Neural Networks Approach to the Detection of Weekly Seasonality in Stock Trading

In this article we investigate the problem of detection the statistically significant dependences of stock trading return, which occur in particular days of the week (usually the first or the last trading day), and which could be important for creating profitable investment strategies. The identifying such days of the week (day-of-the-week effect) is performed by using artificial neural networks. The research results helped to conclude the effectiveness of application of neural networks, as compared to the traditional linear statistical methods for finding stock trading anomalies. The effectiveness of the method was confirmed by exploring impact of different variables to the day-of-the-week effect, evaluation of their influence and sensitivity analysis, and by selecting best performing neural network type. The experimental verification was implemented by using Vilnius Stock Exchange trading data.

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