(SSRN, Slides, Code, Internet Appendix, Blog posts)
- SSRN’s Top Ten download list for: Asset Pricing Models (Topic), European Finance eJournal, Wealth Management eJournal, Decision-Making & Management Science eJournal, DecisionSciRN: Business Analytics (Topic), DecisionSciRN: Predictive Analytics (Sub-Topic), and FinPlanRN: Other Investments (Topic).
- Presentations: FMA European conference 2023, Aalborg, Denmark (scheduled for June 2023) / Nordic Finance Network workshop, March 2023, Levi, Finland / Finance Brown Bag seminars (Aalto university), October 2022 / GSF Winter Workshop, Helsinki, November 2022
Firms with similar characteristics display similar expected returns. Defining neighbouring assets as those with the most similar set of characteristics, I show that past returns of an asset’s neighbours predict its future expected returns. If a majority of an asset’s neighbours have performed poorly (well) in the past, it is likely that this asset also performs poorly (well) in the future. By classifying each asset into a decile portfolio based on the past performance of its neighbours, with 94 characteristics, a long-short portfolio generates an out-of-sample annualized Sharpe ratio of 1.15 with a monthly alpha of 2.72% (t = 8.86).
Without loss of generality, if there exist only three firm characteristics, the k closest assets to the asset j (shown as red dots) are defined as the neighbours of asset j. If expected returns are a function of firm characteristics, regardless of the functional form, neighbouring assets display similar expected returns.
- Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model (With A Sharifi and H Arian) Mathematics and Computers in Simulation, Volume 190, 2021, Pages 1056-1079
Note: This article came out from my master thesis.
- Investments (2022), BSc-level major Finance course
- Course feedback score 4.13/5 (number of students responded: 42)
- Investment management (2021), BSc-level major Finance course
- Course feedback score 4.00/5 (number of students responded: 103)
- Machine Learning in Finance (2019), MSc elective course
- Statistics for Finance (2019)
- Financial Modeling with Python (Workshop) (2019)