Quant LetterNo. 120
74 items across 8 sections, as sent to readers on 19 November 2025. Paper titles open their ML-Quant page; ↗ goes to the source.
Working papers in finance and economics from SSRN.
1 items
The paper proposes a simple early-warning signal that watches hidden market patterns found by a neural network (an autoencoder); when those patterns start moving together it warns of systemic market risk, helping investors time sell-offs and improve returns.
565 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
30 items
Uses machine‑learning copula and regression to spot KSE‑30 stocks that trigger market drops and proposes a quick volatility‑based rebalancing method.
10 sharesSource ↗
Builds and back‑tests an automated ARMA‑GARCH trading system that helps protect emerging‑market portfolios in crises without pulling capital out.
9 sharesSource ↗
Proposes a non‑Gaussian risk‑parity (expected shortfall + generalized hyperbolic) that better handles fat tails and cuts turnover and drawdowns in stress.
9 sharesSource ↗
Examining six countries (1998–2023), finds Sharpe‑ratio trading beats buy‑and‑hold and supports the Adaptive Market Hypothesis by showing cyclical efficiency.
8 sharesSource ↗
Professional forecast disagreement raises stock risk premia: consumption disagreement affects overall returns, while productivity disagreement hits small, low‑profit stocks hardest.
5 sharesSource ↗
Derives best benefit and investment rules for target‑benefit pension plans under random shocks to maximize expected utility.
5 sharesSource ↗
Proposes a time‑linked window DEA and uses the Whale Optimization Algorithm to pick stable, effective trading strategies and firms.
5 sharesSource ↗
Shows optimal early exercise changes expected returns of American puts versus European puts and alters the profitability of many option anomalies.
4 sharesSource ↗
Long-Term Investment Performance: Defines sustainable return (a withdrawal rate that preserves real capital) and shows short‑term averages can mislead long‑term investors because of sequence risk and reinvested cash flows.
4 sharesSource ↗
More accurate news sentiment (GPT‑4 may beat RavenPack) has a strong effect on intraday stock return volatility.
6 sharesSource ↗
JPlus is a gamified app that collects emotional speech and boosts users’ learning and motivation for speech-emotion recognition.
4 sharesSource ↗
Stronger product-market competition makes firms more likely to adopt zero-debt policies, especially when earnings are volatile.
4 sharesSource ↗
New unsupervised mixture autoregressive method splits complex time series into simpler parts and gives accurate, stable financial forecasts.
6 sharesSource ↗
Two deep‑learning models plus a regularized combination better estimate Value at Risk and Expected Shortfall for improved risk management.
6 sharesSource ↗
Extreme house‑price booms and busts transmit across countries more than typical shocks, largely driven by the US market and its interest rate.
5 sharesSource ↗
Volatility in longer‑maturity Chinese treasury futures (especially 10‑year) helps predict stock volatility; SPCA and LASSO outperform standard HAR and boost portfolio returns.
5 sharesSource ↗
SVM vs Transformers: On article abstracts, BERT beats SVM and SPECTER, while GPT‑3.5 shows inconsistent performance in zero‑ and few‑shot discipline classification.
4 sharesSource ↗
Spanish-language Twitter anti-abortion accounts are coordinated, mostly run by men, use hate speech, and link to far-right groups.
4 sharesSource ↗
Reviews show machine learning and deep learning better predict demand for new fashion items despite changing tastes, seasonality, and social-media effects.
4 sharesSource ↗
ML vs DL in Crises: For oil prices (2007–2021), LSTM outperforms SVM, especially for mid‑to‑long‑term forecasts during crises.
4 sharesSource ↗
Machine learning predicts daily VIX better than benchmarks, with weekly jobless claims a key driver.
12 sharesSource ↗
Deep vs Traditional Models: Traditional ML (XGBoost, logistic) outperformed LSTM/BiLSTM for predicting daily stock direction of major Eurozone banks, likely due to limited data for deep learning.
7 sharesSource ↗
The WNSS poorly fits EU online gig workers; a shorter 12-item, three-factor model (survival, social contribution, competence) fits better.
2 sharesSource ↗
AI methods (predictive analytics, reinforcement learning, anomaly detection) can optimize cloud resource management for DevOps, improving efficiency and reliability.
2 sharesSource ↗
Using Eurobarometer 2019, the study profiles young informal workers in EU27—who they are, what they do, and why they work undeclared before the pandemic.
2 sharesSource ↗
Egovernance can boost citizen participation and transparency, but it needs good tech, digital skills, trust, and laws.
2 sharesSource ↗
Online dark patterns trick retail investors into bad choices; stronger rules using behavioral science and AI are needed.
1 sharesSource ↗
Bank performance is shaped by many factors, and COVID, digital transformation, and FinTech/AI make it a priority for more research.
