Volume 16, No. 3, Third Quarter 2018

A New Approach to Goals-Based Wealth Management

by: Sanjiv R. Das, Daniel Ostrov, Anand Radhakrishnan and Deep Srivastav

We introduce a novel framework for goals-based wealth management (GBWM), where risk is understood as the probability of investors not attaining their goals, not just the standard deviation of investor’s portfolios. Our framework is based on a foundation of developments in behavioral economics and finance and is consistent with modern portfolio theory. Using a simple geometric analysis, we determine a specific portfolio that matches each individual investor’s stated goals. Our approach requires information from the investors about their goals, elicited in a clear manner that market research shows is superior to common current practices. This new approach can improve the communication between advisors and clients and produce better advice for enabling clients to attain their goals with high probability through the use of efficient portfolios.

Defined Contribution Pension Plans and Mutual Fund Flows

by: Clemens Sialm, Laura Starks and Hanjiang Zhang

Defined contribution (DC) pension plans constitute an important component of mutual fund assets. Flows into DC plans depend on the decisions of plan sponsors and plan participants: The sponsors select the investment menus made available to employees and the participants decide how to allocate their retirement savings across the investment options.We examine the flows to mutual funds within DC pension plans and contrast these flows with flows from other mutual fund clienteles. We find that flows into funds from DC plans exhibit more performance sensitivity than do flows from non-DC investors.

Picking Through the Alpha Graveyard Correcting for Survivorship Bias in Investment Product Universes

by: Gregory C. Allen, Ivan S. Cliff and Walter J. Meerschaert

The authors propose a practical technique to correct for survivorship bias across return distributions for investment product universes. The technique is designed to work efficiently in a large-scale performance measurement environment. It uses all available data for survivors and non-survivors, corrects for bias across the full distribution (from 1st to 99th percentile), and can be applied to other return-based statistics such as Sharpe ratio, standard deviation, and correlation. The technique is applied to a variety of product universes over a 10-year period to highlight the practical ways that it can be used to improve the investment decision-making process.

Distributed Ledger and Blockchain Technology: Framework and Use Cases

by: Seoyoung Kim and Atulya Sarin

Since its first widespread implementation in 2009, distributed ledgers in general, and blockchain technology in particular, have rapidly become a part of the FinTech vernacular. In this paper, we provide an overview of the history of trade settlement and discuss this nascent technology that may now transform traditional methods of verifying and settling transactions. In so doing, we discuss current and potential use cases of this technology and provide a business-oriented framework for proper as well as improper implementations and applications of blockchains and distributed ledgers.

Adaptive Markets: Financial Evolution at the Speed of Thought

by: Andrew Lo • reviewed by: Savannah Smith

“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. 16, No. 3

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.

Macro-Based Parametric Asset Allocation

by: Richard Franz

Without doubt the financial returns of asset classes are interlinked with the economy. However, a direct link between financial returns and return-driving forces has not been discovered yet. Moreover, there exist many robust approaches for within-asset-class allocation but few advances have been made for between-asset-class allocation. To address these topics I propose a direct modeling of the weights with macroeconomic risk factors. This allows to implicitly identify capital-market dynamics and thus provides a framework which can help in the tactical asset allocation decision.