Steve Yang

Associate Professor

School: School of Business

Building: Babbio Center

Room: 536

Phone: (201) 216-3394

Fax: (201) 216-5541

Email: syang14@stevens.edu

Website

Education
  • PhD (2012) University of Virginia (Systems and Information Engineering)
  • MS (2008) University of Virginia (Systems and Information Engineering)
  • MS (1997) Virginia Polytechnic Institute and State University (Computer Science)
  • BA (1992) Beihang University (Aerospace Engineering)
Research

Microstructure, Behavioral finance, Portfolio optimization, Algorithmic trading, Financial systemic risk, and Financial decision systems

General Information

Dr. Steve Yang is an Associate Professor of the School of Business at Stevens Institute of Technology. He holds a Ph.D. in Systems and Information Engineering from University of Virginia with concentration on Financial Engineering. His research has been focused on understanding markets’ irrationality and its impact on trading, portfolio, risk management, and systemic risk using decision science tools such as Markov decision processes, reinforcement learning, and other artificial intelligence (AI) methods. His current research interests include market microstructure, behavioral finance, algorithmic trading, portfolio optimization, and agent based financial market simulation. His research has also been founded by a variety of NGOs, government agencies and industry firms such as, NSF, SWIFT, IRRC, IAAER, CFTC, DoD, Accenture, Northrop Grumman, KPMG, etc. He graduated at the top of his class with a B.S. degree in Aerospace Engineering from the Beijing Institute of Aeronautics and Astronautics.
Dr. Steve Yang has worked with several major federal financial regulators such as, the Securities and Exchange Commission (SEC), the Commodity Futures Trading Commission (CFTC) and Treasury in the capacity as either a research consultant or visiting scholar. As an expert in modeling algorithmic trading, he provides consulting services to the Chief Economist Office and the Division of Enforcement at the Commodity Futures Trading Commission in Washington DC. He is an Associate Editor for Expert Systems With Applications (ESWA) journal, and he also served as a guest editor for Quantitative Finance and European Journal of Finance, as well as NSF review panels. His research has been published on journals such as Quantitative Finance, Decision Sciences, Journal of Banking & Finance, Expert Systems with Applications, Neurocomputing, etc.

Experience

Associate Professor at Stevens Institute of Technology, 2019 - present
Visiting Scholar at The Securities Exchange Commission - 2020
Assistant Professor at Stevens Institute of Technology, 2012 - 2019
Consultant at U.S. Commodity Futures Trading Commission, 2009 - 2012

Institutional Service
  • School of Business PhD Committee Member
  • Stevens Data Science PhD Committee Member
  • BoT – Research Enterprise and Technology Commercialization Member
  • FE PhD Committee Chair
Professional Service
  • Center for Research toward Advancing Financial Technology Director
  • IEEE CIS IEEE Computational Intelligence in Finance and Economics Committee Member
  • Expert Systems with Applications (Journal) Associate Editor
  • U.S. Commodity Futures Trading Commission Research Consultant
Consulting Service

Enforcement Division of the U.S. Commodity Futures Trading Commission Consultant (2014-present)

Appointments

Visiting Academic Scholar (2020)
Division of Economic and Risk Analysis
US Securities and Exchange Commission

Professional Societies
  • INFORMS – Institute for Operations Research and the Management Sciences Member
  • AFA – American Finance Association Member
  • IEEE CIS – IEEE Computational Intelligence Society Member
  • RBF – Risk Banking and Finance Society Member
Grants, Contracts, and Funds

S.Y. Yang (PI), $862K, "IUCRC Phase I Stevens: Center for Research toward Advancing Financial Technologies (CRAFT)", NSF, Co-PI with Dr. Nickerson, Dr. Calhoun, Dr. Darkinka, Dr. Subalachimi, 2021-2026
S.Y. Yang (CoPI), $25K, “IAAER and KPMG Foundation”, IAAER, Co-PI with Dr. Elaine Henry, 2020-2022.
S.Y. Yang (PI), $15K, “NSF IUCRC Planning Grant”, NSF, Co-PI with Dr. Jeff Nickerson, Dr. Giuseppe Antenese, Dr. Darinka Dentchva and George Calhoun, 2020-2021.
S.Y. Yang (PI), $80K, “Investment Banking Value Chain Modeling”, Accenture LLP, Co-PI with Dr. William Rouse and Mike Pennock, 2016.
S.Y. Yang (Co-PI), $470K, “Technical Leadership Development Framework”, DoD/DAU, Co-PI with Dr. Wilson Felder, 2016.
S.Y. Yang (PI), $50K, “Financial Transaction System Modeling with XBRL”, Northrop Grumman, 2016-2017.
S.Y. Yang (PI), $100K, “Elite Sourced Financial Information Modeling”, Northrop Grumman, 2014-2016.
S.Y. Yang (Co-PI), $35K, “Spoofing Detection and Methodology”, U.S. Commodity and Futures Commission, 2014-2015.
S.Y. Yang (Co-PI) , $20K, “Financial Fraud Detection with Unstructured Data”, Stevens Ignition Grant, with Dr. P.K. Subalachimi, 2014.
S.Y. Yang (Co-PI) , $68K, “High Frequency Trading White Paper”, IRRC, Co-PI with Dr. Khaldoun Khashanah and Dr. Ionut Florescu, 2013-2014.
S.Y. Yang (Co-PI), $32K, “Financial Big Data and Standardization”, SWIF, Co-PI with Dr. Suzanne Morsfield (Columbia University), 2013.
S.Y. Yang (PI), $35K, “Financial Market Simulation and Fraud Detection”, Northrop Grumman, 2013.
S.Y. Yang (PI), $96K, "HFT Trading Behavior Modeling and Fraud Detection Analytics", Commodity and Futures Trading Commission, 2012.

