Quant LetterNo. 70
149 items across 10 sections, as sent to readers on 17 October 2024. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
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The article discusses how two collective pension funds can mutually insure each other against systematic longevity risk, with the success of this insurance depending on how similar their risk preferences are.
2 shares2 citations todaySource ↗
Working papers in finance and economics from SSRN.
32 items
The study explores key success factors in risk management within the Nigerian banking system from 1960 to now, using interviews and questionnaires.
6 sharesSource ↗
The article assesses various machine learning frameworks for multiphase flow in oil and gas production to enhance prediction accuracy.
6 sharesSource ↗
The paper examines the use of machine learning algorithms, specifically Transformers, to predict solar flares and lessen their impact on Earth.
6 sharesSource ↗
A new machine learning model is created to predict and improve the plasmacatalytic dry reformation of methane over NiAl2O3 catalysts in a specific reactor.
4 sharesSource ↗
The study contrasts the forecasting performances of OSBetatEGARCH and MSBetatEGARCH in the US stock market, proving OSBetatEGARCH's superiority.
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The article discusses how multinational companies like CocaCola and IBM manage currency exposure using a mix of financial derivatives and natural hedges.
2 shares2 citations todaySource ↗
The paper examines the effect of statistical learning on arbitrage pricing, revealing that estimation errors can limit arbitrageurs from fully capitalizing on pricing errors.
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The research focuses on optimal portfolio delegation in a random default time scenario, creating a theoretical model to analyze the investment process and portfolio manager's decisions.
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The study shows that deep learning algorithms can effectively eliminate noise from seismic data, especially when the algorithm is trained to learn the signal instead of the noise.
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The article explores factors influencing post-Initial Coin Offerings (ICOs) market stability, suggesting that high-quality signals like venture capital backing can reduce market volatility and increase investor trust.
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Machine learning can predict the success of mergers and acquisitions using accounting and macroeconomic data, but not immediate post-deal earnings.
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Data lakes are vital in modern data architecture for storing diverse data and supporting advanced analytics, with query optimization enhancing performance.
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Credit spread expectation errors, seen as signs of market optimism, can predict economic downturns, showing the importance of credit market sentiment in economic cycles.
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Big Data analytics enhances IT service delivery risk management by enabling real-time risk identification, assessment, and mitigation.
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Combining machine learning with financial data lakes improves predictive analytics, aiding in accurate predictions, fraud detection, and operation optimization.
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The paper explores the impact of credit derivatives on risk management, banking stability, and financial growth in India's financial sector.
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The study uses infrared satellite imagery and machine learning to monitor real-time economic activity in the global cement industry.
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The article discusses the incorporation of advanced analytics into enterprise systems for improved real-time decision-making and the challenges in implementing these technologies.
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The research investigates the correlation between the intensity of news arrival, volatility, and volume at an intraday frequency using a global dataset and natural language processing.
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The paper highlights the rapid advancement of AI and Machine Learning due to large datasets, leading to increased accuracy, efficiency, and versatility.
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The article explores the use of machine learning in finance, specifically in portfolio management, discussing its current drawbacks and potential future developments.
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The research indicates that substantial investments in hedge funds can be justified by their diversification benefits, even without alpha, but these investments are greatly influenced by alpha assumptions.
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The article assesses the performance of Large Language Models in time series forecasting, emphasizing their potential for precise predictions and the necessity for further model improvements.
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The study investigates hidden liquidity in U.S. equity exchanges and introduces an AI-based algorithm that predicts the likely locations of price-improving nondisplayed orders.
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The research investigates if macroeconomic factors and policy interest rates can benefit a risk-averse investor diversifying her wealth in different hedge fund strategies.
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The study shows that AI-rewritten SEC filings increase positive sentiment, positively affecting stock prices, emphasizing the need for careful AI use in financial disclosures.
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The paper discusses the potential negative societal impact if Bitcoin's price continues to rise, as it does not enhance the economy's productive capacity.
5 shares2 citations todaySource ↗
The research explores portfolio optimization in a generalized lifecycle model, where an individual manages a portfolio to maximize consumption, death benefit, and terminal wealth.
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The paper points out unregulated Bitcoin exchanges, especially Binance, as the main source of price discovery, underlining regulatory challenges as Bitcoin products gain popularity.
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A Chinese study reveals that commodity futures data significantly enhances the precision of macroeconomic predictions, especially for the Producer Price Index (PPI).
3 shares1 citation todaySource ↗
A study using technical analysis tools to categorize India's primary market conditions as 'hot' or 'cold' found a correlation with major external events like elections, global depression, terror attacks, and the pandemic.
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Research indicates that companies with CEOs having high social capital often produce less readable 10K reports, especially if the CEOs are influential, their peers also issue less readable reports, and they operate in competitive sectors.
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Economics working papers from RePEc's NEP field reports.
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Algorithmic trading in China has improved market liquidity and reduced costs for investors, with more sophisticated traders emerging.
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A new system of factor models has been introduced, offering superior risk and return dynamics and outperforming benchmark policies in the U.S. market.
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A study of U.K. stock returns found that all multifactor models are inefficient, with the eight-factor model of Chib and Zeng performing best.
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A new four-factor model based on momentum effect in China outperforms traditional models, explaining stock, industry, and regional momentum.
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A study of an electronic market model examines the impact of trading halts and performance-based leverage limits.
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Machine learning, particularly the random forest model, is more effective in predicting the Gross Regional Product growth of Russia's Sverdlovsk region than traditional models, according to a study.
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Machine learning techniques like extreme gradient boosting and random forest are more accurate in predicting the aggregated stock market risk premium based on online investor sentiment than traditional linear models.
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The SHARV-MGJR model, which includes volatility leverage effects and current return data, is more accurate in predicting cryptocurrency market volatility than GARCH-type models.
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A study of 125 nonfinancial firms in Pakistan found that including nonfinancial disclosures like narrative disclosure tone and corporate governance indicators in financial predictive models significantly improves the prediction of firm performance.
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Research shows AI and big data investors tend to follow the crowd during stable, low-volume market days, but act independently during volatile, high-volume days.
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A study finds that Turkish banking firms adopting enterprise risk management see improved performance and value, and reduced risks, suggesting the use of a partial least squares regression model for predictions.
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A hybrid model combining wavelet transform and multi-input LSTM proves more accurate (72.19%) in predicting the trend of the SSE composite index than other models.
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Croatian graduates with cultural, human, and bridging social capital, and who engaged in high-impact practices during studies, are more likely to find suitable, well-paying jobs quickly post-graduation.
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Machine learning models like gradient boosting and regularised regression can offer more precise inflation predictions than conventional methods, especially when forecasting individual components of consumer inflation.
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Machine learning models using Sequential Floating Forward Selection (SFFS) and a few indicators can accurately predict German business cycles, especially during quantitative easing periods.
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A study reveals that only 20.31% of machine learning papers in political science journals report their hyperparameters and tuning methods, indicating a need for more transparency and robustness in machine learning models.
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A new algorithm has been created to identify collusion in public procurement auctions, finding a high likelihood of such activity in Turkey and Europe, leading to a 3-7% increase in procurement costs.
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A model has been developed to detect fake news articles using linguistic features and feature extraction techniques, with the most accurate model being generated through logistic regression and feature hashing vectorisation.
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Machine learning forecasts have improved the prediction quality for volatility of WTI futures prices, with portfolios built using these forecasts outperforming other models and leading to economic benefits.
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This article discusses the application of machine learning and deep learning to analyze financial news headlines, aiming to select low volatility stocks that perform better than the Standard and Poor’s 500 Index.
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A study reveals that a pricing model based on news text from The Wall Street Journal is more effective in predicting investment opportunities than traditional models, using topic modeling and latent factor analysis.
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Research using administrative data and machine-learning models shows that increased regulatory intensity raises costs and discourages companies from investing and hiring, especially financially constrained firms.
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Machine learning analysis of language used in Congress and new SEC rules shows a significant increase in partisanship among SEC Commissioners from 2010-2019, while the Federal Reserve Board remains relatively nonpartisan.
