Quant LetterNo. 65
153 items across 11 sections, as sent to readers on 10 September 2024. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
14 items
The article introduces a new technique for estimating the linear relationship between multiple variables with measurement errors. This method extends the geometric mean functional relationship to multiple dimensions, making it useful for variables measured in different units and enabling unbiased estimation of relationships.
3 shares1 citation todaySource ↗
The article introduces a new method for estimating the relationship between multiple variables with measurement error. This method expands the geometric mean functional relationship to multiple dimensions, treating all variables equally. It doesn't rely on minimizing distances, but on the symmetric concept of correlation.
9 shares1 citation todaySource ↗
The article talks about how lapse-supported premiums can raise the costs of adverse selection in life insurance, particularly when high-risk individuals don't cancel their policies. It proposes three ways to handle this problem.
2 shares1 citation todaySource ↗
A German study found that 31% of a VAT increase on restaurant services was immediately passed onto consumers, rising to 58% after six months, with the UEFA Euro 2024 having no effect on price changes.
8 shares2 citations todaySource ↗
Research from Ethiopia, Malawi, and Nigeria showed that livelihood diversification did not enhance household resilience or decrease food insecurity during the COVID-19 pandemic, indicating traditional coping strategies may not work for large-scale crises.
7 shares4 citations todaySource ↗
Using the diffusion model as a denoiser for financial time series data improves data predictability and trading performance, leading to more profitable trades and enhanced trading efficiency.
7 shares20 citations todaySource ↗
A study found that post-Berlin Wall, East German inventors in tech fields with access to Western knowledge were more likely to continue inventing, while those from strong socialist-supporting communities were less likely to continue patenting but more likely to move to West Germany.
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The article suggests using Canonical Correspondence Analysis (CCA) for a more comprehensive calculation of economic complexity, incorporating country-specific variables.
5 sharesSource ↗
The study develops a superior forecasting method for multi-level limit order books using advanced multidimensional sequence-to-sequence models.
5 shares2 citations todaySource ↗
The paper highlights how high-risk individuals can increase life insurance policy lapse rates, leading to higher adverse selection costs, particularly in diverse populations with relaxed financial underwriting.
4 shares1 citation todaySource ↗
The article introduces a new method for evaluating the impact of new technologies in data-poor areas, combining Earth observation data, machine learning, and socioeconomic surveys, demonstrated through the case of stress-tolerant rice in Bangladesh.
4 shares1 citation todaySource ↗
The article examines conditional linear factor models in asset pricing. It explores the relationships and characteristics of returns and factors using conditional means and covariances. This research lays the groundwork for defining and estimating these models.
3 shares1 citation todaySource ↗
Research shows that enhancing the computational training of Large Language Models (LLMs) greatly boosts the efficiency of professional translators, especially those with lower skills, indicating potential significant economic impacts with further model scaling.
45 shares11 citations todaySource ↗
A study on predicting GDP growth in various countries using deep learning algorithms reveals that linear regression outperforms when only GDP growth values are considered, but deep learning excels when specific economic indicators are included, and light intensity data doesn't necessarily enhance prediction accuracy.
7 shares4 citations todaySource ↗
Working papers in finance and economics from SSRN.
27 items
Research shows that aligning with International Standards on Auditing (ISA) improves audit quality, particularly in areas with strong enforcement and high ISA convergence.
12 shares9 citations todaySource ↗
Satellite data from Earth Observation systems could aid in creating green financial products by filling data gaps, as per a central banking viewpoint.
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Machine learning is utilized to estimate the stiffness of reinforced concrete hollow piers, using data from previous studies to train the predictive models.
2 sharesSource ↗
Machine learning, specifically the Gradient Boosting model, is effectively used to predict the yield strength of irradiated type 316 stainless steels.
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The minimum profit a monopoly can make from alphaconcave demand is expanded from logconcave demand, with calculated maximum limits for consumer surplus and deadweight loss.
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Research shows large language models used in sentiment analysis display social biases, linking specific jobs with certain genders.
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Query Processing with ML Models: TrainDB, a machine learning-based query processing engine, is launched to provide approximate results for aggregate queries in large or sensitive datasets.
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UK and US: A study reveals that a risk-free portfolio, including gold, T-bills, silver, platinum, and palladium, doesn't always equate to a zero-beta portfolio, based on UK and US data.
