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Artificial intelligence and big holdings data: Opportunities for central banks

Author

Listed:
  • Xavier Gabaix
  • Ralph S J Koijen
  • Robert Richmond
  • Motohiro Yogo

Abstract

Asset demand systems specify the demand of investors for financial assets and the supply of securities by firms. We discuss how realistic models of the asset demand system are essential to assess ex post, and predict ex ante, how central bank policy interventions impact asset prices, the distribution of wealth across households and institutions, and financial stability. Due to the improved availability of big holdings data and advances in modelling techniques, estimating asset demand systems is now a practical reality. We show how demand systems provide improved information for policy decisions (eg in the context of financial contagion, convenience yield or the strength of the dollar) or to design optimal policies (eg in the context of quantitative easing or designing climate stress tests). We discuss how recent AI methods can be used to improve models of the asset demand system by better measuring asset and investor similarity through so-called embeddings. These embeddings can for instance be used for policymaking by central banks to understand the rebalancing channel of asset purchase programs and to measure crowded trades.

Suggested Citation

  • Xavier Gabaix & Ralph S J Koijen & Robert Richmond & Motohiro Yogo, 2024. "Artificial intelligence and big holdings data: Opportunities for central banks," BIS Working Papers 1222, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1222
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    References listed on IDEAS

    as
    1. Saleem Bahaj & Robert Czech & Sitong Ding & Ricardo Reis, 2023. "The market for inflation risk," Bank of England working papers 1028, Bank of England.
    2. Koijen, Ralph & Yogo, Motohiro, 2020. "Exchange Rates and Asset Prices in a Global Demand System," CEPR Discussion Papers 14874, C.E.P.R. Discussion Papers.
    3. Xavier Gabaix & Ralph S. J. Koijen, 2024. "Granular Instrumental Variables," Journal of Political Economy, University of Chicago Press, vol. 132(7), pages 2274-2303.
    4. Leonid Kogan & Dimitris Papanikolaou & Lawrence D.W. Schmidt & Bryan Seegmiller, 2023. "Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data," NBER Working Papers 31846, National Bureau of Economic Research, Inc.
    5. Valentin Haddad & Alan Moreira & Tyler Muir, 2023. "Whatever it Takes? The Impact of Conditional Policy Promises," NBER Working Papers 31259, National Bureau of Economic Research, Inc.
    6. Daron Acemoglu, 2025. "The simple macroeconomics of AI," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 40(121), pages 13-58.
    7. Babina, Tania & Fedyk, Anastassia & He, Alex & Hodson, James, 2024. "Artificial intelligence, firm growth, and product innovation," Journal of Financial Economics, Elsevier, vol. 151(C).
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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