Neural networks for Stock analysis
The purpose of this research was to compare two statistical methods: one that based on Cycle Analysis, another – on a simple Neural Network. Price and volume data were used to train this particular Neural Network. These statistical forecasts were built using historical data of S&P-500 index for six months (from June 2009 to January 2010).
The charts below shows how actual 5-day performance (yellow line) differ from predicted performances by these two methods. The top half is the comparison of Neural Network prediction, bottom half – Cycle Analysis. Green bars mean buy signals, red – sell*.
Three major conclusions for this particular historical period:
- Cycle Analysis prediction gives signals too early, Neural Network prediction – too late.
- In average, the prediction by Cycle Analysis showed slightly better accuracy than the one by Neural Network.
- It seems logical to combine these two methods to improve the accuracy.
* ) The calculations have been performed by an integrated experimental system that combined two applications: Cycle Analysis predictor SMAP-3 and Neural Network predictor NNSTP-2
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