Non-integrated Networks

Yao et al. (1999) applied a neural network model to relate technical indicators to future trends in the Kuala Lumpur Composite Index (KLCI) of Malaysian stocks. These authors attempted predictions without the use of extensive market data or knowledge. The technical indicators used as inputs for the neural network model included moving average, momentum and relative strength index (RSI) (Yao et al., 1999). Their experiment used many neural networks with the training method of back-propagation. However, they did not train the neural networks sufficiently nor use fundamental factors for their predictions. Therefore, the robustness of their model for prediction involving other time periods was found to be poor.

In working towards online stock forecasting, Lee (2004) introduced the iJADE stock advisor system which incorporated hybrid radial basis-function recurrent network (HRBFN). The author used prices for 33 major Hong Kong stocks over a ten year period for testing the iJADE stock advisor and structured the HRBFN into three layers; input, hidden, and output. The input layer comprised of two portions with the first being past network outputs, fed back into the network and governed by a decay factor, and the second involved factors related to the prediction problems (Lee, 2004). The author added a structural learning technique as a “forgetting” factor in the back-propagation algorithm and a “time different decay” facility within the network. When compared

18 with other stock prediction models, the iJADE stock advisor produced promising results in terms of efficiency, accuracy and mobility (Lee, 2004).

Similarly, Pan et al. (2003) used neural networks to predict a stock market successfully. They employed neural networks to predict the Australian stock market index (AORD) and attempted to optimize the design as adaptive neural networks. The inputs were relative returns derived from basic factors of the Australian stock market, and inter-market influences on the Australian stock market. They found that a neural network with a hidden layer of two nodes achieved 80% accuracy for directional prediction. Tilakaratne (2004) had discovered a 6-day cycle in the Australian stock market. She also applied neural networks trained with a back-propagation algorithm to discover the optimal neural network architecture and the relative returns series of the open, high, low and closed prices in the Australian stock market. Her optimal neural network architecture comprised three layers; an input layer with 33 nodes, a hidden layer with 3 nodes and an output layer with 4 nodes. The best neural network developed in this study achieved accuracy of at least 81% when predicting the next-day direction of relative returns of open, low, high, and closed prices for the Australian stock market (Tilakaratne, 2004).

Jaruszewicz and Mandziuk (2004) attempted to predict the next day opening value of the Japanese NIKKEI index by developing a multilayered neural network which was structured into separate modules according to input data types. Technical data collected from Japanese, American and German stock markets were pre-processed to prepare them as inputs into the neural network. They found that, for a relatively stable period in the Japanese market (average NIKKEI index volatility of 0.96%) predictive efficiency was very high, with a prediction error of only 0.27%.

Based on companies listed on the Australian Stock Exchange during 2000-2004, Luu and Kennedy (2006) predicted performance using back-propagation neural networks. They measured company performance by using beta, market capitalisation, book to market ratio and standard deviation, finding that approximately sixty percent of companies were classified correctly. The authors also compared the performance of the back-propagation neural network with a support vector machine (SVM); however, the results from these two techniques were not significantly different.

19 To lessen risks, investors usually spread their investment over stocks in different sectors or industries. Abdelmouez, Hashem, Atiya and El-Gamal (2007) applied back- propagation neural networks and linear models, Box-Jenkins methods and multiple regression for stock sector predictions. They used data collected during the period January 1988 to July 2001 from American stock markets such as New York Stock Exchange (NYSE), American Stock Exchange (ASE), the National Association of Securities Dealers Automated Quotations (NASDAQ) and S&P500. They reported that the best results were achieved from back-propagation neural networks.

Focusing on central Europe stock markets, Barunik (2008) proposed an application to predict stock returns by using neural networks in the prediction tasks and using the Jarque-Bera, a statistical method, to test how the daily and weekly returns vary from the normal distribution. Data from Czech, Hungarian, German and Polish stock markets during the period from 1999 to 2006 were used (Barunik, 2008). He found the prediction accuracy achieved for the Prague Stock Exchange 50 Index (PX-50), Budapest Stock Exchange Index (BUX) and Deutscher Aktien Index (DAX) to be 60 percent for both daily and weekly analysis. However, the author reported that the prediction of the Warszawski Indeks Gieldowy (WIG) was not successful for the economic aspect.

To forecast stock prices of Iran Tractor Manufacturing Company, Omidi, Nourani and Jalili (2011) also used neural networks with a back-propagation algorithm. They designed special returns to be used as inputs. The return was computed by dividing price of day t by price of day t-1. By using a sliding window approach with a window size of 30 to the stock prices to be compute inputs, they fixed a neural network topology to 30-8-8-1. However, the authors did not provided detailed explanation of their result but they claimed that in analysing neural network simulation by using regression, their model was appropriate. In addition, Yixin and Zhang (2010) used three-layer neural networks to predict trends of the prices of 6 stocks trading in China‟s stock market. They assigned 21 inputs, three hidden nodes at a single hidden layer and one output node. Their experiment found that the trends of future prices of the 6 sample stocks were well predicted.

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