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Big data recommendations and portfolio diversification: evidence from account-level data

Author

Listed:
  • Wang, Chenhao
  • Zhang, Ting
  • Zhu, Shanyi

Abstract

Retail investors are often underdiversified, limiting potential gains from broader asset allocation. This paper studies whether big data–driven recommendations can improve portfolio diversification. We exploit a natural experiment in a large Chinese commercial bank, where a data fusion platform provides personalized recommendations in FinTech branches. Using proprietary account-level data and a difference-in-differences design, we find that portfolios in FinTech branches become 7.6% more diversified, with higher investment income and lower overall risk. Clients reduce their share of deposits and increase their holdings of wealth management products. Mechanism analyses show that recommendations alleviate information frictions, and the effects are more pronounced for clients whose portfolios underutilize their risk-bearing capacity. The impact is stronger in central business district branches and those lacking human advisors, as well as among male and non-wealthy clients.

Suggested Citation

  • Wang, Chenhao & Zhang, Ting & Zhu, Shanyi, 2026. "Big data recommendations and portfolio diversification: evidence from account-level data," Finance Research Letters, Elsevier, vol. 92(C).
    • Handle: RePEc:eee:finlet:v:92:y:2026:i:c:s1544612326000723
    DOI: 10.1016/j.frl.2026.109541
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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance

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