2018 McKinley Capital Scientific Advisory Board Conference

September 11, 2018

Robert A. Gillam, CFA Chief Investment Officer McKinley Capital Management, LLC
John B. Guerard Jr., Ph.D. Director of Quantitative Research McKinley Capital Management, LLC

On April 19, 2018, McKinley Capital convened a conference of its Scientific Advisory Board Members to present and discuss relevant research topics. Below are executive summaries of the ideas presented and their application and possible enhancement to McKinley Capital’s investment process.

Donuts: A Picture of Optimization Applied to Fundamental Portfolios
Dr. Ian Domowitz

Dr. Ian Domowitz is formerly the Vice Chairman of ITG. Dr. Domowitz works with McKinley Capital on transactions costs analysis and effective portfolio implementation.

Dr. Domowitz asks an important question: how do you incorporate the benefits of portfolio optimization without disturbing the core beliefs of the fundamental manager? A solution is characterized graphically as a “donut,” and captures a variety of portfolio strategies. The simplicity of the framework permits the evaluation of multiple measures of portfolio performance along only two dimensions. Applied to risk reduction programs, the cost of implementing rebalancing decisions falls sharply, perhaps changing capacity. Risk-adjusted return increases monotonically with the degree of liquidity enhancement. The benefits to McKinley Capital of the Domowitz “donut” approach are extremely important when extended to its clients and strategies. We can create, in the “donut,” the largest five stocks in the index and optimize the remaining client universe to produce “donut” holdings that maximize portfolio returns and maintain a comparable Information Ratio to the traditional portfolio construction technique. Liquidity and tracking error considerations are specifically advanced in this strategy to create a portfolio with high liquidity and large Information Ratios and Sharpe Ratios.

The significance of “donuts” to our clients is that the highly statistically significant McKinley Capital stock selection models, which works best on smaller stocks, can be used in effective portfolio construction for the vast majority of the portfolio weights. The top five stocks are optimized as a separate group to create a portfolio that offers support from stock rallies of large, “non-McKinley Capital styled (largely-capitalized stocks that may not meet our discipline) stocks.”

Robust Regression and Data Mining of Financial Data
Dr. John B. Guerard, Jr. and Dr. Ganlin Xu

Dr. Ganlin Xu is the Chief Technology Officer at Guided Choice. He works with McKinley Capital on Data Mining Corrections testing.

In this analysis of the risk and return of stocks in global markets, Drs. Guerard and Xu build a reasonably large number of models for stock selection and create optimized portfolios to outperform a global benchmark. However, financial data is plagued with outliers. Drs. Guerard and Xu apply the robust regression techniques of the McKinley Capital Public Model, GLER, to deal with outliers and compare the McKinley Capital robust regression with more modern LASSO, LAR, and the Leamer S-Regression models in producing stock selection models. They then apply Markowitz-based optimization techniques and the Markowitz-Xu (1994) Data Mining Corrections test to a global and Chinese A-Share stock universes and report interesting results. They find that: (1) the GLER robust regression applications continue to be appropriate for modeling stock returns in global markets and work as well as LASSO and LAR in building effective stock selection models. The Leamer S-Regression models and the Maronna, Martin, and Yohai Optimal Influence Function robust regression modeling techniques appear to be most useful areas of future research for stock selection modeling. They also find: (2) mean-variance techniques continue to produce portfolios capable of generating excess returns above transactions costs, and (3) McKinley Capital’s models pass data mining tests such that the models produce statistically significant asset selection for global stocks.

The significance of robust regression to our clients is that the McKinley Capital stock selection modeling techniques continue to be highly statistically significant and are highly competitive with recently developed statistical models.

Guerard, Markowitz, and Xu have applied robust regression in a series of published articles to establish the determinants of U.S. and global stock returns.

Emerging Markets: A Case Study
Dr. Anureet Saxena

Dr. Anureet Saxena, an investment officer of Lazard, joined the McKinley Capital Scientific Advisory Board to use his expertise in quantitative portfolio implementation, primarily using the Alpha Alignment Factors (AAF).

Emerging Markets (EM) have for the past thirty years produced higher returns relative to risk than domestic or developed markets. In McKinley Capital’s 20-year analysis of Global, Non-US, and Emerging Markets universes, we report that the EM GLER Model Efficient Frontier, showing the maximum return for a given level of risk, dominates the Non-US and Global Frontiers. The McKinley Capital real-time EM results are extremely consistent with the EM backtest and analysis. Moreover, the EM Efficient Frontier Information Ratios and Sharpe Ratios peak at 8% tracking errors whereas Non-US and Global Ratios peaked at 6 % tracking errors. The application of the Domowitz “donut” strategy is also demonstrated in the EM analysis.

The significance to our clients of the EM case study is that the top decile product performance, and its highly statistically significance Active Returns, should be maintained with the implementation of the “donuts” portfolio construction algorithm in a universe where the top five stocks combine for the largest universe weight.

Passive Aggressive Behavior: How Active Managers Can Generate Alpha from the Rise of Passive Ownership
Mr. Rochester Cahan

Mr. Rochester Cahan, from Empirical Partners, joined the McKinley Capital Management Scientific Advisory Board to share his expertise in quantitative model implementation, primarily using Neural Language Processing (NLP) and Machine Learning (ML) algorithms.

Passive ownership of U.S. stocks soared from 2003–2017, reaching 15% of capitalization. ETFs account for 6% and index mutual funds the other 9%. US stocks with market capitalizations of $5–15 billion USD are most heavily owned by passive investors. Evidence from the mutual fund market suggests that retail investors have a long history of mistiming entry and exit points, due to their performance-chasing tendencies. Those same tendencies are also evident in the ETF market, but are magnified due to the instant liquidity, real-time price charts, and commission-free trades on offer in those vehicles. As a result, price momentum is a negative signal among US stocks with the highest passive ownership from 2002–2017. The McKinley Capital proprietary momentum signal also fared poorly in the subset of stocks with the highest passive ownership over the 2010–2017 period, whereas it added value in stocks with lower passive ownership. ETFs with the largest recent inflows tend to underperform in the future, as do the stocks held by those ETFs. Sector ETFs with large inflows are particularly prone to mean reversion.

The significance to our clients of the passive-aggressive behavior analysis is that the McKinley Capital signal worked well in the US from 2010–2017 and could potentially be further enhanced by overlaying knowledge of ETF inflows and outflows. Stocks with a favorable signal and ETF outflows tend to outperform and those with a poor signal and ETF inflows tend to underperform.

This presentation was also featured at an Institutional Investor conference in May 2018 in New York City.


Domowitz, I. (2018). Donuts: A picture of optimization applied to fundamental portfolios. The Journal of Portfolio Management 44, 103-113.

Guerard Jr., J. B., Xu, G., & Gultekin, M. N. (2012). Investing with momentum: the past, present, and future. Journal of Investing 21, 68-80.

Guerard, J.B., Jr, Markowitz, H. & Xu, G L. (2013). Global Stock Selection Modeling and Efficient Portfolio Construction and Management. Journal of Investing 22, 121-128.

Guerard, J.B., Jr, Markowitz, H. & Xu, G L. (2015). Earnings Forecasting in a Global Stock Selection Model and Efficient Portfolio Construction and Management. International Journal of Forecasting, 31, 550-560.

Guerard, J.B., Jr, Gillam, R.A., Markowitz, H. Xu, G., Deng, S. & Wang, E. (2018). Data mining corrections testing In Chinese stocks. Interfaces 48, 108-120.

Guerard, J.B., Jr, & Chettiappan, S. (2017). Active Quant: Applied Investment Research in Emerging Markets. Journal of Investing 26, 138-152.

Guerard, J.B., Jr & Saxena, A. (2018). A Case Study of Forecasted Earnings Acceleration and Stock Selection in Global and Emerging Stock Markets. Frontiers in Applied Mathematics and Statistics doi. 10.3389/fams.2018.0004


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