To continue improving how we deliver the Financial Analysts Journal, in 2019 CFA Institute will partner with the publisher Taylor & Francis/Routledge to provide a variety of services, including web hosting, printing, fulfillment, and subscription sales. Members will continue to enjoy free access to the Journal both online and in print.

Congratulations to the 2018 Graham and Dodd Award Winners

Top Award

“Buffett's Alpha”

Andrea Frazzini, David Kabiller, CFA, and Lasse Heje Pedersen

Scroll Award

“Hedge Funds and Stock Price Formation”

Charles Cao, Yong Chen, William N. Goetzmann, and Bing Liang

CFA Institute Research, Analysis, and Insights Have a New Home

CFA Digest, CFA Institute Magazine, CFA Institute Research Foundation, Standards, and Advocacy publications have moved to cfainstitute.org.

Go Digital, Forgo Print

Learn about how you can help reduce our carbon footprint and use our resources more responsibly and ethically by forgoing your print copy of the Financial Analysts Journal. Members can continue to receive print copies of the Journal; let us know here and we’ll keep you on the print mailing list for no additional fee.

Read the Latest Edition of the Financial Analysts Journal

"Missing the Mark: Mortgage Valuation Accuracy and Credit Modeling," by Alexander N. Bogin, William M. Doerner, and William D. Larson

"The Returns to Private Debt: Primary Issuances vs. Secondary Acquisitions," by Douglas Cumming, Grant Fleming, and Zhangxin (Frank) Liu

"Trends’ Signal Strength and the Performance of CTAs," by Gert Elaut and Péter Erdős

"Comparing Cost-Mitigation Techniques," by Robert Novy-Marx and Mihail Velikov

“Financial Statement Anomalies in the Bond Market”

Steven S. Crawford, Pietro Perotti, Richard A. Price III, and Christopher J. Skousen

We investigate the association between bond returns and 32 financial statement variables. Our findings show that 17 of the 32 financial statement measures we examine are significantly related to future bond returns. Evidence of inefficiency is more pronounced when institutional investors are less active and when there is more uncertainty about the creditworthiness of the issuer. We contribute to the literature by significantly expanding the number of anomalies analyzed and by providing practitioners with actionable guidance on which trading strategies may be profitable in the bond market.

“Trusting Clients' Financial Risk Tolerance Survey Scores”

Neil Hartnett, Paul Gerrans, Robert Faff

We examine whether and to what extent financial advisers can trust financial risk tolerance scores derived from client survey responses. We propose using the standard deviation of standardized survey responses as a simple practical measure for determining the reliability of client risk tolerance measures. Our findings suggest that advisers will better discharge their fiduciary responsibilities by re-examining a client's survey results where there is substantial variation in that client's standardized survey responses, and resurveying such clients to better gauge their risk tolerance scores.

“Brokers or Investment Advisers? The Public Perception”

Patrick A. Lach, CFA, Leisa Reinecke Flynn, and G. Wayne Kelly

We find that individuals are confused by titles used by investment professionals and cannot distinguish between investment advisers and brokers. This confusion persists even among those with investment industry experience, a college degree, or a high level of perceived knowledge. Surveyed individuals better identified the responsibilities of an “investment sales representative” with brokerage compared to more ambiguous titles brokers often use such as “financial advisor.”

“Machine Learning for Stock Selection”

Keywan Christian Rasekhschaffe and Robert C. Jones, CFA

Machine learning is an increasingly important and controversial topic in quantitative finance. A lively debate persists as to whether machine learning techniques can be practical investment tools. Although machine learning algorithms can uncover subtle, contextual, and nonlinear relationships, overfitting poses a major challenge when one is trying to extract signals from noisy historical data. We describe some of the basic concepts of machine leaning and provide a simple example of how investors can use machine learning techniques to forecast the cross-section of stock returns while limiting the risk of overfitting.

“Spending Policy Customization for Institutional Preferences”

James Yaworski, CFA

Many research papers have demonstrated the shortcomings of popular spending rules—specifically, the tendency for rules to cause a loss of purchasing power over time. This study identifies the negative correlation between portfolio purchasing power and recommended spending rates as the primary cause of these shortcomings and the source of considerable fiduciary risk. Using this research, I outline a new spending rule, the “purchasing power rule,” which is designed to sustain portfolio value in a reliable manner. I present a framework based on the purchasing power rule for customizing spending rules to match organizational preferences and goals.