Portfolio - September 29, 2018

Company Name	Ticker	Weight
Intel Corporation	INTC	18.97%
CVS Caremark Corporation	CVS	11.12%
BlackRock Inc	BLK	10.60%
Total SA	TOT	9.92%
Comcast Corporation	CMCSA	9.43%
T Rowe Price Group Inc	TROW	7.34%
Goldman Sachs Group Inc	GS	5.72%
Chevron Corporation	CVX	5.68%
FedEx Corp	FDX	4.04%
BB and T Corporation	BBT	3.61%
Cummins Inc	CMI	3.36%
Deere and Co	DE	3.26%
Motorola Solutions Inc	MSI	2.28%
PACCAR Inc	PCAR	2.09%
Cognizant Technology Solutions Corp	CTSH	1.40%
Discover Financial Services	DFS	0.77%
Northrop Grumman Corp Holding Co	NOC	0.42%
		100.00%

The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds

The authors show that sentiment of Tweets related to FOMC meeting results can provide information for market returns.  They use a polarity of positive or negative when tweets are made about FOMC, Bernanke, Federal Reserve, or Yellen, and create a regression to show that positive or negative sentiment can provide significant betas, even when controlling for the Fama-French value, size, and momentum factors.  They then use these sentiment indicators to backtest a strategy, and show that using the sentiment of tweets related to FMOC meetings can outperform the market, especially a strategy that only modifies from the market portfolio on FOMC meeting days.


AZAR, P. D., & LO, A. W. (2016). The Wisdom of Twitter Crowds: Predicting Stock Market Reactions to FOMC Meetings via Twitter Feeds. Journal of Portfolio Management, 42(5), 123–134.

Portfolio - September 22, 2018

Company Name	Ticker	Weight
Intel Corporation	INTC	26.34%
CVS Caremark Corporation	CVS	13.82%
BlackRock Inc	BLK	11.32%
Chevron Corporation	CVX	8.23%
T Rowe Price Group Inc	TROW	7.93%
Comcast Corporation	CMCSA	7.25%
Laboratory Corp	LH	4.64%
Motorola Solutions Inc	MSI	3.96%
FedEx Corp	FDX	3.44%
Deere and Co	DE	3.15%
Northrop Grumman Corp Holding Co	NOC	2.91%
Cummins Inc	CMI	2.66%
Goldman Sachs Group Inc	GS	2.06%
Cognizant Technology Solutions Corp	CTSH	1.55%
BB and T Corporation	BBT	0.73%
		100%

Stability-Adjusted Portfolios

The authors introduce Stability-Adjusted Portfolios: a methodology for incorporating estimation error in covariances into the portfolio formation process.

The authors compute covariances from all independent subsamples of a chosen size and measure composite errors in these subsamples. These composite errors com­prise small-sample error, independent-sample error, and interval error. They then add these errors to a base-case covariance matrix and, assuming normality, generate stability-adjusted return distributions for all subsamples.  They then combine these distributions into a stability-adjusted return distribution, which is non-normal.

The authors then use full-scale optimization (that works with non-normal distributions) and utility functions to derive optimal portfolios.  These portfolios tend to be less volatile.

KRITZMAN, M., & TURKINGTON, D. (2016). Stability-Adjusted Portfolios. Journal of Portfolio Management, 42(5), 113–122.

Seeking Alpha? It's a Bad Guideline for Portfolio Optimization

The authors argue that alphas do not provide an optimal guide to portfolio optimization.  Although it is widely believed that overweighting assets with positive alphas and underweighting assets with negative assets is a good policy for increasing the Sharpe Ratio, the authors argue that for shifts greater than 1% of the benchmark weight, weighting by alpha can be detrimental to the portfolio.  The authors show that when portfolio weights are shifted, the alphas a changed, causing the portfolio to be suboptimal in large weight shifts.


LEVY, M., & ROLL, R. (2016). Seeking Alpha? It’s a Bad Guideline for Portfolio Optimization. Journal of Portfolio Management, 42(5), 107–112.

Issues in Applying Financial Econometrics to Factor-Based Modeling in Investment Management

The authors summarize techniques, pitfalls, and strategies for developing, testing, and using factor models.  In defining the optimum factors to have in the model, they summarize that it is when the residuals are uncorrelated.  They then summarize the Principal Components Analysis (PCA) method for determining the number of factors.  They then explain the problems of overfitting (too many polynomial degrees) and dimentionality (too many parameters), and techniques for preventing both: for example, Regularization Theory proposes a penalty to the error for in-sample models.  The authors then go into the dangers of relying on in-sample backtesting, and they note other research that posits a 50% penalty on in-sample Sharpe ratios.

