Comparing Different Explanations of the Volatility Trend

We analyze the puzzling behavior of the volatility of individual stock returns over the past few decades. The literature has provided many different explanations to the trend in volatility and this paper tests the viability of the different explanations. Virtually all current theoretical arguments that are provided for the trend in the average level of volatility over time lend themselves to explanations about the difference in volatility levels between firms in the cross-section. We therefore focus separately on the crosssectional and time-series explanatory power of the different proxies. We fail to find a proxy that is able to explain both dimensions well. In particular, we find that Cao et al. (2008) market-to-book ratio tracks average volatility levels well, but has no crosssectional explanatory power. On the other hand, the low-price proxy suggested by Brandt et al. (2010) has much cross-sectional explanatory power, but has virtually no time-series explanatory power. We also find that the different proxies do not explain the trend in volatility in the period prior to 1995 (R-squared of virtually zero), but explain rather well the trend in volatility at the turn of the Millennium (1995-2005).Volatility; Trend; Turnover

Node-Centric Detection of Overlapping Communities in Social Networks

We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms

Information Systems and Stock Return Volatility

Measuring Information Systems (IS) value has been constantly attracting much attention and debate within the IS research community. Since information systems effects are often difficult to quantify, traditional payoff evaluation methods often yield conflicting results. In this paper we suggest that some information systems can be evaluated on the basis of their effect on stock return volatility. Systems which facilitate information sharing and decision-making can improve the quality of company information to stakeholders, thus reducing surprise levels in financial markets. Specifically, these systems can lead to more consistent and predictable company performance. Hence, we hypothesize that information systems can help to reduce a company’s stock return volatility. To test this hypothesis, we have conducted an empirical analysis on a sample of firms that have deployed a Business Intelligence (BI) system. The results indicate a significant reduction in stock return volatility after BI deployment

Economic Externalities of Autocomplete: Empirical Analysis of Financial Markets

Individuals increasingly rely on automatic mechanisms to suggest quick completion of their input text. In this research we test for such an interface\u27s economic effects. Theory on exploratory behavior suggests that exposure to text alternatives can facilitate diversion of the users from their initially intended text to the new terms that appear in the domain and become available. We analyze whether such diversions make an economic impact in terms of transaction loss. We test our hypotheses in the context of the financial industry by analyzing changes in turnover following the introduction of a new security (ticker). Consistent with our hypothesis, upon ticker introduction, turnover of securities that are syntactically similar to the new introduced security is significantly reduced, by 3%-5%, around the starting day of trade of the new security. These results support the notion that the practice of providing text alternatives as an interface can make a significant economic impact