Towards Automated Share Investment System

Predictability of financial time series (FTS) is a well-known dilemma. A typical approach to this problem is to apply a regression model, built on the historical data and then further extend it into the future. If however the goal for FTS prediction would be to support or even make investment decisions, prediction generated by regression-based models are inappropriate as on top of being uncertain and unnecessarily complex, they require lots of investor attention and further analysis to make an investment decision. Rather than precise time series prediction, a busy investor may prefer a simple decision on the current day transaction: buy, wait, sell, that would maximise his return from the investment. Based on such assumptions a classification model is proposed that learns the transaction patterns from optimally labelled historical data and accordingly gives the profit-driven decision for the current-day transaction. The model is embedded into an automated client-server platform which automatically handles data collection and maintains client models on the database. The prototype of the system was tested over 20 years of NYSE:CSC share price history showing substantial improvement of the long-term profit compared to a passive long-term investment.