1 sharesSource ↗
Strong AI capabilities lift firm performance, especially when backed by a data‑driven culture and solid AI infrastructure.
1 sharesSource ↗
Social‑media analysis shows clear, connected climate messaging and coordinated local‑to‑global policies are essential to reach net‑zero.
1 sharesSource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
4 items
MiroThinker v1.0 — An open-source agent that boosts reasoning by training models to interact more with tools and environments, enabling long multi-step workflows and strong benchmark results.
743 shares
P A family of open-source, reinforcement-learned models that excel at physics reasoning (winning top medals at IPhO 2025) and also perform well on math and coding.
44 shares
PartXMLLM: Turns a colored 3D point cloud and a text prompt into a compact program that lists part-level boxes, labels, and edit commands to drive geometry tools.
33 shares
LoopTool: An automated loop that repeatedly generates tool-using examples, refines data and models, and retrains LLMs to improve their tool-use and overall performance.
12 shares
Repositories the letter featured.
10 items
Teaches practical math and coding methods to build and manage investment portfolios.
69 shares
Autonomous system that automates machine‑learning engineering tasks like training and deployment.
560 shares
Pandas extension providing 200+ technical indicators and candlestick pattern signals for trading analysis.
102 shares
Otterlang — announces a new programming language and outlines its core ideas and features.
595 shares
Alpaca Python client — a Python library to access Alpaca’s trading API for trading and market data.
1,843 shares
AI phone-call guide — shows how to make an AI agent place a phone call using an API.
2,897 shares
SwiftUI Wine app — a modern macOS app that wraps Wine so you can run Windows programs on macOS.
14,767 shares
Claude Skills roundup — a curated list of resources and tools for customizing Claude AI workflows.
2,695 shares
Episodes on markets, quant methods and economics.
10 items
Markets stayed strong despite missing October data; experts say trend-following and managed futures can better protect retirement portfolios.
8 shares
Experts say AI will reshape markets, boost growth and infrastructure spending, so investors should adapt strategies.
7 shares
A volatility veteran recommends protection and convex strategies to make portfolios more resilient and potentially improve returns.
5 shares
Ratederivatives strategists outline the drivers and outlook for US and Eurex bond-futures rollovers into Dec 2025–Mar 2026.
5 shares
Jay Hatfield highlights chances in small-cap, value, and dividend ETFs as AI changes valuations and Fed policy expectations.
4 shares
Leveraged ETFs can amplify losses — daily compounding and timing can badly hurt returns.
4 shares
A new Python library standardizes and checks recommender datasets so research is reproducible.
4 shares
Visser: AI is like electricity — it drives huge compute demand, supply bottlenecks, market and labor shifts.
3 shares
Alex Imas studies how people decide, plus AI and discrimination, using experiments and data.
3 shares
US Policy & China Strategy: Mark Farrington focuses on global macro: US industrial policy, China’s supply strategy, dollar dominance, and systemic risks.
3 shares
Posts from quant and economics blogs and newsletters.
10 items
AI research analysts can run nonstop to help quantitative investing, but humans must supervise and validate their findings.
3 shares
Course announced; three ML/LLM papers summarized: Swiss news boosts short-term GDP forecasts, ClusterShapley fairly attributes parts of LLM summaries, and RAGIT automates earnings analysis.
3 shares
Exinterest-rates developer describes a day fixing and supporting front- and middle-office trading spreadsheets at a big bank.
3 shares
Repeat) Course announced; Swiss news helps short-term GDP forecasting, ClusterShapley credits LLM summary parts fairly, and RAGIT automates earnings analysis.
3 shares
Repeat) Course announced with short ML/LLM paper summaries: Swiss news improves GDP nowcasts, ClusterShapley allocates credit in LLM summaries, and RAGIT speeds up earnings analysis.
3 shares
Stocks that can set prices tend to keep steady gross margins, and a fund tracking them might beat the market.
2 shares
A portfolio of stocks with stable five-year gross margins showed higher returns over the past two years.
2 shares
Most of that outperformance happened when momentum was strong, so momentum may have driven the gains.
2 shares
The strategy looks promising, but it’s unclear whether pricing power itself causes the outperformance.
2 shares
Posts from quant researchers on X.
3 items
- SignTrade - SigTrade - Signals - Signature - TradeSign Want a single choice picked for you or a different tone (techy, professional, playful)?: The paper shows a mathematical method (path signatures) that compresses recent price movements and other signals into a small set of features, helping automated trading systems make better decisions.
1 shares
~25% Wash Trading: Study finds about 25% of Polymarket trading is wash trading (some markets 90–95% fake), so high volume can be misleading.
1 shares
Diversifying Liquid Alts: AQR shows liquid “diversifying alternatives” truly diversify portfolios, while many popular illiquid alternatives do not.
0 shares
Threads from r/quant, r/algotrading and friends.
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