Ann Marie Hibbert

We examine institutional investors’ herding behavior around credit rating changes. We find that institutions herd into stocks with recent rating upgrades and herd out of stocks with recent downgrades, with a stronger effect for rating downgrades, large rating changes, and high‐risk firms. These results remain after controlling for earnings surprises and analysts’ recommendation revisions. Results from price impact tests and cross‐sectional analyses by firm‐ and institution‐type suggest that the institutional herding following credit rating changes is mainly an overreaction to rating downgrades and is more consistent with reputational herding.

There are significant differences in the performance of Target Date Funds (TDFs) with the same target year. Using a unique dataset from Morningstar, we show that within the same target year, funds with lower than the average expense ratio, or higher than average allocation to equities, outperform similar funds. This outperformance exists across all target year groups and is economically meaningful. Furthermore, deviations in the equity allocation have a greater impact on performance than does expense ratio. Using bootstrap simulations to investigate the impact over a longer horizon, we show that deviations from the average allocations or expense ratios have a meaningful impact on the retirement savings of an average investor.

The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom Bubble, the 2008 Housing Crisis, and the 2015 Chinese Bubble. We employ three main empirical methods; the LPPL model to identify asset bubbles, the DCC-GARCH model to measure volatility clustering, and the Diebold-Yilmaz volatility spillover index to measure the level of financial contagion. We provide robust evidence that during the DotCom bubble there was very limited spillover between the S&P 500, the Shanghai, and the Shenzhen Composite Indexes. However, there was significantly more spillover effects in the two more recent crises, i.e., the Housing crisis and the 2015 Chinese Bubble. Together, these results highlight the fact that as financial markets have become more globalized, there are greater levels of volatility transmission and correspondingly fewer potential benefits from international diversification.

We use daily data for a panel of 34 countries to investigate regional differences in sovereign credit default swaps (CDS) spread determinants and the significance of local versus global market factors. Similar to prior studies, we find a high level of commonality among CDS spreads, but our results show that this effect is stronger in Latin American CDS. The results of our quantile panel regression model show that although global forces drive spreads across the conditional distribution, changes in credit ratings are significant in explaining CDS spreads only in the upper quantiles. We also confirm the existence of regional differences in spread determinants.

The authors use data on a large sample of investment-grade and high-yield corporate bonds of non-financial firms to investigate the stability of the relationship between yield spreads and both Treasury term structure and market risk variables. The sample spans before, during, and after the recent financial crisis. Regression model estimates reveal a negative relationship between credit spread changes and changes in the term structure variables, as well as a significant effect of stock market conditions, bond volatility, and aggregate liquidity on spreads. Results from a Markov switching-regime model confirm the presence of two regimes and show different effects of certain spread determinants undereach regime.

We investigate the determinants of daily changes in credit spreads in the U.S. corporate bond market. Using a sample of liquid investment grade and high-yield b