Research Review | 30 March 2018 | Portfolio Analysis

Factor Momentum
Robert D. Arnott (Research Affiliates), et al.
January 31, 2018
Past industry returns predict the cross section of industry returns, and this predictability is at its strongest at the one-month horizon (Moskowitz and Grinblatt 1999). We show that the cross section of factor returns shares this property, and that industry momentum stems from factor momentum. Factor momentum is transmitted into the cross section of industry returns via variation in industries’ factor loadings. Momentum in industry-neutral factors spans industry momentum; factor momentum is therefore not a by-product of industry momentum. Factor momentum is a pervasive property of all factors; we show that factor momentum can be captured by trading almost any set of factors. Factor momentum does not resolve the puzzle of momentum in individual stock returns; it significantly deepens this puzzle.

The Conservative Formula: Quantitative Investing Made Easy
Pim van Vliet and David Blitz (Robeco Asset Management)
March 21, 2018
We propose a conservative investment formula which selects 100 stocks based on three criteria: low return volatility, high net payout yield, and strong price momentum. We show that this simple formula gives investors full and efficient exposure to the most important factor premiums, and thus effectively summarizes half a century of empirical asset pricing research into one easy to implement investment strategy. With a compounded annual return of 15.1 percent since 1929, the conservative formula outperforms the market by a wide margin. It reduces downside risk and shows a positive return over every decade. The formula is also strong in European, Japanese and Emerging stock markets, and beats a wide range of other strategies based on size, value, quality, and momentum combinations. The formula is designed to be a practically useful tool for a broad range of investors and addresses academic concerns about ‘p-hacking’ by using three simple criteria, which do not even require accounting data.

Understanding Alternative Risk Premia
Ing-Chea Ang (AQR Capital Management), et al.
March 13, 2018
While equities and bonds are often viewed as reliable sources of long-term returns, many investors are perhaps overly-dependent on them. As such, we believe that investors can benefit by diversifying to other sources of return – such as a market-neutral Alternative Risk Premia (ARP) strategy. In our paper, we illustrate that harvesting six well-known long/short risk premia – Value, Momentum, Carry, Defensive, Trend and Volatility – across multiple liquid asset groups may have the potential to deliver attractive risk-adjusted returns. This is because correlations across risk premia and asset groups tend to be low, resulting in strong diversification benefits when combined in a single portfolio.

Testing Moving Average Trading Strategies on ETFs
Jing-Zhi Huang (Penn. State U.) and Zhijian Huang (Rochester Inst. of Tech.)
March 12, 2018
This paper tests the technical trading rule of moving average (MA) in a long-only portfolio using exchange traded funds (ETFs). We also propose a quasi-intraday version of the MA strategy (QUIMA) that allows investors to trade immediately upon observing MA crossover signals. We find that 1) this QUIMA strategy outperforms the traditional version of the MA strategy that only trades at the close of a trading day, when the long-term MA lag length is not too long, 2) the documented profitability of MA strategy on indices is greatly reduced on ETFs, mainly due to more frequent and larger opening gaps on ETF prices than those on indices, and 3) relative to the buy-and-hold strategy, MA strategies have lower return, but better risk-adjusted performance measures such as the CAPM alpha. In addition, we find that among various long-term MA lengths, the 10-day MA turns out to be overly exploited by investors as its performance is significantly lower than those of surrounding MA lengths. Overall, our findings indicate that profitability of the MA trading rule reduces on tradable ETFs than on non-tradable indices.

Does Mean-CVaR Outperform Mean-Variance? A Practical Perspective
Linh Nguyen (De Montfort University), et al.
October 19, 2017
We examine the relative performance between the two popular portfolio optimisation methods: mean-variance and mean-Conditional Value-at-Risk (CVaR). Using portfolios representing the whole US stock market, we confirm the theoretical outperformance of mean-CVaR in the frictionless market. However, we show that the superiority of mean-CVaR weakens in practical investment context when simple historical sample inputs and transaction costs are incorporated. We further find evidence that the relative performance between the two optimisation methods is significantly influenced by the characteristics of the constituent stocks in the portfolio and the stock market condition. These findings have important implications in shaping investment decisions.

Screening Rules and Portfolio Performance
Angel Leon (Universidad de Alicante), et al.
March 5, 2018
We analyze the use of alternative performance measures to rank and select assets. Previous literature centers on the effects of non-normality on rank correlations between orderings. Instead, we select the assets recommended by each performance measure (ordering) and analyze out-of-sample returns of the portfolio that contains them. The overall empirical findings show that performance measures are definitively relevant for subsequent portfolio returns. Assets selected by the Generalized Rachev ratio dominate other selections showing high cumulative returns after the 2008 downturn. The good performance is connected to the fact that these asset returns show high excess kurtosis but positive skewness and are insensitive to the momentum risk factor.

Momentum and Crash Sensitivity
Stefan Ruenzi (U. of Mannheim) and Florian Weigert (U. of St. Gallen)
December 23, 2017
This paper proposes a risk-based explanation of the momentum anomaly on equity markets. Regressing the momentum strategy return on the return of a self-financing portfolio going long (short) in stocks with high (low) crash sensitivity in the USA from 1963 to 2012 reduces the momentum effect from a highly statistically significant 11.94% to an insignificant 1.84%. We find additional supportive out-of sample evidence for our risk-based momentum explanation in a sample of 23 international equity markets.

Hedging Risk Factors
Bernard Herskovic (University of California, Los Angeles), et al.
February 8, 2018
Standard risk factors can be hedged with minimal reduction in average return. This is true for “macro” factors such as industrial production, unemployment, and credit spreads, as well as for “reduced form” asset pricing factors such as value, momentum, or profitability. Low beta versions of the factors perform close to as well as high beta versions, hence a long short portfolio can hedge factor exposure with little reduction in expected return. For the reduced form factors this mismatch between factor exposure and expected return generates large alphas. For the macroeconomic factors, hedging the factors also hedges business cycle risk by significantly lowering exposure to consumption, GDP, and NBER recessions. We study implications both for optimal portfolio formation and for understanding the economic mechanisms for generating equity risk premiums.

Investment Horizons, Systematic Risk, and Managerial Skills of Institutional Investors
Jiun-Lin Chen (University of Adelaide), et al.
December 8, 2017
We examine the relationship between portfolio risk and equity returns over different investment horizons of institutional investors. Compared to long-term institutions, portfolios held by short-term institutions exhibit higher factor loadings in market, size, and momentum. In particular, they tend to hold smaller stocks and momentum stocks in the bull market. They also tend to trade for stocks with lower profitability in the bull market. Nevertheless, we find that systematic risk can account for the overall superior performance in the buy and sell portfolios of short-term institutions. Our results suggest that while short-term institutions exhibit market timing ability, they do not always generate abnormal returns after controlling for systematic risk.

When will the next recession strike? Monitor the outlook with a subscription to:
The US Business Cycle Risk Report

One thought on “Research Review | 30 March 2018 | Portfolio Analysis

  1. Pingback: Quantocracy's Daily Wrap for 03/30/2018 | Quantocracy

Leave a Reply

Your email address will not be published. Required fields are marked *