This paper examines the distortionary effects of inflation volatility on the allocation of bank loans. We argue that inflation volatility would render bank managers to behave more conservatively in issuing new loans. In contrast, when inflation volatility is low, bank managers would have the latitude to lend more idiosyncratically. Using a large panel of commercial bank data gathered from 15 countries, we provide support for our hypothesis by demonstrating a strong negative relation between inflation volatility and the dispersion of loans-to-assets ratio. Similar results are obtained when we split the sample between EU and non-EU country groups. The robustness of our findings is confirmed by a battery of sensitivity checks.

Monetary Instability, the Predictability of Prices, and the Allocation of Investment: An Empirical Investigation Using U.K. Panel Data by Paul Beaudry, Mustafa Caglayan and Fabio Schiantarelli. Published in volume 91, issue 3, pages 648-662 of American Economic Review, June 2001

Recent studies have documented that institutional investors trade contrary to the predictions of the book-to market anomaly. We examine whether a prominent sub-group of institutional investors, namely hedge funds, differ from other institutions in terms of their trading behavior with respect to the book-to-market effect. We find that hedge funds significantly alter their trading preferences with respect to growth and value stocks, after book-to-market values become public information. More importantly, we show that hedge funds are better able to identify overpriced growth stocks compared to other institutions. Our results contribute to the literature on institutional investors’ trading with respect to stock return anomalies.

Although investors accept a negative premium for lottery-like stocks, it is puzzling that the opposite effect is not observed among stocks experiencing large daily losses. We find that many stocks that experience large negative daily returns (MIN) also display large positive daily returns (MAX); therefore the MIN effect is subdued. Once stocks ranked as high-MAX within MIN deciles are removed, we find that the MIN effect produces significantly higher next-month returns. The subsequent-month returns following MIN are particularly higher when stocks experience negative cumulative monthly returns, when firm-specific investor sentiment is low, and when stocks are near their 52-week lows.

We examine how institutional investors trade stocks with high research and development (R&D) expenses and investigate whether they can detect value-relevant R&D. We document significant differences between hedge funds and other institutional investors in their trading in high R&D stocks. We find that hedge funds (other institutional investors) invest more (less) in high R&D stocks compared to all other stocks. Moreover, hedge funds exhibit strong stock-picking ability in high R&D stocks, and hedge funds with larger allocations to high R&D stocks generate higher future fund returns. Our findings suggest that hedge funds have superior skill in identifying value-relevant R&D.

This paper estimates hedge fund and mutual fund exposure to newly proposed measures of macroeconomic risk that are interpreted as measures of economic uncertainty. We find that the resulting uncertainty betas explain a significant proportion of the cross-sectional dispersion in hedge fund returns. However, the same is not true for mutual funds, for which there is no significant relationship. After controlling for a large set of fund characteristics and risk factors, the positive relation between uncertainty betas and future hedge fund returns remains economically and statistically significant. Hence, we argue that macroeconomic risk is a powerful determinant of cross-sectional differences in hedge fund returns.

This paper investigates the extent to which market risk, residual risk, and tail risk explain the cross-sectional dispersion in hedge fund returns. The paper introduces a comprehensive measure of systematic risk (SR) for individual hedge funds by breaking up total risk into systematic and fund-specific or residual risk components. Contrary to the popular understanding that hedge funds are market neutral, we find that systematic risk is a highly significant factor explaining the dispersion of cross-sectional returns while at the same time measures of residual risk and tail risk seem to have little explanatory power. Funds in the highest SR quintile generate 6% more average annual returns compared with funds in the lowest SR quintile. After controlling for a large set of fund characteristics and risk factors, systematic risk remains positive and highly significant, whereas the relation between residual risk and future fund returns continues to be insignificant. Hence, systematic risk is a powerful determinant of the cross-sectional differences in hedge fund returns.

This paper investigates hedge funds' exposures to various financial and macroeconomic risk factors through alternative measures of factor betas and examines their performance in predicting the cross-sectional variation in hedge fund returns. Both parametric and non-parametric tests indicate a significantly positive (negative) link between default premium beta (inflation beta) and future hedge fund returns. The results are robust across different subsample periods and states of the economy, and after controlling for market, size, book-to-market, and momentum factors as well as the trend-following factors in stocks, short-term interest rates, currencies, bonds, and commodities. The paper also provides macro-level and micro-level explanations of our findings.

This paper investigates the relationship between upside potential and future hedge fund returns. We measure upside potential based on the maximum monthly returns of hedge funds (MAX) over a fixed time interval, and show that MAX successfully predicts cross-sectional differences in future fund returns. Hedge funds with strong upside potential generate 0.70% per month higher average returns than funds with weak upside potential. After controlling for alternative risk and performance measures, funds’ market-timing ability, and a large set of fund characteristics, the positive link between MAX and future returns remains highly significant. We conclude that MAX, as a simple proxy for realized noln-normalities in hedge funds, offers incremental information on future hedge fund returns above and beyond provided by standard risk, performance, and market-timing measures.

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds.