Abstract
In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two “traditional”, rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments.
Highlights
Most financial data is non-stationary by default, this means that the statistical properties, such as the mean and variance, of the data changes over time
These changes are a result of various business and economic cycles such as the high demand for air travel in the summer months effecting on exchange rates and fuel prices [1]
While isolated information is usually taken into account, for example in the current closing price of a stock, share or exchange rate, the consequences of this knock-on effect means that the long term study of the behaviour of a specific variable is not always the best indicator of future market behaviour
Summary
Most financial data is non-stationary by default, this means that the statistical properties, such as the mean and variance, of the data changes over time. These changes are a result of various business and economic cycles such as the high demand for air travel in the summer months effecting on exchange rates and fuel prices [1]. Trend is an identifiable long term variation in the stock market time series, while the periodic variations follow either seasonal patterns or the business cycle in the economy. Short-term and day-to-day variations usually appear at random and are difficult to predict with the exception of the case of ‘‘special events’’ such as public holidays, specific product launch dates or predicable breaking news, but these are often the source for stock trading gains and losses, especially in the case of day traders [2,3,4]
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