Investing in Deflation, Inflation, and Stagflation Regimes
Guido Baltussen (Erasmus University Rotterdam), et al.
We examine asset class and factor premiums across inflationary regimes. As periods of high inflation and deflation are relatively uncommon in recent history, we use a deep sample starting in 1875. Moderate inflation scenarios provide the highest returns across asset class and factor premiums. During deflationary periods, nominal returns are low, but real returns are attractive. By contrast, real equity and bond returns are negative during a high inflation regime, and especially so during times of stagflation. During these ‘bad times’ factor premiums are positive, which helps to offset part of the real capital losses.
Beyond Fama-French Factors: Alpha from Short-Term Signals
David Blitz (Robeco Quantitative Investments), et al.
Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by combining signals applied on a liquid global universe with simple buy/sell trading rules. The composite model consists of short-term reversal, short-term momentum, short-term analyst revisions, short-term risk, and monthly seasonality signals. The resulting alpha is present across regions, translates into long-only applications, is robust to incorporating implementation lags of several days, and is uncorrelated to traditional Fama-French factors.
Siyuan Ma (Stevens Institute of Technology)
This paper examines to what extent the momentum spread ratio (MSR) can predict momentum profits. The momentum spread ratio as a potential proxy of investor underreaction can significantly predict the momentum, industry momentum, and residual momentum, especially after 1994, suggesting that behavioral bias to the firm-specific news is indeed a source of momentum.
Understanding Negative Risk-Return Trade-offs
Aoxiang Yang (University of Wisconsin)
In the data, stock market volatility negatively predicts short-run equity and variance risk premia, at odds with leading asset pricing models. I show that only infrequent large volatility shocks negatively drive risk premia, whereas the predictability is strongly positive at all other times. I develop a micro-founded learning model in which investors underreact to structural breaks in financial distress times and overreact to transitory volatility shocks in normal times. The model can successfully match the novel time-varying volatility-risk premia relationship across various horizons. The model can further account for many other salient data features, such as a time-invariant positive correlation between equity and variance risk premium, a robust leverage effect, and negative observations of equity and variance risk premia at the onsets of recessions, i.e., structural breaks.
Forecasting Long-Horizon Factor Volatility
Tom Oskar Karl Zeissler (Vienna University of Economics and Business)
This paper investigates forecasts of long-term volatility for the fast-growing field of long-short factor strategies in an extensive in- and out-of-sample framework. More in detail, the study follows previous authors by empirically comparing various forecast configurations to provide guidance to academics and practitioners on how to accurately predict future volatility for a broad set of factor strategies. The data set spans various well-known factors over multiple asset classes, factor styles, and a long historical data period. As the in-sample results suggest, forecast accuracy is higher for longer historical lookback periods and forecasting windows, both indicating notable mean reversion effects on average across strategies. Furthermore, the evidence supports previous researchers who reported low forecast accuracy of the common approach of merely extrapolating past realized volatility. In contrast, fitted models that consider short-term volatility clustering and additionally exploit external predictors motivated by the asset-pricing literature perform remarkably better. Specifically, the study provides further evidence on the relevance of macro- and market-based state variables, such as fiscal balance, inflation, or term spread indicators, as determinants of the long-term risk of aggregated future asset prices. However, the subsequent out-of-sample analysis shows that most of these findings would have been hard to identify and exploit for investors. Especially the ‘industry standard’ approach of simply extrapolating historic volatility proves its right of existence by representing a tough benchmark to beat out-of-sample. Nevertheless, some patterns of the in-sample results remain intact after the out-of-sample analysis, for instance suggesting notoriously higher accuracy when using longer forecast windows or focusing on carry-styled factor strategies.
Short-term Relative-Strength Strategies, Turnover, and the Connection between Winner Returns and the 52-week High
Chen Chen (Old Dominion University), et al.
We contribute with two principal findings that suggest a material role for 52-week-high price anchors in understanding the short-run behavior of one-month stock returns. First, we find that short-term momentum in high-turnover stocks is only evident for stocks whose prices are relatively close to their 52-week high. Conversely, strong reversals are evident for high-turnover stocks whose prices are relatively far from their 52-week high. Second, we find that the apparent price-to-52-week-high (PTH) anchoring biases are asymmetric with a concentration in past winners. High-PTH winners strongly outperform low-PTH winners, across all the different turnover and size segmentations that we examine. Conversely, a comparable PTH-based performance variation in past losers is not evident. This asymmetry is behind our first principal finding. We conduct three supplemental investigations that support a PTH anchoring-bias interpretation for the winner-PTH relation; also evaluating market sentiment, dispersion in analyst forecasted earnings, and firm size.
A Factor-Based Risk Model for Multifactor Investment Strategies
Frederic Abergel (BNP Paribas), et al.
This paper presents a novel, practical approach to risk management for multifactor equity investment strategies. Our approach lies in the construction of a cross-sectional risk model using the stock return betas and a small number of style factors and macro-sector indicator functions as explanatory variables in a cross-sectional regression. The model leads to a covariance structure that incorporates in an intuitive fashion both the stocks’ characteristics and good conditioning properties that lead to robust optimization problems. Various portfolio constructions are analyzed in detail, and some concrete examples are provided.
Am I Getting it Right? : A Framework for Attributing Style Factor Exposures
Rehan Mohamed (Birla Institute of Technology & Science and Nivedita Sinha
The growing popularity of factor investing has led to a proliferation of factor indices targeting the same factors, as well as factor definitions and portfolio instruction techniques. For an investor, what matters is to know what kind of risks they are taking on and whether they are getting the intended kind of factor exposures. While risk-model based attribution systems assist with the former, investors do not have a good way of tracking the latter. We propose a simple Brinson based framework to attribute factor exposures in a given portfolio.
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