Quant Letter: April 2024, Week-2

Weekly (41st Edition)

SSRN

Recently Published

Quantitative

Machine Learning for CAT Bond Pricing: The study introduces a machine learning approach for pricing catastrophe bonds, offering more accuracy and robustness than conventional methods, and highlighting key nonlinear relationships between risk factors and bond spreads. (2024-04-10, shares: 2.0)

Quantum Algorithm for Investment Strategy Testing: A quantum algorithm is shown to outperform traditional algorithms in classifying probability distributions in quantitative finance, offering superior discriminatory power and linear scaling with data samples. (2024-04-09, shares: 3.0)

Options Strategies for ETFs: The research examines various option strategies for ETFs, finding their effectiveness varies based on the risk profile of the underlying asset, with some strategies potentially improving risk-adjusted returns. (2024-04-07, shares: 3.0)

Negative Premium in A-Share Market: The research reveals that stocks with higher volatility have significantly lower returns, an anomaly that can't be explained by market volatility or ambiguity aversion. (2024-04-04, shares: 2.0)

Reconsidering ML: The article critiques the term 'machine learning', arguing it doesn't accurately represent machine algorithms and suggests a reevaluation of the narratives around these technologies. (2024-04-04, shares: 18.0)

Financial

Bank Complexity Measurement Using XAI: A machine learning technique shows a link between the complexity and opacity of banks, with complex firms seeing decreased trading activity. (2024-04-05, shares: 13.0)

Bond and Equity ETF Hidden Liquidity: A study shows hidden liquidity is higher for bond ETFs than equity ETFs, with trading costs and informed trading probability not explaining the difference. (2024-04-09, shares: 2.0)

Asset Pricing with Cognitive Biases: A deep learning model shows cognitive biases can explain the equity premium puzzle and excess volatility puzzle in asset pricing. (2024-04-07, shares: 6.0)

Interval Data for FX Volatility Forecasting: The research explores the use of interval-valued data in foreign exchange markets to enhance volatility forecasts, utilizing threshold autoregressive interval models for four major exchange rates. (2024-04-05, shares: 2.0)

Pairs Trading in the German Stock Market: The study uses various methods to identify and leverage mispricing in the German stock market, revealing that a copula-based method provides a consistent average portfolio return after transaction costs. (2024-04-05, shares: 2.0)

ESGInvesting: A Psychological Phenomenon: The study expands traditional portfolio selection and asset pricing theory to include ESG investing, introducing two behavioral innovations related to investor preferences and biased judgments about ESG impact and return distributions. (2024-04-06, shares: 2.0)

Recently Updated

Quantitative

Nonparametric Time Series Bounds: A study explores the properties of empirical risk minimization for time series, focusing on predicting a univariate time series belonging to a class of location-scale parameter-driven processes. (2021-08-13, shares: 148.0)

Model Risk Management for AI: An article suggests that model risk management can be used in national systemic and cyber risk projects like Project Maven, to transition from AI automation to AI augmentation. (2022-05-05, shares: 2.0)

Multi-Country Macroeconomic Monitoring: A new model accounting for differences across countries and macroeconomic time series effectively assesses the impact of global shocks on country-level macroeconomic risks. (2023-05-23, shares: 2.0)

Volatility Risk Pricing: The paper recommends using variance-dependent pricing kernels for option valuation, as they resolve anomalies, fit options well, and provide accurate estimates of equity and variance risk premiums. (2022-02-08, shares: 2.0)

Financial

ESG Impact on Private Equity Fundraising: A study reveals that private equity advisers who disclose negative environmental information in regulatory filings struggle to raise capital. (2023-09-09, shares: 237.0)

Economic Narratives in Portfolio Management: Machine learning models incorporating economic narratives into market portfolio management have been found to outperform benchmarks, particularly during recessions and high investor sentiment periods. (2023-11-16, shares: 275.0)

European Institutional Portfolio Comparison: A study of 800 European institutional portfolios shows they are primarily equity risk-focused, have lower carbon emissions but also lower ESG scores compared to the MSCI ACWI equity benchmark. (2024-03-06, shares: 17.0)

Factor Model for Private Company Valuation: A new method for valuing private companies in private equity, based on actual transactions, can enhance asset allocation and risk management in private markets. (2024-01-19, shares: 2.0)

High Frequency Risk Pricing for Real Estate Securities: A paper has found that the pricing of default interest rate, liquidity, and excess liquidity risks for securitized commercial real estate securities can yield significant returns, with the first estimates made at intraday frequencies. (2024-02-12, shares: 2.0)

Customizing Allocation: The article presents a strong framework for customizing asset allocation portfolios, emphasizing the advantages of automation and transparency in portfolio construction. (2021-09-16, shares: 2.0)

AI Consensus on Pricing: The article discusses the disagreement among machine learning models about which factors affect returns, suggesting that a unified model is impossible with current data. (2021-11-23, shares: 2.0)

Value of Alternative Data: The article investigates the influence of social media data on mutual fund managers' decisions and performance, indicating that using such data improves future returns and stock-picking skills. (2024-03-04, shares: 4.0)

Machine Learning for Forecasting: The article shows the superior accuracy of machine learning in predicting dividends, highlighting its effectiveness in complex information structures and potential influence on corporate finance and investment decisions. (2024-04-02, shares: 2.0)

Subjectivity in Selection: The article delves into the principles of active portfolio selection, emphasizing the potential of subjectivity in improving portfolio performance and the efficiency of a unified active portfolio selection framework. (2022-09-16, shares: 2.0)

ArXiv

Finance

Coherent Risk Measures: The study links coherent risk measures in finance with uniform integrability in probability theory, using a tool called the folding score of distortion risk measures. (2024-04-04, shares: 6)

Social Media Emotions: The research finds that investor emotions expressed on social media can predict daily asset price movements, particularly in low liquidity or high short interest situations. (2024-04-04, shares: 3)

The PEAL Method: The paper introduces the PEAL Method, a mathematical framework for structuring securitizations, aimed at improving market transparency, regulatory oversight, and risk management. (2024-04-08, shares: 3)

Improved Multi-Asset Options Bounds: The first article explores the calculation of model-free bounds for multi-asset options, emphasizing the importance of prioritizing relevant information for accuracy and efficiency. (2024-04-02, shares: 4)

StockGPT: The study introduces StockGPT, a model that predicts stock return dynamics using AI, showcasing the potential of AI in complex financial investment decisions. (2024-04-07, shares: 3)

Generalized Black-Scholes Equation: The research presents a generalized version of the Black-Scholes model, considering option price dynamics to depend on a measure representing investors' uncertainty. (2024-04-08, shares: 2)

Miscellaneous

Judgment in US Output Growth: The study finds that while US output growth rate predictions are generally unbiased, the use of judgment does not necessarily improve their accuracy. (2024-04-05, shares: 2)

