The Parlour $249 / month

The Parlour is a Substack that delves into management science, finance and society, and quantitative studies, offering insights into quantitative finance analytics, risk management strategies, financial modeling, machine learning applications in finance, and portfolio optimization. It provides summaries and analysis of recent academic research, highlighting innovations and trends in the field.

Quantitative Finance Analytics Risk Management Strategies Financial Modeling Machine Learning in Finance Portfolio Optimization Financial Market Simulations Algorithmic Trading Predictive Modeling in Finance Cryptocurrency and Blockchain Statistical Arbitrage Strategies

The hottest Substack posts of The Parlour

And their main takeaways

Quant Letter: January 2025, Week-4

34 implied HN points • 23 Jan 25
💰 Finance Quantitative Machine Learning Portfolio Management Market Analysis Investment Strategies
  1. Advanced models like the MDQR help understand market dependencies, which can make it easier for traders to create effective strategies.
  2. New methods for portfolio optimization can handle many assets at once, moving beyond the traditional limits that were previously in place.
  3. Research shows AI can effectively forecast financial risks and rewards, highlighting the growing importance of technology in finance.

February 2025, Week-1

4 implied HN points • 05 Feb 25
💰 Finance Machine Learning Risk management Statistical Analysis
  1. The study on Network Linear Covariance Models shows that using GNAR models can help better predict stock price movements in the S&P 500, especially during busy trading times.
  2. Agent-Based Modelling is a new method introduced to simulate financial markets, which can help us understand market behavior more clearly.
  3. These research efforts highlight how machine learning techniques can be applied to finance, providing insights that can improve trading strategies.

Quant Letter: January 2025, Week-1

12 implied HN points • 01 Jan 25
💰 Finance Quantitative finance Portfolio Management Market Analysis Investment Strategies
  1. AI is being used in finance to help with investment decisions, but it might make the gap between experts and novices bigger.
  2. New research is exploring better ways to model financial risks and predict market movements using advanced tools, like machine learning.
  3. Legislative events can influence stock market performance, as seen when Congress is in session, which may lead to declines in equity values.

Quant Letter: November 2024, Week-4

21 implied HN points • 27 Nov 24
💰 Finance Quantitative Machine Learning Investment Strategies Portfolio Management
  1. Quanto options pricing can be improved using a mix of models that handle various aspects of finance and asset behavior. This could help in more accurate predictions and simulations.
  2. Hedge funds adapt their activist strategies to align with the preferences of major investors, leading to better results when trying to influence company decisions. This emphasizes the importance of understanding stakeholder interests.
  3. Simple machine learning models can sometimes outperform more complex ones when it comes to predicting financial markets. This shows that less can be more in data analysis.

Quant Letter: December 2024, Week-3

12 implied HN points • 18 Dec 24
💰 Finance Quantitative Investing Market Analysis Machine Learning Cryptocurrency
  1. This week had exciting new research in quant finance, especially on generative AI and crypto forecasting. It shows that this field is active and evolving even during the holiday season.
  2. Recent studies highlighted the influence of machine learning on portfolio management, making it possible to choose better predictors and lower risks. This can help investors make smarter choices.
  3. Insights about investor behavior suggest that emotions and external factors can weigh heavily on trading volume and financial decisions. Understanding these factors can lead to better investment strategies.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Quant Letter: November 2024, Week-2

25 implied HN points • 13 Nov 24
💰 Finance Investment Algorithms Risk management Quantitative Analysis Machine Learning
  1. A new computational method can measure the shadow rate, which helps in comparing different investment types. This can give investors better insights.
  2. Using multi-agent systems for investment research allows adaptation to changing market conditions, leading to improved performance over traditional models.
  3. Machine learning continues to show promise in finance, with various models effectively predicting market behavior and improving investment strategies.

