Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction

Abstract

Predicting future prices by using time series forecasting models has become a relevant trading strategy for most stock market players. Intuition and speculation are no longer reliable as many new trading strategies based on artificial intelligence emerge. Data mining represents a good source of information, as it ensures data processing in a convenient manner. Neural networks are considered useful prediction models when designing forecasting strategies. In this paper we present a series of neural networks designed for stock exchange rates forecasting applied on three Romanian stocks traded on the Bucharest Stock Exchange (BSE). A multistep ahead strategy was used in order to predict short-time price fluctuations. Later, the findings of our study can be integrated with an intelligent multi-agent system model which uses data mining and data stream processing techniques for helping users in the decision making process of buying or selling stocks.Keywords: Data Mining, Neural Networks, MLP, Multi-agent System, Majority Voting, Time series Forecasting(ProQuest: ... denotes formulae omitted.)1 IntroductionPrice estimation using time series predic- tion models has been largely studied as being a key topic in finance for helping users in the decision making process. By developing a strong and reliable time series forecasting model, traders can adjust their trading deci- sions by following specific buy-sell strate- gies. Even though many traders rely their de- cision on speculation, experience and intui- tion, specific strategies based on data analy- sis are still more reliable for inexperienced users. In order to prevent random trading, a general trading method should be developed to help users in defining their position. Buy- ing and selling stocks can be extremely diffi- cult when changes occur unexpectedly. A general forecasting method should be includ- ed as a measure of guidance in order to pre- vent massive losses or poor decisions.A large interest regarding forecasts on stock exchange market and currency exchange rates has been observed in the current litera- ture [1-3],Neural networks are considered a popular and useful strategy as it offers a variety of different models which can be used and adapted according to each specific problem. Trading strategies and patterns can be ob- served using neural networks and can be ex- ploited in order to obtain competitive ad- vantages.This paper presents extends [4], which is a short introduction to financial markets and neural network modeling. Here, we present a case study where price prediction methods are evaluated in order to find whether using neural networks can be considered an ac- ceptable trading strategy among other trading methods.The main purpose of this paper is to evaluate the neural network time series forecasting models by building and creating a series of neural networks and by trying to predict the future price value for three main Romanian stocks in order to find suitable learning mod- els in the forecasting process. After compu- ting hundreds of tests and consulting the cur- rent literature a prediction strategy was dis- covered. A multi step ahead strategy was evaluated and results were computed using Weka and Matlab.2 Related WorkThis paper focuses on evaluating time series forecasting strategies using neural networks as main learning method. In order to evaluate prediction results a general understanding on how neural networks work is needed.In [5], artificial neural networks are consid- ered universal approximators capable of learning and adapting their structure to each particular input data. Designing an optimal network usually implies constant work based on a trial and error process.The main idea behind neural networks is re- lated to the way information is processed by the human brain. Having its origins in the bi- ological nervous system, a neural network is organized in large interconnection streams between neurons that are designed to work together in solving a specific task. …