There is growing literature on search behavior and using search for prediction of market share or macroeconomic indicators. This research explores investors' stock search behaviors and investigates whether there are patterns in stock returns using those for return prediction. Stock search behaviors may reveal common interest among investors. In the first study, we use graph theory to find investment habitats (or search clusters) formed by users who search common set of stocks frequently. We study stock returns of stocks within the clusters and across the clusters to provide theoretical arguments that drive returns among search clusters. In the second study, we analyze return comovement and cross-predictability among economically related stocks searched frequently by investors. As search requires a considerable amount of cognitive resources of investors, they only search a few stocks and pay high attention to them. According to attention theory, the speed of information diffusion is associated with the level of attention. Quick information diffusion allows investors to receive relevant information immediately and take instantaneous trading action. This immediate action may lead to correlated return comovement. Slow information diffusion creates latency between the occurrence of an event and the action of investors. The slower response may lead to cross-predictability. Making use of the discrepancy in information diffusion, we implement a trading strategy to establish arbitrage opportunities among stocks due to difference in user attention. This research enriches the growing IS literature on information search by (1) identifying new investment habitats based on user search behaviors, (2) showing that varying degrees of co-attention and economic linkages may lead to different speed of information diffusion (3) developing a stock forecasting model based on real-time co-attention intensity of a group economically linked stocks and (4) embarking a new research area on search attention in stock market. The methods in handling complex search data may also contribute to big data research. / text

This paper investigates the influence of hedging on firm value and stock return exposures in U.S. oil and gas industry from 1998 to 2004. Previous empirical results show that the relationship between firm value and corporate hedging activities is mixed. We find that the trend and volatility of oil and gas prices play important roles in the issue. Our results indicate that corporate exposures to oil and gas prices are asymmetric. We also find that gas reserve hedging has significant impacts on firm value when volatility of gas price is high. In conclusion, our results show that corporate hedging policies may add firm values in some special situations.

Around the world, knowledge and technology transfer have moved to the forefront of attention in economic, social and industrial policy. As the origins of future development increasingly derives from innovation, attention is paid more and more to non-traditional sources that have the potential to become the basis for creation of new businesses or the catalyser for the rejuvenation of old ones. Among those sources, we find university. These last years, academic patents have been one of the emerging phenomena witnessing the growing evolvement of university in the innovation process. The aim of this doctoral dissertation is to analyse the transfer of technology from university to industry through the analysis of patents. This work pursuits a threefold approach. First, it intends to analyse which characteristics determine the propensity of a university to get involved in technology transfer and more specifically to apply for a patent. Second, it disentangles the underlining value determinants of the patents to decode the value of academic patents and to identify the research processes that are leading to the most valuable inventions. Finally, it investigates the relevancy of academic patenting for innovation in general and wonders if on the long run, such practices could put innovation at risk.

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Very accurate methods, based on boundary integral techniques, are developed for the study of multiple, interacting fluid interfaces in an Eulerian fluid. These methods are applied to the evolution of a thin, periodic layer of constant vorticity embedded in irrotational fluid. Numerical regularity experiments are conducted and suggest that the interfaces of the layer develop a curvature singularity in infinite time. This is to be contrasted with the more singular vorticity distribution of a vortex sheet developing such a singularity in a finite time.

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