STOCHASTIC MODEL OF SHORT-TERM PREDICTION OF STOCK PRICES AND ITS PROFITABILITY IN THE CZECH STOCK MARKET
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Technická Univerzita v Liberci
Technical university of Liberec, Czech Republic
Technical university of Liberec, Czech Republic
Abstract
This paper deals with stochastic modelling and short time prediction of share price development in Czech stock market. The aim of this research is to create such models which can be used for creating automatic trading strategies that will beat the market. Reliability of these models is being checked in three highly liquid shares from Prague Stock O2, CEZ and KB in seven years long period in years 2006–2012. We used Markov chain analysis for modelling. In our models a state space is defi ned on the basis of cumulative daily changes of share price and a state space with eight states is used. The state space is defi ned parametrically as a multiple of standard deviation of daily yields for each share. There were 14 parameters calculated in total and for each parameter nine trading strategies for all shares were applied. It means that 378 trading strategies were calculated. We succeeded in fi nding a set of compact state space models and in applying a compact group of trading strategies on these models which always beat the market when invested in portfolio. The average annual market yield was 3.6%. The average yield of our portfolios oscillates between 4.7% and 14.8%. Strategies overcame the market also even after including transaction costs. After including transaction costs in amount of 0.1% from the trade volume a decrease of average annual yields would occur in the range from 0.45 to 2.1 percentage points. We reached the best results in the sideway trend and in shares with less changing volatility. Conclusions of this research are in contradiction to the Effi cient Market Hypothesis. Results indicate that Czech stock market is not effective in any of its form.
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technical analysis indicators, stock market predication, trading strategies, Markov chain analysis, algorithmic trading
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12123609