Enhanced Electricity Price Forecasting: The study reveals that LQ and elastic net penalty functions provide more precise electricity price predictions than other methods, including the popular LASSO. It also confirms that cross-validation is a useful tool for optimizing parameters. (2024-04-05, shares: 2)

Crypto & Blockchain

Cryptocurrency Volatility: The paper analyzes the factors influencing cryptocurrency volatility from 2020 to 2022, highlighting that positive market returns, positive signed volatility, and negative daily leverage increase price volatility. (2024-04-07, shares: 8)

Fee Choice in AMMs: The study explores the workings of arbitrage in decentralized finance automated market makers (AMMs), aiming to understand how AMMs can optimize revenue or minimize losses, and models the dynamics of arbitrage activity. (2024-04-05, shares: 6)

ArXiv ML

Recently Published

AutoWebGLM: Better Web Navigation: AutoWebGLM is a new web navigation tool that surpasses GPT-4 in performance, using a unique HTML simplification algorithm and a combined human-AI method to enhance webpage understanding and browser functionality, tested using a bilingual benchmark. (2024-04-04, shares: 7)

Evaluating Adversarial Robustness: The study investigates adversarial attacks on Deep Neural Networks for image classification, highlighting the Fast Gradient Sign Method and the Carlini-Wagner approach, and suggests defensive distillation as a defense, effective against FGSM but vulnerable to CW attacks. (2024-04-05, shares: 7)

player2vec: Player Behavior in Games: A new technique for learning hidden user profiles from player behavior data in video and mobile games is presented, utilizing a long-range Transformer model from natural language processing, showing promising results in matching behavior event distribution. (2024-04-05, shares: 7)

Spatial Reasoning in LLMs with Visualization: The paper presents Visualization-of-Thought prompting, a method that improves the spatial reasoning abilities of large language models by visualizing their thought processes. (2024-04-04, shares: 14)

Historical Trending

EditFriendly Noise Space: A novel latent noise space has been proposed for denoising diffusion probabilistic models, enabling a variety of image editing operations and perfect image reconstruction. (2023-04-12, shares: 84)

NeuralCSA: Causal Sensitivity Analysis: The article introduces NeuralCSA, a new neural framework for analyzing causal sensitivity, capable of handling various models, treatments, and queries, and providing accurate causal query bounds. (2023-11-27, shares: 54)

Ziya: Data-centric Learning for LLMs: The authors present Ziya2, a large language model with 13 billion parameters, focusing on pre-training techniques and data-centric optimization, outperforming other models in multiple benchmarks. (2023-11-06, shares: 19)

KV Cache Quantization: KVQuant, a new method for quantizing cached KV activations in large language models, has been developed, allowing the LLaMA-7B model to be served on an 8-GPU system with minimal degradation. (2024-01-31, shares: 64)

ChatGLM-Math: Problem-Solving in LLMs: The authors propose a Self-Critique pipeline to enhance the mathematical problem-solving skills of large language models without affecting their language abilities, showing significant improvements in both areas. (2024-04-03, shares: 47)

RePec

Finance

OR Insights on Finance: The article explores the use of operational research techniques like stochastic programming and machine learning to comprehend risks in financial and economic systems. (2024-04-10, shares: 18.0)

Sectoral Volatility Contagion: The study examines the structure of risk contagion across sectors, emphasizing the need for accurate identification of this structure for effective regulation. (2024-04-10, shares: 14.0)

Insider Silence Trading Strategies: The research finds that trading strategies based on insider trading, specifically buying insider purchases and selling insider sales, perform better over extended periods. (2024-04-10, shares: 12.0)

Portfolio Optimization with Pareto-Dirichlet Method: The article presents a new portfolio optimization method, the Pareto–Dirichlet approach, which surpasses other methods in speed and precision. (2024-04-10, shares: 11.0)

Forecasting CPI with Multisource Data: The research uses Chinese news and Internet search data to enhance the accuracy and timeliness of CPI forecasting, emphasizing the significance of alternative data in economic downturns or uncertain times. (2024-04-10, shares: 9.0)

Auto Insurance Risk Management: Value of Vehicles: The article criticizes traditional vehicle insurance pricing methods as outdated, proposing a shift towards flexible price-to-value methods that consider the actual features and values of vehicles. (2024-04-10, shares: 8.0)

Statistical

Predicting Systemic Financial Risk: Research suggests that machine learning models and financial stress index can effectively predict systemic financial risk, with stock and money markets being the most influential. (2024-04-10, shares: 26.0)

Innovative ML Workflow for China's Financial Crisis: A study introduces a multistep workflow using machine learning models and macroeconomic indicators to predict China's systemic financial crisis, accurately identifying six risk periods from 1990 to 2020. (2024-04-10, shares: 15.0)

Trip Misreporting in Household Travel Survey: The study uses a data-driven method to detect trip misreporting in household travel surveys in Suzhou, China, by incorporating mobile phone signaling data, showing that 23% of total trips were not recorded due to misreporting. (2024-04-10, shares: 8.0)

Machine Learning

Data Sensitivity in Machine Learning Reuse: The article explores the difficulties in reusing machine learning applications due to data sensitivity and domain specificity, categorizing applications into four types based on reuse strategies. (2024-04-10, shares: 23.0)

CostSensitive Machine Learning for Investments: The study employs cost-sensitive machine learning models to predict startup success, potentially reducing investor risk but possibly limiting gains, and proposes ways to improve successful startup detection. (2024-04-10, shares: 22.0)

US House Price Dynamics: The article presents a new estimator that includes cross-sectional heterogeneity and dependency in machine learning, greatly enhancing the prediction of house prices and detection of housing market bubbles. (2024-04-10, shares: 8.0)

GitHub

Finance

Automated Bug Fixing: SWEagent uses advanced language models like GPT4 to automatically rectify GitHub issues, successfully resolving 12.29% of bugs in the SWEbench test set within 1.5 minutes. (2024-04-02, shares: 8511.0)

StockFormer Implementation: The article showcases a PyTorch implementation of the paper StockFormer: Learning Hybrid Trading Machines with Predictive Coding. (2023-07-30, shares: 62.0)

Machine Learning Models: The article explores quantified machine learning, deep learning models, Alpha factors, quantified resources, and provides related paper codes. (2023-11-06, shares: 57.0)

Unified Time Series Forecasting: The article presents a unified training method for universal time series forecasting transformers. (2024-02-07, shares: 283.0)

Trending

LocalSearch: The article explains LLocalSearch, a local search aggregator that uses LLM Agents for user queries, eliminating the need for OpenAI or Google API keys. (2024-03-23, shares: 4226.0)

Python Client: The piece introduces a Python client specifically designed for QuestDB's InfluxDB Line Protocol. (2022-06-13, shares: 47.0)

Karpathy LLM: The article discusses the process of training LLMs in raw CCUDA, a straightforward programming language. (2024-04-08, shares: 8604.0)

Rust Market Simulation: The piece unveils a Rust Market Simulation Library equipped with a Python API. (2024-02-20, shares: 13.0)

LinkedIn

Trending

CFM Women in Quantitative Finance Scholarship: The École Polytechnique is accepting applications for the CFM Women in Quantitative Finance scholarship, supporting a deserving candidate's research in quantitative finance. (2024-04-10, shares: 1.0)

New TTM Models for Forecasting: IBM Research has made TinyTimeMixers, compact pre-trained models for Time-Series Forecasting, available to the public. (2024-04-09, shares: 1.0)

Mandelbrot's Financial Contributions: Benoit Mandelbrot revolutionized financial risk management with new concepts such as heavy tails, fractal markets, and the Multifractal Model of Asset Returns. (2024-04-10, shares: 2.0)

Podcasts

Quantitative

Alpha Mining with Bogdan: AlphaCube's Patrick Zoro and Bogdan Ivaniuk discuss their alpha mining algorithm, capable of generating 40 million strategies daily on a single CPU. (2024-04-05, shares: 13)

Cliff Asness Investment: Cliff Asness of AQR Capital Management talks about diversification, expectation management, and the use of AI in investment processes in a podcast. (2024-04-05, shares: 12)

Global FX Flow: FX Strategists Patrick Locke, James Nelligan, and Ladislav Jankovic discuss USD & G10 FX trends, the impact of higher oil and weaker CAD data on the dollar, and the potential for CHF depreciation. (2024-04-05, shares: 7)

Twitter

Quantitative

Craftsmanship Alpha Revisited: Article 1: The piece highlights the crucial role of craftsmanship alpha in developing and applying investment styles or beta exposures. (2024-04-07, shares: 6)

Machine Learning and Asset Pricing Notes: Article 2: The article offers detailed lecture notes on the convergence of machine learning and asset pricing. (2024-04-07, shares: 5)

Quantitative Macro Machine Learning: Article 3: The article investigates the use of machine learning in the field of quantitative macroeconomics. (2024-04-06, shares: 5)

Jamie Dimon AI augments JPMorgan jobs: JPMorgan has over 2000 AI and machine learning professionals on its payroll. (2024-04-08, shares: 4)

Foundation Models for Time Series Forecasting: The article explores the creation of Chronos, a forecasting model for time series data, built on language model structures. (2024-04-09, shares: 3)

Macro Condition Index Predicts Market Returns: The paper introduces a macro condition index, derived from aggregated macro forecasts, that predicts market trends and behaves countercyclically. (2024-04-10, shares: 2)

Valuation and Security Analysis Lecture Notes: The article presents lecture notes by Bhaskaran Swaminathan on the topics of valuation and security analysis. (2024-04-09, shares: 2)

Weekly Quant Research Recap Subscription: The article introduces a new weekly summary featuring selected quantitative research on investment, macroeconomics, and trading. (2024-04-09, shares: 2)

Miscellaneous

Bitcoin Halving Forecast: Article 1: ManGroup suggests the impending Bitcoin halving may influence the cryptocurrency's value. (2024-04-09, shares: 1)

Time Series Forecasting: Article 2: TimeLLM, a new time series forecasting method that reprograms large language models, has been released on GitHub. (2024-04-06, shares: 1)

Information Asymmetry Modeling: Article 3: A recent study explores the dynamics between regular-speed traders and high-frequency traders, emphasizing the information imbalance in trading. (2024-04-06, shares: 1)

Transformers for Forecasting: The article provides a guide on using transformers for timeseries forecasting, with a Jupyter notebook example. (2024-04-09, shares: 0)

Matturck 2024 AI Map: Matturck's 2024 AI Market Map showcases 2011 company logos and highlights new companies in different AI fields. (2024-04-08, shares: 0)

New Episode with Asness: The latest episode discusses global diversification, AI, and inefficient markets with Clifford Asness. (2024-04-05, shares: 0)

LLM Papers with Code Collection: The article shares a selection of LLM Papers, often providing accompanying code for reference and implementation. (2024-04-04, shares: 0)

ABFR Webinar Markus K.: Markus K. Brunnermeier from Princeton University is set to speak about Strategic Money and Credit Ledgers at an ABFR seminar, a group interested in AI and big data in economics and finance. (2024-04-06, shares: 0.0)

Applying Kalman Filter to Financial Data: The author investigates the application of Kalman filters in streamlining price data with minimal lag, but expresses skepticism about the appropriateness of employing a physical motion model in financial data. (2024-04-09, shares: 5)

Paper with Code

Trending

Unified Structure Learning for Visual Document Understanding: The article highlights the importance of structure information in Visual Document Understanding and presents Unified Structure Learning as a method to improve MLLMs performance. (2024-04-06, shares: 673.0)

UniTable: Table Structure Recognition Framework: The factual and quantitative data in tables, along with human-made conventions, make them difficult for machines to interpret. (2024-04-09, shares: 78.0)

AutoWebGLM: Web Navigation Model: Large language models have difficulty processing real-world webpages due to the diverse actions, extensive HTML text, and complex decision-making involved. (2024-04-08, shares: 85.0)

Advancing LLM Reasoning with Trees: Eurus is a collection of large language models specifically designed to handle reasoning tasks. (2024-04-08, shares: 83.0)

Mamba Model Change Detection: The article presents three mechanisms for modeling spatiotemporal relationships in change decoders, which can be integrated with the Mamba architecture for precise change information. (2024-04-09, shares: 67.0)

Quant Letter: April 2024, Week-1

Weekly (40th Edition)

ArXiv

Finance

RL Agents in Market Simulation: The research introduces a market simulation framework using reinforcement learning agents that can mimic real-world market dynamics and adapt to major market events. (2024-03-28, shares: 2)

Curb Appeal with Deep Learning: Incorporating image data into econometric models through deep learning enhances the accuracy of residential real estate price predictions. (2024-03-29, shares: 4)

Optimal Rebalancing in AMMs: A new method for optimally rebalancing asset ratios in Dynamic Automated Market Maker pools could potentially increase pool profit and loss by about 25% for a BTC-ETH-DAI pool from July 2022 to June 2023. (2024-03-27, shares: 4)

Revisiting String Models of Interest Rates: A revised model of the forward interest rate curve, considering market forces and return correlation, accurately replicates the curve's correlation structure from 1994-2023 with less than 2% error, confirming that perceived time in interest rate markets is a sub-linear function of real time. (2024-03-26, shares: 3)

Neural Networks for Finance: The study investigates the use of supervised autoencoders in improving financial forecasting through precise parameter tuning. (2024-04-02, shares: 3)

SSRN

Recently Published

Quantitative

Enhanced Equity Market Strategy: The article introduces a novel stock market strategy that enhances performance by merging a financial stress indicator with sentiment analysis. (2024-04-02, shares: 29.0)

Extending Financial Portfolio Methods with Deep RL: The paper suggests that deep reinforcement learning can potentially improve traditional portfolio allocation strategies by incorporating contextual data and future rewards. (2024-04-01, shares: 2.0)

