This paper describes our participation in the Intent Mining track of NTCIR-11. We present our methods and results for both document ranking and subtopic mining. Our ranking methods are based on several data fusion techniques with some variations. Our subtopic mining method is a very simple technique that uses query dimensions’ items to form a subtopic

Arti cial Neural Networks can be used to predict future returns of stocks in order to take nancial decisions Should one build a separate network for each stock or share the same network for all the stocks In this paper we also explore other alternatives in which some layers are shared and others are not shared When the prediction of future returns for di erent stocks are viewed as di erent task...

In this paper we propose a generalised autoregressive conditional heteroskedasticity (GARCH) model-based test for a unit root. The model allows for two endogenous structural breaks. We test for unit roots in 156 US stocks listed on the NYSE over the period 1980 to 2007. We find that the unit root null hypothesis is rejected in 40% of the stocks, and only in four out of the nine sectors the null...

This paper tests whether information advantages help explain why some individual investors concentrate their stock portfolios in a few stocks. Slock investments made by households that choose lo concentrate their brokerage accounts in a few stocks outperform those made by households with more diversified accounts (especially among those with large portfolios). Excess returns of concentrated rel...

In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by th...

In this thesis, we study the behavior of bankrupt stocks. Bankrupt stock is a special case of the Hard-to-Borrow stocks. Besides the general nice feature of the Hard-to-borrow feedback for the buy-in demand, the bankrupt stocks could exclude the diffusive effects. This nice property would modify the Marco Avellaneda and Mike Lipkin’s jump-diffusion model for the Hard-to-Borrow stocks into the p...

Four genetic stocks of Leghorn pullets were used to evaluate the effects of genetic selection on growth and fearfulness behavior. Three of the stocks were the Ottawa randombred control stocks from 1950 (CS5), 1959 (CS7), and 1972 (CS10). The fourth stock was a 1993 commercial laying stock (CCS) whose ancestors were involved in the formation of the randombred control stocks. Pullets were reared ...