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The general machine-learning papers the letter carried in 2023-25.
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The article presents Depth Any Video, a new model that uses synthetic data and video diffusion models to estimate video depth more accurately and consistently than previous models.
74 shares66 citations todaySource ↗
The research shows that Mixture-of-Experts Large Language Models can be effective embedding models without finetuning, and suggests a combination of routing weights and hidden state for better performance.
47 shares42 citations todaySource ↗
Simple Tracking: The paper introduces CoTracker3, a new tracking model that uses semi-supervised training to generate pseudo-labels from real videos, improving point tracking performance with less data.
14 shares127 citations todaySource ↗
Efficient 3D Reconstruction: The article discusses Long-LRM, a 3D Gaussian reconstruction model that can efficiently reconstruct large scenes from long sequences of input images, surpassing previous feed-forward models.
13 shares116 citations todaySource ↗
The research investigates model merging in a multilingual context for Large Language Models, finding that objective-based and language-based merging methods enhance performance and safety.
11 shares21 citations todaySource ↗
A new 3D visual understanding benchmark shows that while specialized models are accurate, they are not robust, and human vision is still the most reliable 3D visual system.
9 shares14 citations todaySource ↗
4D Language Embedded Gaussian Splatting: A new method uses 4D representation to connect language with a dynamic model of the world, enabling users to locate events in a video from text prompts.
8 shares15 citations todaySource ↗
A new method combining pre-experiment and in-experiment data increases the sensitivity of online controlled experiments, improving variance reduction and speeding up decision-making.
8 shares4 citations todaySource ↗
The Geometry-Aware Generative Autoencoder (GAGA) addresses challenges of high-dimensional datasets by combining manifold learning with generative modeling.
8 shares23 citations todaySource ↗
LongAlign, a new method for processing long texts, improves alignment in text-to-image diffusion models, overcoming limitations of existing encoding methods.
7 shares21 citations todaySource ↗
Experiments have confirmed the existence of scaling laws in Diffusion Transformers, aiding in determining optimal model size, data needs, and predicting text-to-image generation loss.
155 shares42 citations todaySource ↗
A study has found that language models aligned with human-annotated preference data are preferred by humans in over 70% of cases, even without language-specific data for supervised finetuning.
119 shares32 citations todaySource ↗
The Stability-Aware Boltzmann Estimator Training has been introduced to improve the stability, data efficiency, and agreement with reference observables in Machine Learning Force Fields used in molecular dynamics simulations.
96 shares17 citations todaySource ↗
A new framework represents each new ability in deep learning models as a basis function, providing analytic expressions for the emergence of new skills and scaling laws of the loss with various factors.
49 shares19 citations todaySource ↗
The article discusses CoTaEval, a new framework for evaluating methods to prevent AI from generating copyrighted content, highlighting the need for further research as no method was found to be completely effective.
42 shares52 citations todaySource ↗
The paper introduces SPELL, a method that enhances the diversity of text-to-image diffusion models while avoiding protected images, proving its superiority over other diversity methods.
28 shares25 citations todaySource ↗
The study introduces cedar, a new programming framework for machine learning input data pipelines that enhances performance by applying a mix of optimizations, outperforming other systems.
23 shares17 citations todaySource ↗
The article presents Sequence-aware Sharded Sliced Training (S3T), a new framework for machine unlearning that improves system deletion capabilities with minimal impact on model performance, proving more effective than other methods.
16 shares37 citations todaySource ↗
The paper proposes a new differentiable simulation framework for learning quadruped locomotion, showing its efficiency and effectiveness compared to traditional reinforcement learning methods.
15 shares49 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
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A new method for flow step sampling can be incorporated into current models without the need for retraining.
2,862 shares
Despite not reaching the quality of self-supervised learning methods, generative diffusion models have been found to produce substantial discriminative representations.
332 shares
AI Agent Evaluation: MLEbench is a new benchmark tool designed to evaluate the performance of AI agents in machine learning engineering.
254 shares
GPT4o Capabilities: GPT4o's multimodal and interactive features are important for practical use, but it lacks a high-performing open-source counterpart.
