Quant LetterNo. 25
113 items across 10 sections, as sent to readers on 8 November 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
14 items
Portfolio Predictability Maximization using ML: A stock portfolio called the maximally predictable portfolio (MPP), created using machine learning and a Kelly criterion strategy, consistently performs better than the benchmark.
5 shares3 citations todaySource ↗
The article presents a theoretical model to analyze arbitrage opportunities in a market with unlimited investors, confirming the existence of a unique mean field equilibrium.
3 shares6 citations todaySource ↗
The paper discusses the concept of transfer risk in transfer learning, showing its significant relation with performance and its effectiveness in selecting suitable source tasks in stock return prediction and portfolio optimization.
2 shares10 citations todaySource ↗
Power Law in Volterra Volatility Model: The Sandwiched Volterra Volatility (SVV) model accurately reproduces the power-law behavior of the at-the-money implied volatility skew, provided the correct Volterra kernel is chosen.
4 shares2 citations todaySource ↗
The study investigates the optimal stopping issue in pricing a variable annuity contract, introducing new valuation algorithms and showing how fee and surrender charge functions affect early and optimal surrender boundaries.
2 shares1 citation todaySource ↗
Longitudinal and Spatio-Temporal Survival Model: The Spatio-Temporal Joint Model (STJM) is a new method for credit risk analysis that uses spatial and temporal data to predict a borrower's risk, showing better results when spatial data is included.
7 shares4 citations todaySource ↗
The paper introduces PORS, a heuristic-based framework for finding high-quality rule subsets in fraud prevention, and SpectralRules, a new sequential covering algorithm, showcasing their effectiveness in two real Alipay scenarios.
4 shares1 citation todaySource ↗
Nonstationary Phenomenon: The article discusses the theory of rational asset price bubbles, highlighting that bubbles linked to real assets like stocks and housing are nonstationary phenomena tied to unbalanced growth.
4 shares43 citations todaySource ↗
The article studies how decentralization in blockchain-based governance affects the financial efficiency of Decentralized Autonomous Organizations (DAOs). It uses the Gini coefficient to measure inequality among token owners and discusses the pros and cons of this method.
4 sharesSource ↗
The paper presents a new algorithm that uses deep self-consistent learning for better and more robust calibration of local volatility from market option prices.
15 shares3 citations todaySource ↗
The study suggests that companies that set salaries at round numbers, typically less sophisticated firms, tend to perform worse in the market due to their coarse wage-setting approach.
125 shares3 citations todaySource ↗
The article offers a guide on using the Heath-Jarrow-Morton framework in energy markets, specifically in European power and gas markets, covering market structure, model calibration, simulations, and derivatives pricing.
56 sharesSource ↗
The research presents multimodal learning in bankruptcy prediction models to tackle the problem of missing MDA section in Form 10-K, showing improved classification performance and addressing the limitation of previous models.
33 shares3 citations todaySource ↗
The research investigates the Bachelier model with linear price impact, identifying a set of portfolios that are optimally effective in a scenario of diminishing price impact.
10 shares1 citation todaySource ↗
Working papers in finance and economics from SSRN.
26 items
The research examines statistical arbitrage issues, taking into account the nonlinear and temporary price impact of metaorders, and shows that simple trading rules can be established even with nonparametric alpha and liquidity signals.
120 shares14 citations todaySource ↗
A model is created to understand how investors' disagreement on future volatility affects their trading of volatility derivatives, showing that trading decreases in more volatile periods and the variance risk premium can become positive when future volatility is underestimated.
3 sharesSource ↗
The research evaluates the performance of hedge funds, especially those using a top-down investment approach, and discovers a significant drop in risk-adjusted alpha for global macro managers and managed futures managers after the global financial crisis.
8 sharesSource ↗
The paper explores the link between stock market volatility and trend factor profits, finding that the trend factor performs better after high volatility periods as investors depend more on trend signals.
3 sharesSource ↗
A study of 700 Indian companies shows no significant link between ESG scores and investment returns from 2013 to 2023.
10 shares1 citation todaySource ↗
A paper suggests using the Kelly criterion and Monte Carlo simulation to estimate the optimal portfolio in stock investment.
7 sharesSource ↗
A study shows that exchange rate dynamics are affected by the diversity of investors and their price impact, with more concentrated markets having a stronger price impact.
3 sharesSource ↗
The article introduces a new simulation-based method for pricing and managing risk of financial derivatives during rare events, proving to be more efficient, accurate, and flexible than traditional methods.
85 sharesSource ↗
The study explores the impact of commodity sectors on commodity futures risk premiums, revealing that excluding the precious metal sector from a portfolio increases the Sharpe ratio, suggesting precious metals' role as hedging tools affects commodity performance.
3 sharesSource ↗
Forward Price Ratios: The research criticizes the use of trailing price ratios for predicting stock market returns due to changes in cash flow growth, suggesting the use of forward price ratios scaled by cash flow forecasts for better valuation.
2 sharesSource ↗
The paper examines Quanto Credit Default Swaps, a financial tool that transfers credit risk with foreign exchange exposure, focusing on its theory, pricing, and use in emerging markets like Brazil.
75 sharesSource ↗
The study finds that hedge funds' positions in ETF options predict volatility in underlying ETF returns, particularly in nonequity ETFs like fixed income and currency ETFs.
2 sharesSource ↗
Do Nothing?: The research indicates that ETFs often close after positive returns and flows, with these factors predicting closure decisions, and smaller ETFs earning higher daily returns than larger ones with the same investment objective.
60 sharesSource ↗
Arbitrage-Free Volatility Surfaces: The paper presents a framework for creating arbitrage-free transformations of an implied volatility surface using optimal transport maps, which can be applied to a broader range of synthetic market data generation applications.
2 shares3 citations todaySource ↗
The study shows that the low volatility anomaly in stock prices is connected to mutual funds performance evaluation against benchmark indexes, as mutual fund managers' heavy investment in certain stocks leads to higher trade volumes and lower volatility.
2 sharesSource ↗
The article presents new methods for estimating leverage effect and volatility using high frequency data, tested through simulation and real data analysis.
3 sharesSource ↗
A new machine learning algorithm, SANN, is used to predict insurance company insolvency, showing better accuracy than traditional models.
3 sharesSource ↗
The study reveals a pattern in high-beta stock returns around macroeconomic announcements, indicating that investor risk appetite significantly influences these returns.
3 sharesSource ↗
Performance and Risk Metrics: A diversified portfolio strategy, Sector Portfolio HRP, outperforms the MSCI All Country World Index in annualized return and risk evaluation from 1996 to 2022, a study shows.
4 sharesSource ↗
Machine learning has identified periods of fiscal dominance in the euro area from 2000 to 2019, including during the financial and sovereign debt crises.
2 sharesSource ↗
Research using machine learning indicates that the threat of hostile takeovers can significantly weaken a company's culture, supporting the managerial myopia hypothesis.
3 shares2 citations todaySource ↗
Missing Data Filling: The article highlights the role of data imputation in risk management, explaining its use in filling gaps in incomplete data for a better understanding of risk factors.
189 sharesSource ↗
The research analyzes the execution time of machine learning models in empirical asset pricing, finding that XGBoost is the fastest and most accurate, and that reducing features and time observations can significantly cut execution time.
2 sharesSource ↗
Predictors & Returns: The research suggests that future stock returns can be predicted using machine learning models that consider characteristics and macroeconomic variables, resulting in portfolios that perform better than benchmarks.
494 sharesSource ↗
Momentum Spillovers: The article uncovers momentum spillovers in the corporate bond market, proposing a strategy of buying bonds from high-performing peers and selling bonds from low-performing peers, yielding a monthly alpha of 36 basis points.
2 sharesSource ↗
Regression Parameter Estimation: The article presents a new NAS method for univariate regression problems, comparing it with standard methods and suggesting a generalized approach for calculating the cost function's partial derivatives.
2 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
14 items
The study uses credit card data to enhance real-time GDP forecasting in Japan, demonstrating that this data improves early-stage forecasting by accurately capturing consumer spending.
15 sharesSource ↗
The paper analyzes the performance of ESG equity ETFs in the U.S. from 2019 to 2021, revealing that these ETFs, on average, outperform the S&P 500 Index.
15 sharesSource ↗
The article proposes a new portfolio optimization method using a tree-structured portfolio sorting technique, demonstrating that this strategy outperforms others in terms of Sharpe ratios, standard deviation, and turnover.
15 sharesSource ↗
The research examines the relative significance of push and pull factors for portfolio flows during financial crises, finding that the importance of push factors has increased over time, especially for EU countries.
14 sharesSource ↗
The paper proposes a new framework for dynamic bond portfolio optimization over multiple periods, which outperforms single-period optimization.
26 sharesSource ↗
The study finds that considering volatility clustering in dynamic multiperiod portfolio choices reduces the need for hedging.
23 sharesSource ↗
Performance Evaluation: A study found that actively managed ETFs in the US from 2018 to 2021 did not yield significant above-market returns and their managers lacked superior market timing skills.
18 sharesSource ↗
A study uses machine learning to forecast gender diversity in Chinese company boards, with the extreme Gradient Boosting model showing the best performance.
22 sharesSource ↗
The SHapley Additive exPlanations technique is used in a paper to pinpoint key factors influencing bond excess return predictions made by machine learning models.
21 sharesSource ↗
A study finds that high-quality political signals can predict increased stock market volatility.
16 sharesSource ↗
Research shows a belief-based momentum indicator can predict equity market volatility in China, with the HAR-LCPR model being the most effective.
16 sharesSource ↗
The article talks about a new inventory management framework that uses a deep learning model. This model suggests order quantities based on online reviews and demand data, reducing costs by 28.7% compared to other models.
16 sharesSource ↗
High-Dimensional Data and Mixed-Frequency Method: The first article explores the application of machine learning to improve demand prediction and restocking decisions in newsvendor problems, utilizing complex and varied historical data.
27 sharesSource ↗
The second article introduces a Continuous Audit-Based Certification for MLOps systems, which uses automated audits to manage certificates, enhancing efficiency and reliability in the machine learning system.
14 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
10 items
Unifying RL: Uni-o4 is a novel method that merges offline and online reinforcement learning, enhancing the adaptability of the learning process.
5 shares38 citations todaySource ↗
The BayesBag study introduces a technique of applying bagging to Bayesian posteriors to enhance reproducibility and uncertainty quantification in model misspecification.
5 shares5 citations todaySource ↗
Efficient Inference: PPI++ is a new approach that utilizes a small labeled dataset and a larger machine-learning predictions dataset to boost computational and statistical efficiency.
5 shares135 citations todaySource ↗
Model for Code Generation: CodeFusion is a new model for generating code that improves on previous models by iteratively cleaning up a complete program, matching top systems in initial accuracy and surpassing them in subsequent accuracy checks.
3,953 shares61 citations todaySource ↗
The paper introduces a new framework for sampling and generative modelling based on divergences in path space, leading to the creation of a new score-based annealed flow technique and a regularised iterative proportional fitting objective.
81 shares22 citations todaySource ↗
The research examines the learned representation of a generative model trained on two-qubit density matrices, showing a direct correlation with the entanglement measure concurrence, providing insights into machine learning of quantum states.
22 shares15 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
7 items
Efficient Transformer Model at Unprecedented Scale: DeepSpeed Inference improves latency and throughput performance in different situations.
1,071 shares
Advancing Language Models: The article explores the use of both expert and suboptimal data in general SFT training, without favoring any specific labels.
1,915 shares
Learning to Retrieve, Generate, and Critique through Self-Reflection: The framework develops a unique language model that adaptively retrieves and reflects on passages using special 'reflection' tokens.
439 shares
The article emphasizes the superiority of diffusion models over previous generative models in terms of sample quality and training stability.
35 shares
LLM Benchmarking for GPT4+: The rapid advancement of LLMs necessitates an immediate need for a comprehensive evaluation system to identify their pros and cons.
146 shares
The article discusses strategies to address the issues of sparse feedback and poor side information in recommendation systems.
54 shares
The comprehensive review is designed to stimulate more research into assessing LLMs to ensure their ethical development.
192 shares
Repositories the letter featured.
8 items
The article explores Time Series Analysis and Interpretable Machine Learning, focusing on Python packages such as Darts, PyCaret, Nixtla, Sktime, MAPIE, and PiML.
13 shares
The piece reviews a fixed income library for pricing bonds, bond futures, and derivatives, featuring tools for Curveset construction and risk sensitivity calculations.
14 shares
The article introduces JAXLOB, a GPU-accelerated limit order book simulator aimed at improving large scale reinforcement learning for trading.
26 shares
The article introduces a Time Series Visualization Tool designed to enhance user experience.
3,702 shares
The piece explores the use of Named Tensors to improve the readability of Deep Learning in JAX.
73 shares
The article is a guide to resources for learning and implementing mathematical optimization, including educational materials and software tools.
93 shares
The article provides a collection of resources for understanding and implementing waitfree and lockfree programming techniques.
1,565 shares
The article explores pix2tex, a tool that uses Vision Transformer technology to convert equation images into LaTeX code.
6,218 shares
Industry news: funds, hiring, markets and regulation.
5 items
Unlimited, a firm co-founded by former Bridgewater Associates executive Bob Elliott, is set to launch a tech-based multi-manager hedge fund replication strategy.
3 shares
Ahead of the general election, South Korean regulators have completely banned hedge funds and short sellers from selling borrowed stocks, as per Bloomberg.
3 shares
Hedge funds and private equity firms have started a legal fight against the US Securities and Exchange Commission over the implementation of new regulations.
5 shares
SS&C Technologies reports that hedge funds suffered losses in September, with a decrease in capital movement in October.
7 shares
BNY Mellon is looking for seed capital for Newton Investment Management, its London-based boutique, to break into the multi-manager hedge fund sector.
5 shares
Episodes on markets, quant methods and economics.
10 items
A CIO call discusses the potential of artificial intelligence for investors, identifying companies that could benefit or be at risk, and how AI could disrupt the asset management industry.
4 shares
Ben Hunt explores the role of narrative archetypes in understanding artificial intelligence, their influence on industries and money management, and their effect on market trends and investment decisions.
4 shares
Quantum Solutions: In the QuantSpeak podcast, Dr. Araceli Venegas-Gomez discusses the potential impact of quantum computing on finance, its adoption in various industries, and her shift from aeronautical engineering to quant finance.
4 shares
In a podcast, Professor Aswath Damodaran shares his love for teaching, critiques ESG investing, and discusses market efficiency and the future of education.
6 shares
Lauren Taylor Wolfe, co-founder of Impactive Capital, talks about the advantages of a collaborative private equity approach for sustainable long-term returns as an ESG-focused activist investor.
6 shares
Lessons from Fischer Black, Peter Carr, and More: The article highlights the common traits of renowned figures like Fischer Black, Peter Carr, Rick Rubin, George Box, Gilbert Strang, and John Nash, focusing on their soft skills and unique contributions.
3 shares
Analyzing Market Developments Impact: Jonny Goulden and Saad Siddiqui discuss the impact of recent market developments on the EM fixed income asset class in a podcast recorded on 03 November 2023.
3 shares
Economics, Interest Rates, and Volatility: The recent IBKR Podcast discusses economics, interest rates, and volatility with experts Andrew Wilkinson, Jose Torres, Joe Burke, and Steve Sosnick.
4 shares
Insights from Tom Luongo: Tom Luongo analyzes the bond market, Federal Reserve policy, the shift from Libor to SOFR, and potential threats to the Federal Reserve and global markets.
3 shares
Conversation with Chris Martenson: Chris Martenson talks about the link between energy and economic prosperity, the global oil market, the Petrodollar system, and his investment strategies, including gold prices and real rates.
3 shares
Posts from quant researchers on X.
9 items
The article reviews a study assessing the performance of different reinforcement learning trading strategies during unpredictable, extreme market events.
5 shares
The article highlights a new study suggesting that trading currencies based on their skewness risk premium can yield high returns and Sharpe ratio.
1 shares
The article discusses a study indicating that the effectiveness of a 12-month momentum strategy has decreased due to analysts' improved reaction to news.
1 shares
The article examines a study by Chin and Povala that investigates the factors influencing the returns of listed and unlisted real estate, noting a correlation with return horizon.
1 shares
The NeurIPS 2023 paper presents LLMTime, a large language model that predicts time series data by converting numbers into text and managing missing data.
1 shares
The episode offers useful knowledge on commodity strategies and spreads.
0 shares
Microsoft's DeepSpeedRLHF simplifies chat-style inference, allowing the training of OPT13B in 9 hours and OPT30B in 18 hours for less than $300 and $600 respectively.
0 shares
Open Source Library for DNLS Optimization: Theseus is Meta's open-source library for DNLS optimization, developed on PyTorch for structured machine learning.
0 shares
Language Models for Time Series: LLM4TS is a large language model for time series that uses fine-tuning, layer normalization tuning, and LoRA.
0 shares
Threads from r/quant, r/algotrading and friends.
10 items
6 shares
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227 shares