Welcome to Financial Engineering Research Lab @ CS Department - OSU

Financial engineering is a multidisciplinary field that involves different financial aspects such as financial theory, the methods of financing, mathematical tools, computation and the practice of programming to achieve the desired end results. The financial engineering methodologies apply engineering methodologies, social theories and quantitative methods to finance. It is normally used in the stock market, securities, banking, and financial management and consulting industries, or as quantitative analysts in corporate treasury and finance departments of general manufacturing and service firms. Financial Engineering Research Lab is basically concerned and working on three different approaches to forecast stock market. These approaches include Concordance and Genetic Programming, Multiple Regressions with Dependent Dummy Variables and Hidden Markov Chain Method.





On Multiple Regressions with Dummy Variables approach, we propose a risk estimation model as a potential prediction model for stock market data. An ad hoc risk management system (ARMS) is a frame work composed of statistical model and surveyed field data for forecasting. The primary concern in ARMS is dynamically forecasting or predicting a risky state. . The proposed model called MRDDV (multiple regression with dependent dummy variable) depends on quantitative independent variable and qualitative independent variable, with dependent dummy variable technique to set the criterion of risk state. In this paper, we propose to use the MRDDV model for financial data analysis, specifically stock market data analysis. The model is applied to two different financial time series. We interpret the model with the dependent dummy variable as a predictor for turns in the market.





Hidden Markov chain (HMC) model is widely used for various problems, including signal and image processing, finance, economical prediction, health sciences and other issues. We have used this approach to model the dynamic behavior of the stock markets and individual equities using probabilistic finite state automata models. In particular, Hidden Markov Chain models and algorithms model and predict stock price fluctuations for various time frames.