Selected Publications
Book Chapter
  1. Liu, A.; Chen, J.; Yang, S. Y.; Hawkes, A. G. (2021). Information Transition in Trading and Its Effect on Market Efficiency: An Entropy Approach. Financial Mathematics and Fintech. NYC, NY: Springer.
  2. Yang, S.; Liu, F.; Zhu, X. (2018). The impact of XBRL on financial statement structural comparability. Network, Smart and Open (pp. 193--206). Springer.
  3. Yang, S.; Mo, S. Y. (2016). Social media and news sentiment analysis for advanced investment strategies. Sentiment Analysis and Ontology Engineering (pp. 237--272). Springer.
Conference Proceeding
  1. Liu, A.; Chen, J.; Hawkes, A.; Yang, S. (2020). : Information Transition in Trading and its Effect on Market Efficiency: An Entropy Approach. Proceedings of the First International Forum on Financial Mathematics and Financial (pp. 19). Singapore: Springer.
  2. Zhang, X.; Mo, C. Y.; Yang, S. (2018). Bank Contagion Risk through Fire-Sales: A Heterogeneous Agent Model. 2018 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2321--2328).
  3. Song, Q.; Almahdi, S.; Yang, S. (2017). Entropy based measure sentiment analysis in the financial market. 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1--5).
  4. Xiaodi Zhu, Steve Y. Yang,; Mozani, S. (2016). Forecasting Equity Risk Using Firm Risk Disclosures. IEEE Symposium Series on Computational Intelligence (SSCI.
  5. Polacek, G. A.; Verma, D.; Yang, S. (2016). Influencing Message Propagation in a Social Network Using Embedded Boolean Networks: A Demonstration Using Agent-Based Modeling. INCOSE International Symposium (1 ed., vol. 26, pp. 1509--1523).
  6. Andrew Todd, Peter Beling, William Scherer; Yang, S. (2016). Agent-based financial markets: A review of the methodology and domain. IEEE Symposium Series on Computational Intelligence (SSCI 2016) (pp. 1207--1215).
  7. Song, Q.; Liu, A.; Yang, S.; Deane, A.; Datta, K. (2015). An extreme firm-specific news sentiment asymmetry based trading strategy. 2015 IEEE Symposium Series on Computational Intelligence (pp. 898--904).
  8. Yang, S.; Kim, J. (2015). Bitcoin market return and volatility forecasting using transaction network flow properties. 2015 IEEE Symposium Series on Computational Intelligence (pp. 1778--1785).
  9. Yang, S.; Qiao, Q.; Beling, P. A.; Scherer, W. T. (2014). Algorithmic trading behavior identification using reward learning method. 2014 International Joint Conference on Neural Networks (IJCNN) (pp. 3807--3414).
  10. Yang, S.; Mo, S. Y.; Zhu, X. (2014). An empirical study of the financial community network on twitter. 2014 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics (CIFEr) (pp. 55--62).
  11. Yang, S.; Liu, A.; Mo, S. Y. (2014). Twitter financial community modeling using agent based simulation. 2014 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics (CIFEr) (pp. 63--70).
  12. Mo, S. Y.; Paddrik, M.; Yang, S. (2013). A study of dark pool trading using an agent-based model. 2013 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics (CIFEr) (pp. 19--26).
  13. Yang, S.; Cogill, R. (2013). Balance sheet outlier detection using a graph similarity algorithm. 2013 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics (CIFEr) (pp. 135--142).
  14. Hayes, R.; Paddrik, M.; Todd, A.; Yang, S.; Beling, P.; Scherer, W. (2012). Agent based model of the e-mini future: application for policy making. Proceedings of the 2012 Winter Simulation Conference (WSC) (pp. 1--12).
  15. Yang, S.; Paddrik, M.; Hayes, R.; Todd, A.; Kirilenko, A.; Beling, P.; Scherer, W. (2012). Behavior based learning in identifying high frequency trading strategies. 2012 IEEE Conference on Computational Intelligence for Financial Engineering \& Economics (CIFEr) (pp. 1--8).
Journal Article
  1. Yang, S.; Liu, Y.; Yu, Y.; Mo, S. (2021). Energy ETF return jump contagion: a multivariate Hawkes process approach. European Journal of Finance (pp. 26). United Kingdom: Chapman & Hall.
