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[This chapter constructs aggregate stock market portfolios that outperform general market indexes in out-of-sample tests. Given that most mutual funds cannot consistently outperform general market indexes over time, the performance of our aggregate portfolios is impressive. To achieve these results, we apply the ZCAPM to develop weights for individual stocks in well-diversified portfolios. The analyses employ U.S. stock returns from 1965 to 2018. As in previous chapters, we estimate the empirical ZCAPM using one year of daily returns. Zeta risk coefficient estimates from the ZCAPM are employed to create 24 stock portfolios with different zeta risk levels. One-month-ahead (out-of-sample) returns are computed for these portfolios. Next, the 24 portfolios are formed into 12 long/short portfolios with increasing levels of zeta risk. These portfolios’ average returns are shown to increase linearly with both zeta risk and total risk. Lastly, we add the long/short zeta risk portfolios to the CRSP index to form aggregate portfolios with different total risk levels. Average one-month-ahead returns in our sample period and their standard deviation of returns are plotted to trace out the shape of a mean-variance parabola. Further analyses utilize the empirical ZCAPM to estimate a proxy for the minimum variance portfolio (denoted g). When this portfolio is added to the long/short zeta risk portfolios, a more efficient frontier is obtained with higher returns per unit total risk than when using the CRSP index. Additionally, long only aggregate portfolios are formed using either the CRSP index or portfolio g in combination with long zeta risk portfolios. In general, our results show that superior aggregate portfolios with higher Sharpe ratios than the CRSP index can be created using the empirical ZCAPM. Institutional investors could utilize these procedures and adaptations thereof to create diversified portfolios with relatively high average returns per unit total risk for individual, business, and government clients.]
Published: Mar 2, 2021
Keywords: Aggregate portfolios; Asset pricing; Beta risk; CAPM; Diversified portfolios; Efficient portfolios; Empirical ZCAPM; Expectation–maximization (EM) algorithm; Fama and French; Global minimum variance portfolio; Investment parabola; Long only portfolios; Long/short portfolios; Markowitz; Multifactor models; Out-of-sample returns; Return dispersion; Securities investment; Signal variable; Stock market; Test assets; ZCAPM; Zero-beta CAPM; Zero-investment portfolios; Zeta risk
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