Asset Allocation:
A Recommendation for Resolving the Collision between Theory and Practice
Larry J. Prather (Southeastern Oklahoma State University), et al.
April 26, 2016
We examine the creation of a low-cost optimal risky portfolio that individual investors can easily construct and manage. We consider five index mutual funds and three precious metals that are easy for investors to trade. Collectively, the mutual funds track the returns of the entire U.S. equity market, 98% of foreign stocks, U.S. investment grade bonds, all domestic REITs, and emerging markets. The three precious metals are gold, platinum, and palladium. Because these mutual funds are available in ETF form, we provide optimization results with and without short selling. Optimization results differ greatly from conventional wisdom regarding optimal asset allocation.

The Dynamics of Value Comovement Across Global Equity Markets
Mayank Gupta and Jan Novotny (City University London)
April 1, 2016
The ratio between share price and current earnings per share, the Price Earning (PE) ratio, is widely considered to be an effective gauge of under/overvaluation of a corporation’s stock. Arguably, a more reliable indicator, the Cyclically-Adjusted Price Earning ratio or CAPE, can be obtained by replacing current earnings with a measure of permanent earnings i.e. the profits that a corporation is able to earn, on average, over the medium to long run. In this study, we aim to understand the cross-sectional aspects of the dynamics of the valuation metrics across global stock markets including both developed and emerging markets. We use a time varying DCC model to exploit the dynamics in correlations, by introducing the notion of value spread between CAPE and the respective Market Index from 2002 to 2014 for 34 countries. Value spread is statistically significant during the 2008 crisis for asset allocation. The signal can be utilized for better asset allocation as it allows one to interpret the common movements in the stock market for under/overvaluation trends. These estimates clearly indicate periods of misvaluation in our sample. Furthermore, our simulations suggest that the model can provide early warning signs for asset mispricing in real time on a global scale and formation of asset bubbles.

Introduction into Multi-Factor Investing
Yury Polyakov (Independent)
May 9, 2016
Asset managers worldwide face the new market reality: significantly decreased global economic growth, the lowest interest rates and inflation, an increase in asset cross-correlation due to accelerated globalization and spikes of event driven volatility, high assets valuation levels.
In response to market changes the new approaches to asset management have been developed: risk based asset allocation and factor investing. In this introductory document we briefly cover historical evolution of asset management from traditional methods to alternative beta and “pure” alpha strategies. Then we describe each category and analyze it based on our own examples. At the end we outline the new trends and future fields of research in modern asset management and alpha generating strategies.

My Factor Philippic
Clifford S. Asness (AQR Capital Management)
June 22, 2016
Arnott, Beck, Kalesnik, and West (2016) (ABKW) study smart beta or factor-based strategies and come to the following conclusions: (1) Aside from value, most popular factor strategies currently look expensive. (2) These expensive factor valuations portend lower future returns and a strong possibility of a future “factor crash” in which they go “horribly wrong.” And (3) many of these non-value factors were never real to start with because their historical performance was due to factor richening. That is, researchers mistook the one-time returns from factor richening for truly repeatable “structural alpha.” ABKW’s implied bottom line (their many protestations to only making modest recommendations aside): stick with value, dump the other factors. This essay elaborates on my response in Asness (2016). In summary: (1) I find non-value factor valuations moderately expensive, but not as expensive as ABKW. (2) I argue that ABKW exaggerate the power of factor timing by improperly using long-horizon regression techniques. More proper short-horizon regressions suggest some weak factor timing ability and given this predictability, I construct value-based tactical factor timing strategies to test them. Unfortunately, these strategies add little to portfolios that are already invested in the value factor. It turns out that this “newly” discovered timing tool is, yet again, mostly just a version of regular old value investing. And (3) I examine ABKW’s claim that factor richening drives much of non-value long-term factor performance and find that this very serious allegation about other researchers’ work is totally without merit. Overall, these results suggest that one should be wary of aggressive factor timing. Instead, investors are better off identifying factors they believe in, and staying diversified across them, unless we see far more extreme pricing than we do today.

Low-Volatility Investing: Empirical Evidence of the Defensive Properties of Low Volatility Enhanced Portfolios
Thomas Merz (Zurich Universtity) and Pawel Janus (VU University)
March 2, 2016
Our study provides further insights into the evidence of excess returns of low volatility enhanced portfolios. Based on the framework presented by Campbell and Vuolteenaho (2003), we analyze through-the-cycle as well as stress periods to provide an insight into which portfolio construction technique is most beneficial in enhancing portfolio returns on a risk-adjusted basis. Analyzing a new data set from 2000 through 2015, we find that low volatility enhanced portfolios exhibit extraordinary excess returns during stressed market conditions. Empirically, we find that enhancing portfolios with low volatility building blocks produces on average an excess return between 5.6% and 17.2% for US equity and 1.8% and 16.7% for European equity portfolios during strong market corrections. We provide evidence that across different portfolio construction techniques, relative excess returns become more pronounced the more severe the market correction becomes. While equal weight techniques contribute very steadily to the overall excess return in down cycles, switching techniques show more relative outperformance towards the deeper end of market down cycles.

Time Series Momentum and Volatility Scaling
Abby Kim (Securities and Exchange Commission), et al.
May 31, 2016
Moskowitz, Ooi, and Pedersen (2012) show that time series momentum delivers a large and significant alpha for a diversified portfolio of international futures contracts. We find that their results are largely driven by volatility-scaling returns (or the so-called risk parity approach to asset allocation) rather than by time series momentum. Without scaling by volatility, time series momentum and a buy-and-hold strategy offer similar cumulative returns, and their alphas are not significantly different. This similarity holds for most sectors and for a combined portfolio of futures contracts. Cross-sectional momentum also offers a higher (similar) alpha than unscaled (scaled) time series momentum.

Country Risk and Expected Returns across Global Equity Markets
Adam Zaremba (Poznań University of Economics and Business)
May 10, 2016
Assessing and pricing country risk poses a considerable challenge to tactical asset allocation across national equity markets. This research examines the relationship between the country composite risk (together with its component risks related to: sovereign credit, currency, banking sector, economic structure, and political situation) and the expected returns, also identifying general investment practice implications. The equal-weighted portfolio of risky countries proved to outperform the safe countries by approximately 0.50 percentage points per month. The application of this cross-sectional pattern, however, still poses a significant challenge for investment practice. The abnormal performance proved insignificant for capitalization-weighted and liquidity weighted portfolios, as well as within the subgroups of the full sample. We also observed profitability of the risk-based strategies disappear in the years following the global financial crisis.