Quant LetterNo. 76
150 items across 10 sections, as sent to readers on 27 November 2024. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
9 items
The study explores quanto options involving multiple assets in different currencies, concluding that a mix of GARCH-Jump SV, Weibull SC, and Ornstein Uhlenbeck (OU) SER is most effective for Monte Carlo simulation pricing.
4 sharesSource ↗
The paper presents a Markov-functional method to build local volatility models calibrated to a set of marginal distributions, expanding on the volatility interpolation of Bass, Conze, and Henry-Labordère.
3 sharesSource ↗
The research indicates that hedge funds tailor their activist campaigns to match the preferences of large-stake institutional holders in the target company, resulting in increased shareholder attention, votes, and success.
2 sharesSource ↗
Research using Tokyo Stock Exchange data supports the econophysics theory that average price impact follows a power law in relation to transaction volume.
2 shares20 citations todaySource ↗
A study of sell-side analysts' reports shows that the text information accounts for over 10% of current stock returns, with income statement analyses being the most influential.
2 shares3 citations todaySource ↗
The article investigates the credit risks of decentralized stablecoin issuance, such as overcollateralized lending and business credit, and suggests possible risk reduction strategies.
2 sharesSource ↗
Research shows that machine learning algorithms can unknowingly learn collusive behavior in competitive scenarios, posing a challenge for regulators to prevent algorithmic collusion.
4 shares3 citations todaySource ↗
A study comparing four open-source electricity system models found that they yield similar results when harmonized, emphasizing the need for clear policy guidelines and a standardized approach in clean energy planning.
3 shares5 citations todaySource ↗
The article introduces a new deep learning framework for predicting multi-step time series, which improves the performance of deep learning models. It has been effectively tested on Bitcoin and Ethereum, demonstrating its ability to manage volatility and provide useful information for decision-making.
5 shares8 citations todaySource ↗
Working papers in finance and economics from SSRN.
40 items
Research indicates that large-scale mutual fund redemptions can decrease stock liquidity, especially in the Chinese equity market.
6 sharesSource ↗
Deep learning can effectively tackle the issue of dimensionality in quantitative economics, particularly in dynamic equilibrium models.
5 sharesSource ↗
The approval of spot Bitcoin ETFs and their options in 2024 could impact market liquidity and price volatility, but also introduces regulatory risks.
2 sharesSource ↗
Securitization: The author suggests a regulatory framework for financial innovation, including FinTech, cryptoassets, and DeFi, applicable to past and future securitization transactions, including NFTs.
19 sharesSource ↗
The paper proposes that machine learning predictions can be interpreted as a linear combination of in-sample values of the predicted variable.
4 sharesSource ↗
A machine learning model has been developed that can predict credit bond defaults in the Chinese market with over 90% accuracy, surpassing traditional methods.
2 sharesSource ↗
A new portfolio selection process based on fuzzy integral and measure theories has been introduced, providing a fresh take on traditional portfolio theory.
3 sharesSource ↗
A high-performance computing engine designed for machine learning in FinTech has been presented, showing enhanced runtime performance and strategy evaluations.
2 sharesSource ↗
The use of financial derivatives has been found to lower stock price crash risk in the Chinese market by controlling self-interested managerial behaviors and improving information disclosure.
2 sharesSource ↗
The introduction of broadband internet in Spain has led to an increase in digital labor and territorial cohesion, along with a slight population growth in the regions affected.
15 sharesSource ↗
The article highlights Geoffrey Hinton's key contributions to neural networks, including backpropagation, deep learning, and Capsule Networks, and their role in current AI applications.
224 sharesSource ↗
The study reveals a lack of transparency in predictive machine learning studies in top business and economic journals, leading to fewer citations due to not benchmarking against traditional statistical models.
15 sharesSource ↗
An S&P 500 Case Study: The paper suggests that simple forecasts can effectively stabilize volatility in the SP 500 and Treasury bills, similar to complex models.
6 sharesSource ↗
The article discusses the potential of Big Data analytics in improving risk management in IT service delivery through real-time risk identification, assessment, and mitigation.
2 sharesSource ↗
The study presents a semi-parametric data-driven decision-making framework for inventory and financial hedging of new products, using proxy return factors to infer derivatives returns and demand-return relationships.
4 sharesSource ↗
The article explores the use of high-frequency data to identify similar assets for statistical arbitrage strategies, assessing the effectiveness of various clustering algorithms and trading rules on different asset classes.
7 sharesSource ↗
The study examines the key success factors for effective risk management in the Nigerian banking system from 1960 to now, using a combination of interviews and questionnaires.