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The feasibility of energy efficiency projects using the Energy Service Companies business model is assessed, given customer demand and the high risks due to upfront costs and uncertain returns.
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Research indicates that the use of financial derivatives in China's emerging market reduces stock price crash risk by curbing managers' self-interest and improving firms' information disclosure.
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Research shows both listed and unlisted real estate assets can protect against inflation, with listed real estate more exposed to short-term risk premium shocks.
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A paper discusses the potential of predictive analytics and machine learning in forecasting energy use and enhancing efficiency in Nigeria's power sector.
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A study explores the pros and cons of merging high-frequency trading with artificial intelligence, proposing regulatory steps to ensure market stability.
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The research studies the link between beta wave activity in the motor cortex, keyboard force, and their effect on typing speed and precision.
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The study investigates the volatility connections between cryptocurrencies and stock markets, emphasizing the diversification potential of cryptocurrencies and the Shanghai Stock Exchange.
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The study introduces a new dictionary for analyzing volatility in finance, which can help explain changes in implied volatility dynamics and levels.
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The research models the dynamics of stochastic discount factors, attributing high volatility and conditional heteroskedasticity to factor momentum, macro variables, and Bayesian learning.
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The study uses machine learning to measure board diversity appearance, indicating that firms with visually diverse boards and less public attention generally perform better.
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Analysts' personal discount rates and growth expectations significantly influence valuation fluctuations, primarily driven by risk-free rates and subjective betas.
6 shares1 citation todaySource ↗
The third edition of Financial Modeling Excellence: Innovative Approaches to Stock Predictions delves into probabilistic models for stock price predictions, focusing on time series data and AR and MA models.
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A review of 200+ studies on the cryptoasset market highlights variations in asset-wise, time-varying, and time-frequency domains, emphasizing market connectedness and sentiment analysis during COVID-19.
3 shares1 citation todaySource ↗
The stock market's response to unexpected monetary policy changes is mainly due to alterations in the default-free term structure of yields, not equity premium changes, as per a new model using dividend futures prices.
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A study found that Google's Gemini 1.5 Flash language model does not outperform a basic portfolio or the S&P 500 index in making investment decisions.
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Volatility-managed investment strategies work better for stocks with low uncertainty and during times of low overall uncertainty, a study found.
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Contrary to some claims, a study using the Arbitrage Gordan theorem found that the expected return rate of an asset does impact the option pricing model.
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A study using nonlinear machine learning methods found that an optimized neural network predictor can accurately predict future market movements.
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A study using eye-tracking technology found that visual attention influences trading behavior, with attentional priority leading to a greater tendency to follow the crowd when information is disparate and tournament incentives are present.
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Economics working papers from RePEc's NEP field reports.
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The research uses German political party support and US income shares data to model compositional volatility.
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The article finds that Principal Component Analysis and Long Short-Term Memory are effective in predicting Poland's yield curve and managing interest rates.
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The study reveals that hedge funds with higher fees and minimum investments are better at hedging geopolitical risks, while global macro hedge funds excel at timing.
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The research identifies significant changes in volatility and day-of-the-week effects on cryptocurrency returns during the COVID-19 pandemic, indicating the need for robust trading strategies.
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The research uses language models to create sentiment indices for shipping markets, showing they can accurately predict freight rates, outperforming traditional sentiment analysis.
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The research uses a model to show that China's commodity futures can accurately predict changes in the Baltic Dry Index, aiding decision-making in the shipping industry.
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The research uses remote-sensed and individual data from Sub-Saharan Africa to show that droughts significantly affect child growth, suggesting data-driven policies can help mitigate climate change effects.
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The research finds that machine learning models, particularly the Random Forest model, outperform traditional statistical models in mapping flood susceptibility in New Orleans.
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The research uses machine learning to predict daily stock returns, finding improved performance with feature selection.
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The study suggests a machine learning method for hedging foreign exchange risk in international equity portfolios, improving currency risk hedging.
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The paper introduces a new hybrid model for accurate stock market forecasting, showing a significant increase in prediction accuracy.
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The article uses a random effects model and machine learning to predict commercial real estate values, achieving a low error rate and providing useful data visualizations.
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The research shows that deep transfer learning, specifically the BERT-NLI model, improves the analysis of large political text corpora compared to traditional models.
17 sharesSource ↗
The study uses machine learning, specifically WEKA software, to accurately predict stock prices of the FTSE Bursa Malaysia Kuala Lumpur Composite Index.
