Quant Letter: August 2023, Week 2

Weekly quantitative finance newsletter

Aug 09, 2023

This Week’s Research

It’s out another impressive week for quantitative finance. I'll touch upon a few key papers, but there's plenty more for you to sift through.

Did you know that this list is already highly curated? There is a direct stream with 100s of links a day on www.ml-quant.com if you want to discover research by yourself.

Paper 1: Black-Litterman End-to-End (Deep Learning)

Paper 2: Path Shadowing Monte-Carlo

Paper 3: Would Automated Market Makers Improve Equity Trading?

Paper 4: A Quantitative Approach to Historical Stress Tests

Paper 5: Commodity Dependence and Optimal Asset Allocation

SSRN

Recently Published

Quantitative

Optimizing Portfolio Allocation: The BlackLitterman model, BLEnd2End, uses deep learning to optimize portfolio allocation, outperforming mean-variance benchmarks and other traditional strategies. (2023-08-07, shares: 2.0)

Automated Market Makers in Equity Trade: The study suggests that optimally designed automated market makers could potentially save U.S. investors billions in transaction costs each year. (2023-08-04, shares: 2.0)

Quantitative Approach to Stress Tests: The paper introduces a new method for defining historical stress tests in finance, classifying them into four types and using volatility as a key component in their definitions. (2023-08-04, shares: 2.0)

Commodity Dependence and Allocation: The study demonstrates the benefits of adding commodities to a portfolio for investors in low-commodity dependence countries. (2023-08-07, shares: 2.0)

Analyzing Stock Prices with ChatGPT: The study uses large language models to interpret business data from the Japan Company Handbook, Shikiho, to classify firms and build equity portfolios, suggesting the market may overlook some factual information in Shikiho's text. (2023-08-08, shares: 3.0)

Financial

Volatility and Leverage in Campbell-Cochrane Model: The article critiques the Campbell and Cochrane 1999 habit model, arguing it doesn't account for increased volatility during recessions, and proposes a model with cyclical leverage. (2023-08-05, shares: 2.0)

Forecasting Oil Prices with VRP and Google Data: The paper suggests that incorporating variance risk premium and Google search data into models improves real oil price forecasts, with penalized regressions providing the best results. (2023-08-08, shares: 3.0)

Investor Sentiment and Futures Market Mispricing: The study reveals that investor sentiment significantly influences futures mispricing, with excessive optimism leading to overvaluation, especially when individual trading is dominant. (2023-08-05, shares: 2.0)

Quantitative

Investment Grade Rating with Explainable AI: The article explores the use of explainable AI in formulating rules on financial ratios to help companies enhance their credit ratings. (2022-07-15, shares: 2.0)

Switching Volatility in an Economy: Using a dynamic stochastic general equilibrium model, the research analyzes the impact of the global financial crisis on the euro area, emphasizing the significant influence of US shocks and the need to consider nonlinearities in financial market variables. (2020-12-03, shares: 2.0)

Financial Learning Under Uncertainty: A study suggests that market participants learn from equilibrium prices under uncertainty, which can lead to overreactions and volatility in asset prices. (2022-06-07, shares: 3.0)

Financial

Financial Index Tracking: Reinforcement Learning and Deep RL Method: A new model for tracking financial indices has been proposed, which improves on existing models by including market information variables, exact transaction cost calculation, and new decision variables for cash injection or withdrawal. (2023-07-27, shares: 3.0)

Asset Return Covariance Forecasting with Errors: The study introduces new models for predicting the covariance of asset returns, taking into account measurement errors and maintaining high volatility and correlation persistence. (2021-04-10, shares: 72.0)

Market Sentiment Index for Stock Analysis: The research proposes a dynamic design for aggregating market sentiment, which adjusts to sentiment indicator changes and shows that ignoring these changes can skew model construction. (2022-10-27, shares: 150.0)