6 sharesSource ↗
The article assesses various machine learning frameworks for multiphase flow in oil and gas production, aiming to improve model effectiveness by predicting uncertainty.
6 sharesSource ↗
The research investigates alpha-attractor quintessential inflation in relation to inflationary observables and dark energy equation parameters, considering constraints from cosmic measurements and observations.
5 sharesSource ↗
The study investigates the use of machine learning algorithms to predict the success of mergers and acquisitions, finding that nonlinear models predict post-deal returns well, but not post-acquisition earnings.
3 sharesSource ↗
The Recourse Rule, which reduced capital requirements for top-rated ABS CDO tranches, contributed to the financial distress of large commercial banks during the 2007-2009 crisis.
62 sharesSource ↗
The performance of machine learning models in predicting stock returns is greatly influenced by their design choices, with nonstandard errors in portfolio returns surpassing standard errors by 59%.
12 shares5 citations todaySource ↗
The financial stability risks posed by the rapid growth of digital assets are minimal due to the small size of the digital ecosystem and its limited links with the traditional financial system.
10 sharesSource ↗
Calibrating cross-asset hybrid models for valuing and simulating exposure of complex financial instruments is challenging but necessary for accurate simulations of financial risk factors.
3 sharesSource ↗
The empirical models and distributional properties of prices in nonferrous metals spot and futures markets significantly affect the selection of contract, data frequency, variables, model type, estimation methods, and diagnostic tests.
4 sharesSource ↗
A study reveals that US companies that violate environmental regulations face higher financing costs as investors penalize them by not buying their debt or equity.
4 sharesSource ↗
A new model suggests that short sellers provide negative information to securities lenders, impacting institutional investors' decisions on lending, trading, and governance.
2 sharesSource ↗
An analysis of the rise and fall of special purpose acquisition companies (SPACs) as an alternative to public listing reveals trends, characteristics, concerns, and returns up to 2023.
6 sharesSource ↗
Measures of systemic risk based on the comovements of US financial firms' stock returns under stress can predict market outcomes, bank failures, and balance-sheet results from 1927 to 2023.
3 sharesSource ↗
A new framework for continuous-time model-based reinforcement learning, which reduces the discrepancy between the model-based value function and empirical rewards, could improve upon traditional statistical methods.
2 sharesSource ↗
The article discusses the challenges of trendfollowing investment strategies, suggesting that replicating a broad index of such funds can help mitigate these issues, despite potential tracking errors.
205 sharesSource ↗
The paper reviews the use of machine learning in portfolio management, discussing its limitations and potential future developments in areas like systematic trading strategies and portfolio optimization.
8 sharesSource ↗
The research argues that significant investments in hedge funds can be justified due to their diversification benefits, with equity and event-driven hedge fund strategies offering the most advantages.
7 sharesSource ↗
The study reveals that US banks are reluctant to sell underwater bonds at a discount, especially those that do not recognize unrealized losses in regulatory capital and those with low stock market valuations.
6 sharesSource ↗
The article evaluates the performance of Large Language Models in forecasting stock market data, finding that while some models excel in stable environments, others are better suited for complex data scenarios.
21 sharesSource ↗
Prospectus vs. Ratings: Retail and institutional fund flows are primarily driven by sustainability statements in fund prospectuses, not external sustainability ratings, as per an analysis of over 23,000 equity mutual funds and ETFs.
22 sharesSource ↗
Sentiment Spillover: A study on index investing shows that sentiment spillover among index stocks results in higher prices, increased trading volume, and stronger negative price autocorrelation compared to non-index stocks.
4 sharesSource ↗
Private video game companies that receive private equity investment perform better in terms of growth and returns than public markets and other private equity deals, based on an analysis of 540 private video game investment deals.
2 sharesSource ↗
Despite the aim for transparent trading, hidden orders account for up to 75% of the dollar volume traded for high-priced stocks, and an AI model can predict where these orders will likely appear.
4 sharesSource ↗
Sell-side stock analysts often over-recall distant historical events and under-recall them during crises, with past earnings and forecasts being overweighted and past positive events being selectively forgotten.
7 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
18 items
A new investment strategy uses clustering to reduce asset selection and ranks them by Sharpe ratio, potentially outperforming traditional portfolios.
21 sharesSource ↗
A superior algorithmic trading model has been developed, incorporating volume data and price indicators, outperforming benchmarks and previous models.
19 sharesSource ↗
The Fama-French seven-factor model effectively estimates returns on Indonesian property and real estate stocks, which can hedge inflation and interest rates.
