The Avoidable Costs of Index Rebalancing
Robert D. Arnott (Research Affiliates), et al.
Traditional capitalization-weighted indices generally add stocks with high valuation multiples after persistent outperformance and sell stocks at low valuation multiples after persistent underperformance. For the S&P 500 Index, in the year after a change in the index, additions lose relative to discretionary deletions by about 22%. Simple rules, such as trading ahead of index funds or delaying reconstitution trades by 3 to 12 months, can add up to 23 basis points (bps). This benefit doubles when we cap-weight a portfolio not selected on market value, but based on the fundamental size of a business or its multi-year average market cap.
A New Perspective on the Corporate Bond Liquidity Factor
Fabian Dienemann (PIMCO)
This study documents properties of market-wide corporate bond liquidity and demonstrates that liquidity risk is an important determinant of returns. During market downturns, transaction costs rise for sellers and fall for buyers. The negative relation between buyer and seller liquidity motivates a new across-measure liquidity factor that incorporates an asymmetric liquidity component. Shocks to market-wide liquidity explain a large fraction of bond return variation in the time series. Primarily driven by the asymmetric component, the liquidity factor attracts a cross-sectional risk premium that is robust to controls for credit, equity, and interest rate factors as well as the illiquidity level.
Illiquidity premiums in international corporate bond markets
Delong Li (University of Guelph), et al.
This article examines the impact of illiquidity levels on corporate bond pricing with a novel international dataset, including both advanced and emerging economies. Results show that less liquid corporate bonds which possess wider bid-ask spreads display higher expected returns and credit spreads globally. However, after controlling for common risk factors, illiquidity premiums remain significant exclusively in emerging markets, where investing in less liquid corporate bonds can generate sizable abnormal returns both before and after transaction costs. We further show that money-market liquidity, capital-account openness, and financial stability can contribute to cross-country differences in illiquidity premiums.
Robust Portfolio Choice With Frictions
Dantong Chu (Chinese University of Hong Kong), et al.
This paper studies a robust portfolio choice problem with return predictability and price impacts in continuous time. Asset returns are modeled by some stochastic factors and trades incur both transient and permanent price impacts. Assuming ambiguity aversions toward asset returns and return-predicting factors, an investor aims to maximize the accumulated local mean-variance preference on his investment returns, netting costs due to price impacts. We characterize the robust solution of the investor’s portfolio choice problem in terms of the solution to a coupled Riccati differential system, and derive sufficient conditions, in terms of the model parameters only, to address the well-posedness of the coupled system. Compared with the non-robust case, the ambiguity-averse investor adopts a more conservative optimal strategy where he puts fewer weights on assets with more volatile factors. While the permanent price impact of his trades induces the investor to trade more aggressively, his aggressiveness is constrained by the magnitude of his ambiguity aversion. Finally, our simulations indicate that the investor’s robust trading strategy outperforms his non-robust counterpart, with the performance measured by the Sharpe ratio of daily returns netting the execution costs.
Machine Learning and the Implementable Efficient Frontier
Bryan T. Kelly (Yale U. and AQR Capital Management)
We propose that investment strategies should be evaluated based on their net-of-trading-cost return for each level of risk, which we term the “implementable efficient frontier.” While numerous studies use machine learning return forecasts to generate portfolios, their agnosticism toward trading costs leads to excessive reliance on fleeting small-scale characteristics, resulting in poor net returns. We develop a framework that produces a superior frontier by integrating trading-cost-aware portfolio optimization with machine learning. The superior net-of-cost performance is achieved by learning directly about portfolio weights using an economic objective. Further, our model gives rise to a new measure of “economic feature importance.”
The Failure of Market Efficiency
William J. Magnuson (Texas A&M University School of Law)
Recent years have witnessed the near total triumph of market efficiency as a regulatory goal. Policymakers regularly proclaim their devotion to ensuring efficient capital markets. Courts use market efficiency as a guiding light for crafting legal doctrine. And scholars have explored in great depth the mechanisms of market efficiency and the role of law in promoting it. There is strong evidence that, at least on some metrics, our capital markets are indeed more efficient than they have ever been. But the pursuit of efficiency has come at a cost. By focusing our attention narrowly on economic efficiency concerns—such as competition, friction, and transaction costs—we have lost sight of other, deeper values within our economic system, including wider conceptions of duty, fairness, and morality. And while regulators sometimes pay lip service to these values, they often treat them as merely a subset of efficiency: the best way to treat investors fairly, to promote equality, and to prevent immoral, exploitative behavior, in this view, is simply to create an efficient market. We have seen the consequences of this emphasis play out in spectacular fashion in the last decade. New market structures and technologies, from special purpose acquisition companies to social-media oriented trading apps to cryptocurrencies, have emerged to eliminate barriers to trade and compete with institutional incumbents. These strategies may well lead to more efficient markets in so much as they facilitate access to capital, but they also have the side effect of placing unsophisticated regular citizens into complex contractual arrangements with sophisticated market actors. The result is an “efficient” market, but one with steep moral and social costs. This Article examines the limits of market efficiency as a regulatory goal and suggests a set of structural and substantive reforms aimed at better balancing efficiency with the other goals of markets. It concludes that regulators, courts and scholars alike need to adopt a more comprehensive understanding of the proper ends of market regulation, one that emphasizes the purpose and spirit of finance over the false promise of efficiency.
Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno