Research Review | 8 October 2021 | Dynamic Portfolio Strategies

Time-Varying Factor Allocation
Stefan Vincenz and Tom Oskar Karl Zeissler (Vienna U. of Economics and Business)
September 15, 2021
In this empirical study, we provide evidence on how predictive information can be utilized to profitably allocate a cross-asset factor portfolio, covering various well-known factors over the asset classes equity, commodity, fixed income, and foreign exchange. We investigate the performance of a meaningful set of predictors, which we broadly divide into macro and market indicators. Our analysis shows that tilting a global factor portfolio according to signals derived from business cycle indicators, inflation, and short-term interest rates, among other predictors, significantly outperforms a static factor benchmark. The established results are based on practical considerations, survive conservative transaction cost assumptions, and are validated over an extensive out-of-sample period. In sum, we highlight the potential benefits of an asset-allocation framework conditioned on predictive variables, but caution to time factors on a standalone basis.

Equity premium predictability over the business cycle
Emanuel Moench and Tobias Stein (Bundesbank)
12 September 2021
The US equity market follows a V-shaped pattern around recessions, with sharply negative returns heading into recessions and a strong recovery as the recession unfolds. In addition, recessions are usually preceded by an inverted yield curve. This column shows that the term spread is a robust predictor of recessions, and that model-implied recession probability forecasts do a good job of predicting the equity premium out-of-sample. An investment strategy based on the recession probability model could be used to time the equity market and lead to higher and less volatile profits over time.

The Quant Cycle
David Blitz (Robeco Quantitative Investments)
September 24, 2021
Traditional business cycle indicators do not capture much of the large cyclical variation in factor returns. Major turning points of factors seem to be caused by abrupt changes in investor sentiment instead. We infer a Quant Cycle directly from factor returns, which consists of a normal stage that is interrupted by occasional drawdowns of the value factor and subsequent reversals. Value factor drawdowns can occur in bullish environments due to growth rallies and in bearish environments due to crashes of value stocks. For the reversals we also distinguish between bullish and bearish subvariants. Empirically we show that our simple 3-stage model captures a considerable amount of time variation in factor returns. We conclude that investors should focus on better understanding the quant cycle as implied by factors themselves, rather than adhering to traditional frameworks which, at best, have a weak relation with actual factor returns.

Allocating to Thematic Investments
Koye Somefun (BNP Paribas Asset Management), et al.
September 07, 2021
In this paper we introduce the notion of themes as an additional investment dimension beyond asset classes, regions, sectors and styles, and we propose a framework to allocate to thematic investments at a strategic asset allocation level. The goal of thematic investments is to provide the means to invest in assets that have their returns significantly impacted by the structural changes underlying the theme. Such changes come about through megatrends that shape societies: Demographic shifts, social or attitudinal changes, environmental impact, resource scarcity, economic imbalances, transfer of power, technological advances and regulatory or political changes. Allocating to themes requires discipline because thematic investments are not only exposed to the theme but also to the traditional risk factors. Our approach to allocating to thematic investments uses a framework based on robust portfolio optimisation, which takes into account the expected excess return derived from the exposure to the theme as well as exposures to traditional risk factors. As an illustration, we provide an example where thematic investments in energy transition, environmental sustainability, healthcare innovation, consumer innovation and disruptive tech are added to a traditional multi-asset portfolio.

The Gain-Pain Index: Asset Allocation for Individual (And Other?) Investors
Javier Estrada (IESE Business School)
August 12, 2021
Individual investors typically determine their asset allocation using investor questionnaires, which can be viewed as black boxes that generate a result without highlighting the benefits and costs of the portfolios considered. This article introduces an asset allocation tool, the gain-pain index (GPI), that overcomes this shortcoming. The tool proposed incorporates two critical variables found in investor questionnaires, the portfolio holding period and the investor’s risk tolerance, and broadens the definition of risk beyond volatility by also considering the probability of suffering a loss and the magnitude of the loss. The model is used to determine optimal asset allocations for 21 countries and the world market, for different holding periods and levels of risk aversion.

State-dependent Asset Allocation Using Neural Networks
Reza Bradraniaa (U. of South Australia) and Davood Pirayesh Neghab (Koc U.)
April 2021
Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.

Bayesian Portfolio Selection: Application to Tactical Asset Allocation
Majeed Simaan (Rensselaer Polytechnic Institute)
July 7, 2021
This article discusses the portfolio selection problem from a Bayesian perspective. In doing so, I first provide an overview of the portfolio problem and motivate the decision-making process from an expected utility point of view. Then, I demonstrate the analytical solution to the problem and stress the intuition behind the Bayesian application. In particular, in the case of risky assets, the optimal portfolio corresponds to three funds. The first is the minimum variance portfolio, whereas the others denote two self-financing portfolios corresponding to the mean returns. The combination between the first and the second funds is consistent with the conventional mean-variance portfolio. Furthermore, with the inclusion of the third fund, the portfolio incorporates the priors/beliefs of the decision-making into the portfolio selection. Based on the analytical insights, I conduct a small empirical experiment using two ETFs. The experiment emulates a tactical asset allocation problem. All the empirical analysis is conducted using R.

Currency News and International Bond Markets
Moustafa Abuelfadl (University of New England) and Ehab Yamani (Chicago State University)
September 10, 2021
We use a sample of 27 countries and 63 currency news announcements in an event study framework to examine the impact of currency news on international government bond markets. Our findings reveal a significant spillover of currency news into bond markets. Specifically, the evidence shows significant negative abnormal bond returns, whether measured in dollar terms or local currency terms, implying that currency news plays a role in changing the performance of international government bond markets. We also show that abnormal bond returns remain significantly negative even after controlling for macroeconomic variables. Our results are robust to using alternative risk model specifications, country-level data, and corporate bond data. Our evidence of the significant impact of currency news on bond markets provides essential insights to professional traders, policymakers, and academic researchers.

Alternative Investing: The Fairy Tale And The Future
Richard Ennis (co-found of EnnisKnupp)
August 14, 2021
A fairy tale has sustained alternative investing since the Global Financial Crisis (GFC) of 2008. Here I parse the fairy tale and then set the stage for the future of institutional investing. Freed of the misperception that maintaining several asset-class silos is necessary to achieve efficient diversification, institutional investors will begin to simplify asset allocation. Implicit here is the understanding that alternative investments are purely active strategies. Their role in the portfolio is more transitory than that of stocks and bonds, which are the essential building blocks of efficient diversification. Over time, we can expect to see fewer, more comprehensive asset classes; allocators becoming more discriminating in their choice of individual alt investments; and lesser allocations to alternative investments overall. Successful allocators will use many fewer managers and incur lower costs. There really is no other way forward.

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

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