Fire Sale Risk and Expected Stock Returns

George O. Aragon (Arizona State U.) and Min S. Kim (Michigan State U.)

**July 29, 2020**

*We measure a stock’s exposure to fire sale risk through its ownership links to equity mutual funds that experience outflows during periods of systematic outflows from the fund industry. We find that more exposed stocks earn higher average returns: a portfolio that buys (shorts) stocks with the highest (lowest) exposure outperforms by 3-7% per annum. Our findings cannot be explained by several known determinants of average returns and are consistent with the ex-ante pricing of the risk of future fire sales. We conclude that stocks’ exposures to risks inherited from the constraints of shareholders have important implications for stock prices.*

Understanding Volatility-Managed Portfolios

G. Cejnek (ZZ Vermögensverwaltung) and F. Mair (Vienna U. of Econ. & Business)

**June 17, 2020**

*Contrary to the intuition that the standard risk-return tradeoff should lead to underperformance of a portfolio that scales down exposure during volatile periods a recent paper by Moreira and Muir (2017) actually shows that volatility-managed portfolios produce robust and significant alphas. The present paper investigates the mechanisms that lead to the outperformance of volatility management. By implementing timing regressions and relating returns of a volatility-managed portfolio to discount-rate, cash-flow and expected volatility news we provide evidence that volatility management outperforms by levering up good times without increasing downside exposure to fundamental risk drivers. On the contrary, during the most severe cumulative news shocks (either to cash flows, discount rates or expected volatility) the scaling strategy suffers less than the buy-and-hold portfolio and, thus, increases investor utility.*

To Trend or to Revert – A Portfolio Perspective on Time Series Momentum

Balaji Thiruvengadasamy (EDHEC)

**May 27, 2020**

*Time series momentum or trend following strategies have grown in popularity among institutional investors, with over $300 billion assets under management in managed futures strategies. A portfolio approach on recent return history of 60 liquid futures across different asset classes shows that evidence for both time series momentum and its opposite, reversal, is weak. While recent literature highlights evidence for reversal over long time horizons, this study shows reversal is significant even in short time horizons and that there is no clear pattern across asset classes.*

On the Relevance of Strategic and Tactical Asset Allocation for Portfolio Insurance

Martin Kolrep (Invesco), et al.

**April 27, 2020**

*Portfolio insurance can be an appropriate means to preserve a given capital floor, yet the associated risk budgeting parameters need to be tailored to align with the underlying investment strategy. The main determinants are strategic asset allocation as well as the range and accuracy of tactical asset allocation decisions that would help mitigate downside risk. We evaluate the performance of a multi-asset allocation strategy across a vast number of alternative scenarios using block-bootstrap simulations. Based on a simulated tactical asset allocation model, our framework enables us to gauge the impact of assumed forecast accuracy and the tactical asset allocation range on the ultimate portfolio return distribution under a classic dynamic portfolio insurance risk budgeting framework.*

Equally Diversified or Equally Weighted?

Gianluca Fusai (University of London), et al.

**June 16, 2020**

*The aim of this paper is to shed new light on the concept of diversification showing that it is not necessarily related to the reduction of the volatility of a portfolio, as it is commonly perceived. We introduce a diversification index that exploits the decomposition of portfolio volatility into undiversified volatility and a diversification component. The diversification component offsets the undiversified part leaving as a final result the portfolio volatility itself. Our decomposition has a clear statistical interpretation because it relates the diversification component to the so-called partial covariances, i.e. the covariances between the residuals of the regressions of the weighted asset returns with respect to the portfolio return. On this basis, we advocate the construction of an equally diversified portfolio versus an equally weighted portfolio. An empirical analysis illustrates the superior performance of the equally diversified portfolios with respect to the equally weighted portfolio.*

The Jury is Still Out On the Performance of Naive Diversification (1/N rule)

Redouane Elkamhi and Marco Salerno (University of Toronto)