ENGLE, R. F., FOCARDI, S. M., & FABOZZI, F. J. (2016). Issues in Applying Financial Econometrics to Factor-Based Modeling in Investment Management. Journal Of Portfolio Management, 42(5), 94-106.

Flexible Indeterminate Factor-Based Asset Allocation

The authors propose a new asset allocation process (i.e., FIFAA) that blends the quantitative aspects of modern portfolio theory with the flexibility to make subjective decisions.  The process goes like this: 1) selecting factors, 2) measuring asset class or investment universe factor exposures, 3) choosing desirable factor exposures, and 4) determining the most appropriate asset class targets and ranges for achieving our long-term investment objectives, while maintaining our preferred factor exposures.

BLYTH, S., SZIGETY, M. C., & XIA, J. (2016). Flexible Indeterminate Factor-Based Asset Allocation. Journal Of Portfolio Management, 42(5), 79-93.

Adjusted Factor-Based Performance Attribution

The authors show that if an investor were to use factor based attribution with a returns model containing factors that are either exactly the same as or similar to the factors used to construct the portfolio, adjusted attribution is needed to correct between the factor and specific contributions.



STUBBS, R. A., & JEET, V. (2016). Adjusted Factor-Based Performance Attribution. Journal Of Portfolio Management, 42(5), 67-78.

Alpha Signals, Smart Betas, and Factor Model Alignment

The authors compare errors of omission and of commission of factor risks in portfolios.  An error of omission happens when an investor thinks excess return is caused by alpha, when in fact it is due to an unperceived factor risk; an error of commission happens when an investor thinks excess return is caused by excess factor risk, when in fact it is due to alpha.  The authors find that if an investor commits an error of omission, the losses can be twice as large as those incurred by committing an error of commission.

If the alpha is true alpha, the investor should not adjust portfolios for factor risks; however, it is prudent to balance the risks of omission and of commissions by normalizing alphas and betas accordingly.  This can be done by tempering alphas for the potential of alpha noise. 

MARSH, T., & PFLEIDERER, P. (2016). Alpha Signals, Smart Betas, and Factor Model Alignment. Journal Of Portfolio Management, 42(5), 51-66.

Portfolio - September 8, 2018

Company Name	Ticker	Weight
Intel Corporation	INTC	20.66%
BlackRock Inc	BLK	14.11%
CVS Caremark Corporation	CVS	12.52%
Chevron Corporation	CVX	9.66%
Comcast Corporation	CMCSA	8.34%
T Rowe Price Group Inc	TROW	8.29%
Laboratory Corp	LH	5.04%
Cummins Inc	CMI	4.94%
Deere and Co	DE	4.33%
Northrop Grumman Corp Holding Co	NOC	3.35%
FedEx Corp	FDX	2.95%
Cognizant Technology Solutions Corp	CTSH	2.19%
Goldman Sachs Group Inc	GS	1.84%
PACCAR Inc	PCAR	1.49%
Discover Financial Services	DFS	0.27%
BB and T Corporation	BBT	0.02%

Can the Whole Be More Than the Sum of the Parts? Bottom-up versus Top-Down Multifactor Portfolio Construction

The authors compare a bottom-up and a combination approach to allocating factors in a portfolio.  The bottom-up approach calculates security weights as a function of multiple factors simultaneously, while the combination approach combines individual single-factor portfolios.  In their study, they find the bottom-up approach to be superior (on both a nominal and risk-adjusted basis) because it captures the nonlinear cross-sectional interaction effects between factors that the combination approach does not.

BENDER, J., & WANG, T. (2016). Can the Whole Be More Than the Sum of the Parts? Bottom-Up versus Top-Down Multifactor Portfolio Construction. Journal Of Portfolio Management, 42(5), 39-50.

A Trustee Guide to Factor Investing

The authors summarize various techniques and pros/cons for investing in factor strategies or diversifying according to factors (as opposed to mean-variance optimization); they do so from the perspective of a fiduciary or a trustee who manages the investment managers and makes the ultimate investment decision.  In particular, they suggest 1. Treat factor investing as an investment belief 2. Determine what the investment managers are talking about 3. Be consequential 4. Focus on benchmark construction 5. Educate your pension fund's stake holders. 6. Regularly review the economic rationale and relevance of factors 7. Take and active stance on active choices, and 8. Decide whether allocation to factors will be static or dynamic.  They go on to summarize three ways to implement factors into the portfolio:  risk due diligence, factor tilts, and factor optimization.


KOEDIJK, K., SLAGER, A., & STORK, P. (2016). A Trustee Guide to Factor Investing. Journal Of Portfolio Management, 42(5), 28-38.

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.