The DOI matches its Crossref record.
Skulls, Financial Turbulence, and Risk Management
Open accessAbstract
Based on a methodology introduced in 1927 to analyze human skulls and later applied to turbulence in financial markets, this study shows how to use a statistically derived measure of financial turbulence to measure and manage risk and to improve investment performance. View a webinar based on this article.
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FinRL employs the turbulence index to measure extreme fluctuation.
“FinRL employs the turbulence index turbulence 𝑡 to measure extreme fluctuation [23]:”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [23]
FinRL employs the financial turbulence index to control risk in a worst-case scenario such as the financial crisis of 2007-2008.
“To control the risk in a worst-case scenario, such as financial crisis of 2007-2008, FinRL employs the financial turbulence index turbulence t that measures extreme asset price fluctuation [25]:”
From FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance (2020) · cites this paper as [25]
FinRL employs the turbulence index to measure extreme fluctuation, as in [23].
“For a worst case scenario as the 2008 global financial crisis, FinRL employs the turbulence index turbulence 𝑡 to measure extreme fluctuation [23]:”
From FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance (2021) · cites this paper as [23]
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Research works printed but not linked: 12
- Ang A., 2002, Review of Financial Studies, 15, 1137, 10.1093/rfs/15.4.1137 · DOI
- Ang A., 2002, Journal of Financial Economics, 63, 443, 10.1016/S0304-405X(02)00068-5 · DOI
- Bollerslev T., 1986, Journal of Econometrics, 31, 307, 10.1016/0304-4076(86)90063-1 · DOI
- Chow G., 1999, Financial Analysts Journal, 55, 65, 10.2469/faj.v55.n3.2273 · DOI
- Cremers J.H., 2005, Journal of Portfolio Management, 31, 70, 10.3905/jpm.2005.500356 · DOI
- Das S.R., 2004, Journal of Finance, 59, 2809, 10.1111/j.1540-6261.2004.00717.x · DOI
- Jorion P., 1986, Journal of Financial and Quantitative Analysis, 21, 279, 10.2307/2331042 · DOI
- Kritzman M., 2002, Financial Analysts Journal, 58, 91, 10.2469/faj.v58.n3.2541 · DOI
- Ledoit O., 2003, Journal of Empirical Finance, 10, 603, 10.1016/S0927-5398(03)00007-0 · DOI
- Mahalanobis P.C., 1927, Journal of the Asiatic Society of Bengal, 23, 301
- Mahalanobis P.C., 1936, Proceedings of the National Institute of Sciences of India, 2, 49
- Mayhew S., 1995, Financial Analysts Journal, 51, 8, 10.2469/faj.v51.n4.1916 · DOI
Cited by
Sorted by citations
Deep reinforcement learning for automated stock trading: an ensemble strategy
Yang, H., Liu, X.-Y., Zhong, S., & Walid, A. (2020) · Proceedings of the First ACM International Conference on AI in Finance · doi:10.1145/3383455.3422540 · Cited by 196
“When turbulence t is higher than a threshold, which indicates extreme market conditions, we simply halt buying and the trading agent sells all shares.”
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Liu, X.-Y. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3737257 · Cited by 90
Liu, X.-Y., Yang, H., Gao, J., & Wang, C. D. (2021) · Proceedings of the Second ACM International Conference on AI in Finance · doi:10.1145/3490354.3494366 · Cited by 87
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance
Liu, X.-Y., Yang, H., Chen, Q., Zhang, R., Yang, L., Xiao, B., & Wang, C. (2025) · doi:10.2139/ssrn.3737859 · Cited by 53
“To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index turbulencet that measures extreme asset price fluctuation [25]: turbulencet = (yt − μ)Σ −1(yt − μ) ′ ∈ R, (1)”
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance
Liu, X.-Y., Yang, H., Gao, J., & Wang, C. (2021) · doi:10.2139/ssrn.3955949 · Cited by 46
Metadata
- Authors
- Kritzman, M., & Li, Y.
- Affiliations
- Mark Kritzman, CFA, is at Windham Capital Management, LLC, Cambridge, Massachusetts.
- Yuanzhen Li is at Windham Capital Management, LLC, Cambridge, Massachusetts.
- Venue
- Financial Analysts Journal
- Publisher
- Informa UK Limited
- Published
- 2010-09
- Volume
- 66
- Issue
- 5
- Pages
- 30-41
- ISSN
- 0015-198X (ISSN-L)
- 1938-3312
- OpenAlex
- W2099233100
- Cited by
- 214
- References
- 0
- Citations by year
- 2026: 13 · 2025: 12 · 2024: 14 · 2023: 14 · 2022: 17 · 2021: 14 · 2020: 21 · 2019: 17 · 2018: 15 · 2017: 15 · 2016: 12 · 2015: 8 · 2014: 14 · 2013: 8 · 2012: 11
- FWCI
- 15.34
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Financial Analysts Journal (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Risk and Volatility Modeling
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- financial turmoil, risk management, investment performance, financial market volatility
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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