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Institutionalizing Countercyclical Investment: A Framework for Long-term Asset Owners

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  • Bradley Jones

Abstract

Do portfolio shifts by the world’s largest asset owners respond procyclically to past returns, or countercyclically to valuations? And if countercyclical investment (with both market-stabilizing and return-generating properties) is a public and private good, how might asset owners be empowered to do more of it? These two questions motivate this study. Based on analysis of representative portfolios (totaling $24 trillion) for a range of asset owners (central banks, pension funds, insurers and endowments), portfolio changes typically appear procyclical. In response, I suggest a framework aimed at jointly bolstering long-term returns and financial stability should: (i) embed governance practices to mitigate ‘multi-year return chasing;’ (ii) rebalance to benchmarks with factor exposures best suited to long-term investors; (iii) minimize principal-agent frictions; (iv) calibrate risk management to minimize long-term shortfall risk (not short-term price volatility); and (v) ensure regulatory conventions do not amplify procyclicality at the worst possible times.

Suggested Citation

  • Bradley Jones, 2016. "Institutionalizing Countercyclical Investment: A Framework for Long-term Asset Owners," IMF Working Papers 2016/038, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2016/038
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    Cited by:

    1. Amariei, Cosmina, 2020. "Asset Allocation in Europe: Reality vs. Expectations," ECMI Papers 27304, Centre for European Policy Studies.
    2. Bradley Jones, 2018. "Central Bank Reserve Management and International Financial Stability—Some Post-Crisis Reflections," IMF Working Papers 2018/031, International Monetary Fund.
    3. Daniela Gabor, 2018. "Goodbye (Chinese) Shadow Banking, Hello Market†based Finance," Development and Change, International Institute of Social Studies, vol. 49(2), pages 394-419, March.
    4. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).

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