Big data/machine learning
- Agency MBS Prepayment Model Using Neural NetworksJiawei “David” Zhang, Xiaojian “Jan” Zhao, Joy Zhang, Fei Teng, Siyu Lin and Hongyuan “Henry” LiThe Journal of Structured Finance January 2019, 24 (4) 17-33; DOI: https://doi.org/10.3905/jsf.2019.24.4.017
- Machine Learning for Structured FinanceEric KolchinskyThe Journal of Structured Finance October 2018, 24 (3) 7-25; DOI: https://doi.org/10.3905/jsf.2018.24.3.007
- Seeking Alpha from Dividend Announcements: Big-Data Insights from Joining CAR and EVA Style AnalysisAtreya Chakraborty, James L. Grant, Emery A. Trahan and Bhakti VarmaThe Journal of Investing July 2021, 30 (5) 107-126; DOI: https://doi.org/10.3905/joi.2021.1.183
- Machine Learning for Active Portfolio ManagementSöhnke M. Bartram, Jürgen Branke, Giuliano De Rossi and Mehrshad MotahariThe Journal of Financial Data Science July 2021, 3 (3) 9-30; DOI: https://doi.org/10.3905/jfds.2021.1.071
- Weak Supervision and Black-Litterman for Automated ESG Portfolio ConstructionAlik Sokolov, Kyle Caverly, Jonathan Mostovoy, Talal Fahoum and Luis SecoThe Journal of Financial Data Science July 2021, 3 (3) 129-138; DOI: https://doi.org/10.3905/jfds.2021.1.070
- Stock Portfolio Selection with Deep RankNetYan Li and Zheng TanThe Journal of Financial Data Science July 2021, 3 (3) 108-120; DOI: https://doi.org/10.3905/jfds.2021.1.069
- Confronting Machine Learning with Financial ResearchKristof Lommers, Ouns El Harzli and Jack KimThe Journal of Financial Data Science July 2021, 3 (3) 67-96; DOI: https://doi.org/10.3905/jfds.2021.1.068
- An Inside Peek at AI Use in Private EquityThomas ÅstebroThe Journal of Financial Data Science July 2021, 3 (3) 97-107; DOI: https://doi.org/10.3905/jfds.2021.1.067
- Interpretable Machine Learning for Diversified Portfolio ConstructionMarkus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock and Peter SchwendnerThe Journal of Financial Data Science July 2021, 3 (3) 31-51; DOI: https://doi.org/10.3905/jfds.2021.1.066
- Does the CFTC Report Have Predictive Power: Machine Learning ApproachOleksandr ProskurinThe Journal of Financial Data Science July 2021, 3 (3) 139-151; DOI: https://doi.org/10.3905/jfds.2021.1.065
Pages
Explore our content to discover more relevant research
- Behavioral Finance
- Theory (32)
- In Markets (20)
- In Wealth Management (57)
- Derivatives
- Options (275)
- Credit default swaps (67)
- Counterparty risk (10)
- Other (124)
- Factors, risk premia
- Factor-based models (241)
- Style investing (92)
- Other (23)
- Fixed income and structured finance
- Project finance (79)
- International Investing
- Legal/regulatory/public policy
- Long-term/retirement investing
- Wealth management (296)
- Retirement (338)
- Social security (68)
- Pension funds (98)
- Other (35)
- Mutual funds/passive investing/indexing
- Mutual fund performance (121)
- Passive strategies (28)
- Other (133)
- Performance measurement
- Volatility measures (86)
- Performance measurement (716)
- Portfolio management/multi-asset allocation
- Portfolio theory (286)
- Portfolio construction (693)
- ESG investing (189)
- Manager selection (97)
- Other (94)
- Quantitative methods
- Statistical methods (518)
- Simulations (166)
- Quantitative methods (318)
- Real assets/alternative investments/private equity
- Real estate (87)
- Commodities (87)
- Other real assets (72)
- Currency (92)
- Private equity (218)
- Risk management
- Credit risk management (184)
- Tail risks (64)
- Risk management (345)
- Security analysis and valuation
- Technical analysis (39)