Quant Letter: January 2025, Week-3

4 implied HN points • 15 Jan 25
💰 Finance Quantitative Analysis Risk management Financial Modeling Market Analysis
  1. A model for pricing VIX options has proven effective in markets like Germany's power and TTF gas markets. This model uses multiple factors to improve accuracy.
  2. The HJM and Lifted Heston Model aims to connect historical data of futures contracts with current implied volatility. This helps better predict market behaviors.
  3. Understanding these models can enhance strategies in quantitative finance, especially for those working with options and futures trading.

Quant Letter: January 2025, Week-2

4 implied HN points • 09 Jan 25
💰 Finance Machine Learning Investing Economics Data Analysis
  1. Quant finance uses advanced math and data analysis to make investment decisions. It's all about finding patterns in numbers to predict market trends.
  2. Machine learning is becoming increasingly important in finance. It helps in automating processes and analyzing large amounts of data quickly.
  3. Staying updated with recent research and findings in quant finance can provide valuable insights. It's key to adapt and grow in this fast-changing field.

Predict a Mockingbird: November Week-2

17 implied HN points • 11 Nov 24
💰 Finance Investment Analytics Market Trends Economic Indicators
  1. Companies can show strong or weak financial health based on key metrics like cash flow and profitability. This helps investors decide where to put their money.
  2. Insider trading activities can hint at stock movements. If insiders are buying, it might be a good sign, but heavy selling could be a warning.
  3. Using tools like search interest and news sentiment helps track how a company is viewed in the market. Positive buzz can mean good things for stock performance.

Quant Letter: January 2024, Week-2

30 implied HN points • 09 Jan 24
💰 Finance Quantitative Machine Learning Market Dynamics Financial Models
  1. The Combinatorial Purged Cross-Validation (CPCV) method is superior in financial analytics for reducing overfitting risks.
  2. SPX options data analysis finds limitations in accurately capturing implied volatility using Volterra Bergomi models.
  3. Incorporating Risk premia strategies in portfolios can lessen left-tail exposure, but diversification within options requires maximizing volatility parameters.

Quant Letter: January 2024, Week-4

21 implied HN points • 23 Jan 24
🕹 Technology Quantitative Machine Learning Finance Blockchain Artificial Intelligence
  1. The blog post discusses various research papers on topics like financial risk modeling, interest rate models, and credit risk stress testing.
  2. New methods for predictive modeling in finance, including data-driven option pricing and generative modeling for financial time series, are introduced in the presented papers.
  3. The research covers diverse areas such as economics, crypto, and blockchain, offering insights on market responses, equity premium puzzles, and AI investment rankings in Latin America.

Quant Letter: February 2024, Week-3

17 implied HN points • 21 Feb 24
💰 Finance Research Analysis Simulation Detection
  1. Research suggests Double Deep Q-learning can learn optimal trading strategies in fluctuating liquidity conditions.
  2. Investors decide to buy additional information about an asset's trajectory based on the indifference price of information.
  3. The RAGIC model predicts future stock prices accurately with a consistent 95% coverage using a Generative Adversarial Network.

Quant Letter: January 2024, Week-1

21 implied HN points • 03 Jan 24
🕹 Technology Finance Miscellaneous Cryptocurrency
  1. The post shares summaries and links to various recent articles and research papers related to quantitative finance and machine learning in finance.
  2. Topics covered include forecasting models, risk management strategies, trading algorithms, AI applications, and financial market simulations.
  3. Quantitative finance professionals can stay updated on the latest developments and trends in the industry through various sources like podcasts, news articles, research papers, and online communities.

Quant Letter: February 2024, Week-2

17 implied HN points • 14 Feb 24
🕹 Technology Finance Crypto Machine Learning GitHub Quantitative
  1. Using Autoencoder architectures in Statistical Arbitrage can simplify strategy development and improve returns compared to traditional methods.
  2. A new method, Causal-NECOVaR, provides reliable risk predictions for financial risk analysis regardless of market shocks and systemic changes.
  3. The Merton investment-consumption problem is expanded to incorporate transaction costs and stochastic differential utility in Portfolio Optimization for a better understanding of parameter combinations.

Quant Letter: December 2023, Week-3

21 implied HN points • 20 Dec 23
💰 Finance Quantitative Financial Market Analysis Investment Strategies
  1. Recent research is exploring innovative methods for quantitative investing, such as using deep learning algorithms and new portfolio optimization models.
  2. There are profitable opportunities in the ETF lending market due to cost differences between borrowing ETFs and stocks, creating room for cross-ETF arbitrage.
  3. Studies are showcasing the importance of adaptive investment strategies focused on resilience, active ownership, and broader financial models to navigate fast-changing environments.

Quant Letter: November 2023, Week 4

21 implied HN points • 29 Nov 23
💰 Finance Machine Learning Quantitative finance Financial Modeling Portfolio Management
  1. The paper introduces a methodology using Shapley values to understand the contribution of different factors in portfolio performance.
  2. It presents the versatile SPPC method for evaluating predictor group contributions to portfolio success.
  3. The SPPC method quantifies predictor impacts and offers insights into changing dynamics over time in financial machine learning.

Quant Letter: March 2024, Week-1

12 implied HN points • 06 Mar 24
💰 Finance Quantitative finance Investment Advice Machine Learning Predictive Models
  1. The author analyzed over 3,450 sources to compile 80 relevant links for their subscribers, who now total 5,200.
  2. The SSRN recently published papers on predicting inflation volatility, intraday volatility in financial data, assessing banking stability, and investment advice.
  3. Readers can access the full post archives with a 7-day free trial to Machine Learning & Quant Finance.

Quant Letter: November 2023, Week 2

17 implied HN points • 08 Nov 23
💰 Finance Quantitative finance Machine Learning
  1. Machine learning methods can enhance portfolio predictability and performance in finance.
  2. Research on transfer risk shows its relevance in stock return prediction and portfolio optimization.
  3. Understanding power-law behavior in volatility models can lead to more accurate pricing and risk management strategies.

Quant Letter: October 2023, Week 3

17 implied HN points • 18 Oct 23
💰 Finance Quantitative finance
  1. Don't rely solely on influencers or academics with a marketing budget for the latest quantitative finance techniques.
  2. Access information directly from academic-practitioners at blog.ml-quant.com for a broader view of quantitative finance research.
  3. Subscribe to Machine Learning & Quant Finance for a 7-day free trial to access more content and archives.

Quant Letter: December 2023, Week-2

12 implied HN points • 13 Dec 23
🕹 Technology Quantitative Blockchain Machine Learning Finance Social Sciences
  1. The ML-Quant website has been revamped and is now free for all users to enjoy the newsletter.
  2. Research papers on SSRN cover various topics like volatility modeling, portfolio asset selection, and sentiment analysis using machine learning.
  3. In the field of quantitative finance, there have been recent advancements in areas such as optimal portfolio selection, volatility forecasting, and financial sentiment analysis.

Quant Letter: November 2023, Week 1

12 implied HN points • 02 Nov 23
💰 Finance Quantitative finance
  1. A new method for analyzing high-frequency financial data shows intraday market changes are mainly driven by intraday correlation changes.
  2. The Chiarella-Heston model, an advanced agent-based model, enhances deep hedging in finance.
  3. Subscribe to Machine Learning & Quant Finance to access more content with a 7-day free trial.

Quant Letter: July 2023, Week 2

17 implied HN points • 12 Jul 23
💰 Finance Quantitative Financial Market Dynamics
  1. Weekly quantitative finance newsletter discussing 'Informed Trading Intensity' using ML indicators in asset management.
  2. Machine learning techniques in finance include diversifying portfolios, tabular learning, and predicting fund performance.
  3. Research in financial markets covers topics like bond fund performance, equity premia, thematic investing, and corporate bond returns prediction.