Machine Learning for Factor Prediction: The paper presents a Machine Learning model that uses residual factors from the FamaFrench threefactor model to identify significant alpha factors, providing significant alpha return even when style factors are controlled. (2023-06-10, shares: 2.0)

Portfolio Management Strategy using VIX: The research suggests a portfolio management strategy that adjusts leverage based on the implied volatility index (VIX), resulting in more stable weights, less rebalancing, and higher alphas when considering transaction costs. (2023-07-20, shares: 2.0)

Liquidity and Portfolio Segmentation Study on Cross-Impact and Price Bubbles: A study found that financial bubbles are larger and cross-market impact is more asymmetric in markets with both human and artificial agents, especially when these agents have unique portfolios. (2023-08-01, shares: 6.0)

Derivative Instruments in Portfolio Management: Unified Framework: The article suggests a unified framework for managing derivative instruments in portfolios, addressing issues related to exposure, notional, and market value price separation, and provides Python code for replication. (2022-09-25, shares: 2.0)

Off-Exchange Trading Impact on Stock Price Crashes: Dark Trading: Dark trading, or trading that occurs off-exchange, has been found to significantly increase the risk of stock price crashes, especially for stocks with high institutional ownership and negative earnings news. (2022-08-09, shares: 2.0)

Market Participation and Liquidity Effects of Arms Sales in Financial Markets: The sale of trading advantages can lead to lower market participation and liquidity, causing less sophisticated investors to leave the market due to perceived unfairness. (2022-06-30, shares: 2.0)

ArXiv

Quantitative

Reinforcement Learning for Index Tracking: The article suggests a new dynamic model for tracking financial indexes, which overcomes existing model limitations and offers better accuracy and profit potential. (2023-08-05, shares: 4)

Anomaly Detection in Financial Markets: A study using Graph Neural Networks to identify anomalies in global financial markets found that the interconnected structure of highly correlated assets decreases during a crisis, with the number of anomalies varying based on the crisis stage. (2023-08-05, shares: 2)

Path Shadowing Monte-Carlo: Improved Predictions: The paper presents a Path Shadowing Monte-Carlo method that uses past data to predict future financial paths, showing its effectiveness in predicting future volatility and determining conditional option smiles for the S&P500. (2023-08-03, shares: 5)

Options: Black-Scholes Smiles: The research presents a new perspective on pricing a European Call option with a higher strike, suggesting it can be seen as a Call option on a Call option with a lower strike, and introduces new pricing formulas. (2023-08-08, shares: 6)

Statistically Consistent Term Structures and Affine Geometry: The research examines finite dimensional models for energy futures' term structure, revealing that the compatibility between potential yield curves and diffusion coefficient enforces a specific geometry of possible yield curves. (2023-08-04, shares: 4)

Miscellaneous

DeRisk: Credit Risk Deep Learning Framework: DeRisk, a deep learning framework for predicting credit risk using real-world financial data, has been shown to outperform traditional statistical learning methods. (2023-08-07, shares: 3)

AI Exposure and Unemployment Risk: Research indicates that individual AI exposure models don't predict unemployment or job separation rates, but a combination of these models does, highlighting the need for dynamic, context-aware AI exposure assessment methods. (2023-08-04, shares: 2)

Historical Trending

Reddit's Bull Runs: Social Contagion and Asset Prices: The study uses machine learning to analyze Reddit's WallStreetBets forum, concluding that social forces and peer effects can influence asset prices and cause market bubbles. (2021-04-05, shares: 91)

Temporal Tabular Prediction with Online Learning: A machine learning pipeline is suggested for ranking predictions on temporal panel datasets, showing improved performance with Gradient Boosting Decision Trees models. (2022-12-30, shares: 38)

Volatility and Liquidity in Portfolio Execution: The research proposes a unique solution for optimal trading rate under uncertain volatility and liquidity, using a multidimensional Markovian stochastic factor. (2021-01-07, shares: 40)