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The study reviews literature on financial fraud detection using machine learning, noting a trend towards real datasets and credit card fraud detection models.
15 sharesSource ↗
The study introduces Housing GANs, a data-driven model that successfully generates realistic housing data.
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The paper presents a framework for managing virtual companies in smart urban environments, using Sustainable Information Systems for various business processes.
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The paper explores the impact of investor sentiment and opinion divergence on herd behavior in the Russian stock market, particularly on shares with lower average return and risk.
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The study compares institutional ownership and stock market volatility in China and the USA, revealing that US investors are more risk-averse than their Chinese counterparts.
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The research indicates that long-term exposure to high volatility can lead to underestimation of volatility by market participants, and offers a trading strategy to capitalize on this.
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The piece introduces a new method called quantitative probing for validating causal models, showcasing its success in simulations and offering a guide for its application in causal modelling.
12 sharesSource ↗
VIX and GCP: The study shows a significant correlation between Global Consciousness Project data and the Standard & Poors 500 Volatility Index, suggesting potential for market sentiment prediction.
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The research estimates that 1.23% of global GDP is laundered annually, highlighting the detrimental effects of money laundering on economic and financial indicators.
10 sharesSource ↗
The research suggests that insider trading at a firm can provide valuable information about all securities held by the insider, even if the sales are driven by liquidity needs.
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The paper finds that investment strategies tied to momentum and valuation styles in leveraged loans can yield significant returns.
6 sharesSource ↗
The general machine-learning papers the letter carried in 2023-25.
20 items
The tutorial gives an in-depth look at Topological Machine Learning, specifically persistent homology and the Mapper algorithm, with practical examples and applications.
72 shares22 citations todaySource ↗
The article delves into the reasoning processes of Large Language Models, focusing on the interpretability of attention heads, and suggests future research areas.
36 shares99 citations todaySource ↗
The study conducts a large-scale analysis of quantum kernel methods in quantum machine learning, comparing different types of kernels to understand what makes them effective.
11 shares72 citations todaySource ↗
Enhancing LLMs: The research presents a method for large language models to generate responses with detailed sentence-level citations, enhancing their accuracy and verifiability, and provides a performance assessment benchmark.
11 shares100 citations todaySource ↗
A Synthetic Mobility Dataset: The paper presents NUMOSIM, a synthetic mobility dataset for testing anomaly detection techniques, simulating realistic mobility scenarios and anomalies to improve geospatial mobility analysis.
10 shares24 citations todaySource ↗
PLANSEARCH is a new search algorithm that creates diverse solutions for natural language problems, outperforming traditional methods in various benchmarks.
10 shares93 citations todaySource ↗
WildVis is a new interactive tool for analyzing large-scale conversations, providing search and visualization features for user-chatbot interactions.
9 shares24 citations todaySource ↗
Reinforcement Learning from Prediction Feedback (RLPF) is a method that refines Large Language Models to produce concise, human-readable summaries, enhancing task performance and summary quality.
9 shares16 citations todaySource ↗
The study investigates the use of foundation models in creating realistic medical images, specifically chest x-rays, and shows performance improvement with fine-tuning.
9 sharesSource ↗
The paper introduces an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem, improving approximation factor and solution quality in various applications.
8 sharesSource ↗
The article discusses the influence of foundation models like large language models on the music industry, emphasizing their potential in music comprehension and creation, and calls for more ethical research.
405 shares62 citations todaySource ↗
A Small Language Model: The paper presents InkubaLM, a small language model that performs as well as larger models in tasks such as machine translation and question-answering, showing that effective models can be created with limited resources.
177 shares31 citations todaySource ↗
The study suggests a prompting framework that creates adversarial versions of math word problems to enhance the performance of large language models, indicating their struggle with irrelevant information and recommending fine-tuning for increased robustness.
50 shares13 citations todaySource ↗
Bayesian Framework for Imaging: The research introduces a Bayesian framework, $\mu$GUIDE, for estimating tissue microstructure parameters from any biophysical model or MRI signal, providing a solution to the high computational and time cost of traditional Bayesian methods.
49 shares1 citation todaySource ↗
The paper introduces Kun, a new method for creating high-quality instruction-tuning datasets for large language models without manual annotations, demonstrating its robustness and scalability and providing a scalable solution for enhancing these models' instruction-following abilities.
