The paper "Home Country-Bias, Investment Strategy GMM " is a perfect example of a finance and accounting assignment. Geometric mean maximization is the portfolio selection criterion that consists of maximization of invested capital growth and maximizing terminal wealth. As gambling strategy GMM criterion originated with Kelly (1956) in his analysis of a gambler’ s optimal strategy of betting with private information. Kelly considered a gambler with noisy or (without certain) private information. Such a gambler made numerous bets with cumulative effects, that is, the gambler reinvested his losses and gains and each round he betted a constant proportion of his capital.
The question that gave rise to GMM criterion was how much the gambler was supposed to bet is his aim was to maximize his expected terminal wealth? As an investment strategy GMM criterion originated with Latane (1959) who sought to determine how an individual making rational choices under uncertainty would maximize his expected terminal wealth. The connection between GMM criterion and the expected utility maximization arises when the underlying function of utility is logarithmic which means that the utility function must bear the property of showing decreasing risk aversion features. The first argument for the GMM criterion is that it has the highest probability of leading to more wealth at the end of a large number of cumulative and uncertain decisions.
The argument against GMM criterion was that maximizing geometric mean return is not the same as maximizing expected utility. One of the empirical literature on GMM criterion is that it is statistically indistinguishable from the portfolio in the market. The other empirical literature on GMM criterion is that just like the SRM criterion GMM criterion does not produce a highly diversified portfolio. Question two Hong and Stein (199) identify the following criteria for an acceptable behavioural model of asset prices.
First, the behavioural model of asset prices should rest on the assumption about the behaviour of the investor that is consistent or priori plausible with casual observation. Second, it should provide an explanation of the existing evidence in a unified and parsimonious way. Third, it should make a number of further predictions which can be ultimately validated and tested. “ Boundedly rational” means that each of the two agents “ news-watchers” and the “ momentum traders” are only able to gather or process some segment of public information available to them. The “ news-watchers” make their forecasts based on private observable signals about the fundamentals of the future.
Momentum traders condition themselves on changes in past prices. “ Momentum traders” use the information on changes in prices in the past while the news-watchers use the information on future fundamentals. The other model assumption made by Hong and Stein (1999) is that overreaction and underreaction are unified in the sense that the existence of underreaction sows the seeds for overreaction by making it profitable for momentum traders to enter the market. When only “ news-watchers” are active prices never overshoot their long-run value which means that at nay horizon there is never any negative serial correlation in returns. When both agents are active, prices respond positively to new information by shooting up because the news watchers buy more aggressively knowing that new information would bring in a new series of momentum traders. The phenomenon is explained by considering two different proxies for the rate of diffusion of information, that is, by considering the size of the firm and second with respect to residual coverage by the analyst.
Kelly, John (1956). “A New Interpretation of Information Rate.” Bell System Technical Journal,35, 917-926.
Latane, Henry (1959). “Criteria for Choice among Risky Ventures.” Journal of Political
Economy, 67, 144-155.
Hong, H., and Stein, J. (1997). A unified theory of underreaction, momentum trading and overreaction in asset markets, NBER Working paper #6324.
Fama E, French K. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance 47:427-465.
Coval, J. & Moskowitz, T. (1998). The geography of investment: informed trading and asset prices, CRSP working paper. Chicago: University of Chicago.
Lee, M., & Swaminathan, J. (1999). What is the intrinsic value of the Dow? Journal of Finance 54, 1693–1741.
Chan, K. and Lakonishok, J. (2004). Value and growth investing: Review and update. Financial analysis journal, 71-84.
Solnik, H. (1974). Why not diversify internationally rather than domestically? Financial analyst journal, vol. 30. 48-52.
Cavaglia, J., Brightman, C and Aked, M. (2000). The Increasing Importance of Industry Factors, Financial analyst journal, vol. 56, p. 41-54.