Predicting Relative Returns

Predicting Short Term Stock Returns

As the capital markets evolve and expand, more and more data is being created daily. This explosion of data has made the flow of information much more efficient. As market participants act on this information flow, it drives market prices to more efficient values, . One of the driving forces in this march to efficiency, is the application of various algorithmic learning techniques on both marke...

Predicting Daily Returns for the IBM Stock

The goal of the work described in this paper is to predict the daily returns of the closing prices for the IBM stock. From the original data of IBM daily quotes a new data set was built using technical indicators as predictor variables. Using this new data set, two modelling approaches were tried: regression and classification. Early analysis and experiments suggested that this prediction probl...

Predicting Local Returns with Macroeconomic Variables

Predictability of the return on the market portfolio is a well established fact. This study shows that predictability is a more general phenomenon and it extends to return indices of the U.S. states. At the state level, the consumption trend deviation of Lettau and Ludvigson, and the collateral ratio of Lustig and Van Nieuwerburgh can predict short-term and long-term state-level returns. The st...

Predicting a Rank Measure for Stock Returns

Predicting Equity Returns from Securities Data

Our experiments with capital markets data suggest that the domain can be e ectively modeled by classi cation rules induced from available historical data for the purpose of making gainful predictions for equity investments. New classi cation techniques developed at IBM Research, including minimal rule generation (R-MINI) and contextual feature analysis, seem robust enough for consistently extra...