We investigate the long-term (30-year) efficient frontier weights in five common asset class indexes by adding classes one-by-one to the stock-bond frontier. Our results show that bonds are the most effective diversifier for stocks, real estate is helpful only at higher risk levels, and international stocks and commodities add little diversification benefits over the longer time horizon. Overall, our results highlight the difficulties using modern portfolio theory to quantify asset class allocations. The efficient frontier (the highest expected return for a given standard deviation), holds a sacred place in academic finance and has driven much of the world’s movement towards increased diversification. Further, diversification across broad asset classes (stocks, bonds, cash, commodities and real estate for example) is commonly believed to account for the vast majority of long-term portfolio returns and picking the right assets within asset classes for only a smaller proportion. Given the means, standard deviation and covariance’s of the asset classes, mathematical programming can solve for the weights in the asset classes for different levels of risk tolerance (portfolio standard deviation) so the portfolio is “optimized” (has the highest return/risk profile). A central issue for this research is that asset class weights calculated using the efficient frontier do not significantly differ depending on the combination of asset classes chosen to estimate the inputs (asset class mean return, standard deviation, and covariance). Many efficient portfolios have zero weights in most asset classes and huge weights in others, seemingly contradictory for a theory that touts diversification. Variability in asset class weights is well documented in a vast academic literature, but much of the published research in this area considers only stock diversification or stock/bond diversification issues, not the more practical problem of portfolio weights across a broader, commonly-employed spectrum of asset classes. The emphasis for this study is to document the contribution that commonly employed asset classes make to the standard stock/bond efficient frontier.

Hedge funds claim higher returns with lower risk and low correlation with the U.S. stock market, albeit at a higher cost than alternative investments. This paper examines the corresponding properties of mutual funds that employ hedge fund type strategies. Results show that mutual funds in the Morningstar long-short category, compared to a matched mutual fund samples, have slightly lower total returns but higher risk-adjusted performance measures based on lower risk statistics. Lower beta and R2 for the long-short funds highlight their main benefit as a portfolio diversifier. Expense ratio and turnover costs are higher for long-short funds.

Mutual fund performance persistence has been well documented in the finance literature. This study extends the previous work by looking at a longer time period of returns over annual intervals, rather than longer time series groupings. The results document that persistence varies significantly through time, especially when the stock market turns. Clear long-term investing and trading rules based on persistence are not supported by the paper's conclusions.

This article presents tools to improve student learning in financial analysis beyond what is achieved through traditional textbook methods. The two-pronged approach includes real-world financial data in electronic form and integrative student assignments. The three most popular electronic academic financial databases (EDGAR, Disclosure, and Compustat) are described, with emphasis on hands-on classroom assignments that make financial analysis relevant to students. We also include financial analysis modules designed to synthesize material from a variety of business disciplines. These include a key ratio comparison module, a growth rate calculation module, a quality cash flows module, and a module for developing a sample of companies based on annual report text.