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An effective approach for predicting daily stock trading decisions using fuzzy inference systems

  • Soft computing in decision making and in modeling in economics
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Abstract

To achieve success in stock trading, the ability to forecast future market behavior is crucial. Many professional traders favor traditional technical indicators as their preferred price projection method. However, making decisions based on various information sources can enhance the accuracy of market predictions. To address this, we have developed fuzzy inference systems (FISs) that utilize fundamental and technical data as inputs, enabling daily trading decisions such as buy, hold, and sell signals. The results of our study indicate that the incorporation of fundamental financial data alongside technical data significantly enhances the accuracy of predicting future prices compared to systems relying solely on past price data. To further assess the effectiveness of our FISs, we conducted t-tests to compare their results with those of traditional technical trading strategies and the Buy–Hold strategy. The outcomes of the t-tests confirm that the proposed FISs outperform both the Buy–Hold strategy and traditional technical trading strategies, including RSI, MACD, and SO.

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Data availability

Daily price data for the selected company is collected from (http://www.nasdaq.com/Markets/).

References

  • Ahmadi E, Jasemi M, Monplaisir L, Nabavi MA, Mahmoodi A, Jam PA (2018) new efficient hybrid candlestick technical analysis model for stock market timing on the basis of the support vector machine and heuristic algorithms of imperialist competition and genetic. Expert Syst Appl 94:21–31

    Article  Google Scholar 

  • Anbalagan T, Uma Maheswari S (2015) Classification and prediction of stock market index based on fuzzy metagraph. Proc Comput Sci 47:214–221

    Article  Google Scholar 

  • Atsalakis GS, Protopapadakis EE, Valavanis KP (2016) Stock trend forecasting in turbulent market periods using neuro-fuzzy systems. Oper Res Int J 16:245–269

    Article  Google Scholar 

  • Brock W, Lakonishok J, LeBaron B (1992) Simple technical trading rules and the stochastic properties of stock returns. J Financ 47:1731–1764

    Article  Google Scholar 

  • Casanova IJ (2012) Portfolio investment decision support system based on a fuzzy inference system. In: Madani K, Dourado Correia A, Rosa A, Filipe J (eds) Computational intelligence, Springer, Berlin, Heidelberg, pp 183–196

  • Chavarnakul T, Enke D (2009) A hybrid stock trading system for intelligent technical analysis-based equivolume charting. Neurocomputing 72:3517–3528

    Article  Google Scholar 

  • Chiang WC, Enke D, Wu T, Wang R (2016) An adaptive stock index trading decision support system. Expert Syst Appl 59:195–207

    Article  Google Scholar 

  • Chourmouziadis K, Chatzoglou PD (2016) An intelligent short term stock trading fuzzy system for assisting investors in portfolio management. Expert Syst Appl 43:298–311

    Article  Google Scholar 

  • da Costa TR, Nazario RT, Bergo GSZ, Sobreiro VA, Kimura H (2015) Trading system based on the use of technical analysis: a computational experiment. J Behav Exp Financ 6:42–55

    Article  Google Scholar 

  • Dourra H, Siy P (2002) Investment using technical analysis and fuzzy logic. Fuzzy Sets Syst 127:221–240

    Article  MathSciNet  Google Scholar 

  • Dymova L, Sevastianov P, Bartosiewicz P (2010) A new approach to the rule-base evidential reasoning: stock trading expert system application. Expert Syst Appl 37:5564–5576

    Article  Google Scholar 

  • Gogocken M, Ozcalicic M, Boru A, Dosdogru AT (2019) Stock price prediction using hybrid soft computing models incorporating parameter tuning and input variable selection. Neural Comput Appl 31:577–592

    Article  Google Scholar 

  • Granville JE (1963) New key to stock market profits. Prentice-Hall

    Google Scholar 

  • Gunduz H, Cataltepe Z (2015) Borsa Istanbul (BIST) daily prediction using financial news and balanced feature selection. Expert Syst Appl 42:9001–9011

    Article  Google Scholar 

  • Henderson C (2006) Currency strategy: the practitioner’s guide to currency investing, hedging and forecasting. Wiley

    Google Scholar 

  • Hu Y, Liu K, Zhang X, Su L, Ngai EWT, Liu M (2015) Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Appl Soft Comput 36:534–551

    Article  Google Scholar 

  • Huang Q, Yang J, Feng X, Liew AWC, Li X (2019) Automated Trading Point Forecasting based on Bicluster Mining and Fuzzy Inference. IEEE Trans Fuzzy Syst 28(2):259–272

  • Khan BT, Javed N, Hanif A, Raja MA (2017) Evolving technical trading strategies using genetic Algorithms: a case about Pakistan stock exchange. In: International Conference on intelligent data engineering and automated learning. Springer, Cham

  • Lane GC (1984) Lane’s stochastics. Tech Anal Stocks Commodities 2(3):80

  • Lee KH, Jo GS (1999) Expert system for predicting stock market timing using a candlestick chart. Expert Syst Appl 16:357–364

    Article  Google Scholar 

  • Lincy GRM, John CJ (2016) A multiple of fuzzy inference system framework for daily stock trading. Expert Syst Appl 44:13–21

    Article  Google Scholar 

  • Murphy JJ (1999) Technical analysis of the financial markets. A comprehensive guide to trading methods and applications. Penguin

    Google Scholar 

  • Nakano M, Takahashi A, Takahashi S (2017a) Fuzzy logic-based portfolio selection with particle filtering and anomaly detection. Knowl-Based Syst 131:113–124