45 shares10 citations todaySource ↗
LR-QAT, a new efficient algorithm for large language models, has been proposed by researchers, outperforming other post-training quantization methods and offering wide applicability.
30 shares60 citations todaySource ↗
A new hybrid quantum-classical deep learning model has been introduced for predicting drug binding affinity, showing a 6% increase in accuracy and more stable performance than classical models.
25 shares16 citations todaySource ↗
The Decentralized Intelligence Network (DIN) is a new framework designed to tackle AI development issues like data fragmentation, using personal data stores, a scalable federated learning protocol, and a cryptographic rewards system.
23 shares2 citations todaySource ↗
A physics-informed machine learning model has been introduced for modeling thermal states in complex space missions, offering better generalization and reduced computing costs compared to traditional models.
19 shares10 citations todaySource ↗
A study explores the responsible requirements of generative AI models, highlighting five key considerations and emphasizing the importance of responsible generative AI in various fields.
17 shares14 citations todaySource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
3 items
The article presents the VoiceAssistant400K dataset, designed for fine-tuning models for speech output. The code for this is accessible on GitHub.
1,407 shares
The article presents a Python library named rerankers, which eases the application of prevalent reranking methods.
689 shares
The second article explores FluxMusic, an enhanced version of diffusion-based rectified flow Transformers, developed for converting text into music.
493 shares
Repositories the letter featured.
10 items
Packt has released a guidebook on how to utilize Python in Algorithmic Trading.
71 shares
PyPika is a Python-based SQL query builder, beneficial for data analysis tasks.
2,475 shares
A high-performance gradient boosting framework, based on decision tree algorithms, is available for diverse machine learning tasks.
16,510 shares
Python and C languages are employed for asynchronous event-driven algorithmic trading.
659 shares
The ISLP package offers data and coding resources for laboratory use.
100 shares
The article provides a guide on how to automatically update sources for Internet Protocol Television (IPTV).
6,058 shares
The piece introduces a Python package that uses NARMAX models for system identification.
376 shares
The article offers an open access book on creating scientific visualizations with Python and Matplotlib.
10,239 shares
The article discusses a Machine Learning eXchange (MLX) port of FLUX, based on Huggingface Diffusers.
363 shares
The piece presents a probabilistic programming framework for objective model selection in time-varying parameter models.
150 shares
Industry news: funds, hiring, markets and regulation.
20 items
Ace Capital Gestao de Recursos Ltda, a Brazilian hedge fund firm, has bought quant investment firm Saires Capital Gestão de Recursos Ltda to enhance its computer-based investment strategies.
9 shares
In July 2024, hedge funds almost equaled the performance of global equities, with Preqin's All Hedge Fund Index increasing by 1.61%, just slightly less than the MSCI World Index's 1.78% rise.
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The US Securities and Exchange Commission has abandoned its effort to enforce a rule for higher fee disclosures from hedge funds and private equity, after a legal challenge to its transparency initiative.
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Forge, a company offering high-impact code solutions for hedge funds and financial entities, has secured an investment from Morgan Stanley.
5 shares
Sengu Capital, a Hong Kong-based firm, is planning to launch a new hedge fund targeting Japan's resurging financial market, with a strategy focused on enhancements in corporate management.
5 shares
Baird Capital has sold its share in ecube Solutions Limited, an aircraft services provider, to Unical Aviation Inc, a global aerospace parts supplier.
5 shares
A BNP Paribas report shows increased investor optimism towards hedge funds, with new allocations planned for the second half of 2024 due to fresh capital inflows and portable alpha strategies.
5 shares
A self-proclaimed poor meditator continues to promote value while criticizing private equity.
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Douglas Haynes, ex-President of Point72 Asset Management, has abandoned plans to start a new hedge fund, Norias Research Group, due to capital raising difficulties.
4 shares
Balyasny Asset Management has appointed Aditya Singh, a former Goldman Sachs director, as a macro portfolio manager.
3 shares
New York hedge fund Elliott Management has bought Thames Water's debt at a lower price, potentially influencing the utility company's future.
3 shares
The multi-manager hedge fund sector, including firms like Citadel and Millennium, has seen its first net outflows in seven years, suggesting a possible drop in investor interest.
3 shares
HFR and Radient AI are collaborating to create an AI-powered platform for hedge fund analysis.