RePec

Finance

Long Memory and Fractality in Volatility: The research identifies long memory and fractality in all nine CBOE volatility indices, influencing investment choices and trading strategies. (2022-01-28, shares: 14.0)

Sampling Methods for Increased Volatility: The paper proposes a portfolio composition framework resistant to market volatility, using a modified Markowitz’s approach and sampling methods to enhance allocation efficiency during high market volatility. (2021-02-18, shares: 12.0)

Algorithmic Trading and Block Ownership: Algorithmic trading decreases the chances of block ownership initiation in U.S. public firms by deterring sophisticated investors from gathering information. (2023-08-09, shares: 17.0)

Sectoral and Regional Volatility Connection: A study found increased volatility connectedness between the CDS and equity markets in the US, UK, EU, and Japan during crisis periods, with equity being the main volatility transmitter. (2023-08-09, shares: 17.0)

Heterogeneous Tail GCF Modeling for Financial Factors & Asset Returns: The Factor-HGH, a new model for financial factors and asset returns, performs better than traditional models in managing highly tail heterogeneous cryptocurrencies. (2023-08-09, shares: 14.0)

Portfolio Optimization: A new model combining market predictors and machine learning enhances portfolio optimization by minimizing historical data noise and integrating future-oriented data into expected returns. (2023-08-09, shares: 23.0)

Corporate Capital Structure Modeling: The authors suggest a method for optimizing a company's capital structure using a formula that increases return on equity based on return on sales, resource productivity, and equity multiplier. (2023-08-09, shares: 17.0)

GitHub

Finance

Rust ML Framework: This software introduces a new machine learning framework designed specifically for the Rust programming language. (2023-06-19, shares: 1761.0)

Optiver Ready Trader Go: Automated Market-Making: The AvellanedaStoikov market-making strategy is now part of an automated trading algorithm for the Optiver Ready Trader Go contest. (2023-04-27, shares: 7.0)

Pytorch Constrained Optimization Framework: The software concerns Pytorch-based framework that helps solve optimization problems and enhances system identification and model predictive control. (2020-10-14, shares: 278.0)

Qlib AI in Quantitative Investment: Qlib is an AI platform aimed at improving quantitative investment strategies and research using AI technologies. (2022-06-23, shares: 19.0)

Conformal Prediction: Real Data Implementation: Conformal prediction, a lightweight and practical method, has been used on actual data. (2021-12-25, shares: 415.0)

Informative

Statistical Arbitrage: Origins & Strategies: Ed Thorp's piece discusses the beginnings of statistical arbitrage, a balanced investment strategy, and his experience starting an indicators project in 1979. (2023-08-09, shares: 1.0)

Volatility Modeling & Football: A Conversation with Mauro Cesa: Risk.net's Quantcast episode discusses volatility modeling, option pricing, path-dependent volatility, and World Cup draws and formats. (2023-08-05, shares: 1.0)

Challenges of Learning Quantitative Finance Secrets: The article emphasizes the importance of joining a successful quantitative finance firm early on due to the secretive nature of the industry. (2023-08-09, shares: 1.0)

Transitioning to Finer Grained Quant Strategies: The article discusses the complexities of transitioning from low frequency to finer grained quantitative strategy, including learning new models and high-performance computing. (2023-08-04, shares: 1.0)

Entropy Based Measures in Portfolio Optimization: Talk by Dany C.: Dany C., creator of Riskfolio, spoke about Entropy Based Measures in Portfolio Optimization at the AI Finance Institute Summer Bootcamp 2023 at NYU. (2023-08-09, shares: 1.0)

Inflation-Adjusted Drawdown of Risk-Free Assets: Treasuries are experiencing inflation-adjusted drawdowns due to changes in BOJ bond yield caps, rising commodity prices, and worries about the Fed's potential long-term rate hikes. (2023-08-05, shares: 1.0)

