---
title: Loss-Cutting and Gain-Riding Strategies
url: https://www.ml-quant.com/papers/repec/taf-apmtfi-v-29-y-2022-i-5-p-402-438/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:taf:apmtfi:v:29:y:2022:i:5:p:402-438
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1350486X.2023.2224354%3Bh%3Drepec%3Ataf%3Aapmtfi%3Av%3A29%3Ay%3A2022%3Ai%3A5%3Ap%3A402-438
featured: 2023-07-12
citations: unknown
topic: Trading, Microstructure & Execution
---


# Loss-Cutting and Gain-Riding Strategies

A new trading strategy is proposed that targets left tail risk and generates an annualized alpha of 180 bps over 5 years, outperforming the contrarian mean-variance optimal strategy.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fhdl.handle.net%2F10.1080%2F1350486X.2023.2224354%3Bh%3Drepec%3Ataf%3Aapmtfi%3Av%3A29%3Ay%3A2022%3Ai%3A5%3Ap%3A402-438
- Identifier: RePEc:taf:apmtfi:v:29:y:2022:i:5:p:402-438
- Released: 2022-01-20
- First featured: Quant Letter No. 7 (2023-07-12): https://www.ml-quant.com/issues/2023-07-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

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