Prediction of Stock Trading Signal Using Colored Petri Nets and Genetic Algorithm (Case study: Tehran Stock Exchange)

Document Type : Technology

Authors

1 PhD student, Department of financial management, Babol branch Islamic Azad University, Babol, Iran

2 Professor, Department of Management, Faculty of Economics and Administrative Science, University of Mazandaran, Babolsar, Iran.

3 Department of Business, Islamic Azad University, Babol Branch, , Babol, Iran

Abstract

Deciding when to buy or sell stocks is a challenging problem for investors to increase incoming and decrease loss in stock market. Methods of predicting stock market in literature fall into two main categories: fundamental-analysis-based methods and technical-analysis-based methods. Predicting the trend of stock price movements and detecting changes of trend direction using technical analysis is generally preferred by analyzers in comparison with price prediction methods using fundamental analysis, due to data frequency reduction and less data variations in short-term. Most of the both methods use Artificial Intelligence (AI) techniques such as data mining and meta-heuristic approaches. AI based approaches suffer disadvantage of expert interaction necessity. In this paper a hybrid method of Genetic Algorithm (GA) and Colored Petri Nets (CPN) is proposed to model, simulate and predict buy/sell stock trading signals. CPN is a formal modeling language which supports mathematical simulation and markup language programming that reduces the necessity of human expert interaction in prediction approach. Stock trading rules are extracted from historical data of 162 companies in Tehran stock exchange weekly gathered from April 2016 till April 2018 using GA to maximize earning per share. Simulation results demonstrate that proposed method outperforms other state-of-the-art methods, in terms of classification correctness.

Keywords

References

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History

  • Receive Date: 13 November 2019
  • Revise Date: 16 December 2019
  • Accept Date: 25 January 2020

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