Predicting Global Stock 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 a Rank Measure for Stock Returns

Predicting Stock Market Returns with Aggregate Discretionary Accruals∗

We find that the positive relation between aggregate accruals and one-year-ahead market returns documented in Hirshleifer, Hou and Teoh [2009] is driven by discretionary accruals but not normal accruals. The return forecasting power of aggregate discretionary accruals is robust to choices of sample periods, return measurements, estimation methods, business condition and risk premium proxies, an...

Predicting Stock Returns Using a Variable Order Markov Tree Model

The weak form of the Efficient Market Hypothesis (EMH) states that the current market price fully reflects the information of past prices and rules out predictions based on price data alone. In an efficient market, consistent prediction of the next outcome of a financial time series is problematic because there are no reoccurring patterns that can be used for a reliable prediction. This researc...