Sina Seyfi

I am a 3rd year PhD student in Finance at Aalto University.

I do research in Empirical Asset Pricing and Big Data. I employ machine learning tools to study the cross-section of stock returns.

Email: sina.seyfi[at]


Neighbouring Assets

  • 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.

Previous Research

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)