Academic journal article Business: Theory and Practice

Forecasting OMX Vilnius Stock Index-A Neural Network approach/OMX Vilnius Akciju Indekso Prognozavimas Naudojant Dirbtinius Neuronu Tinklus

Academic journal article Business: Theory and Practice

Forecasting OMX Vilnius Stock Index-A Neural Network approach/OMX Vilnius Akciju Indekso Prognozavimas Naudojant Dirbtinius Neuronu Tinklus

Article excerpt

1. Introduction

Stock market prediction brings a lot of discussion between academia. First of all negotiations arise whether future prices can be forecasted or not. One of the first theories against ability to forecast the market is Efficient Market Theory (EMH). It states that current prices "fully" reflect all available information so there is no possibility to earn any excess profit (Fama 1970). Another important statement was made several years later announcing, that stocks take a random and unpredictable path, stock prices have the same distribution and are independent from each other, so past movement cannot be used to predict the future (Malkiel 1973). This idea stands for Random Walk Theory. According to these statements no one investor could profit from the market without additional unpublicized information or undertaking additional risk. But these theories are facing critics and negotiations that during the time prices are maintaining some trends so it is possible to outperform the market by implementing appropriate forecasting models and strategies.

Researchers provide many models for stock market forecasting. They include various fundamental and technical analysis techniques. Fundamental analysis involves evaluating all the economy as a whole, analyzing exogenous macroeconomic variables, the root is based on expectation. On the contrary, technical analysis is using historical data, such as price and volume variables, preprocessing this data mathematically and making future forecasts rooted in statistics.

Financial time series forecasting brings a lot of challenges because of its chaotic, difficult, unpredictable and nonlinear nature. The most traditional methods are made under assumption that relation between stock price and certain variables is linear. There is evidence that these techniques, such as moving average, do not have acceptable accuracy (Dzikevicius et al. 2010). Most popular linear dependencies are simple moving averages, exponential moving averages and linear regression.

One of the newest approaches to forecast dynamic stock market nature is looking for non-linear techniques such as artificial neural networks (ANN). These methods, inspired by human brain, have an ability to find non-linear patterns, to learn from past and generalize. Neural networks are widely used in physical sciences but the popularity is rising in the financial field as well. The main research paper target is to evaluate the neural network ability to forecast stock market behavior by implementing a multi-layer perceptron (MLP) model to predict stock market index OMX Vilnius (OMXV) future movements (actual value and direction of the index). The model's accuracy is compared with several traditional linear models (moving average and linear regression).

The organization of this paper is as follows. The second section provides a brief review of previous researches, the third parts describes data and chosen methodology, the fourth part presents empirical results. The last section provides a brief summary and conclusions.

2. Literature review

The born year of neural network method can be called the year 1958, when the first neural network structure was defined. It was called perceptron (Rosenblatt 1958). Another important date is the year 1986. The authors introduced the 'back-propagation' learning algorithm that still nowadays is the most popular and will be discussed in a more detailed way in the next section (Rumelhart et al. 1986).

Nowadays modern ANN use of field is really wide: it includes biological, physical science, industry, finance, etc. There are four main reasons of such increasing popularity of use (Zhang et al. 1998). First of them is that oppositely from the other traditional methods ANN have very few assump tions, because they are learning from examples and capturing functional relationships. The second advantage is generalization --the ability to find the unseen part of population from a noisy data. …

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