Factors to Assets: Mapping Factor Exposures to Asset Allocations

The authors demonstrate a methodology for optimizing a portfolio of 15 asset classes to match a preferred exposure to 6 factors.  They also implement various constraints and find they are still able to build a portfolio that closely matches a preferred factor exposure under those conditions.

GREENBERG, D., BABU, A., & ANG, A. (2016). Factors to Assets: Mapping Factor Exposures to Asset Allocations. Journal Of Portfolio Management, 42(5), 18-27.

David and Goliath: Who Wins the Quantitative Battle?

The author promotes the Vanguard index funds by comparing them to the average hedge fund.  In particular, he favors index funds due to their lower cost, higher historical risk-adjusted returns, non-market timing (such as done with smart-beta), and less turnover than ETFs.

BOGLE, J. C. (2016). David and Goliath: Who Wins the Quantitative Battle?. Journal Of Portfolio Management, 43(1), 127-137.

Uncloaking Campbell and Shiller's CAPE: A Comprehensive Guide to Its Construction and Use

The authors propose a number of enhancements to the Shiller CAPE, and they showed the enhancements improve the predictability of forecasting the S&P500's return.  Their enhancements involve normalizing with nominal (as opposed to real) returns, cash flow, sales, GDP, CPI, Hodges-Lehmann mean, or an Thiel Sen estimator.

PHILIPS, T., & URAL, C. (2016). Uncloaking Campbell and Shiller's CAPE: A Comprehensive Guide to Its Construction and Use. Journal Of Portfolio Management, 43(1), 109-125.

An Analysis of the Expense Ratio Pricing of SMB, HML, and UMD Exposure in U.S. Equity Mutual Funds

The authors do a regression on popular ETF expense ratios against the Carhart Four Factor Model.  In doing so, they find that on average fund companies charge 11.9 bps for size exposure, 27 bps for growth/value exposure, and 72.5 bps for momentum exposure.  They also found that different funds price the factors at widely different levels.

GROVER, S., & KIZER, J. (2016). An Analysis of the Expense Ratio Pricing of SMB, HML, and UMD Exposure in U.S. Equity Mutual Funds. Journal Of Portfolio Management, 43(1), 138-143.

Mathematics and Economics: A Reality Check

The author criticizes the common financial education system, and he promotes the study of mathematics instead.  In particular he argues that students of the financial markets would be best served by studying discrete math, information theory and signal processing, machine learning, and quantum computing.  In addition, he criticizes the common practice of backtesting strategies, rather than testing them forward by building a track record; and he suggests that finance professors should have real-world experience rather than just academic knowledge.

DE PRADO, M. L. (2016). Mathematics and Economics: A Reality Check. Journal Of Portfolio Management, 43(1), 5-8.

Identifying Economic Regimes: Reducing Downside Risks for University Endowments and Foundations

The authors model contagion by separating returns of the S&P 500 into two regimes (growth and contraction); in doing so, they illustrate the importance of changing policies in different regimes in order to meet the goals of both spending and preserving capital.  They suggest dynamic spending rules; whereas, in market downturns, they reduce spending from the neutral policy spending percentage.  They give the following examples of potential spending cuts during market crashes: suspend sabbatical leave for faculty, temporary wage freeze, hiring postponement, and hold off on noncritical capital improvements.


MULVEY, J. M., & LIU, H. (2016). Identifying Economic Regimes: Reducing Downside Risks for University Endowments and Foundations. Journal Of Portfolio Management, 43(1), 100-108.

Optimal Dynamic Portfolio Risk Management

The authors formulate and solve the multiperiod optimal portfolio choice problem.  They show that the investor typically behaves myopically, and the multiperiod solution can be reduced to a single-period Markowitz solution.  They find the optimal capital allocation consists in holding a position in the risky port­folio that is inversely proportional to the risky portfolio variance.  Across several models, they find that the minimum variance portfolio with short-sale restrictions performs best among all competing models with dynamic risk control. To forecast the covariance matrix with high precision, practitioners are advised to use the multivariate GARCH forecast.


