in this study, a prediction model based on support vector machines (svm) improved by introducing a volume weighted penalty function to the model was introduced to increase the accuracy of forecasting short term trends on the stock market to develop the optimal trading strategy. along with vw-svm classifier, a hybrid feature selection method was used that consisted of f-score as the filter part ...

This paper considers a problem of multi-period supply portfolio selection and execution with demand information updates. A supply portfolio specifies a buyer’s decision on selecting sourcing mix from among a group of suppliers. We develop a framework for optimal supply portfolio selection and execution. Further, we demonstrate that the optimal portfolio selection follows a base-stock policy and...

This paper discusses the use of multilayer feed forward neural networks for predicting a stock's excess return based on its exposure to various technical and fundamental factors. To demonstrate the effectiveness of the approach a hedged portfolio which consists of equally capitalized long and short positions is constructed and its historical returns are benchmarked against T-bill returns and th...

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...

This paper presents methodologies to select equities based on soft-computing models which focus on applying fundamental analysis for equities screening. This paper compares the performance of three soft-computing models, namely multi-layer perceptrons (MLP), adaptive neuro-fuzzy inference systems (ANFIS) and general growing and pruning radial basis function (GGAP-RBF). It studies their computat...

Once it is decided that investment is to be made in the stock, the obvious question which arises is: which all stocks should be purchased? Past performance will not guarantee the future, but it is still worthwhile to evaluate the investments based on their ability to deliver consistent returns with minimal risk. Therefore, the ability to generate most profitable return from short term stock tra...

In this paper, we propose use of the influence diagram for stock portfolio selection. We use an algorithm that applies the mutual information as the metric to guide the refinement of the influence diagram. We applied the algorithm to the conceptual refinement of the influence diagram. We tested our algorithm to a specific domain – portfolio selection; the result is impressive compared to the S&...

We used the Support Vector Machines in a classification approach to 'beat the market'. Given the fundamental accounting and price information of stocks trading on the Australian Stock Exchange, we attempt to use SVM to identify stocks that are likely to outperform the market by having exceptional returns. The equally weighted portfolio formed by the stocks selected by SVM has a total return of ...

This paper presents methodologies to select equities based on soft-computing models which focus on applying fundamental analysis for equities screening. This paper compares the performance of three soft-computing models, namely Multilayer Perceptrons (MLP), Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and General Growing and Pruning Radial Basis Function (GGAP-RBF). It studies their computati...