Aggregating Autoencoders for Persistent Access Threats: The article presents AEAPT, a deep learning method for detecting and isolating long-term, undetected cyberattacks. (2024-04-02, shares: 6.0)

Uncertainty in Sentiment Analysis: The study explores the uncertainty in sentiment scores derived from text using advanced language processing models, finding moderate uncertainty in the results. (2024-04-01, shares: 14.0)

Portfolio Replication Insights: The paper introduces a new method for decoding investment portfolio strategies using Dynamic Bayesian Graphical Models, resulting in better portfolio allocation decisions and adaptability to various market conditions. (2024-04-01, shares: 12.0)

Financial

Forecasting Trading Costs: The research analyzes trading costs, revealing that large, complex trades can be executed affordably and that trade risk value and complexity extend trade horizons. (2024-04-02, shares: 17.0)

Pricing Liquidity Risk: The research investigates the pricing of liquidity factors in the US stock market, demonstrating that models with a liquidity factor outperform those with a size factor. (2024-03-30, shares: 5.0)

Extreme Liquidity: The article proposes a crypto asset portfolio model that adjusts for liquidity to improve effectiveness and reduce discontinuity. (2024-03-30, shares: 2.0)

US Treasury Yield Forecast: The study introduces a forecasting model for predicting the 10-Year US Treasury Yield based on variables like exchange rates and crude oil prices. (2024-04-01, shares: 7.0)

Intellectual Capital Growth Modeling: The paper discusses a new funding mechanism that uses intellectual capital money to stimulate the generation and exploitation of intellectual capital. (2024-03-31, shares: 3.0)

Market Clearing: The article investigates the role of firms in providing shares to passive investors, particularly in response to index funds' buying. (2024-03-29, shares: 6.0)

Recently Updated

Quantitative

Equity Premium Forecasting: Machine learning techniques, while effective in predicting equity premium within sample, struggle to beat the historical average in out-of-sample predictions. (2023-08-06, shares: 3.0)

China’s Inflation Rate Forecasting: Eight machine learning models, notably the gradient boost decision tree and forecast combination model, excel in predicting China's inflation rate over the autoregressive benchmark. (2024-03-24, shares: 4.0)

Predicting Beta: Machine Learning algorithms enhance the precision of estimating equity betas for private or nontraded assets, particularly for smaller, younger firms with unique capital structures. (2024-01-01, shares: 2.0)

Deep News Sentiment for Finance: The article discusses the use of neural networks to extract hidden economic factors from large news analytics data, showing superior performance in GDP growth forecasting and asset return analysis. (2023-09-12, shares: 2.0)

Behavioral Diversification in Portfolios: The article introduces a new simulation of a diversified portfolio based on consumer products, using linear regression and Monte Carlo Simulation, advocating for a consumer-behavior approach in portfolio structuring. (2023-08-07, shares: 2.0)

Comparative Study of Portfolio Risk Management: The article suggests a new method for portfolio risk management and capital allocation, combining value-at-risk with other statistical measures, proving its effectiveness in reducing potential portfolio losses. (2023-11-01, shares: 2.0)

Arbitrage Risk in MAX Effect: In the Korean stock market, the MAX effect, or the highest daily return from the previous month, is only significant in overpriced stock groups. (2022-05-20, shares: 28.0)

Financial

Global US Stock Integration: The research finds that US stocks with less global integration can improve portfolio diversification and match international index portfolios in risk-adjusted returns and tail risk. (2023-08-03, shares: 86.0)

Asymmetric Info in Asset Markets: The study suggests that reducing information asymmetry in secondary asset markets could potentially harm economic welfare. (2022-08-19, shares: 152.0)

PL Attribution Options: The paper disputes the belief that the gap between implied and realized volatility is the main factor in profit and loss for delta-hedged options, proposing a new formula for understanding this difference. (2023-07-07, shares: 169.0)

Strategic Mutual Fund Convergence: The research finds a trend towards similar allocation strategies in equity mutual funds globally, especially among funds managed by large financial institutions. (2022-04-29, shares: 89.0)

Forecasting TSEC Volatility: The study compares GARCH family models and EWMA models to identify the best algorithm for predicting volatility in Taiwan's stock market, using data from 1997 to 2023. (2023-12-31, shares: 2.0)

Issues with Implied Volatilities: OptionMetrics records stock options prices at 359 p.m., not 400 p.m., causing changes in implied volatility spreads and affecting stock comovement, especially during the COVID-19 pandemic. (2022-03-22, shares: 2.0)

Improved Volatility Strategy: An enhanced strategy for volatility-managed portfolios, based on Moreira and Muir 2017's formation, results in significant real-time performance improvement, including 148 Sharpe ratio increases and 165 positive abnormal returns. (2023-08-25, shares: 2.0)

Domain Influence on Diversification: A study of 251 US retail investors found diversification errors in the gain domain but not in the loss domain, supporting a loss-attention hypothesis. (2021-08-27, shares: 2.0)

False Discoveries in Currency Analysis: A new method, robust to data dependence and estimation errors, is developed to assess predictive models' performance, when applied to currency technical trading rules, it yields a Sharpe ratio around one for about 50 years. (2024-03-06, shares: 2.0)

Media Sentiment and Asset Allocation: US media sentiment about foreign countries affects domestic investors' international asset allocation, with negative media coverage leading to reduced flows to international mutual funds targeting the country. (2023-05-09, shares: 2.0)

ArXiv ML

Recently Published

Rashomon Partitions: Estimating Heterogeneity: The study introduces Rashomon Partition Sets, a new method for partitioning covariate space in statistical analyses, which includes all partitions with posterior values near the maximum, allowing for more robust conclusions. (2024-04-02, shares: 8)

Gecko: Compact Text Embeddings: Gecko is a new text embedding model that improves knowledge extraction from large language models, surpassing other models in the Massive Text Embedding Benchmark. (2024-03-29, shares: 172)

Historical Trending

LightGaussian Compression: LightGaussian is a new method that converts 3D Gaussians into a more compact format, enhancing efficiency in real-time neural rendering and reducing storage needs. (2023-11-28, shares: 519)

Longform Factuality: The Search-Augmented Factuality Evaluator (SAFE) method uses large language models to assess the accuracy of long-form factual content, achieving superior rating performance. (2024-03-27, shares: 323)

FP Deep Learning Quantization: A study finds that FP8 data formats are superior to INT8 in post-training quantization, offering better workload coverage, model accuracy, and versatility across various network architectures. (2023-09-26, shares: 100)

Visual LVLM Grounding: The Rephrase, Augment and Reason (RepARe) framework enhances the performance of large vision-language models in zero-shot tasks by rephrasing questions and extracting image details. (2023-10-09, shares: 127)

Unsupervised Diffusion Segmentation: A new method using self-attention layers in stable diffusion models achieves superior zero-shot segmentation without annotations, outperforming previous methods on the COCO-Stuff-27 dataset. (2023-08-24, shares: 107)

Riemannian Laplace Approximation: A recent improvement to the Laplace Approximation, which uses a Gaussian distribution to approximate a target density, corrects previous biases and narrow approximations, leading to practical improvements in experiments. (2023-11-05, shares: 75)

RePec

Finance

Global Impact on Volatility Persistence: Global factors significantly influence the local volatility persistence in equity indices of 17 developed economies. (2024-04-03, shares: 24.0)

Insider Trading Strategies: Seyhun's 1986 study indicates that insider buying often leads to positive future returns, while insider selling slightly hints at negative returns, possibly due to liquidity needs. (2024-04-03, shares: 12.0)

Accruals-Cash Flow Evaluation: This research clarifies misconceptions about the role of accruals in informative earnings, introducing a new analysis that recognizes non-cash accruals as parts of earnings that do not involve cash flows. (2024-04-03, shares: 9.0)

Forecasting CPI: The study enhances the precision and promptness of Consumer Price Index (CPI) forecasts by using a large Chinese news corpus and Internet search data, and combining penalized regression and mixed-frequency data sampling methods. (2024-04-03, shares: 9.0)

GitHub

Finance

Unified Time Series Forecasting Transformers: The piece investigates a combined training approach for universal time series forecasting transformers. (2024-02-07, shares: 283.0)

PyTorch Implementation for StockFormer: The article showcases a PyTorch implementation of the StockFormer paper, which studies hybrid trading machines using predictive coding. (2023-07-30, shares: 62.0)

Lightweight LLM Evaluation Suite: The article presents LightEval, a lightweight evaluation suite for LLM, used by Hugging Face along with the new LLM data processing library datatrove and LLM training library nanotron. (2024-01-26, shares: 267.0)

Python Package for TradingView Screeners: The piece explores a Python package that enables users to develop TradingView screeners. (2022-05-30, shares: 112.0)

Python Library for Nasdaq Data Links API: The article talks about a Python library that enables access to Nasdaq Data Links' RESTful API. (2021-11-02, shares: 379.0)

Trending

Python QuestDB InfluxDB Client: A Python client has been created for QuestDB's InfluxDB Line Protocol. (2022-06-13, shares: 47.0)

Rust Market Simulation Library: A Rust-based market simulation library with a Python API has been developed. (2024-02-20, shares: 13.0)

Local and API Model Experiments: Local and API-available models are being used in ongoing experiments. (2024-01-13, shares: 601.0)

LinkedIn

Trending

New Publication on Predictability: A new publication focusing on predictability and robustness for a special issue of Econometrics and Statistics has been released, featuring contributions from Lorenzo Camponovo and Fabio Trojani. (2024-04-03, shares: 2.0)

Fair Value Option Model: Thomas Feng from Graham Capital Management lectured at Columbia University on creating an option model for fair value determination across volatility term structures, useful for trading strategies. (2024-04-03, shares: 2.0)

Synthetic Data in Finance: The use of synthetic data is growing in quantitative finance due to its ability to improve modeling, analysis, and testing, while overcoming real-world financial data limitations. (2024-04-03, shares: 2.0)

Ilia Bouchouev Virtual Barrels Talk: Commodities trader Ilia Bouchouev will discuss his new book Virtual Barrels: Quantitative Trading in the Oil Market at a Thalesians Ltd event in London. (2024-04-02, shares: 2.0)

Risk Management in Trading: Will McBride and Dmitry Pargamanik emphasize the significance of risk management tools in option trading, following their webinar on using an options profit calculator. (2024-03-28, shares: 4)

Traders Use Weather Derivatives: Increased extreme climate events are causing a rise in weather trading. (2024-04-02, shares: 3)

Twitter

Quantitative

Turing Institute Report on Language Models in Finance: The report from the Alan Turning Institute explores the application of large language models in finance. (2024-04-02, shares: 0)

Machine Learning for Merger Arbitrage: A recent study utilizes machine learning to enhance the success and potential returns of merger arbitrage trades. (2024-03-29, shares: 8)

Using Option Market Data to Predict ETF Returns: The research paper proposes a novel trading strategy, suggesting that alterations in the implied volatility of ETF options can forecast returns on the underlying ETF. (2024-04-01, shares: 7)

Weekly Recap of New Research in Finance: The latest weekly summary emphasizes new research in fields like asset pricing, machine learning, market microstructure, and options. (2024-04-02, shares: 6)

Machine Learning and Data Science Notes: Ott Toomet from the University of Washington shares extensive lecture notes on machine learning and data science. (2024-04-01, shares: 5)

Equity Risk Premium Update: Aswath Damodaran's 2024 paper update explores the elements affecting the equity risk premium and ways to calculate it. (2024-04-01, shares: 2)

Miscellaneous

ML DL, AI in Asset Management: Article 1: The article explores the use of Machine Learning, Deep Learning, and AI in the field of Asset Management. (2024-04-02, shares: 1)

Mathematics of Neural Networks: Article 2: Bart Smets of Eindhoven University shares lecture notes on the mathematical aspects of Neural Networks for advanced students. (2024-03-30, shares: 1)

Momentum Goes Vertical: Article 3: The article reports on a notable surge in momentum. (2024-04-03, shares: 0)

Generative AI Language Model Market Map: The article introduces the fast-expanding Generative AI Large Language Model Infrastructure Stack and its market landscape. (2024-04-02, shares: 0)

Info for Traders: The article provides useful information for individuals involved in trading. (2024-03-30, shares: 0)

Linear Algebra Review: The article provides a comprehensive review of the subject of linear algebra. (2024-03-29, shares: 0)

Quant Letter: March 2024, Week-3

Weekly (38th Edition)

ArXiv

Finance

Reinforcement Learning for Arbitrage: The study presents a new framework for statistical arbitrage that uses reinforcement learning to optimize asset coefficients and identify the best mean reversion strategies. (2024-03-18, shares: 6)

Deep Learning for Order Book Forecasting: The research applies deep learning techniques to predict mid-price changes for NASDAQ-traded stocks, providing a framework to evaluate the feasibility of these predictions. (2024-03-14, shares: 4)

Rough Volatility and Premium: The paper examines the effect of unpredictable risk on pricing in a rough volatility model, emphasizing the random nature of the market price of volatility risk. (2024-03-18, shares: 3)

Robust Utility Maximization: The study investigates maximizing utility in a frictionless market, introducing projectively measurable functions and proving the existence of an optimal investment strategy under certain conditions. (2024-03-18, shares: 3)

Optimal Portfolio Choice: The research looks at optimal portfolio choices in continuous time, considering the impact of transactions on prices and providing solutions to optimal portfolio and execution problems. (2024-03-15, shares: 3)

MeanField Game Market Entry: The paper explores optimal portfolio liquidation in games with unchangeable trading direction, proving the existence of a unique equilibrium in both mean-field and N-player games. (2024-03-15, shares: 2)

Economics

Composite Likelihood for Gaussian Processes: The first article discusses a framework for understanding parametric continuous-time stationary Gaussian processes, applied successfully to models describing the random log-spot variance of financial asset returns, including cryptocurrency. (2024-03-19, shares: 3)