**June 30, 2020**

*For many decades academics have presented theories on optimal portfolio allocations, with mean-variance models at their front and center. However, the work of DeMiguel et al. (2009) has made a compelling case that estimation error completely dwarfs the benefits of these optimal allocation rules, making naive diversification (1/N) a dominating strategy. In this paper, we show that the jury is still out on the relative performance of 1/N. We demonstrate that risk-based diversification – which rely solely on the variance-covariance matrix – strongly outperform the 1/N naive rule in terms of Sharpe ratio, certainty equivalent returns and turnover. We also show that machine learning and clustering techniques can be used to enhance the benefits from diversification when using risk-based allocation rules. We present simulation exercises that illustrate the source of the outperformance of risk-based diversification and the importance of clustering. Our results are robust across different asset types by considering portfolios of (a) equities only, (b) equities and bonds, and (c) a large set of equity anomalies.*

Dynamic Asset Allocation Based on Valuation and Momentum Applied to a Global All-Equity Portfolio

James White and Victor Haghani (Elm Partners)

**March 23, 2020**

*There has been considerable research into dynamic global tactical asset allocation (GTAA) strategies driven by simple measures of Valuation and Momentum applied to a baseline balanced portfolio of equities and fixed income (see Blitz and van Vliet 2008, Wang and Kochard 2011, Gnedenko and Yelnik 2014, and Dewey and Haghani 2016). In sum, this research has found that historically, these Valuation-and-Momentum-driven investment approaches have produced higher returns and more attractive risk-adjusted returns than a static-weight approach. However, the above-cited research has been mostly silent on the question of how such an approach could be implemented and would have performed in the context of an All-Equity portfolio, where the Valuation and Momentum signals would drive a dynamic allocation between different regional equity markets. This note explores this question. It finds that such a dynamic approach would have produced higher absolute returns, and higher risk-adjusted returns, than a static approach would have produced. However, the improvement in simulated historical returns is less than the improvement that Valuation and Momentum deliver when applied to a balanced baseline portfolio of equities and fixed income. For example, simulated historical returns for the All-Equity strategy described below over the past roughly 40 years had about 50% more variability but only 20% higher returns than did a similar dynamic Global Balanced strategy. We examine a form of the strategy that can be followed by any investor with a simple brokerage account, using low-cost, liquid and widely available ETFs or index funds, and without use of leverage or shorting. The implementation we explore in this note trades monthly, with turnover averaging about 50% per annum. It is be based on data that is freely available in the public domain.*

Monetization Matters: Active Tail Risk Management and The Great Virus Crisis

Vineer Bhansali (LongTail Alpha), et al.

**July 21, 2020**

*We discuss monetization strategies for both “Left” and “Right” tail risk hedging to illustrate potential benefits of active management of hedges. In particular, by including actual data from the sharp COVID-19 pandemic related market correction and subsequent rebound of 2020 we quantify how monetization strategies have the ability to improve the performance of portfolio hedges. This extends previous work on active tail risk hedging published in this journal. We conclude that active management of tail hedging can result in significant increases in the efficacy of tail hedging.*

Portfolio Choice with Time Horizon Risk

Alexis Direr (Laboratoire d’Economie d’Orléans)

**June 24, 2020**

*I study the allocation problem of investors who hold their portfolio until a target wealth is attained. The strategy suppresses final wealth uncertainty but creates an investment time horizon risk. I begin with a simple mean variance model transposed in the duration domain, then study a dynamic portfolio choice problem with Generalized Expected Discounted Utility preferences. Using long-term US return data, I show in the mean variance model that a large amount of time horizon risk can be diversified away by investing a significant share of equities. In the dynamic model, more impatient investors are also more averse to timing risk and invest less in equities. The equity share is downward trending with accumulated wealth relative to its target.*

Relative Sentiment and Machine Learning for Tactical Asset Allocation

Raymond Micaletti (Columbus Macro)

**October 25, 2019**

*We examine Sentix sentiment indices for use in tactical asset allocation. In particular, we construct monthly relative sentiment factors for the U.S., Europe, Japan, and Asia ex-Japan by taking the difference in 6-month economic expectations between each region’s institutional and individual investors. These factors (along with one-month forward equity returns) then serve as inputs to a wide array of machine learning algorithms. Employing combinatorial cross-validation and adjusting for data snooping, we find relative sentiment factors have robust and significant predictive power in all four regions; that they surpass both standalone sentiment and time-series momentum in terms of informational content; and that they demonstrate the ability to identify the subsequent best- and worst-performing global equity markets from along a cross-section. The results are consistent with previous findings on relative sentiment, discovered using unrelated datasets.*

*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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