(21th-February-2021)
• Improving portfolio returns
• A major Japanese securities company decided to user neural computing in order to develop better prediction models. A neural network was trained on 33 months' worth of historical data. This data contained a variety of economic indicators such as turnover, previous share values, interest rates and exchange rates. The network was able to learn the complex relations between the indicators and how they contribute to the overall prediction. Once trained it was then in a position to make predictions based on "live" economic indicators.
The neural network-based system is able to make faster and more accurate predictions than before. It is also more flexible since it can be retrained at any time in order to accommodate changes in stock market trading conditions. Overall the system outperforms statistical methods by a factor of 19%, which in the case of a £1 million portfolio means a gain of £190,000. The system can therefore make a considerable difference on returns.
Making predictions based on key indicators
• Predicting gas and electricity supply and demand
• Predicting sales and customer trends
• Predicting the route of a projectile
• Predicting crop yields
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