121 shares
Large Language Model Linearization: LoLCATs enhances linearizing quality, reducing the disparity between linearized and original Llama 3.1 70B and 405B LLMs by 77.8% and 78.1% on 5shot MMLU.
106 shares
The hybrid tokenizer is introduced to tackle challenges, breaking down continuous latents from the autoencoder into discrete tokens for the overall view and continuous tokens for residual parts.
87 shares
Gaussian Head Avatar: The article presents GAGAvatar, a novel technique for generating animated head avatars using just one image.
77 shares
Agentic Framework for Computer Use: The piece introduces Agent S, a new open-source platform that enables autonomous computer interaction and simplifies complicated tasks.
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Expanding Flow Models: The research suggests Rectified Diffusion, a fresh method that broadens the use of rectification in diffusion models beyond merely flow-matching models.
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RetrievalAugmented Generation: RAG systems enhance the accuracy of large language models by integrating external knowledge for context-specific responses.
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Long PDF Reading Model: PDFWuKong is a large language model aimed at enhancing question-answering capabilities for extensive PDF documents.
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LayoutGuided Scene Generation: Conventional 3D modeling tools complicate and prolong the process of creating intricate, user-specific 3D scenes.
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The storage needs of 3D Gaussian Splatting, a tool for high-quality, real-time rendering, hinder its widespread use.
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Repositories the letter featured.
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The article shares the code repository for the book Feature Engineering for Machine Learning by Alice Zheng and Amanda Casari.
1,434 shares
The article explains the process of forecasting time series data using the PyTorch software.
3,918 shares
The article presents code examples related to the content found on www.systematictrading.org and qoppac.blogspot.
363 shares
The article presents a new feature engineering package that operates similarly to sklearn.
1,888 shares
The article explores a technique to circumvent Deep Packet Inspection (DPI) across different platforms.
7,186 shares
The piece introduces the application of Hidden Markov Models in Python through an API similar to scikitlearn.
3,042 shares
The article presents zap, a multi-language workflow automation platform with 500 plugins, as an alternative to existing platforms like Zapier and Airflow.
9,924 shares
The article offers a series of tutorials and examples for mastering Prompt Engineering methods for effective interaction with large AI language models.
894 shares
The piece presents an autonomous coding agent that can perform tasks within your IDE, always with user permission.
7,579 shares
Industry news: funds, hiring, markets and regulation.
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Cboe Global Markets is launching its new Options on Cboe Volatility Index Futures trading on the Cboe Futures Exchange LLC from October 14.
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Crypto derivatives exchange BitMEX is introducing Trading Bots, a tool for automated 24/7 crypto trading that doesn't require coding experience.
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Citadel Securities Quant Winner: Samson Qian, winner of the Citadel Securities Quant To Watch 2024 Award, is being recognized for his work in Generative AI Driving Alpha Rebellion Research.
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Hedge funds are cutting back on trading due to stricter risk controls, with credit trading being the most impacted, says a global survey by Beacon Platform Inc.
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LGT Capital Partners is launching a new hedge fund vehicle for institutional and wealth management clients, offering access to individual hedge fund strategies in the firm’s primary evergreen LGT Endowment portfolio.
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In 2023, North American hedge fund assets under management rose by 8.3% to $3.66tn, as per Preqin data.
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Andreas Steno Larsen, ex-Nordea strategist, is starting a new hedge fund, AsgardSteno Global Macro Fund, with Asgard Asset Management.
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UBS O’Connor maintains a positive outlook for a potential rebound in merger-arbitrage performance despite a challenging year.
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A survey by the Alternative Investment Management Association and PwC reveals that almost 50% of traditional hedge funds now invest in cryptocurrencies.
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Following Bronte Capital's worst monthly returns in over two years, prominent Australian hedge fund investor John Hempton is reevaluating his strategy.
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Casey Herren has been appointed as the new Director of Business Development at Gatemore Capital Management, focusing on partner relations and capital formation in UK and US public markets.
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A top quant research expert from Two Sigmas is transitioning to Cubist Business Insider Africa.
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A Bank of America survey reveals that hedge fund allocators are worried about crowding risks, but are still concentrating their portfolios into large multimanager firms.
