Skip to main content

Application to Stock Exchange Predictions

  • Chapter
  • First Online:
Mathematical Theory of Democracy

Part of the book series: Studies in Choice and Welfare ((WELFARE))

  • 1635 Accesses

Abstract

The model of representation is adapted to predict fluctuations in stock prices. From the mathematical standpoint, neither the ‘society’, nor ‘representatives’ are necessarily human, so some objects can represent the behavior of other objects. This idea is applied to the major American and German stocks with which the Dow Jones and DAX indices are computed. For this purpose the price fluctuations of the Dow Jones stocks are regarded as representatives of those of the DAX stocks a week later. In particular, during the control period of 24 weeks, the fluctuations in American Express stock prices anticipated, on average, the price fluctuations of 2/3 of the DAX stocks. Some selected groups of three to five Dow Jones stocks arranged into ‘parliaments’, whose predictions are made by majority rule, have even better characteristics. Both single Dow Jones stocks and their parliaments are statistically tested on their potential to be used as predictors. For single stocks, the P-values are derived analytically; for the parliaments they are obtained by Monte Carlo simulation with 1000 experiments. The predictive capacity of the totality of Dow Jones stocks is also evaluated and statistically tested.

In bourgeois society capital is independent and has individuality.

Karl Marx (1818–1883)

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ashford RW, Berry RH, Dyson RG (1988) Operational researchand financial management. Eur J Oper Res 36:143–152

    Google Scholar 

  2. Bachelier L (1900) Théorie de la spéculation. Th`ese annales scientifiquesde l’Ecole Normale Supérieure, 3e série 17:21–86 (Paris, Gauthier-Villars). Reprinted as a book Jacques Gabay, Paris 1995

    Google Scholar 

  3. Bernstein PL (1996) Against the gods: the remarkable story ofrisk. Wiley, New York

    Google Scholar 

  4. Black D (1958) The theory of committees and elections. CambridgeUniversity Press, Cambridge

    Google Scholar 

  5. Black F, Scholes MS (1973) The pricing of options andcorporate liabilities. J Pol Econ 81(3):637–654

    Google Scholar 

  6. Brealey RA, Myers SC, Allen F (2003) Principles ofcorporate finance, 7th ed. McGrow-Hill, Boston

    Google Scholar 

  7. Castelli Ch (1877) The theory of options in stocks and shares. Mathieson,London

    Google Scholar 

  8. Chen P (1996) A random walk or color chaos on the stock market?—Time-frequency analysis of S& P indexes. Nonlinear Dynamics and Econometrics1(2):87–103

    Google Scholar 

  9. Doumpos M, Kosmidou K, Baourakis G, Zopounidis C(2002) Credit risk assessment using a multicriteria hierarchical discriminationapproach: a comparative analysis. Eur J Oper Res 138:392–412

    Google Scholar 

  10. Eberlein E, Keller U, Prause K (1998) New insights intosmile, mispricing and value at risk: the hyperbolic model. J Bus 71:371–406

    Google Scholar 

  11. Hull JC (2002) Fundamentals of futures and option markets, 4th ed.Prentice-Hall, Upper Saddle River NJ

    Google Scholar 

  12. Hull JC (2003) Options, futures, and other derivative securities, 5th ed.Pearson Education, Upper Saddle River NJ

    Google Scholar 

  13. Ikeda N,Watanabe S, Fukushima M, Kunita H (eds) (1996) Itˆo’sstochastic calculus and probability theory. Springer, Tokyo

    Google Scholar 

  14. Jarrow R (2002) Modelling fixes income securities and interest rateoptions, 2nd ed. Stanford University Press, Stanford CA

    Google Scholar 

  15. Lo AW, Mamaysky H, Wang J (2000) Foundations of technicalanalysis: computational algorithms, statistical inference, and empirical implementation.J of Finance 55(4): 1705–1765

    Article  Google Scholar 

  16. Loistl O, Schossmann B, Vetter O (2001) XETRA efficiencyevaluation and NASDAQ modelling by KapSyn. Eur J Oper Res – feature issueFinancial modelling 135(2):270–295

    Google Scholar 

  17. Loistl O, Schossmann B, Vetter O, Veverka A (2002) A comparisonof transaction costs on XETRA and on NASDAQ. Quant Financ 2(3):199–216

    Google Scholar 

  18. Mandelbrot B (1963) The variation of certain speculativeprices. J Bus 36:394–419

    Article  Google Scholar 

  19. Maron O (1998) Learning from ambiguity, PhD thesis. MIT,Cambridge MA.http://www.textfiles.com/bitsavers/pdf/mit/ai/aim/AITR-1639.pdf. Cited 18May 2013

  20. Merton RC (1973) Theory of rational option pricing. Bell J EconManag Sci 4:141–183

    Article  Google Scholar 

  21. Merton RC (1998) Applications of option-pricing theory: twentyfiveyears later. Am Econ Rev 88:323–349

    Google Scholar 

  22. Mulvey JM, Rosenbaum DP, Shetty B (1997) Strategic financialrisk management and operations research. Eur J Oper Res 97:1–16504 13 Application to Stock Exchange Predictions

    Google Scholar 

  23. Osborne MFM (1959) Brownian motion in the stock market. OperRes 7(2):145–173

    Article  Google Scholar 

  24. Samuelson P (1965) A rational theory of warrant pricing. IndustrialManagement Review 6:13–32

    Google Scholar 

  25. Schärlig A (1996) Prartiquer ELECTRE et PROMETHEE. PressesPolytechniques et Universitaires Romandes, Lausanne

    Google Scholar 

  26. Tangian A (2008) Predicting DAX trends from Dow Jones data bymethods of the mathematical theory of democracy. Eur J Operational Research,185:1632–1662

    Article  Google Scholar 

  27. Tay FEH, Shen L, Cao L (2003) Ordinary shares, exotic methods— financial forecasting using data mining techniques. World Scientific, Singapore

    Book  Google Scholar 

  28. Trippi R (1995) Chaos and nonlinear dynamics in the financial markets:theory, evidence, and applications. McGraw-Hill, Irwin

    Google Scholar 

  29. Wilmott P (2001) Paul Wilmott introduces quantitative finance.Wiley, Chichester

    Google Scholar 

  30. Wirtschaftswoche (2002) DAX-Werte and Dow Jones(tables in every issue), 1/2–27

    Google Scholar 

  31. ZopounidisC (1999) Multicriteria decision aid in financialmanagement.Eur J Oper Res 119:404–415

    Google Scholar 

  32. Zopounidis C, Doumpos M (2002) Multi-groupdiscrimination using multi-criteria analysis: illustrations from the field of finance.Eur J Oper Res 139:371–389

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Tangian, A. (2014). Application to Stock Exchange Predictions. In: Mathematical Theory of Democracy. Studies in Choice and Welfare. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38724-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38724-1_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38723-4

  • Online ISBN: 978-3-642-38724-1

  • eBook Packages: Business and EconomicsEconomics and Finance (R0)

Publish with us

Policies and ethics