Quant Letter: March 2024, Week-3

Weekly (38th Edition)

Mar 20, 2024

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)

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)

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

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

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)

Machine Learning & Quant Finance

Discussion about this post

Machine Learning & Quant Finance

Quant Letter: March 2024, Week-3

Weekly (38th Edition)

ArXiv

Finance

Economics

Crypto & Blockchain

Historical Trending

SSRN

Recently Published

Quantitative

Financial

Recently Updated

Quantitative

Financial

RePec

Finance

Statistical

Historical Trending

GitHub

Finance

Trending

LinkedIn

Trending

Informative

Podcasts

Related

Twitter

Quantitative

Miscellaneous

Videos

Quantitative

Paper with Code

Trending

Rising

Discussion about this post