Return Smoothing, Liquidity Costs, and Investor Flows: Evidence from a Separate Account Platform2017
We use a new dataset of hedge fund returns from a separate account platform to examine (1) how much of hedge fund return smoothing is due to main-fund specific factors, such as managerial reporting discretion (2) the costs of removing hedge fund share restrictions. These accounts trade pari passu with matching hedge funds but feature third-party reporting and permissive share restrictions. We use these properties to estimate that 33% of reported smoothing is due to managerial reporting methods. The platform's fund-level liquidity is associated with costs of 1.7% annually. Investor flows chase monthly past performance on the platform but not in the associated funds.
Q Group Panel Discussion: Looking to the Future2016
Moderator Martin Leibowitz asked a panel of industry experts—Andrew W. Lo, Robert C. Merton, Stephen A. Ross, and Jeremy Siegel—what they saw as the most important issues in finance, especially as those issues relate to practitioners. Drawing on their vast knowledge, these panelists addressed topics such as regulation, technology, and financing society’s challenges; opacity and trust; the social value of finance; and future expected returns.
Spectral Portfolio Theory2016
Economic shocks can have diverse effects on financial market dynamics at different time horizons, yet traditional portfolio management tools do not distinguish between short- and long-term components in alpha, beta, and covariance estimators. In this paper, we apply spectral analysis techniques to quantify stock-return dynamics across multiple time horizons.Using the Fourier transform, we decompose asset-return variances, correlations, alphas, and betas into distinct frequency components. These decompositions allow us to identify the relative importance of specific time horizons in determining each of these quantities, as well as to construct mean-variance-frequency optimal portfolios. Our approach can be applied to any portfolio, and is particularly useful for comparing the forecast power of multiple investment strategies. We provide several numerical and empirical examples to illustrate the practical relevance of these techniques.
What Is An Index?2016
Technological advances in telecommunications, securities exchanges, and algorithmic trading have facilitated a host of new investment products that resemble theme-based passive indexes but which depart from traditional market-cap-weighted portfolios. I propose broadening the definition of an index using a functional perspective—any portfolio strategy that satisfies three properties should be considered an index: (1) it is completely transparent; (2) it is investable; and (3) it is systematic, i.e., it is entirely rules-based and contains no judgment or unique investment skill. Portfolios satisfying these properties that are not market-cap-weighted are given a new name: “dynamic indexes.” This functional definition widens the universe of possibilities and, most importantly, decouples risk management from alpha generation. Passive strategies can and should be actively risk managed, and I provide a simple example of how this can be achieved. Dynamic indexes also create new challenges of which the most significant is backtest bias, and I conclude with a proposal for managing this risk.
Pioneered by the Nobel Prize–winning economist Harry Markowitz over half a century ago, portfolio theory is one of the oldest branches of modern financial economics. It addresses the fundamental question faced by an investor: how should money best be allocated across a number of possible investment choices? That is, what collection or portfolio of financial assets should be chosen? In this article, we describe the fundamentals of portfolio theory and methods for its practical implementation. We focus on a fixed time horizon for investment, which we generally take to be a year, but the period may be as short as days or as long as several years. We summarize many important innovations over the past several decades, including techniques for better understanding how financial prices behave, robust methods for estimating input parameters, Bayesian methods, and resampling techniques.
Spectral Analysis of Stock-Return Volatility, Correlation, and Beta2015
We apply spectral techniques to analyze the volatility and correlation of U.S. common-stock returns across multiple time horizons at the aggregate-market and individual-firm level. Using the cross-periodogram to construct frequency bandlimited measures of variance, correlation and beta, we find that volatilities and correlations change not only in magnitude over time, but also in frequency. Factors that may be responsible for these trends are proposed and their implications for portfolio construction are explored.
Reply to “(Im)Possible Frontiers: A Comment”2015
In Brennan and Lo (2010), a mean-variance efficient frontier is defined as “impossible” if every portfolio on that frontier has negative weights, which is incompatible with the Capital Asset Pricing Model (CAPM) requirement that the market portfolio is mean-variance efficient. We prove that as the number of assets n grows, the probability that a randomly chosen frontier is impossible tends to one at a geometric rate, implying that the set of parameters for which the CAPM holds is extremely rare. Levy and Roll (2014) argue that while such “possible”frontiers are rare, they are ubiquitous. In this reply, we show that this is not the case; possible frontiers are not only rare,but they occupy an isolated region of mean-variance parameter space that becomes increasingly remote as n increases. Ingersoll (2014) observes that parameter restrictions can rule out impossible frontiers, but in many cases these restrictions contradict empirical fact and must be questioned rather than blindly imposed.
Hedge Funds: A Dynamic Industry In Transition2015
The hedge-fund industry has grown rapidly over the past two decades, offering investors unique investment opportunities that often reflect more complex risk exposures than those of traditional investments. In this article, we present a selective review of the recent academic literature on hedge funds as well as updated empirical results for this industry. Our review is written from several distinct perspectives: the investor’s, the portfolio manager’s, the regulator’s, and the academic’s. Each of these perspectives offers a different set of insights into the financial system, and the combination provides surprisingly rich implications for the Efficient Markets Hypothesis, investment management, systemic risk, financial regulation, and other aspects of financial theory and practice.
The non-trading or non-synchronous effect arises when time series, usually financial asset prices, are taken to be recorded at time intervals of one length when in fact they are recorded at time intervals of another, possibly irregular, lengths. For example, the daily prices of securities quoted in the financial press are usually "closing" prices, prices at which the last transaction in each of those securities occurred on the previous business day. these closing prices generally do not occur at the same time each day, but by calling them "daily" prices, we have implicitly and incorrectly assumes that they are equally spaces at 24-hour intervals. Such an assumption can generate spurious predictability in price changes and returns even if true price changes or returns are statistically independent. The non-trading effect induces potentially serious biases in the moments and co-moments of asset returns such as their means, variances, covariances, and autocorrelation and cross-autocorrelation coefficients.