KAMULIN, V. (2016). Optimal Dynamic Portfolio Risk Management. Journal Of Portfolio Management, 43(1), 85-99.

 

 

An Asset Class Characterization of the U.S. Equity Index Volatility Risk Premium

The authors created a 32-year return series for short volatility exposure, the Realized Premium Volatility (RVP). Their study suggest that exposure to volatility of the U.S equity markets offers an attractive risk premium; there are however occasional severe crashes that could wipe out years of returns.  The market tends to price volatility effectively in stable regimes (including ones with high but constant volatility), but risk pricing seems slow to adjust to large abrupt regime changes (i.e., a market crash).

FALLON, W., & PARK, J. (2016). An Asset Class Characterization of the U.S. Equity Index Volatility Risk Premium. Journal Of Portfolio Management, 43(1), 72-84.

Labor Conditions and Future Capital Market Performance

The authors find that since 1970, when the unemployment rate is high, the next 24 months of stock and bond returns are high; vis a vis, when the unemployment rate is low, the next 24 months of stock and bond returns are low.  They posit the cause of this relationship is that if labor costs go up (benefiting the labor market), shareholders don't do as well (because more of their earnings had to be paid out in wages); and vise versa.  Also, they suggest that if labor is doing poorly (i.e., the unemployment rate is going up), that might mean interest rates will go up in the future thereby reducing the discount rates and increasing valuations of stocks.

ARNOTT, R., LI, F., & LIU, X. (2016). Labor Conditions and Future Capital Market Performance. Journal Of Portfolio Management, 43(1), 54-71.

The Tesla Run-up: A Follow-up with Investment Implications

The authors had previously valued the stock of Tesla in 2014 at about $100 using overly liberal projections; the price of Tesla subsequently went up to over $250.  This article suggests that Tesla's stock is overpriced according to fundamentals and DCF calculations, and the speeches and promotions by Musk are propping up the stock price, due to long-term prospects over short-term results.  But when a negative event regarding the company soon causes a cascade in the stock price, it will appear like a short-term reaction, but it's actually a Bayesian update that has been a long time coming.

CORNELL, B. (2016, Fall2016). The Tesla Run-Up: A Follow-Up with Investment Implications. Journal of Portfolio Management. pp. 1-4.

A Practitioner's Guide to Market Microstructure Invariance

The authors add to one of their previous papers to show their comprehensive model of market microstructure invariance.  They used their metrics to recast trades as bets, calendar time as business time, and return volatility as dollar volatility.  In doing so, they hypothesized that the amount of risk transferred for each bet is the same for low and high velocity stocks and the dollar cost of executing low and high velocity stocks is the same when measured as the amount of risk transfer.  They support this with detailed equations and charts.

KYLE, A. S., OBIZHAEVA, A. A., & KRITZMAN, M. (2016). A Practitioner's Guide to Market Microstructure Invariance. Journal Of Portfolio Management, 43(1), 43-53.

What Goes into Risk-Neutral Volatility? Empirical Estimates of Risk and Subjective Risk Preferences

RND volatility correlates somewhat with realized volatility; forecasts of realized volatility from a GARCH model are highly significant in explaining RND volatility; non-BS factors such as sentiment and confidence are important determinants of option prices; historical volatility does not contribute strongly to forecasting volatility; the larger and more significant coefficients in the RND volatility regressions suggest that empirical (P Measure) volatility is correlated with returns, but transferring the P Measure into the Q Measure increases the effect; recent wide trading ranges seem to be connected with investors' and market makers' risk aversion, rather than to their objective forecasts of future volatility; high market volatility occurs when consumer and investor sentiment is low.

FIGLEWSKI, S. (2016). What Goes into Risk-Neutral Volatility? Empirical Estimates of Risk and Subjective Risk Preferences. Journal Of Portfolio Management, 43(1), 29-42.

