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Contemporary quantitative and mathematical finance pedagogy, or quant-fin, is evident in courses like ‘Financial Derivatives and Risk Management’.  Quant-FIN courses typically seek to integrate applied mathematical and capital market theories. But, the practice of this integration often reduces to a presentation of tedious asset-pricing formulae, applied statistical methods, and graph theory.  This is further complicated with the growing prevalence of “Big Data.†Today, to be successful in their careers quant-FIN majors need to understand and implement a multitude of different solution techniques. This suggests that today’s business school pedagogy has a need to incorporate a classroom-based quant-FIN directed experiential learning model (ELM). The proposed workshop demonstrates how the cloud-based decision-making system, WinORS_{e-AI 2017 ,} (pronounced and abbreviated as: Win.O.R.S.) is currently used to implement an ELM and, as a consequence, create a ‘flipped’ and ‘Active Learning’ classroom environment. Win.O.R.S is available to students through an Internet browser. Student’s initiate fetches of real-time equity, options, futures, and fixed-income data to: a) create option spread portfolios (e.g., butterfly, straddles, guts, etc.); b) construct, simulate, hedge and maintain real-valued equity and bond portfolios; c) examine volatility hedging concepts and forecast future asset values by using a “Big Data†capable artificial neural network; and, d) implement a dynamic futures hedge against priced assets. Win.O.R.S also takes the student into the competitive field of automated trading system.  Within the operational environment, managed portfolios are traded by machine learning algorithms. It is here where students learn to compare risk-adjusted performance across alternate risk-mitigation instruments and theories.

Derivatives; Quant-FIN Education; Active Learning; Artificial Intelligence