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Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models

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Book cover Advances in Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP 2017)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 81))

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Abstract

Forecasting is an important technique in many industries and business fields for reading the terrain. The category of technology industry stock, which includes 7 independent stocks, in Taiwan Stock Exchange (TWSE) is selected to be the study subject in this paper. The goal is to forecast the return index of the individual stocks base on the information observed from the trading historical da-ta of the subjects. By including the trading volume, the number of trading rec-ords, the opening price, and the closing price in the inputs to the representative models in time-series and computational intelligence: EGARCH(1,1) and the In-teractive Artificial Bee Colony (IABC), respectively, the forecasting accuracy are compared by the Mean Absolute Percentage Error (MAPE) value. The experi-mental results indicate that the IABC forecasting model with the selected input variables presents superior results than the EGARCH(1,1).

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References

  1. Chen, S.-S.: Applied Time-series Econometrics for Macroeconomics and Finance. Tunghua, Taipei (2013)

    Google Scholar 

  2. Bollerslev, T.R.: Generalized autoregressive conditional heteroskedasticity. J. Econometrics 31(3), 307–327 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bollerslev, T.R.: ARCH modeling in finance: A review of the theory and empirical evidence. J. Econometrics 52(1–2), 5–59 (1992)

    Article  MATH  Google Scholar 

  4. Dickey, D.A.: Likelihood ratio statistisc for autoregression time series with a unit root. Econometrica 49, 1057–1072 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  5. Engle, R.F.: Autoregressive conditional heteroskedasticity with estimates of the variance of united kindom inflation. Econometrica 50(4), 987–1007 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  6. Abedinia, O., Barazandeh, E.S.: Interactive artificial bee colony based on distribution planning with renewable energy units. In: 2013 IEEE PES Innovative Smart Grid Technologies (ISGT), pp. 1–6. IEEE Press, Washington, DC (2013)

    Google Scholar 

  7. Tsai, P.-W., Pan, J.-S., Liao, B.-Y., Chu, S.-C.: Enhanced artificial bee colony optimization. J. Innov. Comput. Inf. Control 5(12B), 5081–5092 (2009)

    Google Scholar 

  8. Tsai, C.-F., Hsiao, C.-T., Tsai, P.-W., Chang, J.-F.: Applying interactive artificial bee colony algorithm and the time-series methods in the foreign exchange rate forecasting. Econ. Manage. 36(Z1), 147–150 (2014)

    Google Scholar 

  9. Tsai, P.-W., Liu, C.-H., Zhang, J., Chang, J.-F.: Structuring interactive artificial bee colony forecasting model in foreign exchange rate forecasting with consumer confidence index and conventional microeconomics factors. ICIC Expr. Lett. Part B: Appl. 7(4), 895–902 (2016)

    Google Scholar 

  10. French, K.R., Roll, R.: Stock return variance: the arrival of information and the reaction of traders. J. Financ. Econ. 17(1), 5–26 (1986)

    Article  Google Scholar 

  11. Nelson, D.B.: Conditional heteroskedasticity in asset returns: a new approach. Economietrica 59(2), 347–370 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  12. Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Dario, P., Sandini, G., Aebischer, P. (eds.) Robots and Biological Systems: Towards a New Bionics?, 1993. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 102, pp. 703–712. Springer, Berlin, Heidelberg (1993)

    Chapter  Google Scholar 

  13. Karaboga, D.: An Idea Based on Honey Bee Swarm for Numerical Optimization. In: Technical Report-TR06, 2005 (2005)

    Google Scholar 

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Acknowledgement

This work is funded by the Key Project of Fujian Provincial Education Bureau (JA15323).

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Authors and Affiliations

  1. College of Information Sciences and Engineering, Fujian University of Technology, Fuzhou, 350118, Fujian Province, China

    Tien-Wen Sung

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, Fujian Province, China

    Tien-Wen Sung

  3. Department of International Business, National Kaohsiung University of Applied Sciences, Kaohsiung, 807, Taiwan

    Cian-Lin Tu & Jui-Fang Chang

  4. Department of Computer Science and Software Engineering, Swinburne University of Technology, Victoria, 3122, Australia

    Pei-Wei Tsai

Authors
  1. Tien-Wen Sung
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  2. Cian-Lin Tu
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  3. Pei-Wei Tsai
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  4. Jui-Fang Chang
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Corresponding author

Correspondence to Jui-Fang Chang .

Editor information

Editors and Affiliations

  1. Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology , Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Swinburne University of Technology, Hawthorn, Victoria, Australia

    Pei-Wei Tsai

  3. Universiti Teknologi Petronas, Teronoh, Malaysia

    Junzo Watada

  4. University of Canberra, Bruce, Aust Capital Terr, Australia

    Lakhmi C. Jain

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Sung, TW., Tu, CL., Tsai, PW., Chang, JF. (2018). Short-Term Forecasting on Technology Industry Stocks Return Indices by Swarm Intelligence and Time-Series Models. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-319-63856-0_34

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  • DOI: https://doi.org/10.1007/978-3-319-63856-0_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63855-3

  • Online ISBN: 978-3-319-63856-0

  • eBook Packages: EngineeringEngineering (R0)

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