IDEAS home Printed from https://ideas.repec.org/a/ove/journl/aid11075.html
   My bibliography  Save this article

Modelling and trading the English stock market with novelty optimization techniques

Author

Listed:
  • Andreas Karathanasopoulos

Abstract

The scope for this paper is to introduce short term adaptive models to trade the FTSE100 index. There are five major innovations on this paper which include the introduction of an input selection criteria when utilising an expansive universe of inputs, adaptive sliding window modelling, a hybrid combination of PSO and RBF algorithms, the application of a PSO algorithm to a traditional ARMA model, and finally the introduction of a multi-objective algorithm to optimise statistical and trading performance when trading an equity index.

Suggested Citation

  • Andreas Karathanasopoulos, 2016. "Modelling and trading the English stock market with novelty optimization techniques," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 50-57.
  • Handle: RePEc:ove:journl:aid:11075
    as

    Download full text from publisher

    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/11075
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Charalampos Stasinakis & Georgios Sermpinis & Konstantinos Theofilatos & Andreas Karathanasopoulos, 2016. "Forecasting US Unemployment with Radial Basis Neural Networks, Kalman Filters and Support Vector Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 569-587, April.
    2. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Sermpinis, Georgios & Stasinakis, Charalampos & Theofilatos, Konstantinos & Karathanasopoulos, Andreas, 2015. "Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms—Support vector regression forecast combinations," European Journal of Operational Research, Elsevier, vol. 247(3), pages 831-846.
    5. Georgios Vasilakis & Konstantinos Theofilatos & Efstratios Georgopoulos & Andreas Karathanasopoulos & Spiros Likothanassis, 2013. "A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 415-431, December.
    6. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    7. Pesaran, M Hashem & Timmermann, Allan, 1995. "Predictability of Stock Returns: Robustness and Economic Significance," Journal of Finance, American Finance Association, vol. 50(4), pages 1201-1228, September.
    8. Bennell, J. & Sutcliffe, C., 2000. "Black-Scholes Versus Neural Networks in Pricing FTSE 100 Options," Papers 00-156, University of Southampton - Department of Accounting and Management Science.
    9. Teo Jasic & Douglas Wood, 2004. "The profitability of daily stock market indices trades based on neural network predictions: case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965-1999," Applied Financial Economics, Taylor & Francis Journals, vol. 14(4), pages 285-297.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andreas Karathanasopoulos, 2017. "Modelling and trading the London, New York and Frankfurt stock exchanges with a new gene expression programming trader tool," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 24(1), pages 3-11, January.
    2. Leandro Maciel & Rosangela Ballini, 2021. "Functional Fuzzy Rule-Based Modeling for Interval-Valued Data: An Empirical Application for Exchange Rates Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 743-771, February.
    3. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    4. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    5. Moreno, David & Olmeda, Ignacio, 2007. "Is the predictability of emerging and developed stock markets really exploitable?," European Journal of Operational Research, Elsevier, vol. 182(1), pages 436-454, October.
    6. Sermpinis, Georgios & Stasinakis, Charalampos & Rosillo, Rafael & de la Fuente, David, 2017. "European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression," European Journal of Operational Research, Elsevier, vol. 258(1), pages 372-384.
    7. Andreas Karathanasopoulos & Christian Dunis & Samer Khalil, 2016. "Modelling, forecasting and trading with a new sliding window approach: the crack spread example," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1875-1886, December.
    8. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    9. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    10. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    11. McCracken,M.W. & West,K.D., 2001. "Inference about predictive ability," Working papers 14, Wisconsin Madison - Social Systems.
    12. Ahoniemi, Katja & Lanne, Markku, 2009. "Joint modeling of call and put implied volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 239-258.
    13. repec:ipg:wpaper:2013-020 is not listed on IDEAS
    14. Alina Bărbulescu & Cristian Ștefan Dumitriu, 2021. "On the Connection between the GEP Performances and the Time Series Properties," Mathematics, MDPI, vol. 9(16), pages 1-19, August.
    15. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    16. Leland E. Farmer & Lawrence Schmidt & Allan Timmermann, 2023. "Pockets of Predictability," Journal of Finance, American Finance Association, vol. 78(3), pages 1279-1341, June.
    17. Monticini, Andrea & Ravazzolo, Francesco, 2014. "Forecasting the intraday market price of money," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 304-315.
    18. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    19. Christiane Baumeister & Lutz Kilian, 2014. "What Central Bankers Need To Know About Forecasting Oil Prices," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 869-889, August.
    20. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    21. Clark, Todd E. & McCracken, Michael W., 2015. "Nested forecast model comparisons: A new approach to testing equal accuracy," Journal of Econometrics, Elsevier, vol. 186(1), pages 160-177.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ove:journl:aid:11075. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Francisco J. Delgado (email available below). General contact details of provider: https://edirc.repec.org/data/deovies.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.