Navigating Hedge Funds Guide: A guide provides insights into the unique benefits and challenges of starting or joining small, mid-sized, or large hedge funds. (2023-08-09, shares: 1.0)

Cross-Dimensional Evolution of Large Language Models: Arpit Narain from MathWorks will discuss the development of Large Language Models at an AI Finance Institute Summer Bootcamp hosted by NYU Tandon School of Engineering. (2023-08-09, shares: 1.0)

Venture Capital Financing via Social Media: Elisabetta B.'s blog post discusses how social media can boost venture capital financing and address funding disparities faced by startups led by women and those lacking social capital. (2023-08-05, shares: 1.0)

Finance's Multi-Agent Deep Reinforcement Learning: A resource guide highlights the use of multi-agent deep reinforcement learning in trading and derivative pricing, and compares different algorithms for nonstationarity, scalability, and observability. (2023-08-08, shares: 1.0)

Podcasts

Quantitative

Tactical Asset Allocation and Trading Volatility Products: The article highlights the significance of rules-based investing, strategy diversification, and understanding systematic models in tactical asset allocation and trading volatility products. (2023-08-06, shares: 25)

Options Trading and Market Dynamics: The article delves into the complexities of options trading, the effect of market dynamics on volatility and risk, and the role of artificial intelligence in shaping market dynamics. (2023-08-07, shares: 19)

Financial Planning and Portfolio Management: The article presents a discussion with Martin Tarlie from GMO on the redefinition of risk in portfolio management and the challenges it brings to the portfolio optimization process. (2023-08-07, shares: 15)

Systematic Trading and Investment Strategies: The article offers strategies for systematic trading in volatile markets, stressing the importance of backtesting, recording statistics, sticking to the chosen system, and the need for diversification. (2023-08-06, shares: 13)

Systems Investing, Central Banks: Jeff Ross talks about using a systems-based approach to investing, considering factors like inflation, economic growth, and liquidity. (2023-08-05, shares: 8)

Trading Insights, Predictions: Francis Hunt, the Market Sniper, discusses his military-inspired trading strategy and shares his views on the current and future bond market. (2023-08-08, shares: 7)

Oil's Role in Financialization: Ryan C. Smith's book examines how the 1973 OPEC Oil Embargo and the 1979 Oil Shock contributed to the growth of the global financial industry. (2023-08-06, shares: 7)

Zero-DTE Options: Market Chameleon cofounders, Will McBride and Dmitry Pargaminik, discuss zero days to expiration options with IBKR’s Jeff Praissman. (2023-08-03, shares: 6)

News

Quantitative

UK's Openness Competitiveness: UK's Openness: The European leader of Cboe, a major equity trading platform, is planning to also dominate the derivatives market. (2023-08-06, shares: 4)

Hedge Fund with Lightning-Fast Calculations: Lightning-Fast Calculations Hedge Fund: Castle Ridge executives believe that AI technology has progressed enough to make better investment decisions than human portfolio managers. (2023-08-06, shares: 4)

Tech revolution transforming finance: The article investigates the transformative potential of Artificial Intelligence in the existing system. (2023-08-07, shares: 1)

GAM's future at risk, urgent action required: The fate of GAM, which has been in turmoil since a 2018 scandal involving a star fund manager, will be determined in the near future. (2023-08-03, shares: 0)

Barclays mulls EU headquarters move to Paris: The bank is considering a possible move that may lead to job transfers, with Paris potentially benefiting post-Brexit. (2023-08-04, shares: 0)

Linklaters partner joins Millennium as GC: Wong is now employed by Millennium, reporting to global general counsel Gil Raviv. (2023-08-03, shares: 0)

Twitter

Quantitative

CNNs Predict Stock Returns: Kelly and team's research shows that convolutional neural networks can successfully predict monthly stock returns using images of single stock implied volatility surfaces. (2023-08-06, shares: 3)

Forensic Finance: Market Manipulation and Fraud: A literature review on Forensic Finance discusses various topics such as market manipulation, fraud, insider trading, and greenwashing. (2023-08-09, shares: 2)

