One thorny issue with neural networks is that the inputs need to be coded properly in order that the network can extract useful information from them. For instance, training the network simply on the share prices themselves would be unsatisfactory, as that particular pattern of prices would depend on the absolute value of the stock market. In many cases, the obvious strategy is the correct one, and in the case of stock market prediction an obvious strategy suggests itself. It is not the absolute value of the stock prices that is of interest, but the changes in prices from one time period to the next. This is where the money is to be made, regardless of whether the general value of shares is hovering around the 4000 mark, the 5000 mark or the 6000 mark. For this reason, it would make more sense for the inputs to the ANN to be the changes in share price.
ANNs essentially associate input patterns with output patterns. The inputs could be the raw stock market data, since this is the material that technical analysts use to predict movements in the market. The outputs could be any one of several things. For instance, given inputs representing the share prices on day 1, day 2 and day 3, the output might be a prediction of the share prices on day 4 (or possibly even on days 4 and 5). Alternatively, the outputs might be simple “buy” and “sell” signals for shares in particular companies.
The advantage of ANNs is that they are flexible. They will, if correctly trained, learn to classify any pattern in the training library correctly: if they are given a pattern on which they have been previously trained, they will produce the correct output. However, they will also produce the correct output if they are given a pattern similar to one they have seen during training. They will therefore classify patterns that they have never seen before, based on the closest matching training pattern.