Skip to main content

Abstract

If we trade in financial markets we are interested in buying at low and selling at high prices. We suggest an active trading algorithm which tries to solve this type of problem. The algorithm is based on reservation prices. The effectiveness of the algorithm is analyzed from a worst case and an average case point of view. We want to give an answer to the questions if the suggested active trading algorithm shows a superior behaviour to buy-and-hold policies. We also calculate the average competitive performance of our algorithm using simulation on historical data.

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 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

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. Chavarnakul, T., Enke, D.: Intelligent technical analysis based equivolume charting for stock trading using neural networks. Expert Systems and Applications 34, 1004–1017 (2008)

    Article  Google Scholar 

  2. El-Yaniv, R.: Competitive solutions for online financial problems. ACM Computing Surveys 30, 28–69 (1998)

    Article  Google Scholar 

  3. El-Yaniv, R., Fiat, A., Karp, R., Turpin, G.: Optimal search and one-way trading algorithm. Algorithmica 30, 101–139 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  4. El-Yaniv, R., Fiat, A., Karp, R., Turpin, G.: Competitive analysis of financial games. In: IEEE Symposium on Foundations of Computer Science, pp. 327–333 (1992)

    Google Scholar 

  5. Feng, Y., Ronggang, Y., Stone, P.: Two Stock Trading Agents: Market Making and Technical Analysis. In: Faratin, P., Parkes, D.C., Rodriguez-Aguilar, J.A., Walsh, W.E. (eds.) Agent Mediated Electronic Commerce V: Designing Mechanisms and Systems. LNCS (LNAI), pp. 18–36. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  6. Ratner, M., Leal, R.P.C.: Tests of technical trading strategies in the emerging equity markets of Latin America and Asia. Journal of Banking and Finance 23, 1887–1905 (1999)

    Article  Google Scholar 

  7. Ronggang, Y., Stone, P.: Performance Analysis of a Counter-intuitive Automated Stock Trading Strategy. In: Proceedings of the 5th International Conference on Electronic Commerce. ACM International Conference Proceeding Series, vol. 50, pp. 40–46 (2003)

    Google Scholar 

  8. Shen, P.: Market-Timing Strategies that Worked. Working Paper RWP 02-01, Federal Reserve Bank of Kansas City, Research Division (May 2002)

    Google Scholar 

  9. Silaghi, G.C., Robu, V.: An Agent Policy for Automated Stock Market Trading Combining Price and Order Book Information. In: ICSC Congress on Computational Intelligence Methods and Applications, pp. 4–7 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mohr, E., Schmidt, G. (2008). Empirical Analysis of an Online Algorithm for Multiple Trading Problems. In: Le Thi, H.A., Bouvry, P., Pham Dinh, T. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2008. Communications in Computer and Information Science, vol 14. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87477-5_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-87477-5_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87476-8

  • Online ISBN: 978-3-540-87477-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics