Quant LetterNo. 97
144 items across 6 sections, as sent to readers on 14 May 2025. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
25 items
A study reveals that constant block intervals in blockchain settings provide the best protection against arbitrage for Automated Market Makers' liquidity providers, using random walk theory.
47 shares13 citations todaySource ↗
High-Frequency Trading: FlowHFT, a new imitation learning framework, outperforms traditional high-frequency trading models by learning from multiple expert models.
26 shares4 citations todaySource ↗
FINSABER, a backtesting framework, shows that Large Language Models' effectiveness in stock trading decreases over longer periods and larger symbol universes, emphasizing the need for trend detection and risk controls.
19 shares26 citations todaySource ↗
A study finds that resampling techniques in backtests can cause bias in Sharpe Ratio estimates, suggesting a need for structure-preserving resampling methods.
19 shares1 citation todaySource ↗
Discount Factor Estimation: NewsNet-SDF, a deep learning framework, successfully integrates pretrained language model embeddings with financial time series for asset pricing and risk assessment, outperforming traditional models.
18 shares3 citations todaySource ↗
The study compares the effectiveness of two deep learning algorithms, the Deep Galerkin Method and the Time Deep Gradient Flow method, in solving PDEs for option pricing.
16 sharesSource ↗
The Chiarella model is expanded to handle long-term value drifts, revealing excess volatility and bimodal mispricings in various assets.
15 shares2 citations todaySource ↗
The use of the soft actor-critic (SAC) algorithm in multi-asset portfolio selection is explored, showing superior performance in both simulated and real markets.
15 sharesSource ↗
The rough Bergomi model is extended by replacing the fractional Brownian motion with a generalised grey Brownian motion, improving SPX/VIX options calibration.
14 sharesSource ↗
The paper introduces an algorithm for creating fair trade relations based on a world trade model, aiming to prevent tariff wars and discussing the impact of tariff restrictions on trade prices and recession.
13 sharesSource ↗
Large Language Models (LLMs) have potential in mimicking human behavior in economic markets, but need more research for improved diversity and accuracy.
21 shares19 citations todaySource ↗
A new pairs trading strategy using path signature techniques enhances futures trading by providing better interpretability, robustness, and returns.
17 shares4 citations todaySource ↗
Inflation driven by supply increases unemployment risks for vulnerable workers, emphasizing the need for inflation control for fair labor market growth.
16 shares1 citation todaySource ↗
A literature review identifies five key themes in the impact of Generative AI on entrepreneurship, calling for more broad-scale research and effective regulations.
16 shares21 citations todaySource ↗
Research on Davao del Sur's coffee value chain dynamics reveals that trust, risk, and transaction costs influence key interactions, and a balanced system benefiting all can be achieved with adjustments to demand, pricing, trust, and risk attitudes.
15 sharesSource ↗
A study on Sultan Kudarat's coffee farmers' profits shows positive annual profit from selling all dried cherries, but negative profit from producing and selling all green coffee beans due to extra costs and minimal price differences.
15 sharesSource ↗
A study on smart meter adoption in the US shows that federal funding and state legislative actions positively impact adoption and energy efficiency savings, emphasizing the need for multi-level governance in policy integration.
14 sharesSource ↗
The chapter highlights the potential of using large-scale data in entrepreneurship and innovation research, suggesting that machine-learning models and big data can create precision measurements and 'digital doubles' for virtual experimentation.
13 sharesSource ↗
The article introduces a new method for estimating the leverage effect using high-frequency data, offering two new estimators that simplify the process and increase noise resistance, as confirmed by simulations and empirical analyses.
16 sharesSource ↗
The paper proposes a framework for transferring learning of discount curves across different fixed-income product classes, enhancing kernel ridge regression and introducing a term that promotes curve smoothness, resulting in improved extrapolation performance and tighter confidence intervals.
15 sharesSource ↗
The research investigates the use of hidden states in large language models to estimate and fill in economic and financial statistics, showing that a simple linear model trained on these hidden states performs better than the models' text outputs and requires minimal labelled examples for training.
11 shares3 citations todaySource ↗
The research applies a unified asymptotic theory to high-frequency cryptocurrency ETF trading data, revealing infinite-mean durations for all five cryptocurrencies studied.
18 shares1 citation todaySource ↗
The article introduces an analytical method for calculating the collateral liquidation probability in DeFi stablecoin single-collateral lending, improving risk assessment efficiency.
14 sharesSource ↗
The paper suggests a lightweight, exact solution for computing the collateral liquidation probability in DeFi stablecoin single-collateral lending, offering a more efficient alternative to complex simulations.
14 sharesSource ↗
The article presents a reinforcement learning framework for managing risk objectives. This is achieved through a specialized Actor-Critic algorithm and an auxiliary variable sampling method. The effectiveness of this approach is confirmed through simulation experiments in statistical arbitrage trading.
19 shares3 citations todaySource ↗
Working papers in finance and economics from SSRN.
60 items
The article explores the use of topological data analysis in computer vision research to create a generalized feature engineering framework.
5 sharesSource ↗
The study finds that machine learning models can effectively forecast U.S. GDP, especially during economic volatility.
2 sharesSource ↗
The paper highlights the gap between academic training and practical AI skills, suggesting improvements for higher education models.
4 sharesSource ↗
The research shows that firms with advanced enterprise risk management are more likely to use currency derivatives for foreign exchange rate hedging.
2 sharesSource ↗
The study suggests that aggressive policy responses can manage macroeconomic effects of uncertainty shocks during low inflation cycles.
3 sharesSource ↗
The article examines the potential impact of emerging computing paradigms like quantum and DNA computing on the digitization of education.
2 sharesSource ↗
The paper discusses the use of multimodal AI in assistive devices for disability inclusion, and the ethical considerations involved.
3 sharesSource ↗
The SWOT analysis of AI in economic forecasting shows its ability to handle complex data and improve predictions, but also reveals its transparency issues and high data requirements.
2 sharesSource ↗
A study suggests using iron-based catalysts prepared by pyrolysis for resource utilization, with machine learning predicting catalyst performance and analyzing key factors.
2 sharesSource ↗
A new web-based tool uses transformer-based models to analyze customer review sentiment and create summaries, paving the way for future sentiment analysis tool improvements.
2 sharesSource ↗
A research paper suggests using digital signatures for object-level authentication in Building Information Models (BIMs), addressing the need for better data integrity and trust in the construction industry.
2 sharesSource ↗
The Informer, a new deep learning algorithm, is used for ultra-short-term offshore wind power prediction, effectively extracting features and capturing sequence dependency from long time-series data.
2 sharesSource ↗
A study on ESIPT regulation in trifluoroanthraquinone derivatives provides insights for designing new WOLED materials, revealing dual fluorescence and ambipolar properties in some derivatives.
2 sharesSource ↗
A Decoupled Likelihood Modeling framework is proposed to address class imbalance in Incremental Generalized Category Discovery, showing strong scalability in large-scale class settings.
2 sharesSource ↗
The paper discusses the potential of quantum computing and DNA computing to improve predictive analytics in cybersecurity for educational institutions, suggesting a hybrid quantum-DNA forecasting framework.
2 sharesSource ↗
The research uses machine learning to predict national financial crises, with the Balanced Random Forest model proving most effective.
7 sharesSource ↗
The study highlights risks and inefficiencies in the Solana token market, advocating for better transparency and regulation.
8 sharesSource ↗
The paper uses machine learning to predict the exercise of American call options, outperforming traditional methods.
3 sharesSource ↗
The article explores the use of artificial intelligence in improving financial risk management in financial institutions.
3 sharesSource ↗
The research suggests pension funds can promote ESG goals without major portfolio changes, but warns of potential overreach without proper accountability.
