Financial Applications in Signal Processing Curricula

Modern signal processing heavily relies on probability and statistics. Naturally, these subjects are emphasized in the signal processing curricula, at both the undergraduate and graduate levels. Historically, however, both probability and statistics were developed in order to solve various applied problems arising in economics and finance. Specifically, probability theory owes its existence to games of chance [1], [2], and statistics made its first significant appearance in the pricing of insurance premia and bonds [1]. More recently, both finance and economics have been steady sources of data—much of it 1D temporal signals—generating demand for the statistical analysis and modeling tools commonly developed and used by signal processing researchers and practitioners. Such interrelationships suggest that courses on applied probability and random processes taught to signal processing students should frequently turn to finance for motivating examples, illustrations, homework problems, and lab exercises. In addition, courses dedicated to financial applications of signal processing should perhaps be as common as those on applications to biomedical imaging and speech analysis. In reality, however, this happens very rarely. Financial signal processing is absent from most electrical engineering curricula. The main objectives of this column are to share several ways in which financial signal processing has been incorporated into the undergraduate and graduate curricula at Purdue’s School of Electrical and Computer Engineering (ECE), and to encourage other schools to do the same. The most obvious spot in the undergraduate curriculum for a cornucopia of finance-related examples and problems is an undergraduate course on applied probability. At Purdue’s ECE, it is a required course, typically taken during the junior year. In addition to students who will ultimately pursue signal processing, the audience usually includes many other students with varied interests, coming both from Electrical Engineering and from Computer Engineering. It is therefore advisable to use motivating examples from many different application areas, in order to insure that as many students as possible are engaged and