Signature Kernel Solver for Path-Dependent PDEs: The article introduces a new, verifiably effective kernel-based solver for path-dependent partial differential equations (PPDEs). This provides a practical alternative to Monte Carlo methods, especially for option pricing under rough volatility. (2024-03-18, shares: 2)

Crypto & Blockchain

Physics & Finance: The study uses Kelvin waves and advanced math to link physics and financial engineering, aiming to solve complex problems like hedging losses in cryptocurrency trading. (2024-03-14, shares: 6)

Scaling Uniswap: The paper finds that cheaper, faster chains improve Uniswap v3 Protocol's performance and profitability, and suggests that issues with Automated Market Makers may stem from chain dynamics. (2024-03-14, shares: 4)

Historical Trending

MeanField Microcanonical GD: The study presents a new model that improves the efficiency of sampling multiple data points simultaneously, particularly in high-dimensional financial time series. (2024-03-13, shares: 9)

Capital Return Path-dependency: The research challenges the belief that profit rate and return rate on equity depend on divestments, capitalization path, and market interest rate, respectively, in periodic growth processes. (2024-03-13, shares: 4)

Large Order Trading with HFT: The study finds that faster or more accurate high-frequency traders may harm themselves but benefit the normal-speed informed trader in a market. (2024-03-13, shares: 4)

Pairs Trading Graphical Matching Approach: The paper proposes a new method for pairs trading that selects pairs with strong cointegration but no shared assets, leading to lower portfolio variance, reduced trading costs, and better risk-adjusted performance. (2024-03-12, shares: 4)

SSRN

Recently Published

Quantitative

Cryptocurrency Hedging Strategy: QuantPedia's article suggests a strategy to hedge cryptocurrency portfolios in cold storage using a Top 5 cryptocurrency index and BTC derivatives to reduce market risk. (2024-03-17, shares: 3.0)

Weather Derivative Pricing Rainfall Model: The paper discusses the modeling and pricing of rainfall-based weather derivatives using the Markov Chain Analogue Year Mixed Exponential model and the Esscher transform. (2024-03-19, shares: 4.0)

Seasonality in Cryptocurrencies: The study explores seasonality patterns in ten cryptocurrencies, noting lower trading volume and volatility during weekends and no consistent calendar effects on returns. (2024-03-20, shares: 4.0)

Robust Linear Regression for Portfolio Optimization: The research presents a new optimization framework that minimizes unknown parameters and addresses estimation error in portfolio optimizations by focusing on the row sums of precision matrix estimates. (2024-03-16, shares: 3.0)

Financial

Arbitrage Efficiency: The study suggests that portfolios can use asset mispricing to increase efficiency, particularly during high-sentiment periods. (2024-03-15, shares: 8.0)

Timing Volatility Premiums: The research shows that managing a portfolio based on volatility risk premium timing strategies can improve long-term performance, especially during periods of high volatility. (2024-03-16, shares: 3.0)

Equity Options Trading: The study uses a multi-asset model to show that components of informed trading can predict high-volatility events in equity options. (2024-03-19, shares: 3.0)

Bond vs Equity ETFs Liquidity: The research reveals that bond ETFs and equity ETFs have different trading dynamics, with bond ETFs offering more hidden liquidity and dark trading volume due to the lack of transparency in bond markets. (2024-03-19, shares: 2.0)

ChatGPT Trading Strategy: The paper shows that ChatGPT can use Twitter news to generate profitable stock tickers for day trading, demonstrating the AI's ability to turn non-specific news into firm-specific mispricing signals. (2024-03-14, shares: 4.0)

Merger Arbitrage with Machine Learning: Machine learning can enhance the profitability of merger arbitrage trades, offering valuable financial insights and substantial economic benefits for investors. (2024-03-19, shares: 2.0)

Improved Volatility Forecasting: Enhancing the Heterogeneous Autoregressive Regression model with new methods for deriving volatility estimators from option price data improves daily stock volatility forecasts. (2024-03-19, shares: 2.0)

Short-Selling Hedge Funds Success: Hedge funds involved in short-selling show superior performance and unique trading patterns, often trading against retail trading trends, contributing to their exceptional performance. (2024-03-19, shares: 2.0)

The Forking Effect in Cryptocurrency: Cryptocurrency forking events don't significantly impact bitcoin's returns but increase its volatility, which remains high for three days post-fork. (2024-03-17, shares: 2.0)

Recently Updated

Quantitative

Volatility Forecasting Deep Estimation: The article suggests using deep neural networks to estimate volatility models, aiming to improve volatility forecasting. (2023-06-06, shares: 2.0)

VIX Forecasting Illusion: The paper uses daily volatility measures to forecast stock market volatility, finding inconsistent results with different evaluation metrics. (2023-06-30, shares: 2.0)

Superkurtosis in Trading: Traditional risk measures may underestimate losses in intraday trading, posing a risk to financial market stability. (2023-04-01, shares: 2.0)

Mergers Economic Impact in Africa: Mergers and acquisitions can help companies survive economic crises, affecting market share, shareholder wealth, employment, and economies of scale. (2023-01-20, shares: 2.0)

Financial

Investor Reactions to Alt Data: Investors only significantly respond to alternative data in financial decisions if it aligns with previous financial reports, according to a study. (2024-03-12, shares: 3.0)

Firm Characteristics and Equity Options: A research found that firm characteristics greatly affect equity option prices, and machine learning can enhance option pricing by pooling similar stock information. (2023-01-31, shares: 2.0)

Short Selling and Info Risk Pricing: Short selling influences the pricing of the probability of informed trading (PIN), especially after good news and among small firms, a study reveals. (2023-05-05, shares: 2.0)

Reducing Carbon Footprint of Index: An index-based portfolio's carbon footprint can be reduced by over 50% with low active risk by focusing on low emission stocks, according to the authors. (2024-02-13, shares: 2.0)

Valuation of Levered Equity and Debt Shield: The note argues that discounting the debt tax shield at the cost of debt capital gross of corporate tax has several advantages, including not needing to forecast speculative tax shields due to future net borrowings. (2023-07-10, shares: 2.0)

Retail Investor Trading Effects: Google Trends data shows that inexperienced retail investors can negatively affect the cost of capital, future performance, and value of real options, especially for smaller firms with less institutional ownership. (2024-02-13, shares: 2.0)

Bitcoin Inflation Hedge: Research suggests that Bitcoin's returns significantly increase after a positive inflation shock, indicating its potential as an inflation hedge. (2024-03-06, shares: 2.0)

Public Firm Ownership: A new dataset, derived from regulatory filings, offers a detailed view of public firms' ownership structures and institutional managers' investment holdings, surpassing the limitations of commercial databases. (2023-03-09, shares: 2.0)

Insider Trading Dynamics: A study on insider trading in the U.S. shows that most of it happens during periods of high information asymmetry, with trading during low asymmetry periods showing a strong self-selection bias. (2022-03-18, shares: 2.0)

