Published Paper Details:

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instruments traded on an exchange. The successful prediction of a stock�s future price could yield significant profit. Stock prediction has a high level of complexity involved and there exists no algorithm that can successfully predict stock prices to a 100 percent accuracy. The most prominent technique involves the use of artificial neural networks (ANNs) and Genetic Algorithms(GA). The most common form of ANN in use for stock market prediction is the feed forward network utilizing the backward propagation of errors algorithm to update the network weights. These networks are commonly referred to as Backpropagation networks. Another form of ANN that is more appropriate for stock prediction is the time recurrent neural network (RNN) or time delay neural network (TDNN). For stock prediction with ANNs, there are usually two approaches taken for forecasting different time horizons: independent and joint. The independent approach employs a single ANN for each time horizon, for example,1-day, 2-day, or 5-day. The joint approach, however, incorporates multiple time horizons together so that they are determined simultaneously. In this approach, forecasting error for one time horizon may share its error with that of another horizon, which can decrease performance. There are also more parameters required for a joint model, which increases the risk of overfitting. The predicted low and high predictions are then used to form stop prices for buying or selling. Outputs from the individual �low� and �high� networks can also be input into a final network that would also incorporate volume, intermarket data or statistical summaries of prices, leading to a final ensemble output that would trigger buying, selling, or market directional change. A major finding with ANNs and stock prediction is that a classification approach (vs. function approximation) using outputs in the form of buy(y=+1) and sell(y=-1) results in better predictive reliability than a quantitative output such as low or high price