Article1998

Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

Emad Saad · Danil V. Prokhorov · Donald C. Wunsch

537 citations0/0 files storedIEEE Transactions on Neural Networks

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

Abstract

Three networks are compared for low false alarm stock trend predictions. Short-term trends, particularly attractive for neural network analysis, can be used profitably in scenarios such as option trading, but only with significant risk. Therefore, we focus on limiting false alarms, which improves the risk/reward ratio by preventing losses. To predict stock trends, we exploit time delay, recurrent, and probabilistic neural networks (TDNN, RNN, and PNN, respectively), utilizing conjugate gradient and multistream extended Kalman filter training for TDNN and RNN. We also discuss different predictability analysis techniques and perform an analysis of predictability based on a history of daily closing price. Our results indicate that all the networks are feasible, the primary preference being one of convenience.

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References

1996Designing a neural network for forecasting financial and economic time series 1988Economic prediction using neural networks: the case of IBM daily stock returns 1993Recognizing Financial Distress Patterns Using a Neural Network Tool

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