---
title: Quant Letter No. 116: October 2025, Week 4
url: https://www.ml-quant.com/issues/2025-10-24/
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
issue_date: 2025-10-24
---


# Quant Letter No. 116: October 2025, Week 4

Sent 2025-10-24. 10 items.

## RePEc

### Historical Trending

- __[Reinforcement Learning for Hedging](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fpapers.ssrn.com%2Fsol3%2Fpapers.cfm%3Fabstract_id%3D3355706%3Bh%3Drepec%3Achf%3Arpseri%3Arp1980)__: The article introduces a novel application of reinforcement learning for efficiently managing a portfolio of over-the-counter derivatives, independent of any model. (2019-01-25, shares: 91) · https://www.ml-quant.com/papers/ssrn/3355706/
- __[HighFrequency Trading Impact](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11408-019-00331-6%3Bh%3Drepec%3Akap%3Afmktpm%3Av%3A33%3Ay%3A2019%3Ai%3A2%3Ad%3A10.1007_s11408-019-00331-6)__: The paper discusses the effects of high-frequency trading on market factors like volatility, transaction costs, and liquidity, indicating varied opinions in the financial sector. (2019-01-05, shares: 90) · https://www.ml-quant.com/papers/repec/kap-fmktpm-v-33-y-2019-i-2-d-10-1007-s11408-019-00331-6/
- __[International Arbitrage for Stocks](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1057521923002934%3Bh%3Drepec%3Aeee%3Afinana%3Av%3A89%3Ay%3A2023%3Ai%3Ac%3As1057521923002934)__: The study suggests a new strategy for high-frequency arbitrage on international cross-listed stocks, exploiting price discrepancies. (2023-08-09, shares: 28) · https://www.ml-quant.com/papers/repec/eee-finana-v-89-y-2023-i-c-s1057521923002934/
- __[Nowcasting NZ GDP with ML](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fcama.crawford.anu.edu.au%2Fsites%2Fdefault%2Ffiles%2Fpublication%2Fcama_crawford_anu_edu_au%2F2018-09%2F47_2018_richardson_mulder_vehbi.pdf%3Bh%3Drepec%3Aeen%3Acamaaa%3A2018-47)__: The paper reveals that machine learning algorithms are more effective than traditional statistical models in predicting real GDP growth in New Zealand. (2018-02-23, shares: 175) · https://www.ml-quant.com/papers/repec/een-camaaa-2018-47/
- __[Predicting Vehicle Wait Times at Borders](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0739885921000068%3Bh%3Drepec%3Aeee%3Aretrec%3Av%3A89%3Ay%3A2021%3Ai%3Ac%3As0739885921000068)__: The study explores new data sources and machine learning techniques to forecast short-term wait times at a US-Mexico border crossing, emphasizing the difficulties of high data variability. (2021-10-19, shares: 25) · https://www.ml-quant.com/papers/repec/eee-retrec-v-89-y-2021-i-c-s0739885921000068/
- __[Risk Factor Validation](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs12197-018-9438-x%3Bh%3Drepec%3Aspr%3Ajecfin%3Av%3A43%3Ay%3A2019%3Ai%3A1%3Ad%3A10.1007_s12197-018-9438-x)__: The research disputes the Fama and French three factor model, stating that size and value mimicking factors should not be seen as systematic risk factors. (2019-09-19, shares: 30) · https://www.ml-quant.com/papers/repec/spr-jecfin-v-43-y-2019-i-1-d-10-1007-s12197-018-9438-x/
- __[Cost Estimation with ML](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.conscientiabeam.com%2Fjournal%2F76%2Fabstract%2F5678%3Bh%3Drepec%3Apkp%3Arocere%3A2019%3Ap%3A64-75)__: The article introduces a machine learning method for predicting software costs early in a project with high accuracy. (2019-03-13, shares: 42) · https://www.ml-quant.com/papers/repec/pkp-rocere-2019-p-64-75/
- __[Intraday Volatility Prediction](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.inderscience.com%2Flink.php%3Fid%3D138879%3Bh%3Drepec%3Aids%3Aijecbr%3Av%3A27%3Ay%3A2024%3Ai%3A4%3Ap%3A633-650)__: The paper reveals that range-based volatility forecasting and asymmetric GARCH models are most effective for the Indian stock market, particularly the GKYZ volatility estimator. (2024-06-24, shares: 18) · https://www.ml-quant.com/papers/repec/ids-ijecbr-v-27-y-2024-i-4-p-633-650/
- __[Bank Failure Prediction](https://econpapers.repec.org/scripts/redir.pf?u=https%3A%2F%2Fwww.anserpress.org%2Fjournal%2Fjea%2F3%2F1%2F50%2Fpdf%3Bh%3Drepec%3Abba%3Aj00001%3Av%3A3%3Ay%3A2024%3Ai%3A1%3Ap%3A129-144%3Ad%3A169)__: The study uses machine learning survival models to predict US bank failures, offering insights to enhance risk management in the banking sector. (2024-03-02, shares: 17) · https://www.ml-quant.com/papers/repec/bba-j00001-v-3-y-2024-i-1-p-129-144-d-169/
- __[Brazilian ML Portfolios](https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS1566014122000085%3Bh%3Drepec%3Aeee%3Aememar%3Av%3A51%3Ay%3A2022%3Ai%3Apb%3As1566014122000085)__: The research investigates the use of machine learning to predict stock returns in Brazil, showing that an Equal Risk Contribution approach greatly enhances risk-adjusted returns. (2022-12-02, shares: 16) · https://www.ml-quant.com/papers/repec/eee-ememar-v-51-y-2022-i-pb-s1566014122000085/