Quant Letter: September 2023, Week 2

12 implied HN points • 14 Sep 23
💰 Finance Quantitative finance
  1. Quant Letter: September 2023, Week 2 is a weekly quantitative finance newsletter.
  2. A research on improving high-frequency trading systems through low-latency code optimization was recently published.
  3. You can get 7-day free access to Machine Learning & Quant Finance to read more.

Quant Letter: August 2023, Week 1

12 implied HN points • 02 Aug 23
💰 Finance Quantitative finance Machine Learning Research Financial Markets
  1. The featured papers discussed in the newsletter are 'Displaced by Big Data,' 'Deep Learning for Corporate Bonds,' and 'Exploiting the dynamics of commodity futures curves.'
  2. The newsletter highlights research on whether new data diminishes the advantages of active fund managers with industry expertise.
  3. Readers are encouraged to subscribe for a 7-day free trial to access the full post archives.

Quant Letter: July 2023, Week 1

12 implied HN points • 05 Jul 23
💰 Finance Quantitative Newsletter
  1. The newsletter discusses a study on intraday stock predictability with a large dataset.
  2. The author asks for suggestions about papers they might have overlooked in the comment section.
  3. Readers are encouraged to subscribe for a 7-day free trial to access the full post archives.

Quant Letter: June 2023, Week 3

12 implied HN points • 14 Jun 23
💰 Finance Quantitative finance
  1. The newsletter won't be available on the 21st, returning on the 28th with some changes.
  2. There are around 4,700 non-paying subscribers showing interest in research content.
  3. Interested readers can access a 7-day free trial for the full archives.

Quant Letter: March 2024, Week-2

0 implied HN points • 13 Mar 24
💰 Finance Quantitative finance
  1. The post discusses a new rank volatility model for large equity markets that aligns well with empirical data and allows for relative arbitrage.
  2. A new framework for pricing debt securities under varying short-rate differences is introduced.
  3. Readers can access the full post and archives with a 7-day free trial subscription to Machine Learning & Quant Finance.

Quant Letter: November 2024, Week-1

0 implied HN points • 06 Nov 24
💰 Finance Machine Learning Market Analysis Options trading
  1. A new way to measure market uncertainty has been developed by changing the Volatility Index (VIX). This method helps to get a clearer picture of how volatile the market is.
  2. Research on short-term options could improve risk predictions for quick investments. This can help traders make better decisions in fast-moving markets.
  3. Staying updated with recent research in finance can give you an edge. New methods and findings can lead to smarter strategies in trading.

Quant Letter: November 2024, Week-3

0 implied HN points • 20 Nov 24
💰 Finance Risk management Quantitative finance Market Analysis Financial Models Investment Strategies
  1. Vulnerability Conditional Risk Measures help assess risk during financial crises. They focus on understanding tail risks in the market.
  2. Research on heavy-tailed risks can show how certain extreme events might develop. It looks into the behavior of sums of risk factors.
  3. New studies in finance are slowly changing how we understand and measure risk. Keeping up with these developments can improve investment strategies.

Quant Letter: December 2024, Week-2

0 implied HN points • 12 Dec 24
💰 Finance Investment Market Analysis Quantitative finance Trading Strategies Financial Technology
  1. Leveraged ETFs can be useful for long-term investment strategies when using advanced methods like neural networks. They might outperform regular benchmarks if used wisely.
  2. There is a new AI model designed for predicting market volatility, showing the growing role of technology in finance. This can help investors make better decisions.
  3. The importance of keeping up with the latest research and trends in finance is highlighted, as it can give investors a competitive edge. Staying informed is key to success.

Quant Letter: February 2025, Week-3

0 implied HN points • 19 Feb 25
💰 Finance Investing Credit Risk Quantitative Analysis Market Trends Data science
  1. Using data from US corporate bond holdings can help predict credit risk better than traditional ratings. It means more real-time information for making investment decisions.
  2. A new investment strategy called Betting Against Bad Beta is introduced. This strategy aims to improve how investors can bet against stocks with poor performance.
  3. Machine learning is becoming more important in finance, especially for analysis and predicting risks. This technology helps make smarter investment choices.