Timothy B. Riley

Return Chasing in Bond Funds

We use a unique dataset of SEC Form N-SAR filings to examine the gross flows of U.S. bond funds. We find that gross inflows and outflows average around 4% of total net assets per month, but net flows average only 0.26%. When modeling these flows, we see that, as with equity funds, bond fund investors chase returns. Most of the return chasing is done by inflows. Funds with the highest decile of returns average 1% higher net flow and inflow per month, and the lowest-decile funds lose nearly 0.6% in assets per month. Next, we find that positive net flows cause bond funds to diversify their holdings regardless of fund size. Finally, we test whether flows predict returns and find that an inflow-weighted portfolio generates alphas of 0.8% per year. Bond inflows appear to be “smart money.”

Predictability in Bond ETF Returns

This article studies the persistence of bond exchange-traded fund (ETF) premiums and discounts. Following a day of high or low premiums or discounts over net asset value (NAV), ETFs tend to maintain a premium or discount for up to 30 days. Premiums and discounts also predict distinct patterns of returns after daily closing. Overnight returns are negative following a high premium, while ETFs with large discounts are followed by positive overnight returns. The large discount ETFs have substantially higher returns than high premium ETFs over the subsequent thirty days. We find that traditional liquidity measures, along with prior deviations from NAV, are significant in explaining a fund’s premiums/discounts. Finally, we examine a long–short portfolio strategy to exploit the observed deviations from NAV and find it generates an alpha of 0.96% per month or about 11.5% per year.

Dissecting the Low Volatility Anomaly

We find the low volatility anomaly is present in all but the smallest of stocks. Portfolios can be formed on either total or idiosyncratic volatility to take advantage of this anomaly, but we show measures of idiosyncratic volatility are key. Standard risk-adjusted returns suggest that there is no low volatility anomaly from 1996 through 2011, but we find this result arises from model misspecification. Caution must be taken when analyzing high volatility stocks because their returns have a nonlinear relationship with momentum during market bubbles.

The Long and Short of the Vol Anomaly

On average, stocks with high prior-period volatility underperform those with low prior-period volatility, but that simple comparison paints an incomplete, and potentially misleading, picture. As we show, high volatility is an indicator of both positive and negative future abnormal performance. Among high volatility stocks, those with low short interest experience extraordinary positive returns, while those with high short interest experience equally extraordinary negative returns. Our results show that there is a surprisingly strong connection between the volatility and short interest puzzles and that studying the two together yields new and sharper insights into both.

Portfolio Concentration and Mutual Fund Performance

Mutual fund managers should choose to increase the concentration of their portfolio when they possess information of great enough expected value to offset the risks of increased concentration. Consistent with that idea, we find that fund performance improves after concentration increases. Because the riskiness of increased concentration varies between funds and over time, the expected value of the information required by managers before choosing to increase concentration should also vary. Among other results, we show that the concentration-performance relation is stronger for funds with less institutional ownership and when investor sentiment is low.

Challenging the Conventional Wisdom on Active Management: A Review of the Past 20 Years of Academic Literature on Actively Managed Mutual Funds

Just over 20 years have passed since the publication of Mark Carhart’s landmark 1997 study on mutual funds. Its conclusion—that the data did “not support the existence of skilled or informed mutual...

Skill and Persistence in Mutual Fund Returns: Evidence from a Six-Factor Model

The addition of the Fama and French (2015) profitability (RMW) and investment (CMA) factors to the standard four-factor model reveals persistent positive alpha after fees for mutual funds. Over the period 2000-2014, about 65 percent of fund managers have at least some skill, and about 15 percent have skill in excess of fees. The best performing funds have significant negative exposures to both factors, while the worst performing funds have significant positive exposures. Because of that pattern, failure to account for these factors masks a difference in alpha between the best and worst performers of almost 7 percent per year.

Investible Benchmarks for Actively Managed Mutual Funds

I use exchange traded funds to construct low cost benchmarks for actively managed mutual funds. The benchmarks can be identified in advance, require no leverage or shorting, and require only annual rebalancing. The average fund underperforms its benchmark by 1% per year after expenses, a difference in performance equivalent to a

Average Funds versus Average Dollars: Implications for Mutual Fund Research

25 billion per year opportunity cost for investors selecting active management. The fees charged by actively managed funds are the primary reason they underperform. Active management comes at a significant cost, but without any offsetting benefit.