.S. stock market in the late 1990s as the result of “psychological contagion leading to irrational exuberance.”

Behavioral Finance and Asset Pricing Suggests that real people: Have limited information processing capabilities Exhibit systematic bias in processing information Are prone to making mistakes Tend to rely on the opinion of others (fads); referred to as a “bandwagon” effect.

Conclusions from EMH Tests The studies based on EMH have made an invaluable contribution to our understanding of financial market. The role of information (especially new information) in asset pricing. However, for some there seems to be growing discontentment with the theory’s “rational expectations” focus. However, for an excellent paper in support of the EMH read: “The Efficient Market Hypothesis and its Critics,” by Burton Malkiel, Princeton University, Working Paper #91, April 2003.

Appendix 3: Three Forms of Market Efficiency The following slide discusses the three forms of market efficiency

Three Forms of The Efficient Market Hypothesis There are actually three stages of the EMH model: Weak Form: Current prices reflect all past price and past volume information. The fundamental information contained in the past sequence of prices of a security is fully reflected in the current market price of that security. Semi-strong Form: Current prices reflect all past price and past volume information AND all publicly available information. Information such as interest rates, earnings, inflation, etc. Strong Form: Current prices reflect all past price and past volume information, all publicly available information publicly available information AND all private (e.g., insider) information.