Quant LetterNo. 14
100 items across 9 sections, as sent to readers on 30 August 2023. Paper titles open their ML-Quant page; ↗ goes to the source.
Quantitative-finance and ML-for-finance preprints from arXiv.
15 items
Empirical Analysis on Financial Market: The study presents a new physical model, inspired by statistical physics, that uses Level 3 order book data to predict price volatility and expected returns, surpassing traditional and machine learning methods by incorporating principles of statistical physics.
4 shares2 citations todaySource ↗
JAX-LOB: The paper introduces JAX-LOB, the first GPU-powered limit order book simulator capable of processing multiple books simultaneously, designed for efficient large-scale simulations of LOB dynamics for research, calibration, and reinforcement learning training.
5 shares30 citations todaySource ↗
The article investigates the use of Quantum Algorithms in predicting stock prices of companies like Apple and Visa, comparing the accuracy of these quantum models with traditional models.
4 shares18 citations todaySource ↗
The authors present a non-parametric method for joint calibration of a volatility model and a correlated stochastic short rate model, demonstrating its effectiveness on market data and comparing it with sequential calibration.
3 shares3 citations todaySource ↗
Conversational GPT models are suggested for efficient text classification in finance, providing a practical solution for tasks with limited labels and achieving top-tier results.
2 shares36 citations todaySource ↗
A new deep learning algorithm has been developed to solve complex mathematical equations, offering improved accuracy and less complexity than similar models.
5 shares1 citation todaySource ↗
Financial Fraud Patterns: TimeTrail, a new technique for detecting financial fraud, uses advanced analysis to explain fraud patterns, outperforming traditional methods in accuracy and interpretability.
2 shares8 citations todaySource ↗
The research expands the transaction fee system in blockchain's proof-of-stake protocol, adding a long-term utility model for miners and a new parameter for user-miner incentives and contract validity.
9 shares9 citations todaySource ↗
The study presents explicit oracles for Grover's algorithm to align with investor preferences, possibly choosing portfolios with optimal Sharpe ratios, tested using quantum simulators.
4 shares1 citation todaySource ↗
The study extends the use of generative adversarial networks (GANs) to a full internal market risk model, suggesting that GAN-based models can be a data-driven alternative for market risk modeling.
31 shares22 citations todaySource ↗
The research examines the effect of correlated features on the interpretability of LSTM models for predicting oil company stocks, concluding that adding a correlated feature does not enhance the interpretability of these models.
22 shares6 citations todaySource ↗
The research investigates a differential Riccati equation with indefinite matrix coefficients, using it to address two algorithmic trading issues with a constant absolute risk-aversion utility function.
14 shares17 citations todaySource ↗
Mathematical Framework: The research introduces a new framework for Automated Market Makers, suggesting a unique fee structure and a novel AMM that reduces divergence loss.
19 shares25 citations todaySource ↗
The research uses singular perturbation methods to study optimal trading in a market with stochastic price impact, showing how stochastic trading frictions affect optimal trading through numerical experiments.
48 shares20 citations todaySource ↗
A novel continuous-time stochastic gradient descent method is developed for optimizing stochastic differential equation models, with potential applications in mathematical finance, including training stochastic point process models.
33 shares7 citations todaySource ↗
Working papers in finance and economics from SSRN.
31 items
The paper suggests a reinforcement learning approach for high-frequency algorithmic trading in futures market using news and price data, tested on the NIFTY 50 index.
2 sharesSource ↗
The paper presents a machine learning method to create synthetic cryptocurrency portfolios that mimic the risk-adjusted return profile of cryptocurrencies, providing a safer investment option.
3 sharesSource ↗
The paper finds that reported mutual fund returns in China are influenced by subscription and redemption activities, resulting in a lower actual gain coefficient for investors.
2 sharesSource ↗
The research compares various option strategies for Exchange-Traded Funds (ETFs) to the Buy and Hold approach, highlighting the need for strategies to match investor risk profiles and financial goals.
3 sharesSource ↗
The article introduces a formula for calculating single barrier options in a stochastic volatility framework, not dependent on the model's form or parameters.
46 sharesSource ↗
The research shows that machine learning models can be used to devise investment strategies and construct optimal portfolios, performing better than traditional strategies on the Mexican Stock Exchange.
5 sharesSource ↗
The study explores the impact of a CFO's risk-taking incentives and equity compensation on the hedging strategy of US oil and gas companies.
3 sharesSource ↗
The paper uses machine learning to predict US economic recessions using market sentiment and economic indicators, using the ARIMA method for backcasting.
2 shares9 citations todaySource ↗
The study indicates that banks meet timing requirements for discretionary hedging of fixed-income security and funding risks, but the effectiveness of these strategies is uncertain.
2 sharesSource ↗
Dollar Tree-Family Dollar Acquisition: The paper suggests a new empirical approach using a synthetic control method and machine learning to compare outcomes in mergers and acquisitions research.
134 sharesSource ↗
The paper explores the growing influence of financial markets and actors in the energy industry, a trend known as financialization and assetization.
