We evaluate the out-of-sample performance of the sample-based mean-variance model, and its extensions designed to reduce estimation error, relative to the naive 1/N portfolio. Of the 14 models we ...evaluate across seven empirical datasets, none is consistently better than the 1/N rule in terms of Sharpe ratio, certainty-equivalent return, or turnover, which indicates that, out of sample, the gain from optimal diversification is more than offset by estimation error. Based on parameters calibrated to the US equity market, our analytical results and simulations show that the estimation window needed for the sample-based mean-variance strategy and its extensions to outperform the 1/N benchmark is around 3000 months for a portfolio with 25 assets and about 6000 months for a portfolio with 50 assets. This suggests that there are still many "miles to go" before the gains promised by optimal portfolio choice can actually be realized out of sample.
The modern portfolio theory pioneered by
Markowitz (1952) is widely used in practice and extensively taught to MBAs. However, the estimated Markowitz portfolio rule and most of its extensions not ...only underperform the naive 1/
N rule (that invests equally across
N assets) in simulations, but also lose money on a risk-adjusted basis in many real data sets. In this paper, we propose an optimal combination of the naive 1/
N rule with one of the four sophisticated strategies—the Markowitz rule, the
Jorion (1986) rule, the
MacKinlay and Pástor (2000) rule, and the
Kan and Zhou (2007) rule—as a way to improve performance. We find that the combined rules not only have a significant impact in improving the sophisticated strategies, but also outperform the 1/
N rule in most scenarios. Since the combinations are theory-based, our study may be interpreted as reaffirming the usefulness of the Markowitz theory in practice.
This article introduces the large portfolio selection using gross-exposure constraints. It shows that with gross-exposure constraints, the empirically selected optimal portfolios based on estimated ...covariance matrices have similar performance to the theoretical optimal ones and there is no error accumulation effect from estimation of vast covariance matrices. This gives theoretical justification to the empirical results by Jagannathan and Ma. It also shows that the no-short-sale portfolio can be improved by allowing some short positions. The applications to portfolio selection, tracking, and improvements are also addressed. The utility of our new approach is illustrated by simulation and empirical studies on the 100 Fama-French industrial portfolios and the 600 stocks randomly selected from Russell 3000.
We test the relevance of technical and fundamental variables in forming currency portfolios. Carry, momentum, and value reversal all contribute to portfolio performance, whereas the real exchange ...rate and the current account do not. The resulting optimal portfolio produces out-of-sample returns that are not explained by risk and are valuable to diversified investors holding stocks and bonds. Exposure to currencies increases the Sharpe ratio of diversified portfolios by 0.5 on average, while reducing crash risk. We argue that besides risk, currency returns reflect the scarcity of speculative capital.
Using a comprehensive set of 103 equity strategies, we analyze the value of volatility-managed portfolios for real-time investors. Volatility-managed portfolios do not systematically outperform their ...corresponding unmanaged portfolios in direct comparisons. Consistent with Moreira and Muir (2017), volatility-managed portfolios tend to exhibit significantly positive alphas in spanning regressions. However, the trading strategies implied by these regressions are not implementable in real time, and reasonable out-of-sample versions generally earn lower certainty equivalent returns and Sharpe ratios than do simple investments in the original, unmanaged portfolios. This poor out-of-sample performance for volatility-managed portfolios stems primarily from structural instability in the underlying spanning regressions.
Cover's celebrated theorem states that the long‐run yield of a properly chosen “universal” portfolio is almost as good as that of the best retrospectively chosen constant rebalanced portfolio. The ...“universality” refers to the fact that this result is model‐free, that is, not dependent on an underlying stochastic process. We extend Cover's theorem to the setting of stochastic portfolio theory: the market portfolio is taken as the numéraire, and the rebalancing rule need not be constant anymore but may depend on the current state of the stock market. By fixing a stochastic model of the stock market this model‐free result is complemented by a comparison with the numéraire portfolio. Roughly speaking, under appropriate assumptions the asymptotic growth rate coincides for the three approaches mentioned in the title of this paper. We present results in both discrete and continuous time.
We test the relation between ambiguity aversion and five household portfolio choice puzzles: nonparticipation in equities, low allocations to equity, home-bias, own-company stock ownership, and ...portfolio under-diversification. In a representative US household survey, we measure ambiguity preferences using custom-designed questions based on Ellsberg urns. As theory predicts, ambiguity aversion is negatively associated with stock market participation, the fraction of financial assets in stocks, and foreign stock ownership, but it is positively related to own-company stock ownership. Conditional on stock ownership, ambiguity aversion is related to portfolio under-diversification, and during the financial crisis, ambiguity-averse respondents were more likely to sell stocks.
Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We ...model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.
Volatility-Managed Portfolios MOREIRA, ALAN; MUIR, TYLER
The Journal of finance (New York),
August 2017, Volume:
72, Issue:
4
Journal Article
Peer reviewed
Open access
Managed portfolios that take less risk when volatility is high produce large alphas, increase Sharpe ratios, and produce large utility gains for mean-variance investors. We document this for the ...market, value, momentum, profitability, return on equity, investment, and betting-against-beta factors, as well as the currency carry trade. Volatility timing increases Sharpe ratios because changes in volatility are not offset by proportional changes in expected returns. Our strategy is contrary to conventional wisdom because it takes relatively less risk in recessions. This rules out typical risk-based explanations and is a challenge to structural models of time-varying expected returns.
Genetic Variation in Financial Decision-Making CESARINI, DAVID; JOHANNESSON, MAGNUS; LICHTENSTEIN, PAUL ...
The Journal of finance (New York),
October 2010, Volume:
65, Issue:
5
Journal Article
Peer reviewed
Individuals differ in how they construct their investment portfolios, yet empirical models of portfolio risk typically account only for a small portion of the cross-sectional variance. This paper ...asks whether genetic variation can explain some of these individual differences. Following a major pension reform Swedish adults had to form a portfolio from a large menu of funds. We match data on these investment decisions with the Swedish Twin Registry and find that approximately 25% of individual variation in portfolio risk is due to genetic variation. We also find that these results extend to several other aspects of financial decision-making.
PDF