5 sharesSource ↗
The study discusses the transformation of the wealth management industry through machine learning and cloud computing, improving competitiveness and efficiency.
2 sharesSource ↗
The paper explores the future of Service-Oriented Architecture, enhanced by AI and Machine Learning for better automation, system optimization, and customer experience.
3 sharesSource ↗
Exoplanet Transit Detection: TESSGAIA is a new system that combines data from Gaia and TESS to effectively identify exoplanet transits, with a 90% success rate for Neptune-sized planets.
4 sharesSource ↗
The quadratic rough Heston model, improved with Gatheral 2022's hybrid scheme and finite difference methodology, offers reliable skew-stickiness ratio values and fits to SPX and VIX volatility smiles.
3 sharesSource ↗
The study highlights the role of data preprocessing in improving the efficiency of machine learning algorithms for network intrusion detection.
2 sharesSource ↗
The XGBoost model, using historical weather and PV output data, offers more precise ultrashort-term PV power predictions than the SVR model, contributing to grid stability.
3 sharesSource ↗
Autocratic governance and inter-city competition significantly impact skyscraper development, with autocratic nations more likely to have taller skyscrapers.
5 sharesSource ↗
The creation of AI solutions for specific insurance predictions is vital due to industry consolidation and the rise of InsurTech startups, with gaps in domain knowledge and machine learning guidance.
2 sharesSource ↗
A custom artificial neural network model using data from Airbnb, AirDNA, and the Federal Reserve accurately predicts short-term rental prices, highlighting key features like guest capacity and review count.
3 sharesSource ↗
Machine learning methods can improve cybersecurity in IoT systems and cloud computing in Colombia, with various algorithms assessed for their ability to detect and counter cyber threats.
2 sharesSource ↗
A new model for assessing global mutual funds' financial performance has been validated using data from 35 countries over 34 years, considering factors like risk size, diversification, and liquidity.
4 sharesSource ↗
A study reveals a strong negative correlation between expected inflation sensitivity and firm growth, with low inflation sensitivity firms experiencing high, sustained growth.
3 sharesSource ↗
A proposed model allows optimal risk sharing in dynamic settings with diverse preferences, introducing a new traded security as an endogenous variable.
3 sharesSource ↗
Price movements in catastrophe bonds can be predicted by ambiguity preference in economic outlook and natural disasters, especially during crises and geopolitical conflicts.
3 sharesSource ↗
The adoption of internal carbon pricing (ICP) decreases future profitability and stock returns but increases firm valuation, showing investor value in sustainability commitment.
3 shares2 citations todaySource ↗
A new model for optimal risk sharing introduces a new endogenous variable, allowing for diverse agents and results not possible in the standard model.
3 sharesSource ↗
A study on the Italian CO2-emission allowances derivatives market details its characteristics, risk hedging and investment uses, market development, and price dynamics.
2 sharesSource ↗
The research proposes a dual-risk valuation model that separates market risk and expectation error, improving upon traditional cash flow models.
2 sharesSource ↗
The paper presents a model that predicts day-ahead electricity prices in Central Western Europe by incorporating Flow-Based Market Coupling into a residual demand framework.
3 sharesSource ↗
The paper warns that hierarchical risk clustering strategies in portfolio allocation can be affected by inaccuracies in the covariance matrix.
2 sharesSource ↗
The paper introduces a dual-risk framework that divides systematic risk into external market-based cost and internal belief-driven valuation, improving traditional cash flow valuation methods.
2 sharesSource ↗
The document explores the Central Limit Theorem, detailing its evolution and applications in various fields.
2 sharesSource ↗
The study examines the relationship between the Global Economic Policy Uncertainty Index and WTI crude oil prices from 1997 to 2024.
3 sharesSource ↗
The paper investigates the factors influencing the adoption of internal carbon pricing among publicly listed firms in 54 countries.
