IDEAS home Printed from https://ideas.repec.org/f/pka740.html
   My authors  Follow this author

Bob Kaempff

Personal Details

First Name:Bob
Middle Name:
Last Name:Kaempff
Suffix:
RePEc Short-ID:pka740
[This author has chosen not to make the email address public]

Affiliation

Banque Centrale du Luxembourg

Luxembourg, Luxembourg
http://www.bcl.lu/
RePEc:edi:bclgvlu (more details at EDIRC)

Research output

as
Jump to: Articles Chapters

Articles

  1. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.
  2. Florian Hauser & Bob Kaempff, 2013. "Evolution of trading strategies in a market with heterogeneously informed agents," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 575-607, July.

Chapters

  1. Florian Hauser & Bob Kaempff, 2010. "Trading on Marginal Information," Lecture Notes in Economics and Mathematical Systems, in: Marco Li Calzi & Lucia Milone & Paolo Pellizzari (ed.), Progress in Artificial Economics, pages 15-26, Springer.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

  1. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.

    Cited by:

    1. Brice Corgnet & Cary Deck & Mark DeSantis & David Porter, 2017. "Information (Non)Aggregation in Markets with Costly Signal Acquisition," Working Papers 1735, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    2. Ackert, Lucy F. & Church, Bryan K. & Zhang, Ping, 2018. "Informed traders’ performance and the information environment: Evidence from experimental asset markets," Accounting, Organizations and Society, Elsevier, vol. 70(C), pages 1-15.
    3. Lijian Wei & Xiong Xiong & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2017. "The effect of genetic algorithm learning with a classifier system in limit order markets," Published Paper Series 2017-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    4. Wang, Zongrun & Chen, Songsheng, 2019. "Market efficiency, strategies and incomes of heterogeneously informed investors in a social network environment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 15-32.

  2. Florian Hauser & Bob Kaempff, 2013. "Evolution of trading strategies in a market with heterogeneously informed agents," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 575-607, July.

    Cited by:

    1. Haijun Yang & Harry Wang & Gui Sun & Li Wang, 2015. "A comparison of U.S and Chinese financial market microstructure: heterogeneous agent-based multi-asset artificial stock markets approach," Journal of Evolutionary Economics, Springer, vol. 25(5), pages 901-924, November.
    2. Witte, Björn-Christopher, 2012. "Fund managers - Why the best might be the worst: On the evolutionary vigor of risk-seeking behavior," Economics Discussion Papers 2012-20, Kiel Institute for the World Economy (IfW Kiel).
    3. Hauser, Florian & Schredelseker, Klaus, 2018. "Who benefits from insider regulation?," The Quarterly Review of Economics and Finance, Elsevier, vol. 68(C), pages 203-210.
    4. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.
    5. Marinelli, Carlo & Weissensteiner, Alex, 2014. "On the relation between forecast precision and trading profitability of financial analysts," Journal of Financial Markets, Elsevier, vol. 20(C), pages 39-60.
    6. Wang, Zongrun & Chen, Songsheng, 2019. "Market efficiency, strategies and incomes of heterogeneously informed investors in a social network environment," Journal of Economic Behavior & Organization, Elsevier, vol. 158(C), pages 15-32.
    7. Giulio Bottazzi & Pietro Dindo, 2013. "Evolution and market behavior in economics and finance: introduction to the special issue," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 507-512, July.

Chapters

  1. Florian Hauser & Bob Kaempff, 2010. "Trading on Marginal Information," Lecture Notes in Economics and Mathematical Systems, in: Marco Li Calzi & Lucia Milone & Paolo Pellizzari (ed.), Progress in Artificial Economics, pages 15-26, Springer.

    Cited by:

    1. Florian Hauser & Jürgen Huber & Bob Kaempff, 2015. "Costly Information in Markets with Heterogeneous Agents: A Model with Genetic Programming," Computational Economics, Springer;Society for Computational Economics, vol. 46(2), pages 205-229, August.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Bob Kaempff should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can 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.