Volume 15, No. 1, First Quarter 2017

Moore’s Law Vs. Murphy’s Law in the Financial System: Who’s Winning?

by: Andrew W. Lo

Breakthroughs in computing hardware, software, telecommunications, and data analytics have transformed the financial industry, enabling a host of new products and services such as automated trading algorithms, crypto-currencies, mobile banking, crowdfunding, and robo-advisors. However, the unintended consequences of technology-leveraged finance include firesales, flash crashes, botched initial public offerings, cybersecurity breaches, catastrophic algorithmic trading errors, and a technological arms race that has created new winners, losers, and systemic risk in the financial ecosystem. These challenges are an unavoidable aspect of the growing importance of finance in an increasingly digital society. Rather than fighting this trend or forswearing technology, the ultimate solution is to develop more robust technology capable of adapting to the foibles in human behavior so users can employ these tools safely, effectively, and effortlessly. Examples of such technology are provided.

A Machine Learning Approach to Research Curation for Investment Process

by: Sonya Cates, Stephen Lawrence, Carla Penedo and Viktoriia Samatova

Many investment professionals consider academic research instrumental in improving the quality of the investment process. However, it is hard to extract investment insights from the vast and rapidly expanding research corpus, which requires a large amount of time and human effort in order to absorb. We offer a novel solution to this problem by introducing a machine learning approach to research curation. By comparing the performance and accuracy of humans and machines, we show that a machine learning approach approximates the quality of human curation but offers the strategic benefits of scalability, efficiency and lower cost.

Picking “Winners” Funds

by: Joshua Livnat, Gavin Smith and Martin Tarlie

One of the most crucial decisions for investors and plan sponsors is the selection of funds among the thousands of available alternatives.We find that regardless of the initial criterion used to rank funds based on past performance, more diversified top funds outperform concentrated top funds in the subsequent year. This better performance is attributed to the more consistent returns of funds with diversified holdings. We also find that when the initial criterion is based on a manager’s skill, as measured by a positive intercept in a regression of past fund returns on the five Fama–French factors, active share is a useful tool to predict future winner funds among top skill managers. However, diversified top funds provide slightly higher returns and less severe drawdowns than funds with high active share and top manager skill.

To account for the problem that high information ratio can be associated with low return but even lower tracking error, we introduce the Modified Information Ratio (IR) measure. This measure adjusts the conventional IR to account for an investor’s desired alpha. The Modified IR measure and conventional IR behave similarly with respect to diversification. We find that top Modified IR funds that are also diversified—winner funds—have significantly better future 12-month returns than top Modified IR funds that are concentrated.

Stock Portfolio Design and Backtest Overfitting

by: David H. Bailey, Jonathan M. Borwein and Marcos López de Prado

In mathematical finance, backtest overfitting connotes the usage of historical market data to develop an investment strategy, where too many variations of the strategy are tried, relative to the amount of data available. Backtest overfitting is now thought to be a primary reason why investment models and strategies that look good on paper often disappoint in practice. Models and strategies suffering from overfitting typically target the specific idiosyncrasies of a limited dataset, rather than any general behavior, and, as a result, often perform erratically when presented with new data.

In this study, we address overfitting in the context of designing a mutual fund or investment portfolio as a weighted collection of stocks. Very often a newly minted equity-based fund of this type has been designed by an exhaustive computer-based search of some sort to obtain an optimal weighting that exhibits excellent performance based, say, on the past 10 or 20 years’ historical market data, and the fund often highlights this backtest performance.

In the present paper, we illustrate why this back test-driven portfolio design process often fails to deliver real-world performance. We have developed a computer program that, given any desired performance profile, designs a portfolio consisting of common securities, such as the constituents of the S&P 500 index, that achieves the desired profile based on in sample back test data. We then show that these portfolios typically perform erratically on more recent, out-of-sample data. This is symptomatic of statistical overfitting. Less erratic results can be obtained by restricting the portfolio to only positive-weight components, but then the results are quite unlike the target profile on both in-sample and out-of-sample data.

The Revolving Door

by: Craig W. French

“Case Studies” presents a case pertinent to contemporary issues and events in investment management. Insightful and provocative questions are posed at the end of each case to challenge the reader. Each case is an invitation to the critical thinking and pragmatic problem solving that are so fundamental to the practice of investment management.

Traded Funds and the New Dynamics of Investing

by: Ananth Madhavan • reviewed by: Ben Eischen

“Book Reviews” identifies important, and often popular, new books from a wide range of investment topics. Beyond providing a summary and review of the content and style of the books, “Book Reviews” seeks to contribute to a conscious, critical, and informed approach to investment literature.

Practitioner’s Digest • Vol. 15, No. 1

The “Practitioners Digest” emphasizes the practical significance of manuscripts featured in the “Insights” and “Articles” sections of the journal. Readers who are interested in extracting the practical value of an article, or who are simply looking for a summary, may look to this section.