3 shares1 citation todaySource ↗
Finance Mechanism: The thesis presents NEXUS3, a cryptocurrency governed by an auditable DAO framework, offering a legal alternative to traditional financial systems.
2 sharesSource ↗
Complex algorithms in global models outperform regional models in cross-sectional asset pricing, contradicting previous studies favoring regional methods.
5 shares1 citation todaySource ↗
Cryptocurrencies, unlike stock markets, show a flat volatility profile due to the lack of restricted trading hours.
3 sharesSource ↗
The article analyzes advanced numerical techniques in finance, discussing their uses, limitations, and challenges like computational complexity and model risk.
3 sharesSource ↗
Quantitative Finance: The AGNOSTIC tool, free from assumptions or parametric models, is designed to help tackle dimensionality and overfitting issues in Quantitative Finance.
4 sharesSource ↗
Legalizing stock repurchases enhances equity capital access and investment, indicating that buyback restrictions could hinder efficient capital distribution.
3 sharesSource ↗
A few stocks significantly impact the performance of cross-sectional asset pricing anomalies, implying that a large part of the returns may be due to mispricing.
3 sharesSource ↗
In municipal bond auctions, dealers bid higher when expecting to acquire new clients, resulting in lower winning yields and broader trading networks.
3 sharesSource ↗
The article introduces the Network Basket Loan, a financial tool aimed at funding socially beneficial projects and preventing market failures related to social and partial selection.
3 sharesSource ↗
The research investigates the impact of venture capital partners' diverse human capital on investment choices, startup success, and innovation, with partners of varied backgrounds leading unique, high-risk investments.
2 sharesSource ↗
The paper presents and analyzes six REIT return factors, demonstrating that these factors outperform general equity asset pricing factors and behave differently across economic conditions.
2 sharesSource ↗
The study reassesses the stability of 40 macroeconomic variables across the G7 economies, finding strong evidence of mean reversion in the U.S. and significant variations between countries.
3 sharesSource ↗
Blockchain for Mobility: ARXNAVIS combines six smart-contract protocols, proven through simulations, to achieve fiscal sustainability through Asset-Backed Digital Currency treasuries and a cryptocurrency-based interchange fee mechanism.
3 sharesSource ↗
The paper uses Fourier series expansion on the average asset return function, shedding light on the equity premium puzzle and demonstrating how alpha represents the long-term historical dependence of covariance risk.
2 sharesSource ↗
The research analyzes a large external asset purchase program, discovering that stock prices can be influenced by external order flows even if they don't provide information about future cash flows.
2 sharesSource ↗
The article explores bond trading activity and transaction cost differences between Over-the-Counter and Central Limit Order Book venues in China's interbank government bonds market, showing how market structure and trading mechanism impact asset liquidity.
2 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
30 items
A new Automated Adaptive Trading System may help stabilize emerging markets during downturns, addressing issues caused by the rise of algorithmic trading and passive investing.
27 sharesSource ↗
Machine learning has been utilized to identify assets driving downward trends in the Pakistan Stock Exchange, suggesting a portfolio optimization strategy for effective asset allocation.
25 sharesSource ↗
Accounting for fat-tailed returns in risk parity portfolio optimization can lessen sensitivity to volatility shocks, decrease portfolio turnover during market instability, and enhance risk-adjusted returns.
16 sharesSource ↗
The research finds that Sharpe Ratio Minimae and Maximae trading strategies are more profitable than the buy-and-hold strategy in global markets, supporting the Adaptive Market Hypothesis.
15 sharesSource ↗
The study introduces a new window analysis method for assessing decision-making units' efficiency, using the Whale Optimization Algorithm to identify stable trading strategies and companies.
11 sharesSource ↗
The paper uses a new data augmentation technique to study poverty in the Middle East and North Africa, specifically Lebanon, using alternative data sources when traditional income data is scarce or unavailable.
10 sharesSource ↗
The blockwise reduced modeling (BRM) method is introduced for analyzing incomplete data sets, improving predictive performance for both linear and nonlinear models.