14 sharesSource ↗
A study on Portuguese mutual funds shows fund age negatively affects performance, expense ratios inversely affect domestic equity fund performance, and fund flows are key performance determinants.
13 sharesSource ↗
The article talks about the application of the DLWR-LSTM model to enhance the precision of stock index predictions in the Shanghai Stock Exchange, keeping an accuracy rate of about 1% irrespective of time series changes.
15 sharesSource ↗
Trading algorithms, both symmetric and asymmetric, are tested on stablecoin markets using machine learning to determine profit margins, with the asymmetric algorithm performing better in unbalanced markets.
31 sharesSource ↗
Machine learning is used to analyze the probability of default in financial institutions, finding that markets with more competition are better at managing systemic risks.
24 sharesSource ↗
The effectiveness of machine learning models in predicting global stock market volatility is assessed, with simpler models proving more effective for creating volatility-timing portfolios.
22 sharesSource ↗
The article explores the similarities between data preparation for machine learning and data warehouses, suggesting the possibility of an automated system to expand data warehouse architecture.
21 sharesSource ↗
An unsupervised machine learning algorithm is used to analyze corporate disclosures, revealing a connection between machine-identified risk factors and investor pricing behavior, with most risk factors reducing return volatility.
19 sharesSource ↗
The study uses machine learning to predict China's business cycle using various indicators, with Logistic Regression being the most successful.
18 sharesSource ↗
A new economic uncertainty index, created using machine learning, effectively predicts stock market returns, especially during periods of high uncertainty and sentiment.
18 sharesSource ↗
The use of machine learning models, specifically Random Forest and Adaptive Lasso, improves risk analysis during stress tests by accurately predicting key banking variables.
17 sharesSource ↗
Machine learning is used to study the impact of public debt issuance on macroeconomic equilibrium and wealth distribution, with the income channel having the most significant effect on welfare changes.
17 sharesSource ↗
The Naive Bayes Classifier machine learning algorithm is used to categorize emails as spam or not, with the decision maker choosing error tolerance based on two different Laplace values.
16 sharesSource ↗
High-Frequency Forecasting: The study introduces DeepVol, a model using Dilated Causal Convolutions, which effectively uses high-frequency data to predict next-day market volatility.
27 sharesSource ↗
The paper suggests using a Pointwise Convolutional Autoencoder and Shapley Additive Explanations for index tracking, outperforming other stock selection strategies in various financial markets.
17 sharesSource ↗
The article discusses the profound influence of artificial intelligence on marketing, highlighting the importance of combining data and creativity for the future of the industry.
2 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
17 items
The article presents AIMV2, a method for training large-scale vision encoders using both images and text, which excels in multimodal image understanding and vision benchmarks.
182 shares124 citations todaySource ↗
OminiControl, a new framework that incorporates image conditions into pre-trained Diffusion Transformer models, is introduced, surpassing existing models in conditional generation tasks.
158 shares370 citations todaySource ↗
The research investigates the OpenAI o1 model's ability, enhanced by Chain-of-Thought fine-tuning and innovative reasoning strategies, to adapt to wider domains lacking clear standards.
97 shares156 citations todaySource ↗
The article introduces the Perceptive Internal Model (PIM), a method that uses elevation maps for stable humanoid robot movement across different terrains and sensor setups.
86 shares115 citations todaySource ↗
The paper introduces Insight-V, a system that improves the reasoning abilities of large language models by generating extensive reasoning paths and integrating a multi-agent system for better visual reasoning performance.
62 shares149 citations todaySource ↗
The article introduces XGrammar, a structure generation engine that significantly speeds up context-free grammar execution in large language models.
34 shares91 citations todaySource ↗
The study reveals how Chinese companies like Tencent are bypassing U.S. export controls to use semiconductors in advanced AI models.
33 shares3 citations todaySource ↗
Material Anything is a new framework that creates physically-based materials for 3D objects, adaptable to various lighting conditions.
31 shares39 citations todaySource ↗
The research introduces a method to identify crucial layers in Diffusion Transformer models, improving image editing and inversion methods.
21 shares124 citations todaySource ↗
The study presents FlowMol-CTMC, a model that outperforms others in 3D small molecule generation with fewer learnable parameters.
19 shares15 citations todaySource ↗
The article discusses Reducio-DiT, a technique that encodes videos into a compressed format, enabling the creation of high-resolution videos with limited GPU resources.
83 shares14 citations todaySource ↗
The paper introduces Mixture-of-Logits (MoL), a method that enhances the performance of recommendation systems and language models by approximating similarity functions, reducing latency by up to 66 times.