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Klarna is said to be selling most of its UK buy now, pay later loan portfolio to US hedge fund Elliott, potentially freeing up £30bn for new loans.
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Finance is becoming a more popular career choice for top students, surpassing big tech.
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Bloomberg reports that hedge funds are increasing bets against certain currencies, anticipating a Trump victory in the upcoming US election.
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Institutional Investor reports that commodity trading advisors and trend-following hedge funds, led by Quantedge Global Fund, made a significant comeback in September.
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CoinShares' report indicates that last week, digital asset investment products saw inflows of $407m, influenced more by the upcoming US elections than monetary policy outlooks.
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The conflict between Pfizer and activist hedge fund Starboard Value has escalated, with Starboard demanding an investigation into alleged pressure on former executives, according to Reuters.
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It is currently a favorable time to be employed at Jump Trading.
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Episodes on markets, quant methods and economics.
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The article explores the investment potential of agricultural commodities like wheat and corn, emphasizing their role in portfolio diversification and factors influencing their prices.
16 shares
The piece shares expert advice on handling market fluctuations, emphasizing the significance of market direction, risk management, and strategic use of ETFs.
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The article examines the current state of the ETF industry, the emergence of active ETFs, and the influence of Bitcoin and crypto, featuring insights from expert Eric Balchunas.
11 shares
The piece includes a conversation with Marko Papic discussing the U.S. election, America's foreign policy, and the consumer-driven American economy.
8 shares
The article delves into strategies for successful sector rotation and bond market timing, focusing on sector diversification, market shifts, and potential changes in market leadership.
8 shares
Jay Barry and Phoebe White analyze the September CPI report, recent rate volatility, inflation markets, and Fed balance sheet policy in a podcast recorded on October 11, 2024.
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Sylvain Gugger, a former math teacher and machine learning expert, talks about learning rate schedules, PyTorch bugs, and the importance of reproducibility in training runs on a Jane Street podcast.
8 shares
Marc Cohodes, a seasoned investor, discusses market corruption, the role of social media in fraud exposure, and the transformation of the stock market post-COVID.
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Owen Lamont, a Portfolio Manager, discusses market bubbles, passive investing, trading gamification, and the impact of cultural investment preferences on an Acadian Asset Management podcast.
6 shares
Anthony Pompliano, CEO of Professional Capital Management, shares his experiences in building and selling businesses, investing in 200 companies, and managing a large independent financial media platform.
6 shares
Posts from quant researchers on X.
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Recent studies in quantitative investing explore predicting bond returns, mutual fund trading patterns, statistical arbitrage, and factor allocation with regime-switching.
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Jarrow and Yildirim have reviewed literature on inflation-linked products, including bonds derivatives pricing models and empirical results.
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A recent study indicates that about 9% of mutual funds manipulate Morningstar ratings annually to increase inflows and fees, resulting in lower long-term returns.
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Rod Gordillo and GestaltU discuss the changing ETF market and diversification factors on ReSolveRiffs with Bloomberg's Eric Balchunas.
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A new paper by sbraun27 and Juliusz Jabłecki examines the potential and challenges of trend-following beta replication.
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A free subscription is available for a weekly recap of the latest research on quantitative investing.
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A recent study shows that a fear index based on the Thomson Reuters MarketPsych dataset accurately predicts Treasury bond returns.
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The author provides a summary of the week's top research papers on quantitative investing, highlighting a study on cryptocurrency returns.
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The article explores the SigKAN Signature-Weighted Kolmogorov-Arnold Networks for Time Series, providing links to the Python GitHub and the research paper.
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The article provides a link to an unoptimized Kolmogorov-Arnold Time-Series notebook.
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ManGroup has released a new article discussing risk parity strategies.
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The article provides seven important takeaways from interviews with Jerry Parker.
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The article explores the declining prices of GPU rentals and the factors causing it.
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The article narrates the author's journey and method of transferring his consciousness into an AI.
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The article gives a positive review of a podcast, commending its logical and understandable content.
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Threads from r/quant, r/algotrading and friends.
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