20 sharesSource ↗
A new machine learning strategy, momentum-determined indicator-switching (N-MDIS), is proposed to increase the accuracy of equity premium prediction, outperforming existing strategies.
19 sharesSource ↗
A study reveals that increased product market competition (PMC) encourages firms to adopt zero-leverage (ZL) strategies, particularly those with high earnings volatility.
18 sharesSource ↗
A reevaluation of the role of news sentiment shows that accurately measured sentiment significantly impacts intraday stock return volatility.
16 sharesSource ↗
A version of Stochastic Gradient Boosting is suggested for estimating production possibility sets in Data Envelopment Analysis (DEA), reducing overfitting and aiding in scenarios requiring generalization.
16 sharesSource ↗
A study reveals that machine learning models are more successful than traditional methods in predicting Chinese corporate mergers and acquisitions using 60 explanatory variables.
28 sharesSource ↗
New probabilistic deep learning frameworks have been introduced for estimating Value at Risk and Expected Shortfall measures, providing financial institutions with improved capital allocation methods.
27 sharesSource ↗
Machine learning methods have been found to enhance the accuracy of stock market volatility forecasts in China by utilizing the volatility of long-term treasury bond contracts.
24 sharesSource ↗
The article proposes a new algorithm and machine learning model to improve the efficiency and accuracy of the Lot Streaming and Scheduling Problem with stochastic product arrival times.
16 sharesSource ↗
The paper introduces a new machine learning technique for decomposing and modeling complex time series, providing a potential alternative to the Box-Jenkins method in financial modeling.
13 sharesSource ↗
The study uses financial news and machine learning to create a monetary policy frictions index, showing a significant positive impact on the nonperforming loans of Chinese commercial banks.
12 sharesSource ↗
The research uses quantile connectedness models and machine learning to study the global influence of the US housing market and its interest rates on international housing market.
10 sharesSource ↗
ML vs. DL: Research shows deep learning methods outperform traditional machine learning in predicting oil prices, especially during crises.
31 sharesSource ↗
The newly introduced MFF-CPPM model in China has demonstrated higher accuracy and flexibility in predicting carbon trading prices compared to standard models.
10 sharesSource ↗
The study uses machine learning to predict the VIX, highlighting weekly jobless claim data as a significant factor in market volatility.
23 sharesSource ↗
Traditional machine learning models outperform deep learning models in predicting stock price direction in the Eurozone banking sector, according to the study.
13 sharesSource ↗
The research indicates that AI capability directly affects firm performance, with a data-driven culture mediating this effect and AI infrastructure being vital.
5 sharesSource ↗
The study emphasizes the role of communication and a comprehensive approach in addressing climate change, using machine learning to analyze social media discussions on the subject.
4 sharesSource ↗
The paper investigates the use of dark patterns in retail investment, focusing on the application of behavioral sciences and AI to improve regulation and safeguard investors.
2 sharesSource ↗
Pre-Pandemic Dynamics: The research profiles young informal workers in the EU27, aiming to understand how Covid-19 has impacted youth employment informality.
2 sharesSource ↗
The article discusses how artificial intelligence can improve resource management in cloud environments, boosting DevOps workflows' performance and efficiency.
2 sharesSource ↗
The review investigates the link between e-governance initiatives and citizen participation, emphasizing the need for interdisciplinary research to assess these initiatives' effectiveness.
2 sharesSource ↗
Review and Future Research: The paper analyzes literature on factors influencing banks' performance, proposing new research areas in digital transformation, artificial intelligence, and the COVID-19 pandemic.
1 sharesSource ↗
The study tests the Work Need Satisfaction Scale's (WNSS) suitability for online gig workers, suggesting modifications to the scale to better reflect the specifics of online platform work.
1 sharesSource ↗
Repositories the letter featured.
10 items
Automating Code Generation: Paper2Code is a new tool that can automatically generate code from machine learning scientific papers.