Dividend Investing: High-Dividend Stocks Outperform: Research by Roni Israelov and NDVR Wealth indicates that high-dividend stocks have historically performed better than low-dividend stocks, but this can be explained by common equity factors. (2023-08-08, shares: 2)

Price Targets Predict Stock Returns: A study reveals that sell-side analysts' price targets are generally not good at predicting stock returns, but ranked price targets within the same analyst do have predictive power. (2023-08-08, shares: 2)

ML-Based BlackLitterman Model Outperforms: A new research paper introduces a version of the Black-Litterman model that uses machine learning to optimize view generation and portfolio allocation, showing better performance when applied to 14 liquid ETFs. (2023-08-07, shares: 2)

Miscellaneous

Return Measures and Trading Strategies: The article explores the comparison of different return measures and their impact on multi-period returns and trading strategies. (2023-08-03, shares: 1)

Statistical Learning in Python: A new edition of the book An Introduction to Statistical Learning is now available, featuring applications in Python. (2023-08-08, shares: 0)

Implications of Investments: The third article is incomplete, thus a summary cannot be provided due to lack of information. (2023-08-07, shares: 0)

Blogs

Quantitative

ML Algorithms for Pricing Options: The article explores the application of machine learning algorithms for option pricing in quantitative research and trading. (2023-08-05, shares: 14)

Confirmation of New Volatility Regime: The article investigates if the recent lows of the VIX Index suggest a new volatility pattern in the stock market, using different analytical techniques. (2023-08-08, shares: 9)

Factor Zoo: Uncovering Stock Return Drivers: Sak H., Chang M. T., and Huang T.'s paper applies machine learning to study the progression of financial anomalies over time. (2023-08-03, shares: 6)

US Credit Rating Downgrade: Investment Implications: The Talks article explores the investment and geopolitical implications of Fitch's downgrading of the US credit rating. (2023-08-08, shares: 4)

Rabbit Holes: Journey of Discovery: The article Breadth first depth later emphasizes the need to grasp a broad spectrum of topics before focusing on the details. (2023-08-06, shares: 0)

Paper with Code

Rising

Data-Free Learning of Kinematics: The article investigates physical systems such as elastic bodies and kinematic linkages, focusing on their operation in lower-dimensional subspaces despite being defined on high-dimensional configuration spaces. (2023-08-07, shares: 97.0)

The All-Seeing Project: Panoptic Recognition and Understanding: The article unveils the AllSeeing project, a comprehensive data and model system aimed at recognizing and understanding all elements in the open world. (2023-08-04, shares: 80.0)

Reasoning Segmentation via LLM: The study introduces a new task known as 'reasoning segmentation'. (2023-08-03, shares: 55.0)

Bit Quantization of LLMs: The article discusses a study on the effects of parameter quantization after training in large language models. (2023-08-07, shares: 71.0)

Machine Learning & Quant Finance

Discussion about this post

Machine Learning & Quant Finance

Quant Letter: August 2023, Week 2

Weekly quantitative finance newsletter

This Week’s Research

Paper 1: Black-Litterman End-to-End (Deep Learning)

Paper 2: Path Shadowing Monte-Carlo

Paper 3: Would Automated Market Makers Improve Equity Trading?

Paper 4: A Quantitative Approach to Historical Stress Tests

Paper 5: Commodity Dependence and Optimal Asset Allocation

SSRN

Recently Published

Quantitative

Financial

Recently Updated

Quantitative

Financial

ArXiv

Quantitative

Miscellaneous

Historical Trending

RePec

Finance

GitHub

Finance

Trending

LinkedIn

Trending

Informative

Podcasts

Quantitative

Related

News

Quantitative

Twitter

Quantitative

Miscellaneous

Blogs

Quantitative

Related

Paper with Code

Trending

Rising

Discussion about this post