RePec

Finance

Global Volatility Impact: Global factors significantly influence the local volatility persistence in equity indices of 17 developed economies. (2024-03-20, shares: 24.0)

Currency Hedging Strategy: A new dynamic currency hedging strategy for global investors is introduced, which is more stable, robust, and risk-reducing than other methods. (2024-03-20, shares: 22.0)

TimeFrequency Correlation in Oil Markets: Short-term trading significantly influences price volatility in crude oil futures markets, especially between mature and emerging markets. (2024-03-20, shares: 19.0)

OR Insights on System Dynamics: Operational research methods are used to analyze risks and dynamics in financial and economic systems. (2024-03-20, shares: 18.0)

Return and Volatility in NFT Markets: The relationship between NFTs and conventional currencies is weak, but NFTs have increased influence during the Covid-19 crisis. (2024-03-20, shares: 16.0)

Cryptocurrencies in Stock Markets: High volatility persistence is found in stock and cryptocurrency markets, with Tether being the most effective diversifier during market turmoil. (2024-03-20, shares: 14.0)

GARCH-MIDAS Volatility Models Evaluation: The research suggests that GARCH-MIDAS models may overstate the influence of macro-variables in forecasting total variance, warning of data-mining bias. (2024-03-20, shares: 12.0)

Commodity Financialization Convenience Yield: The paper reveals that soybeans' convenience yield is affected by financial markets volatility and global macroeconomic variables, supporting the concept of commodity financialization. (2024-03-20, shares: 12.0)

Insider Silence Strategy Performance: The study concludes that insider trading strategies, particularly buying insider purchases and selling insider sales, yield higher profits over longer holding periods. (2024-03-20, shares: 12.0)

Portfolio Optimization Numerical Integration: The paper introduces an efficient numerical integration method for Mean-Variance portfolio optimization, demonstrating its effectiveness in various investment scenarios. (2024-03-20, shares: 10.0)

Statistical

Machine Learning Workflow for China's Financial Crisis: Researchers have developed a multistep workflow using machine learning to predict China's systemic financial crises, successfully identifying six high-risk periods from 1990 to 2020. (2024-03-20, shares: 15.0)

Forecasting Gold Price: The paper suggests using the eXtreme Gradient Boosting (XGBoost) machine learning model and Shapley additive explanations (SHAP) for accurate forecasting and interpretation of gold price fluctuations, surpassing other advanced models. (2024-03-20, shares: 13.0)

Panel Data Nowcasting: Price-Earnings Ratio Prediction: The article talks about using structured machine learning regressions to predict corporate earnings. This method, which uses mixed-frequency time series panel data, has shown better results than traditional forecasting methods. (2024-03-20, shares: 20.0)

Historical Trending

LeadLag Relationship Between Futures and Cash Index: Research shows that CSI 300 index futures typically precede the cash index by 0-5 minutes, but this varies with market conditions. (2023-06-07, shares: 21.0)

Mixed-Frequency Volatility Model: The MF-MoP model, based on predictability momentum, is more effective than GARCH and Realized GARCH models in predicting financial asset volatility. (2023-04-01, shares: 17.0)

Bayesian Comparison of GARCH and Stochastic Volatility: Stochastic volatility models, particularly the SV-M model, are more effective than GARCH models in modelling inflation rates across 18 developed countries. (2023-08-23, shares: 17.0)

Jumps and Margin Level Study: The SE-SVCJ model, combined with a GPD, provides a more accurate forecast of margin levels in the stock index futures market. (2023-05-23, shares: 14.0)

GitHub

Finance

DiCE for Diverse Explanations: The article provides a guide on generating different counterfactual explanations for any machine learning model. (2019-05-02, shares: 1251.0)

Jupyterquant: Dockerized Research Environment: The piece offers a tutorial on setting up a Jupyter quant research environment using Docker. (2023-08-26, shares: 31.0)

Chronosforecasting: Language Models for Forecasting: The article introduces Chronos, a pre-trained language model specifically developed for probabilistic time series forecasting. (2024-02-23, shares: 195.0)

LlamaGym: Finetuning LLM Agents: The article explains the process of improving LLM agents through online reinforcement learning methods. (2024-03-01, shares: 725.0)

Ubicloud: Portable Cloud Services: The piece presents a new free, open-source cloud service in its public beta version, featuring elastic compute block storage and managed Postgres. (2023-01-17, shares: 2512.0)

Trending

Grok1: Grok has publicly released its software for open use. (2024-03-17, shares: 33455.0)

DeepSeekVL: DeepSeekVL is advancing in the field of vision-language understanding. (2024-03-07, shares: 1179.0)

bigAGI: A new AI suite, powered by the latest LLMs, offers features like AI personas and voice response, with deployment options on-premises or in the cloud. (2023-03-19, shares: 3235.0)

Llama Inference: A code for inference has been developed specifically for Llama models. (2023-02-14, shares: 51398.0)

Mindgraph: A prototype has been developed that uses AI to generate and query an ever-growing knowledge graph. (2024-03-16, shares: 552.0)

LinkedIn

Trending

Leslie B. Lamport: Distributed Systems Contributions: Leslie B. Lamport, an American computer scientist and mathematician, is recognized for his work in distributed systems and the development of LaTeX, earning him the 2013 Turing Award. (2024-03-20, shares: 4.0)

Productionalizing LLMs: Key Components: Implementing LLMs involves more than just deploying the Langchain/LLamaindex app, it also requires the development of high-quality RAG applications, indexing pipelines, reranking techniques, and finetuning of embeddings and LLMs. (2024-03-20, shares: 1.0)

Efficient Data Processing with Python: Modern Python frameworks like Dask provide efficient solutions for processing large data volumes on local machines, as shown by processing nearly 900 million rows of S&P e-mini futures trade tick data. (2024-03-20, shares: 1.0)

Highlights from Financial Risks International Forum: The 17th Financial Risks International Forum concluded with discussions on various topics including market microstructure, volatility, textual analysis, cryptocurrencies, machine learning, and climate risk. (2024-03-20, shares: 2.0)

Cloudera and NVIDIA: AI Integration: Cloudera and NVIDIA have expanded their partnership to incorporate NVIDIA’s NIM and Retriever microservices into Cloudera Machine Learning, facilitating faster, secure, and simplified generative AI workflows in production. (2024-03-20, shares: 2.0)

Integrating Net Zero Objectives: Asian investors are increasingly incorporating Net Zero/Climate transition goals into factor-based equity solutions, with high equity income strategies gaining popularity. (2024-03-20, shares: 2.0)

ITables v2 with DataTables Support: The new version of ITables, ITables v2, offers improved search and selection interfaces, customizable table appearances, and data download options. (2024-03-20, shares: 3.0)

Concave Price Impact Study: A study on Concave Price Impact with Impact Decay - Theory and Empirics was presented at the Louis Bachelier Risk Forum, offering new trading rules for different market conditions. (2024-03-20, shares: 1.0)