1,572 shares
Python Library for Analysis: The article introduces a new Python library that can calculate technical analysis indicators on live data.
115 shares
Interactive RL Algorithm Demo: The piece presents a web-based demo of basic tabular Reinforcement Learning algorithms in a simple grid world scenario.
32 shares
Rust Stream Processing Engine: The article discusses a high-performance data stream processing engine built in Rust, supporting various data sources and processors.
896 shares
Use Your Own Chat Data: The piece introduces a chat log tool that enables users to easily use their own chat data.
4,195 shares
The article shares a collection of scripts and notebooks used in the author's Medium posts.
467 shares
The article reviews 500 case studies of real-world machine learning system designs from 100 companies, with a focus on GenAI.
868 shares
The article explains how to horizontally scale PostgreSQL using automatic sharding.
1,362 shares
LLM Extraction: The article presents ContextGem, a tool designed to easily extract LLM from documents.
305 shares
Web Testing: The article introduces Playwright, a framework for web testing and automation that supports Chromium, Firefox, and WebKit through a single API.
72,492 shares
Industry news: funds, hiring, markets and regulation.
9 items
Rebellion Research explores the potential impact of machine learning on the future of quantitative finance.
14 shares
UBPs Alternative Investment Solutions group forecasts a favorable climate for hedge fund strategies in 2025, but emphasizes the importance of robust risk management.
11 shares
The SSampC GlobeOp Hedge Fund Performance Index shows slight improvements for hedge funds in April, with a significant rise in the Capital Movement Index.
7 shares
Avantyr Capital Partners, under the leadership of former Viking Global Investors CIO Ning Jin, is expected to surpass 1bn in assets at its inception, making it one of the biggest hedge fund launches of the year, says Bloomberg.
5 shares
Qube Research & Technologies has initiated a new internship program in Dubai to attract talent in quantitative research development and trading.
5 shares
New hedge fund managers are offering better terms and lower fees for investors, except for non-equity strategies, due to fundraising challenges.
3 shares
In April, hedge funds moved their shorting activity from tech stocks to consumer and financial stocks due to macroeconomic issues and trade friction.
2 shares
Matt Maloney has departed from Gramercy Funds Management, an emerging markets-focused hedge fund, after ten years.
2 shares
Eisler has dismissed its leading equities systematic portfolio manager.
2 shares
Episodes on markets, quant methods and economics.
10 items
Michael Crumpler and Jon Hilsenrath discuss the effects of tariffs on credit risk and the necessary strategic responses in a volatile trade environment in a podcast.
16 shares
Rodrigo Gordillo, Riccardo Gambineri, and Frank Seneco discuss the role of private placement life insurance in wealth planning, asset protection, and tax deferral on ReSolve Riffs.
9 shares
Meera Chandan and others discuss the evolution of the USDAsia view following record volatility, central bank meetings, and the impact of the USUK trade deal on FX.
7 shares
Phoebe White, Teresa Ho, and Ipek Ozil predict a fall in Treasury yields due to potential labor market deterioration following the recent Fed meeting and tariff announcements.
7 shares
Jay Hatfield discusses the investment potential of Master Limited Partnerships, emphasizing their resilience during energy price volatility and their role in global natural gas infrastructure trends.
5 shares
Goldman Sachs strategists predict a positive future for US stocks and investors following a rebound after tariff announcements.
4 shares
JPMorgan Chase's commodities research heads suggest that foreign investors reassessing US assets could boost gold prices.
3 shares
High net worth individuals and family offices can use private placement life insurance and offshore strategies for tax efficiency, asset protection, and estate planning.
3 shares
TCW Group CEO, Katie Koch, shares her experience leading the asset manager through a period of significant change in the fixed income market.
3 shares
JPMorgan Chase strategists analyze the effects of dedollarisation on Euro government and swap markets, and recent central bank meetings in the UK and Scandinavia.
3 shares