81 shares4 citations todaySource ↗
The study presents KTO, a new method that uses a Kahneman-Tversky model to align language models with human feedback, performing better than preference-based methods by learning from a binary signal of output desirability.
71 shares162 citations todaySource ↗
The article presents a new approach for Large Language Models that divides the process into memory recall and reasoning, enhancing model performance and interpretability.
63 shares55 citations todaySource ↗
The paper discusses spontaneous concatenation in convolutional neural networks trained on acoustic recordings, offering a potential neural method for modeling syntax from raw acoustic inputs.
53 shares6 citations todaySource ↗
The research presents the Mesh-adsorbed Gaussian Splatting method for 3D reconstruction, combining 3D Gaussians and meshes for improved performance.
42 shares12 citations todaySource ↗
The study introduces Coarse Correspondences, a method that improves Multimodal Language Models' spatial-temporal reasoning using 2D images, enhancing performance without task-specific fine-tuning.
41 shares20 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
12 items
The article discusses how the use of high-quality preference data can significantly enhance instruction adherence and honesty by up to eightfold.
1,435 shares
The piece proposes a method to refine Q and V, which can improve the precision of attention with INT4 QK and FP8 PV.
523 shares
The article highlights recent developments in human animation, which use audio pose or movement maps conditions to produce superior quality animations.
446 shares
The Whisper speech recognition model has improved its word-level timestamp precision by making adjustments to the tokenizer.
371 shares
The synthesis of an expanding body of literature is vital for the advancement of science.
235 shares
BFloat16: The AnchorAttention method has been created to address numerical problems caused by BFloat16, improve long-context capabilities, and speed up training.
135 shares
Large Model Training: Despite progress in variance reduction algorithms for stochastic optimization, their application in training deep neural networks and large language models has not been widely successful.
121 shares
Speech Translation: There is increasing research interest in end-to-end speech-to-speech translation, a process that directly translates spoken language from one form to another.
109 shares
Named Entity Recognition: The article presents WhisperNER, a new model that simultaneously transcribes speech and recognizes entities within it.
97 shares
MPLSandbox is a sandbox for multiple programming languages, providing detailed feedback from compiler and analysis tools for Large Language Models.
93 shares
Sketch animations are a powerful tool for visual storytelling, used in everything from simple flipbook drawings to professional studio work.
89 shares
The study of Multimodal Large Language Models is growing, as more advanced models are required to manage diverse text and visual tasks in practical applications.
21 shares
Repositories the letter featured.
10 items
The article shares Python code to apply the probabilistic machine learning concepts from Kevin Murphy's book.
6,552 shares
The article explores the use of AI agents in Generative AI, specifically in the field of finance and LLMs.
200 shares
The article presents the quickest library for executing Deep Reinforcement Learning.
347 shares
HTML vs Plain Text: The article advocates for HtmlRAG HTML over plain text for better retrieval results in RAG systems.
182 shares
The article introduces an open-source platform that utilizes AI to pull structured data from documents.
906 shares
Payload is a new open-source framework that uses Next.js and TypeScript to provide a backend and admin panel for app development or as a headless CMS.
27,958 shares
The article introduces a new internet scanner tool specifically designed for hackers.
6,787 shares
The piece provides a guide on how to use Llama to implement a paper from scratch without any complications.
523 shares
MATCC introduces a new technique for precise stock price prediction, taking into account market trends and cross-time correlations.
13 shares
Industry news: funds, hiring, markets and regulation.
20 items
Blue Riband Group has selected TS Imagine's Risk Module and Portfolio Risk Management for its global long-short equity investment management.
12 shares
Trading Technologies International is set to offer access to Cboe equity index options, marking its entry into the equity options trading market.
12 shares
Marshall Wace, a UK-based hedge fund, has incorporated Bloomberg's machine learning-powered real-time pricing data service, IBVAL Front Office, to enhance its systematic credit strategies.
7 shares
Cboe Global Markets is set to launch the first cash-settled index options linked to the price of spot bitcoin on December 2.
6 shares
Jason Siegel, ex-head of Brookfield Asset Management’s multi-strategy hedge fund platform, has moved to BW Gestão de Investimentos, the investment office of Brazil’s billionaire Moreira Salles family.
5 shares
Gatemore Capital Management is pushing market research firm YouGov to start a sale process to boost shareholder value.
5 shares
US hedge funds such as Appaloosa Management and Scion Asset Management have upped their stakes in US-listed Chinese firms like JD.com and GDS Holdings.
4 shares
Princeton University is attracting a growing number of students interested in studying quantitative finance.