  2. Liu, A.; Paddrik, M.; Yang, S.; Zhang, X. (2020). Interbank contagion: An agent-based model approach to endogenously formed networks. Journal of Banking and Finance (1 ed., vol. 112, pp. 105-191). Amsterdam: Elsevier.
  3. Liu, A.; Chen, J.; Hawkes, A.; Yang, S. (2020). The Flow of Information in Trading: A Entropy Approach. Entropy (pp. 16). Basel, Switzerland: MDPI.
  4. Almahdi, S.; Yang, S. Y. (2019). A constrained portfolio trading system using particle swarm algorithm and recurrent reinforcement learning. Expert Systems with Applications (2019 ed., vol. 130, pp. 145-156). Oxford: Pergamon.
    https://www.sciencedirect.com/science/article/pii/S0957417419302441.
  5. Yang, S. Y.; Liu, F.; Zhu, X.; Yen, D. C. (2018). A graph mining approach to identify financial reporting patterns: An empirical examination of industry classifications. Decision Sciences (4 ed., vol. 50, pp. 847-876). Hoboken, NJ: Wiley.
    https://onlinelibrary.wiley.com/doi/full/10.1111/deci.12345?casa_token=uhILkk4An3EAAAAA%3AnP-syavVVXFKuQ-nMyc4MNN6pZyY8ES027QN8uvzJ16uI6u3EWQHpvy97zANGW5SV9T45K2XIHnSThw.
  6. Yang, S. Y.; Onur, E. (2018). Interest Rate Swap Market Complexity and Its Risk Management Implications. Complexity (vol. 2018). London: Hindawi.
    https://www.hindawi.com/journals/complexity/2018/5470305/abs/.
  7. Yang, S. Y.; Yu, Y.; Almahdi, S. (2018). An investor sentiment reward-based trading system using Gaussian inverse reinforcement learning algorithm. Expert Systems with Applications (vol. 388-401, pp. 388-401). Amsterdam: Elsevier .
    https://www.sciencedirect.com/science/article/pii/S0957417418304810.
  8. Liu, A.; Mo, C. Y.; Paddrik, M. E.; Yang, S. Y. (2018). An agent-based approach to interbank market lending decisions and risk implications. Information (6 ed., vol. 9, pp. 132-150). Basel: Multidisciplinary Digital Publishing Institute.
    https://www.mdpi.com/2078-2489/9/6/132.
  9. Almahdi, S.; Yang, S. (2017). An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown. Expert Systems with Applications (vol. 87, pp. 267--279). Elsevier.
  10. Yang, S.; Mo, S. Y.; Liu, A.; Kirilenko, A. A. (2017). Genetic programming optimization for a sentiment feedback strength based trading strategy. Neurocomputing (vol. 264, pp. 29--41). Elsevier.
  11. Song, Q.; Liu, A.; Yang, S. (2017). Stock portfolio selection using learning-to-rank algorithms with news sentiment. Neurocomputing (vol. 264, pp. 20--28). Elsevier.
  12. Yang, S.; Liu, A.; Jin, C.; Hawkes, A. (2017). Applications of a Multivariate Hawkes Process to Joint Modeling of Sentiment and Market Return Events. Quantitative Finance (2 ed., vol. 18, pp. 295–310). London: Taylor & Francis Group.
  13. Jin, C.; Hawkes, A.; Khashanah, K.; McMillan, D.; Rosenbaum, M.; Scalas, E.; Yang, S. (2017). Editors’ foreword on ‘Hawkes Processes in Finance’. Quantitative Finance (2 ed., vol. 18, pp. 191–197). London: Taylor & Francis.
  14. Mo, S. Y.; Liu, A.; Yang, S. (2016). News sentiment to market impact and its feedback effect. Environment Systems and Decisions (2 ed., vol. 36, pp. 158--166). Springer.
  15. Yang, S.; Qiao, Q.; Beling, P. A.; Scherer, W. T.; Kirilenko, A. A. (2015). Gaussian process-based algorithmic trading strategy identification. Quantitative Finance (10 ed., vol. 15, pp. 1683--1703). Taylor \& Francis.
  16. Yang, S.; Mo, S. Y.; Liu, A. (2015). Twitter financial community sentiment and its predictive relationship to stock market movement. Quantitative Finance (10 ed., vol. 15, pp. 1637--1656). Taylor \& Francis.
Courses

QF302 - Financial Market Microstructure and Trading
FE610 - Stochastic Calculus for Financial Engineers
FE570 - Market Microstructure and Trading Strategies
FE670 - Algorithmic Trading Strategies
FE622 - Simulation Methods in Computational Finance and Economics
FE900 - Thesis in Financial Engineering
FE960 - PhD Research Topics