Data Mining in Finance: Report From the Post-NNCM-96 Workshop on Teaching Computer Intensive Methods for Financial Modeling and Data Analysis
Year: 1997
Type: preprint
Abstract: The conference Neural Networks in the Capital Markets was followed by a workshop at Caltech on November 23, 1996.The purpose of the workshop was to bring people together to share their experiences and ideas on designing and teaching new courses in financial engineering, bridging the gap between computationally advanced methods for learning from data, and real world problems in finance.This report highlights some of these ideas, and describes in detail one specific course taught at the Leonard N. Stern School of Business at New York University.That course, Data Mining in Finance, focuses on computer intensive data analysis methods that are relevant to financial problems.The methods originate in fields such as statistics and probability, artificial intelligence and machine learning, computational science, applied mathematics, and engineering.They include neural nets, genetic algorithms, bootstrap methods, Bayes nets, Hidden Markov models, and clustering techniques.These methods are applied to problems that include building and evaluating technical trading strategies, and computing and managing market and credit risk.Beside the theory and the lectures, that course has two hands-on components.For each method, a computer assignment on a real world data set is placed between two lectures.The first lecture introduces the problem, discusses the essentials of the method to solve this problem, and demonstrates a software implementation.After students have gained some experience with the method and software in the lab, the second lecture summarizes strengths and weaknesses, and discusses further applications in the capital markets.The second hands-on component is a group project.It explores one of the approaches presented during the semester on a problem and data set selected by each individual group, usually in collaboration with a Wall Street firm.The course concludes with several Wall Street traders giving their perspectives on the practice and promise on data mining in finance.In recent years the application of data mining-statistical artificial intelligence, machine learning, information extraction, knowledge discovery-to technical trading and finance has seen exciting results, as witnessed by several conferences, including NNCM (Neural Networks in the Capital Markets) in 1993 and 1995 (Refenes et al., 1996) in London, in 1994 and 1996 (Weigend et al., Center for Digital Economy Research Stem School of Business IVorking Paper IS-97-19
Source: SSRN Electronic Journal
Author: Andreas S. Weigend
Institution: New York University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
Funder: University of Washington
OpenAlex ID: W1590176672