We study the effects of local religious beliefs on mutual fund risk-taking behaviors. Funds located in low-Protestant or high-Catholic areas exhibit significantly higher fund return volatilities. Similar differences persist when we use the religiosity ratios at fund managers' college locations. Risk-taking associated with local religious beliefs manifests in higher portfolio concentrations, higher portfolio turnover, more aggressive interim trading, and more “tournament” risk-shifting behaviors, but not over-weighting risky individual stocks. Overall, our results suggest that local religious beliefs have significant influences on mutual fund behaviors. This paper was accepted by Brad Barber, finance.

From 1997 to March 2000, as technology stocks rose more than five-fold, institutions bought more new technology supply than individuals. Among institutions, hedge funds were the most aggressive investors, but independent investment advisors and mutual funds (net of flows) actively invested the most capital in the technology sector. The technology stock reversal in March 2000 was accompanied by a broad sell-off from institutional investors but accelerated buying by individuals, particularly discount brokerage clients. Overall, our evidence supports the bubble model of Abreu and Brunnermeier (2003), in which rational arbitrageurs fail to trade against bubbles until a coordinated selling effort occurs. PERCEIVED BUBBLES, SUCH AS the "tech bubble" and the more recent credit and real estate bubbles, pose challenges to efficient market theories and are not well understood. The stock market run-up in the mid to late 1990s was the greatest in the last 140 years of U.S. history in terms of both price appreciation and market-wide valuation multiples. 1 While theoretical models explaining bubbles are plentiful, there is little rigorous empirical work uncovering the complex

In this paper, we study the relationship between default probability and stock returns. Using the market-based measure of Expected Default Frequency (EDF) constructed by Moody's KMV, we first demonstrate that higher default probabilities are not necessarily associated with higher expected stock returns, a finding that complements the existing empirical evidence. We then show that the puzzling and complex relationship between stock returns and default probability is consistent with the implications of existing structural models that account for possible negotiated benefits for equity-holders upon default. Adapting the setting of the Fan and Sundaresan (2000) model that explicitly considers the bargaining game between equity-holders and debt-holders in financial distress, we are able to obtain a theoretical relationship between expected returns and default probability that resembles the empirically observed pattern. Our analysis indicates that, depending on the level of shareholder advantage, the relationship between default probability and equity return may be either upward sloping (low shareholder advantage) or humped and downward sloping (high shareholder advantage). Moreover, we show that distressed firms in which shareholders have a stronger advantage in renegotiation exhibit lower expected returns, and that their default probabilities do not adequately represent the risk of default born by equity. We test these implications using several proxies for shareholder advantage and find strong support in the data.

Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data center to develop such capabilities. First, we search for repeatable workload patterns by exploring cross-VM workload correlations resulted from the dependencies among applications running on different VMs. Treating workload data samples as time series, we develop a co-clustering technique to identify groups of VMs that frequently exhibit correlated workload patterns, and also the time periods in which these VM groups are active. Then, we introduce a method based on Hidden Markov Modeling (HMM) to characterize the temporal correlations in the discovered VM clusters and to predict variations of workload patterns. The experimental results show that our method can not only help better understand group-level workload characteristics, but also make more accurate predictions on workload changes in a cloud.

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic transmission. The difficulty of the problem comes from the fact that it is practically infeasible for a CR to first scan all channels and then pick the best among them, due to the potentially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid collisions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR's average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that addresses all the above considerations is derived by formulating the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to conduct a "second-round" optimization over the operational parameters, such as the sensing and probing times. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone.