Welcome to Korns Associates

(..our agents make money investing...)

Introducing: Korns Associates.

Korns Associates is a privately held applied research company that develops sophisticated agent technology, development tools, and applications. It was founded in June 1993, and has pioneered the use of "intelligent agents" for securities investing using a business model wherein its research is self-funding.

Our Investment Agents.

Korns Associates principal research interest lies in the application of "intelligent agents" to automating mutual-fund-style securities investing. In a traditional mutual fund business, a staff of human traders are continuously evaluated based on their one year, three year, five year, and ten year performance histories. The best performing human traders receive an increasing share of the mutual fund's assets to manage. The worst performing human traders, well ...

Our Business Model.

Korns Associates business model is applied research powered by proprietary investing profits. As an applied research group, Korns Associates searches the academic community looking for new Artificial Intelligence and Machine Learning technologies which might be applied to securities investing. Promising new technologies are implemented in Deep Green as investing agents which will compete for stock market profits in the virtual "survival of the fittest" environment. At each stage of its development, Deep Green is used to rank securities as investment selections in the Korns Associates Real Money Balanced Investing Account. Profits from this proprietary investing activity are used to fund futher Deep Green application development.

Our Agent Technology.

Korns Associates has pioneered the development of Analytic Information Server?, an integrated knowledge management tool. This agent oriented database technology represents a significant step forward in the use of "Intelligent Agents" in integrating both existing and new distributed applications with "Adaptive Intelligence".

Research Publications

Analytic Information Server has aided a number of scientists in valuable peer reviewed research.
Here are the links to a number of peer reviewed scientific research papers for which AIS has provided either subject material, tools, or valuable assistance. All of these papers are published in peer reviewed scientific journals or peer reviewed books. These papers have receives hundreds of citations.
(Note: these downloads are all drafts only as we are not allowed to distribute the final printed papers. Please purchase the print versions from publishers to support continuing research.)

  1. Korns, Michael F., 2015. Trading Volatility Using Highly Accurate Symbolic Regression. In Ryan, et. al., Handbook Of Genetic Programming Applications, New York, New York, USA. Springer.
  2. Korns, Michael F., 2012. Predicting Corporate Forward 12 Month Earnings, 2012. Theory and New Applications of Swarm Intelligence, ISBN 978-953-51-0364-6, edited by Rafael Parpinelli and Heitor S. Lopes, InTech Academic Publishers.
  3. Korns, Michael F., 2012. A Baseline Symbolic Regression Algorithm. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice X, New York, New York, USA. Springer.
  4. Korns, Michael F, 2010. Abstract Expression Grammar Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VIII, New York, New York, USA. Springer.
  5. Korns, Michael F, 2011. Accuracy in Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice IX, New York, New York, USA. Springer.
  6. Korns, Michael F. 2006. Large-Scale, Time-Constrained Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice IV, New York, New York, USA. Springer.
  7. Korns, Michael F. 2007. Large-Scale, Time-Constrained Symbolic Regression-Classification. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice V, New York, New York, USA. Springer.
  8. Korns, Michael F, 2009. Symbolic Regression of Conditional Target Expressions. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VII, New York, New York, USA. Springer.
  9. Korns, Michael F, 2009. Symbolic Regression Using Abstract Expression Grammars. In Proceedings of GECCO Genetic and Evolutionary Computation Conference, Montreal, July 2009. Association for Computing Machinery.
  10. Korns, Michael F., 2015. Highly Accurate Symbolic Regression for Noisy Training Data. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XIII, New York, New York, USA. Springer.
  11. Korns, Michael F., 2016. An Evolutionary Algorithm for Big Data Multiclass Classification. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XIV, New York, New York, USA. Springer.
  12. Korns, Michael F., 2017. Evolutionary Linear Discriminant Analysis for Multiclass Classification Problems. In GECCO Conference Proceedings 17, July 15-19, Berlin Germany 2017.
  13. Korns, Michael F., 2017. Genetic Programming Symbolic Classification: A Study. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XV, New York, New York, USA. Springer.
  14. Korns, Michael F., May, Tim, 2019. Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XVI, New York, New York, USA. Springer.
  15. Korns, Michael F., May, Tim, 2019. Feature Discovery with Deep Learning Algebra Networks. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XVIII, New York, New York, USA. Springer.
  16. Korns, Michael F., and Truscott, Philip, 2011. Detecting Shadow Economy Sizes with Symbolic Regression. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice IX, New York, New York, USA. Springer.
  17. Truscott, Philip, Korns, Michael F., 2015. Predicting Product Choice with Symbolic Regression and Classification. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XIII, New York, New York, USA. Springer.
  18. Korns, Michael F., 2013. Extreme Accuracy in Symbolic Regression. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XI, New York, New York, USA. Springer.
  19. Korns, Michael F, 2009. Mutation and Crossover with Abstract Expression Grammars. In Proceedings of World Summit on Genetic and Evolutionary Computation, Shanghai, June 2009. Association for Computing Machinery.
  20. Korns, Michael F, 2009. Symbolic Regression of Conditional Target Expressions. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VII, New York, New York, USA. Springer.
  21. Truscott, Philip, Korns, Michael F., 2014. Explaining Unemployment Rates and Symbolic Regression. In Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice XI, New York, New York, USA. Springer.
  22. Korns, Michael F., Nunez, Loreyfel, 2008. Profiling Symbolic Regression Classification. In Riolo, Rick, L, Soule, Terrance, and Wortzel, Bill, editors, Genetic Programming Theory and Practice VI, New York, New York, USA. Springer.


Korns Associates

Korns Associates is a privately held R&D company that develops and uses sophisticated agent technology to build artificial intelligence applications for securities analysis and stock ranking. We were founded in June 1993, and have engaged in continuous software research and development of securities analysis software.