Course in Computational Intelligence in Finance and Business

At IIM-C we firmly believe that information is power. In the years to come the need for increased sharing of information will govern the changes in organizational structure. A basic understanding of information systems is thus mandatory. We endeavor to ensure that you drive the e-commerce revolution. These courses not only equip you with software tools but also impart an understanding of the hardware which will help you set design your own database. It's time you started your own dotcom at IIMC.
Course Objectives

Course Objectives

The course is targeted to expose the students to modeling business problems using Computational Intelligence (CI) methods such as ANN, fuzzy systems, genetic and evolutionary algorithms, support vector machines, rough sets, instance based learning, simulation, meta heuristics for optimization such as simulated annealing, ant colony optimization, agent based computing. Emphasis will be on using soft computing (SC) (hybrid) methods in solving business problems in the area of finance and other disciplines.

The course will be based on case studies of financial and other business applications which exploit CI techniques for solving the problems of prediction, classification, clustering, learning, fuzzy decision making, optimization, time series forecasting, etc.

Course Content
Brief introduction to CI techniques: ANN, fuzzy systems, genetic and evolutionary algorithms, and support vector machines (SVM). Some discussions will be on rough sets, instance based learning, simulation, different meta heuristics for optimization as simulated annealing, ant colony optimization, agent based computing and different hybrid SC algorithms. Time Series methods will be briefly discussed.

Cases illustrating application of CI / SC to finance and other business applications. Cases will be also used to illustrate or explain some of the CI techniques.

Financial modeling to be considered are basically problems of prediction, classification, clustering, learning, optimization, data mining, time series forecasting under uncertain, fuzzy (ambiguous and imperfect knowledge) and dynamic situations.

Finance and other business applications to be treated are, for examples, in the areas of capital markets, portfolio optimization, foreign exchange prediction, interest rate prediction, bankruptcy prediction, financial analysis of a firm, insurance, credit appraisal, revenue management, risk management, fraud detection, demand forecasting and market segmentation.
Pedagogy

Lecture; Case discussions and presentation; Demonstration of appropriate packages; Project discussion and presentation.