3 sharesSource ↗
Despite high-risk assets and short-term leverage, Nonbank mortgage companies maintain low bankruptcy rates by quickly reducing operating costs and financing after negative shocks.
2 sharesSource ↗
The research proposes two ways to learn the price of derivatives using neural networks, focusing on price differences and differences between prices of derivatives based on different asset prices.
3 sharesSource ↗
The article suggests a method to calculate future hedging costs of complex financial products using a risk projection algorithm and simulations.
16 shares1 citation todaySource ↗
The article evaluates the reliability of commonly used indicators to identify high frequency traders, showing variations in performance and suggesting that unscaled proxies are more effective at indicating true HFT activity.
4 shares4 citations todaySource ↗
The article suggests a new method for creating characteristic-based equity factors called power sorting, showing its superior performance and applicability to multifactor strategies.
12 shares2 citations todaySource ↗
The research uses CDS and put option prices to gauge the likelihood and impact of climate change policy-related transition risk events, creating a reliable CDS-based index.
4 shares1 citation todaySource ↗
The research connects limited financial flexibility to levered risk premiums, emphasizing leverage gaps and targets, and argues that leverage alone provides limited insights.
3 sharesSource ↗
The study explores the negative correlation between past volatility and stock returns, finding that firms with higher volatility tend to lease more capital.
2 shares2 citations todaySource ↗
The article introduces new methods for asset selection in sparse index tracking portfolios, highlighting its benefits over traditional full replication portfolios.
2 shares1 citation todaySource ↗
The article challenges the belief that passive index funds strictly mimic their underlying index, showing that S&P 500 index funds do not legally commit to this.
4 sharesSource ↗
The paper presents a new text-based measure of disruptive innovation, developed through machine learning and textual analysis of IPO prospectuses, which accurately predicts IPO results and challenges the hype hypothesis about tech stocks.
2 shares2 citations todaySource ↗
High Returns and Outperformance: The research introduces a new data-driven method for statistical arbitrage that uses machine learning to maximize risk-adjusted returns, outperforming all benchmark approaches in a study on daily US equities.
2 sharesSource ↗
Machine learning models, especially random forests, are more effective than traditional models in predicting market trends and reducing errors, improving market-neutral strategies and minimum variance portfolios.
2 sharesSource ↗
Implications of Institutional Demand: The research indicates that corporate bond pricing is significantly affected by institutional demand, with different institutions having different preferences for maturity credit risk and liquidity.
1,744 sharesSource ↗
Differences in Returns: The study reveals that index-linked trading leads to differences in returns and volatilities, with firm fundamentals influencing risk premiums in expected returns.
241 sharesSource ↗
Impact on Endowment Choices: The research finds that university endowments tend to invest in private equity funds managed by their alumni, but this doesn't necessarily lead to better performance.
152 sharesSource ↗
Factors Affecting Stock Return Forecasts: The study shows that while machine learning strategies can predict short-term returns for small firms and early historical data, they have not provided significant economic gains for most of the U.S. market in the past 20 years.
7 sharesSource ↗
The correlation between short-term institutional demand and future stock returns is only significant in hedge funds, not in other institutions with short investment horizons.
72 sharesSource ↗
Pricing errors in the base asset can inflate options prices and affect option Greeks, leading to inefficient risk management and hedging if not considered.
2 sharesSource ↗
There's a balance between tracking error and turnover in rebalancing choices, with calendar-based methods being less efficient than tolerance bands, and no proof that rebalancing choices can consistently boost expected returns.
2 sharesSource ↗
Economics working papers from RePEc's NEP field reports.
9 items
Market Impact of Trading Strategies: The research investigates the trading strategies and market impact of the National Pension Service of Korea, identifying differences between internal and external management methods, with the latter serving as a market stabilizer and the former increasing volatility.
14 sharesSource ↗
The Light Gradient Boosting Machine model has been found to be the most effective at predicting 12 exchange rates due to its ability to extract short-term information and robustness on small datasets.
14 sharesSource ↗
Past & Present: The research confirms previous findings that stock returns are influenced by aggregate-volatility risk and idiosyncratic volatility, and suggests that recent asset-pricing models fail to consistently account for this, except for the models by Stambaugh and Yuan, and Barillas and Shanken.
22 sharesSource ↗
CDS Spreads & Equities: The research examines the volatility connection between the CDS and equity markets in the US, UK, EU, and Japan, finding that this connection is stronger during crisis periods and that equity is the main transmitter of volatility.
17 sharesSource ↗
Social media data and hybrid strategies combining econometrics and machine learning can enhance forecast accuracy in commercial sectors like the film industry.
22 sharesSource ↗
Machine learning can effectively predict housing price trends and variations, considering land use and transportation interactions, as evidenced in a Toronto and Hamilton Area study.
21 sharesSource ↗
Machine-learning methods, particularly the random-forest-regression-tree method, provide a more accurate measure of disclosure sentiment than dictionary-based methods.
13 sharesSource ↗
The suggested Factor-HGH model for the combined distribution of financial factors and asset returns shows benefits in capturing data stylized facts and enhancing portfolio performance, particularly with highly tail heterogeneous cryptocurrencies.