3 shares
Heidrick & Struggles, an executive search firm, has named Daniel Aghdami as a Partner to oversee the firm's European financial clients.
3 shares
The article explores the trend of people frequently shopping for the same individuals.
3 shares
Donald Trump has nominated Scott Bessent, founder of Key Square Group, to be the Treasury Secretary, responsible for US fiscal policy and economic strategy.
2 shares
Agecroft Partners is expanding its Asian operations with a new Hong Kong office, managed by Jerry Wang, to handle capital introduction services.
2 shares
GCQ Funds Management, a Sydney-based hedge fund, is targeting Japan's growing cloud accounting market for its next major growth opportunity.
2 shares
The US Treasury Department is investigating JPMorgan Chase's dealings with a hedge fund allegedly connected to Iranian oil trader Hossein Shamkhani.
2 shares
The growth of the global alternative investments market and the rise of private capital firms have created a need for standardised benchmarks.
2 shares
Employee views on diversity and inclusion initiatives at hedge funds are at their lowest in four years.
2 shares
Global hedge funds are quickly offloading US electric and water utility stocks and investing more in US materials stocks.
2 shares
Despite a historic stock market rally this year, Man Group’s AHL Trend Alternative Fund is facing some of its worst returns.
1 shares
From RAG to riches narrates a story of substantial financial achievement.
1 shares
Ex-billionaire investor Sung Kook Bill Hwang has received an 18-year prison sentence for masterminding one of the biggest financial frauds in US history.
1 shares
Episodes on markets, quant methods and economics.
10 items
Chris Browne highlights the advantages of investing in democratic nations, emphasizing the potential opportunities in the Eurozone due to the current geopolitical landscape.
9 shares
Joel Nagel shares insights on asset protection strategies, including the use of trusts, second passports, dual citizenship, and strategic debt usage.
5 shares
Marco Papic explores how fiscal policies and international tensions influence financial markets, the effects of larger deficits and economic stimulus on bond yields and inflation, and Trump's policies' impact on global markets.
5 shares
The podcast explores the possibility of the US Federal Reserve cutting rates again in December, the effect of a strong dollar on Europe and Japan, and the market's indifference to China's policy support announcement.
4 shares
Pierre Ferragu discusses the potential of AI to increase the wealth gap, the US's dominance in AI design and manufacturing, and the competition in the chip market.
4 shares
The second episode of a sports business series explores the changing ways fans consume sports content and the conflict between traditional broadcasters and tech platforms.
3 shares
Daniel Lacalle discusses the Trump Trade, its current status, and future market predictions on MacroVoices.
2 shares
Wentao Su talks about improving AB testing on social media through network science and developing algorithms to enhance user experience and advertiser performance.
2 shares
Andrew Wilkinson and Jose Torres present ForecastEx, a prediction platform by Interactive Brokers designed to aid investors in making profitable decisions.
2 shares
Trump's energy agenda aims to combat inflation by reducing energy prices, potentially affecting oil prices and international relations with oil-exporting nations.
2 shares
Posts from quant researchers on X.
12 items
The new weekly newsletter discussing recent research on quantitative investing, including bonds, crypto, currencies, equities, and machine learning, is now out.
5 shares
A study using Finnish military draft cognitive tests indicates a correlation between high IQ scores and superior stock market trading performance in later life.
2 shares
Time Series Prediction with Sparse Mixture of Experts: Article 3: MoiraiMoE employs a projection layer and a sparse mixture of experts in Transformers to predict and model various time series patterns, eliminating the need for human-defined heuristics.
2 shares
The increase in passive investing may lead to index frontrunning, resulting in unseen expenses for investors.
1 shares
Combining free cash flow yields with momentum signals can improve investment strategies, increasing Sharpe ratios and decreasing drawdowns.
1 shares
A study of 1056 machine learning models reveals how design choices such as algorithm, target feature, and training affect strategy returns.
1 shares
FReT uses recurrent topology to create sophisticated predictions of unseen timeseries data.
1 shares
Opalesque is recognized for conducting in-depth interviews with hedge fund managers.
1 shares
Opalesque is renowned for its top-notch interviews with hedge fund managers.
1 shares
The author shares their experience visiting the social platform, bluesky, and provides a link to the site.
0 shares
The author provides lecture notes on ridge regressions and lasso elastic net, with a link for more details.
0 shares
The article explores the importance of GPT wrappers in supply chain management, especially for LLM-driven apps.
0 shares
Threads from r/quant, r/algotrading and friends.
2 items
447 shares
61 shares