In this study, we examine the forecastability of a specific neural network architecture called General Regression Neural Network (GRNN) and compare its performance with a variety of forecasting techniques, including Multi-Layered Feedforward Network (MLFN), multivariate transfer function, and...

This study examines the scheduling problem for a two-stage flowshop. All jobs are immediately available for processing and job characteristics including the processing times and due dates are known and certain. The goals of the scheduling problem are (1) to minimize the total flowtime for all...

We study whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks in the parameters and bias induced by autocorrelation in the predictor variable. To do so we apply the Bai and Perron (BP) (1998, 2000) methodology to test for...

Statistical time-series approaches to hedging are difficult to beat, especially out-of-sample, and are capable of out-performing many theory-based derivative pricing model approaches to hedging commodity price risks using futures contracts. However, the vast majority of time-series approaches to...

The present study focuses on the trading of at-the-money straddles using options on foreign currency futures, namely British Pound, Canadian Dollar, and Japanese Yen. The financial performance and economic significance of a direct profit forecast trading strategy are examined. This strategy uses...

This paper retests the signaling hypothesis of dividends by examining whether managers change dividends to signal their expectation of earnings prospects using a simultaneous-equation approach. This approach allows us to more clearly test the earnings prospects signaling hypothesis and...