3 shares
Several US pension funds are planning to significantly boost their investments in private credit in the 2025 fiscal year, indicating increased interest from institutional investors.
2 shares
The article discusses unrelated exits, clarifying that they have no connections.
2 shares
The piece delves into the ideas and viewpoints of Giuseppe Paleologo.
2 shares
Regal Partners Ltd, an Australian hedge fund company, is reportedly raising $1bn for its new Cayman Islands-based fund to expand its operations.
2 shares
The article suggests that electronic market making firms could potentially have a productivity rate that is five times higher per person.
2 shares
Due to disappointing returns and low investor interest, abrdn has closed its Global Risk Mitigation Fund, officially ending operations on August 30.
2 shares
Episodes on markets, quant methods and economics.
10 items
Volatility as a Trading Signal: Dan Russo discusses the use of volatility as a trading signal, the role of market breadth and sector performance in predicting market declines, and the importance of technical analysis for long-term portfolio stability.
14 shares
Federal Reserve Policies and Wealth Gap: Chris Martsenson talks about the potential impact of proposed tax changes, the role of the Federal Reserve in widening the wealth gap, and the challenges faced by day traders due to automated systems and overnight gains in the S&P 500.
14 shares
Inside the Mind Of A Successful Trend Follower: Doug Greenig highlights the benefits of systematic trend-following in over 500 markets, including unusual ones like California carbon emissions and Turkish interest rate swaps, and discusses the macro environment characterized by deep structural change and tail risks.
12 shares
Machine Learning and Index Replication ETFs: Bob Elliott discusses the use of machine learning to create index replication ETFs of alternative investments like hedge funds, draws parallels between the current business cycle and the 1950s and 1960s, and questions if Fed policy is restrictive.
11 shares
Navigating Recessions and Bond Market Shifts: David Hay talks about the potential for companies to succeed during economic downturns, the complexities of the bond market, the implications of the fourth turning for investment strategies, and the potential benefits of investing in scarce resources over easily created financial instruments like treasuries.
11 shares
Hedge Fund Impacts: Dan Gallagher discusses the effects of macro hedge fund liquidations on financial markets, the need for tax expertise for wealthy clients, and potential impacts of rising interest rates in Japan.
10 shares
CPI Manipulation: Mish Schneider discusses the manipulation of CPI numbers, undervaluation of commodities before COVID, inflationary pressures from the pandemic, and the future of markets in relation to electric vehicles.
7 shares
Currency Hedging: Jeremy Schwartz talks about his partnership with Professor Jeremy Siegel, currency hedging in equity markets, and the process of launching a new ETF.
7 shares
Meera Chandan and Jay Barry analyze US employment data, Fedspeak, and the outlook for US rates and FX markets after a volatile Friday.
7 shares
Joe Baratta shares his views on the private equity industry, the current macro environment, and his growth as a leader.
6 shares
Posts from quant and economics blogs and newsletters.
4 items
The article discusses the limitations of AI in investment strategies and proposes network data analysis as a potential solution.
8 shares
The article examines the surge in domestic market flows post-COVID, emphasizing the transition from foreign to local control and its effect on market peaks.
2 shares
The article reanalyzes the post-COVID increase in domestic market flows and the transition from foreign to local control.
2 shares
All five articles discuss the surge in domestic financial flows post-COVID, leading to record market highs, particularly in lower market cap deciles.
2 shares
Posts from quant researchers on X.
10 items
Catastrophe bonds offer returns similar to equity but with less volatility and little correlation with traditional financial assets.
3 shares
Fan and his team found that currency basis momentum provides significant Sharpe ratios and a statistically significant alpha, even when related to carry and momentum factors.
2 shares
Greenwood and Sammon explore the disappearing index effect phenomenon in their paper.
1 shares
The article explores the use of regularised jump models to identify regimes and select features in intraday trading strategies.
1 shares
The Financial Times discusses a study on the global market portfolio, focusing on the changing equity percentages and investors' ability to withstand a 6-year drawdown.
1 shares
The article offers helpful tips for utilizing the pandas software library effectively.
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Bergen and his team suggest a new value measure that surpasses conventional ones, resulting in substantial alphas.
0 shares
The author explores the advantages of high-volatility alternatives in his recent article.
0 shares
The paper offers a fresh viewpoint on the betting-against-beta factor.
0 shares
Threads from r/quant, r/algotrading and friends.
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