Informative

Trading Analytics Recap: The author recounts their experience at the Bloomberg-hosted Trading Analytics and TCA conference in London, discussing recent trading developments and best execution trends. (2024-03-20, shares: 1.0)

Eigenportfolios Understanding: The article outlines the Eigenportfolios strategy, derived from principal components of returns and based on linear algebra, discussing its advantages and drawbacks. (2024-03-20, shares: 1.0)

Tick Size Research Release: Plato has published a new academic paper titled Tick Size, Market Quality, and Market Structure, examining the significance of tick size in financial markets. (2024-03-20, shares: 2.0)

Spring Lab Collaboration Begins: The spring Project Lab session has started, providing students with practical experiences in quantitative research projects in partnership with various employers. (2024-03-20, shares: 1.0)

Quantitative Finance Must-Reads: The article provides a list of essential books for those in the quantitative finance field, covering subjects like algorithmic trading, financial models, and machine learning. (2024-03-20, shares: 1.0)

Forecasting Refinement by Sefton et. al.: The article explores Sefton et. al.'s forecasting approach, which improves the Black/Litterman model by including macro forecasts and security-specific Alpha forecasts. (2024-03-20, shares: 1.0)

29 Volatility-Sorted Datasets: The article introduces a dataset file with two volatility-sorted datasets from 1929 onwards, aimed at aiding investors in understanding the low-risk effect in finance. (2024-03-20, shares: 1.0)

Einhorn's Critique of Market Trends: The article examines David Einhorn's criticism of current financial markets, arguing that the growth of passive investing and algorithmic trading neglects the true value of companies. (2024-03-20, shares: 3.0)

Podcasts

Related

Quantitative Investing: Jim O'Shaughnessy, founder of O'Shaughnessy Asset Management, discusses the importance of data in improving portfolio returns in an interview with Barry Ritholtz. (2024-03-20, shares: 5)

Fed Signals: Ira Jersey, Bloomberg Intelligence's lead US interest rate strategist, explores the Federal Reserve's communication methods and the influence of government fiscal policy on the economy. (2024-03-18, shares: 3)

Twitter

Quantitative

Machine Learning in Finance: The article reviews recent studies on the use of machine learning in asset pricing and corporate finance. (2024-03-14, shares: 11)

Research in Finance: The article recaps the week's research papers on topics such as empirical asset pricing, machine learning, macro options, and industry insights. (2024-03-19, shares: 6)

Stock Option Hedging: The article explores the growing impact of delta hedging on stocks due to increased stock option volume, highlighting a study on expected hedging demand and its strong return predictability. (2024-03-17, shares: 5)

Natural Resource Equities vs. Futures: Article 1: Man Group suggests that investing in natural resource equities offers better diversification benefits than futures. (2024-03-20, shares: 3)

Interview with Clifford Asness: Article 2: In a recent interview, Clifford Asness discusses the 60/40 portfolio diversification strategy and market efficiency. (2024-03-15, shares: 3)

Unified Time Series Model: Article 3: The pre-trained LM UNITS is a comprehensive time series model capable of performing tasks like classification, forecasting, imputation, and anomaly detection. (2024-03-19, shares: 2)

Miscellaneous

Event Risk Impact on Shortdated Options: Article 1: A study explores the effect of event risk on short-term options, deriving higher-order moments for option portfolios and identifying substantial macroeconomic event risk. (2024-03-15, shares: 2)

ESG Integration Across Asset Classes: Article 2: ManGroup provides insights on incorporating Environmental, Social, and Governance (ESG) elements into systematic investing across various asset classes. (2024-03-15, shares: 1)

Macroeconomics Textbook Updates: Article 3: A new textbook on macroeconomics is now accessible in eBook format. (2024-03-20, shares: 0)

GPT4 vs. Causation: Article 1: GPT4 is proficient in differentiating between causation and correlation. (2024-03-18, shares: 0)

Price vs. Accounting: Article 2: The paper suggests that accounting-based information is more precise for predictions longer than a month, contrary to prior studies favoring price-based indicators. (2024-03-16, shares: 0)

Market Timing: Article 3: Jonathan Kinlay explores the frequently misunderstood ability of market timing. (2024-03-14, shares: 0)

Videos

Quantitative

Generating Correlation Matrices: The video on YouTube explains the Onion Method, a technique used to create random correlation matrices for statistical analysis. (2024-03-15, shares: 0.0)

Python AI Finance: Python Quants GmbH is hosting an information session about their Certificate in Python for Finance program. (2024-03-14, shares: 1.0)

Maximizing Internship: A video offers tips on maximizing internships in the quant field, highlighting the significance of networking and diligence. (2024-03-17, shares: 22.0)

Maximum Ignorance: A blog post explores the difficulty of estimating probabilities in scenarios with no previous data, using a surgeon's failure rate as an example. (2024-03-15, shares: 284.0)

Paper with Code

Trending

LLM Data Interpreter: The article explores the effectiveness of Large Language Model (LLM) based agents and provides a GitHub link to the code. (2024-03-18, shares: 36459.0)

Generalization Beyond Overfitting: The study suggests examining the generalization of neural networks on small, algorithmically generated datasets, with the code on GitHub. (2024-03-18, shares: 2517.0)

Decompiling Binary Code: The article announces the release of the first open-access decompilation LLMs, pretrained on C source code and assembly code, with the code on GitHub. (2024-03-18, shares: 1198.0)

GSPMD: Scalable Parallelization: The paper presents GSPMD, a compiler-based parallelization system for machine learning computations, with the code on GitHub. (2024-03-17, shares: 654.0)

pyvene: PyTorch Models Library: The article highlights the significance of interventions on model-internal states in AI, including model editing and robustness, with the code on GitHub. (2024-03-18, shares: 279.0)

Rising

Fast InnerProduct Algorithm: The article discusses the Freepipeline Fast Inner Product (FFIP), a new algorithm and hardware architecture that improves upon the fast inner product algorithm (FIP) introduced by Winograd in 1968. (2024-03-17, shares: 213.0)

Chronos: Time Series Language: The article introduces Chronos, a new framework designed for pre-trained probabilistic time series models. (2024-03-15, shares: 165.0)

Sequoia: Speculative Decoding: The paper presents Sequoia, a new algorithm designed for speculative decoding that is scalable, robust, and hardware-aware. (2024-03-15, shares: 135.0)

D Gaussian Splatting Optimization: The article introduces RAINGS, a new optimization strategy for training 3D Gaussians from random point clouds, following a detailed study of SfM initialization and 1D regression tasks. (2024-03-17, shares: 99.0)

Monolithic Preference Optimization: The article highlights the crucial role of supervised finetuning (SFT) in achieving successful convergence in preference alignment algorithms for language models. (2024-03-18, shares: 70.0)

Sparse MixtureofExperts Implementation: The article presents ScatterMoE, a GPU-based implementation of the Sparse MixtureofExperts (SMoE) model. (2024-03-15, shares: 60.0)