ETFs, High-Frequency Trading, and Flash Crashes

"Banning HFT only increases the severity of flash crashes.  Similarly, banning ETFs may destroy the current HFT-ETF ecosystem and likewise worsen intraday market crashes."

ALDRIDGE, I. (2016). ETFs, High-Frequency Trading, and Flash Crashes. Journal Of Portfolio Management, 43(1), 17-28.

Volatility Wisdom of Social Media Crowds

"Information contained in the volatility sentiment extracted from broader social media data sources can be used to create profitable investment strategies for stock market volatility ... A large database of tweets contains useful information about future stock market volatility.  [Social Anomaly Score strategies] are able to outperform a benchmark by harnessing the volatility wisdom of social media crowds."

KARAGOZOGLU, A. K., & FABOZZI, F. J. (2017). Volatility Wisdom of Social Media Crowds. Journal Of Portfolio Management, 43(2), 136-151.

Currency Crowdedness Generated by Global Bond Funds

"Bond managers rely on currency beta strategies. Both the G10 and Global carry and the value strategy are preferred by professional bond investors... Bond managers [should] pay closer attention to the return attribution of ... currency management, [because] global bond fund returns can be related to currency-risk factors, [and] alpha-generating currency management could improve diversification and the risk-return ratio in a global bond portfolio."

KONSTANTINOV, G. (2017). Currency Crowdedness Generated by Global Bond Funds. Journal Of Portfolio Management, 43(2), 123-135.

The Bad Arithmetic of Active Management

"Although the average return of active managers cannot be any different than the return of the total market portfolio (and will be less, due to fees and expenses), that does not mean that active managers do not add value for investors."

JACOBSEN, B. J. (2017). The Bad Arithmetic of Active Management. Journal Of Portfolio Management, 43(2), 115-122.

Effect of Booms and Busts on the Sharpe Ratio

The Sharpe ratio is inversely related to skewness; since returns subject to disasters are
negatively skewed and returns subject to booms are positively skewed, hedge funds who sell insurance by shorting options typically exhibit a higher Sharpe ratio than those who buy options.

BEDNAREK, Z., & PATEL, P. (2017). Effect of Booms and Busts on the Sharpe Ratio. Journal Of Portfolio Management, 43(2), 105-114.

Quantifying Backtest Overfitting in Alternative Beta Strategies

Alternative beta strategies often exhibit high performance in hypothetical backtests, but their live actual performance can be substantially less than projected by investment banks (most especially in equity value strategies).  "Factor fishing" and data mining allow the construction of products that perform well in historical backtests, but not as well live.

SUHONEN, A., LENNKH, M., & PEREZ, F. (2017). Quantifying Backtest Overfitting in Alternative Beta Strategies. Journal Of Portfolio Management, 43(2), 90-104.

The Moral Hazard Problem in Hedge Funds: A Study of Commodity Trading Advisors

Hedge Fund Managers with performance-based fees are likely to take on more risk when they have discretionary authority over investments in a favorable market environment, because they are more concerned with fee income than survival.  This is an implicit cost to investors in the fund, because more risk is taken without additional return.  To combat this problem, investors can require managers to have a sizable stake in the fund or incorporate a claw-back provision in the fee calculation.

CAI, L., CHENG, J., MARAT, M. (2017). Moral Hazard Problem in Hedge Funds: A Study of Commodity Trading Advisors (CTAs). Journal Of Portfolio Management, 43(2), 77-89. 

Defined Contribution Retirement Plans Should Look and Feel More Like Defined Benefit Plans

The effectiveness of Defined Contribution plans can be improved by increasing the participant savings rate, allowing for longevity-risk pooling, employing professional return/risk management, reducing administrative costs, and optimizing withdrawal timing/amounts.

ILMANEN, A., KABILLER, D. G., SIEGEL, L. B., & SULLIVAN, R. N. (2017). Defined Contribution Retirement Plans Should Look and Feel More Like Defined Benefit Plans. Journal Of Portfolio Management, 43(2), 61-76.