14 sharesSource ↗
Machine learning is enhancing the accuracy and reliability of Value at Risk (VaR), a tool used in risk management for estimating potential portfolio losses.
25 sharesSource ↗
Papers that shipped their code, from the Papers with Code feed (2023-25).
9 items
The article highlights the storage of scientific knowledge predominantly in books, scientific journals, and PDFs.
614 shares
Calibrated Quantization for LLMs: The article presents the OmniQuant technique for LLMs, which delivers strong performance in various quantization settings while preserving computational efficiency.
64 shares
The paper presents Prompt-2-Model, a technique that employs a natural language task description for training deployable models.
158 shares
The article explores the difficulties and potential future paths in a specific field, drawing on past research.
129 shares
The article delves into the application of context length extrapolation methods for managing longer sequences in models.
262 shares
Enhancing Math Reasoning with Language Models: The presented model excels in mathematical reasoning, performing exceptionally well on GSM8k and MATH benchmarks according to comprehensive tests.
6,294 shares
Foundation Models for Code: Code Llama, a series of advanced language models for coding, provides superior performance, supports extensive input contexts, and can execute programming tasks without prior training.
4,628 shares
Enhancing Zero-Shot Chain-ofT-hought Reasoning with Language Models: The quality of reasoning steps is enhanced and calculation mistakes are corrected by providing more detailed instructions in PS prompting.
4,019 shares
Map Pretraining for Navigation: The article details the creation of a local metric map to compile incomplete observations, eliminate duplicates, and model navigation dependency.
94 shares
Repositories the letter featured.
10 items
Explores a web app that integrates technical, fundamental research and sentiment analysis for investment trading.
94 shares
Introduces NVTabular, a tool for processing large-scale tabular data in deep learning-based recommendation systems.
946 shares
Provides a detailed list of resources for understanding and implementing Deep Hedging.
37 shares
Discusses a Python library specifically designed for evaluating option trading strategies.
7 shares
Presents a collection of tools and examples for Quantitative Analysis, including MonteCarlo Simulations, Linear Regression, and TimeSeries Analysis.
10 shares
Python programming language can be integrated and utilized in Microsoft Excel.
271 shares
Python library that allows for the simulation and visualization of random processes.
59 shares
Investigates the use of large language models in automatically generating visualizations and infographics.
820 shares
Official implementation of the Graph of Thoughts method, which uses large language models to solve complex problems.
556 shares
Industry news: funds, hiring, markets and regulation.
3 items
Bessemer Group Inc. has recently acquired a new stock market asset on August 24, 2023.
2 shares
Randel Freeman has been endorsed by an activist investor group to lead a Swiss asset manager.
2 shares
The commission mandates all advisors to review and record their compliance policies and procedures every year.
1 shares
Episodes on markets, quant methods and economics.
3 items
Professor Zoro and Asim Turk discuss the MultiFactor Investing Model, covering topics such as data manipulation for stocks, multiple linear regression for returns, and result visualization.
1 shares
Aahan Menon from Prometheus Investment Research talks about their macro framework, liquidity, and the potential for further growth in the bond market.
2 shares
The rise of AI-powered code generation is causing a decline in software architecture quality, leading to questions about design responsibility.
0 shares
Posts from quant researchers on X.
18 items
Machine learning is used to accurately forecast mutual fund alphas, with fund momentum and investor sentiment as main indicators.
5 shares
A machine learning model shows that the variation in investors' beliefs significantly forecasts stock returns.
5 shares
Lecture notes discuss empirical asset pricing, including return predictability, volatility, interest rates, and other topics.
4 shares
Micaletti uses Sentix survey sentiment measures and machine learning to predict stock returns based on investor sentiment.
4 shares
Micaletti employs Sentix survey sentiment data and machine learning to forecast stock returns from investor sentiment.
4 shares
ManGroup's blog post discusses the use of Shapley values in interpreting machine learning models.
4 shares
The article suggests that using VIX for volatility scaling can enhance equity strategies performance, especially post-transaction costs.
3 shares
QuantInsti's blog post discusses the application of convolutional neural networks in trading and suggests further reading on the topic.
3 shares
The article introduces XGenTimeSeries, a tool that improves understanding of data augmentation in energy time series applications.
2 shares
Jonathan Kinlay's paper examines the pros and cons of creating synthetic market data for trading strategies.
2 shares
The article reports on a study that identifies the Sentix Survey's Treasury market sentiment index as a key forecaster of US bond returns.
2 shares
The paper studies the factors influencing investor overconfidence, using data from the UBS-Gallup Investor Optimism Survey.
2 shares
The article presents David Blitz from Robeco's insights on quantitative investing in emerging markets.
1 shares
The article analyzes the correlation between business confidence fluctuations and commodity futures returns.
1 shares
The article investigates the predictability of returns using four different definitions of earnings acceleration.
0 shares
The article introduces continuous statistical jump models as a method for identifying various market conditions.
0 shares
Threads from r/quant, r/algotrading and friends.
2 items