    Article  Google Scholar 

  • Nakano M, Takahashi A and Takahashi S (2017b) Robust technical trading with fuzzy knowledge-based systems. In SoMeT, pp 652–667

  • Nakashima T, Ariyama T, Kitano H, Isibuchi H (2005) A Fuzzy rule-based trading agent: analysis and knowledge extraction. computational intelligence for modelling and prediction. Springer, Berlin, pp 265–277

    Google Scholar 

  • Naranjo R, Santos M (2019) A fuzzy decision system for money investment in stock markets based on fuzzy candlesticks pattern recognition. Expert Syst Appl 133:34–48

    Article  Google Scholar 

  • Rajab S, Sharma V (2019) An interpretable neuro-fuzzy approach to stock price forecasting. Soft Comput 23:921–936

    Google Scholar 

  • Ravichandra T, Thingom C (2016) Stock price forecasting using ANN method. Information systems design and intelligent applications. Springer, New Delhi, pp 599–605

    Chapter  Google Scholar 

  • Sahin U, Ozbayoglu AM (2014) TN-RSI: Trend-normalized RSI indicator for stock trading systems with evolutionary computation. Proc Comput Sci 36:240–245

    Article  Google Scholar 

  • Simutis R (2000) Fuzzy logic based stock trading system . In: Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on computational intelligence for financial engineering(CIFEr) (Cat.No.00th8520), IEEE, pp 19–21

  • Sugeno M, Kang GT (1988) Structure identification of fuzzy model. Fuzzy Sets Syst 28:15–33

    Article  MathSciNet  Google Scholar 

  • Svalina I, Galzina V, Lujić R, Šimunović G (2013) An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: the case of close price indices. Expert Syst Appl 40(15):6055–6063

    Article  Google Scholar 

  • Takagi T, Sugeno M (1985) Fuzzy identification of system and its applications to modeling and control. IEEE Trans Syst Man Cyben 15:116–132

    Article  Google Scholar 

  • Tan T, Wang S, Wang K (2017) A new adaptive network-based fuzzy inference system with adaptive adjustment rules for stock market volatility forecasting. Inf Process Lett 127:32–36

    Article  MathSciNet  Google Scholar 

  • Tas O, Gursoy OZ (2016) A fuzzy logic based technical indicator for BIST 30 index and Islamic index. Proc Econ Finance 38:203–212

    Article  Google Scholar 

  • Veeramani C, Venugopal R, Edalatpanah SA (2022) Neutrosophic DEMATEL approach for financial ratio performance evaluation of the NASDAQ Exchange. Neutrosophic Sets Syst 51:766–782

    Google Scholar 

  • Veeramani C, Venugopal R, Muruganandan S (2023) An exploration of the fuzzy inference system for the daily trading decision and its performance analysis based on fuzzy MCDM methods. Comput Econ 62:1313–1340

  • Venugopal R, Veeramani C, Edalatpanah SA (2023) Analysis of fuzzy DEMATEL approach for financial ratio performance evaluation of NASDAQ exchange. In: Proceedings of International Conference on Data Science and Applications: ICDSA 2021, Volume 2, pp 637–648. Springer Singapore

  • Wan Y, Si YW (2017) Adaptive neuro fuzzy inference system for chart pattern matching in financial time series. Appl Soft Comput 57:1–18

    Article  Google Scholar 

  • Welles JW (1978) New Concepts in Technical Trading Systems, Hunter Publishing Company, Greensboro, NC

  • Yunusoglu MG, Selim H (2013) A fuzzy rule based expert system for stock evaluation and portfolio construction: an application to Istanbul Stock Exchange. Expert Syst Appl 40:908–920

    Article  Google Scholar 

  • Zhong X, Enke D (2017) A comprehensive cluster and classification mining procedure for daily stock market return forecasting. Neurocomputing 267:152–168

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Applied Science (Mathematics), PSG College of Technology, Coimbatore, Tamil Nadu, 641 004, India

    R. Venugopal & C. Veeramani

  2. Directorate of Online Education, Manipal University Jaipur, Bagru, Jaipur, Rajasthan, 303007, India

    S. Muruganandan

  3. School of Business, Faculty of Business and Leadership, MIT - World Peace University, Pune, 411038, India

    S. Muruganandan

Authors
  1. R. Venugopal
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  2. C. Veeramani
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Correspondence to R. Venugopal.

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Appendix 1. The traditional technical indicator rule and FISs frame work rule for buy, sell signals and its t-Statistics test values

Appendix 1. The traditional technical indicator rule and FISs frame work rule for buy, sell signals and its t-Statistics test values

See Tables 4, 5, 6, 7, 8, 9, 10 and 11.

Table 4 The RSI and MACD total number of buy, sell signals and it’s mean, SD values
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Table 5 The SO and OBV total number of buy, sell signals and its mean, SD values
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Table 6 The multiple of FISs frame work total number of buy, sell signals and mean, SD values
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Table 7 The multiple of FISs frame work total number of buy, sell signals and mean, SD values
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Table 8 The t-statistics test result of technical indicator RSI and MACD vs buy and hold strategy
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Table 9 The t- statistics test result of technical indicat or SO and OBV vs buy and hold strategy
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Table 10 The t-statistics test result of FISs frame work with P/E Ratio and volume vs buy and hold strategy
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Table 11 The t- statistics test result of FISs frame work without P/E Ratio and volume
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Venugopal, R., Veeramani, C. & Muruganandan, S. An effective approach for predicting daily stock trading decisions using fuzzy inference systems. Soft Comput 28, 3301–3319 (2024). https://doi.org/10.1007/s00500-023-09383-3

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