Bull and Bear Markets Research Paper

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The origin of the terms bull and bear markets is unclear. Don Luskin (2001) cites an English book by a Thomas Mortimer, printed in 1785 (Every Man His Own Broker, or, A Guide to Exchange Alley), that identifies a “bull” with a trader who invests heavily in stocks on borrowed money in the hope of selling at a profit before the loan repayment date. In contrast, a “bear” was a short-seller, that is, someone who borrows shares and sells them in the present and perhaps lends out as the proceeds at interest because he expects the price of stocks to decline after which he can buy the securities cheaply and return them to the lender. In any case, the terms bull and bear markets are popularly used and understood to mean durations of successive large stock price increases and large stock price decreases, respectively. The implication is that there is duration dependence in stock prices—that is, once prices begin increasing in a bull market, they tend to continue increasing, whereas decreasing prices in a bear market tend to continue decreasing.

However, there has been disagreement amongst researchers as to whether bull and bear markets exhibiting such duration dependence even exist. It is known that even if price changes are independent, they can after the fact seem to exhibit bull and bear phases; theories based on this idea hold that bull and bear markets are simply the result of after the fact categorization of stock market data. Other theories hold that bull and bear markets do exhibit predictability. And even if there is predictability in prices, they can be of two kinds—rational and irrational. Irrational cycles might be fueled by fads that ignite an increase in stock purchases and then die out, leading to mean reversion in prices. Rational cycles might exhibit bubble-like characteristics—although they might satisfy no-arbitrage conditions, they might still be influenced by nonfundamental factors.

One example of stock price predictability is an economy where, for some reason, a bubble develops, that is, asset prices differ from the present value of all future expected dividends. Prices each period are, nevertheless, equal to the sum of the present values of next period’s expected dividend and next period’s expected asset price. Each period, the economy finds itself in one of two regimes—one where the bubble persists and another where the bubble bursts; the greater the size of the bubble, the greater the probability of the bubble bursting. Simon van Norden and Huntley Schaller (2002) use U.S. stock market returns to test the (irrational) fads hypothesis set in a regime-switching model against a (rational) bubble alternative. They find in favor of the bubble alternative. However, the power of such tests depends strongly on the auxiliary assumptions used, as well as the alternative hypothesis specified. As a result, it is very difficult to reject the “irrationality” hypothesis entirely. For example, Jerry Coakley and Ana-Maria Fuertes (2006) find that market sentiment does play an important transitory role.

There is also a parallel literature that looks at mean reversion in stock prices and the existence of momentum effects in the context of investment strategies (e.g, Jegadeesh and Titman 1993). This literature finds negative autocorrelation in stock prices at short intervals and positive autocorrelation at longer intervals. In any case, there is now a solid body of work that seeks to use quantifiable rules to document and measure the bull and bear markets. There are two widely used algorithms—one by Gerhard Bry and Charlotte Boschan (1971) that mimics the qualitative rules used by the National Bureau of Economic Research to decide upon turning points of business cycles, and another that uses a Markov regime switching model (Maheu and McCurdy 2000).

Asset prices are used as signals by economic agents in several ways. First, because asset prices are considered to be aggregators of information and generally forward-looking, higher asset prices in a given sector are interpreted as greater growth potential in that sector, or concomitantly as a reduction in the cost of capital; this then allows investment to go where there is the greatest potential. Second, they are used as measures of value in various other contexts, such as in executive compensation. Third, asset prices, particularly real estate values, are also used by individuals as measures of wealth to help plan consumption. Finally, the Federal Reserve also uses asset prices to set monetary policy. When asset prices are divorced from true value, all these uses are affected. This is also true when there is a lot of asset price volatility, because the signal to noise ratio drops.

The integrity of the financial infrastructure can also be affected if there is an unexpected swing in asset prices, particularly downward, such as in October 1987, when the U.S. stock market dropped 23 percent in one day. The payments system could be affected, as well as the mechanisms for settling trades in securities markets. Also, because bank loans are often tied to property and stock market values, swings in asset prices are related to swings in lending and hence to swings in consumption and investment. These effects vary across countries. In financial systems characterized by a greater degree of arm’slength transactions, households are more sensitive to asset prices because market forces are used more than customary relationships for borrowing and investment purposes. Of course, such systems are overall more resilient and able to adjust to changes in growth opportunities. The empirical validity of asset market price swings causing swings in real activity is difficult to establish because any correlation in asset prices and real activity might be due simply to the reflection of future real activity in forward-looking asset prices.

There is also research that shows the generally negative effect of fluctuations in economic activity, that is to say business cycles on growth and human welfare. However, Matthew Rafferty (2005) shows that although unexpected volatility is related to lower growth, expected volatility is related to higher growth. Similarly, Jaume Ventura (2004) suggests that asset price bubbles could moderate the effect of financial market frictions and improve the allocation of investment across countries.

There has been more work recently on the globalization of business cycles and asset price cycles. This is related to the issue of correlation between different geographical asset markets, because lower correlation implies greater value for international portfolio diversification. François Longin and Bruno Solnik (2001) find that correlation between international equity markets has increased in bear markets, but not in bull markets. Javier Gómez Biscarri and Fernando Pérez de Gracia (2002) find that European stock markets seem to have become more concordant over time, as would be expected from the continuing integration of European financial markets. Furthermore, Christian Dunis and Gary Shannon (2005) find that, at least for the United Kingdom and the United States, there are still benefits from diversifying over emerging economy stock markets, ranging from Indonesia, Malaysia, and the Philippines to Korea, Taiwan, China, and India. However, the extent of such benefits may drop off with the continuing integration of India and China into world security and product markets.

Bibliography:

  1. Biscarri, Javier Gómez, and Fernando Pérez de Gracia. 2002. Bulls and Bears: Lessons from Some European Countries. Working Paper, University of Navarra.
  2. Bry, Gerhard, and Charlotte Boschan. 1971. Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. New York: National Bureau of Economic Research.
  3. Coakley, Jerry, and Ana-Maria Fuertes. 2006. Valuation Ratios and Price Deviations from Fundamentals. Journal of Banking and Finance 30 (8): 2325.
  4. Dunis, Christian L., and Gary Shannon. 2005. Emerging Markets of South-East and Central Asia: Do They Still Offer a Diversification Benefit? Journal of Asset Management 6 (3): 168–190.
  5. International Monetary Fund. 2006. World Economic Outlook: Financial Systems and Economic Cycles. Ch. 4. September 14.
  6. Jegadeesh, Narasimhan, and Sheridan Titman. 1993. Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance 48: 65–91.
  7. Longin, François, and Bruno Solnik. 2001. Extreme Correlation of International Equity Markets. Journal of Finance 56 (2): 649–676.
  8. Luskin, Don. 2001. The History of “Bull” and “Bear.” http://www.thestreet.com/comment/openbook/1428176.ht ml.
  9. Maheu, John M. and Thomas H. McCurdy. 2000. Identifying Bull and Bear Markets in Stock Returns. Journal of Business and Economic Statistics 18 (1): 100–112.
  10. Rafferty, Matthew. 2005. The Effects of Expected and Unexpected Volatility on Long-Run Growth: Evidence from 18 Developed Economies. Southern Economic Journal 71 (3): 582–591.
  11. Van Norden, Simon, and Huntley Schaller. 2002. Fads or Bubbles? Empirical Economics 27 (2): 335–362.
  12. Ventura, Jaume. 2004. Bubbles and Capital Flows. Working Paper, Centre de Recerca en Economia Internacional, Barcelona.

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