The hottest Quantitative finance Substack posts right now

And their main takeaways
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Top Finance Topics

Quant Letter: May 2025, Week-4

The Parlour • 4 implied HN points • 30 May 25
💰 Finance Investing Trading Economics Financial Analysis Quantitative finance
  1. Using machine learning can help build models to categorize investors based on their behavior. This method faces challenges in being validated and understood.
  2. Research is exploring how to optimize portfolios over a longer time frame. This could help in making better financial decisions.
  3. Synthetic data created by agent-based models can provide valuable insights for testing and understanding trading strategies.

Nerds are better investors

Klement on Investing • 2 implied HN points • 05 Jun 25
💰 Finance Investment Data science Market Analysis Quantitative finance Portfolio Management
  1. More companies are hiring data scientists to help with investment decisions. This often leads to better returns for those companies.
  2. Hiring data scientists can help firms focus more on specific investments, which improves their insight and portfolio performance.
  3. However, too much reliance on data scientists can make the stock market less efficient, leaving room for traditional analysts to find good investment opportunities.

Quant Letter: January 2025, Week-1

The Parlour • 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.
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Quant Letter: November 2023, Week 4

The Parlour • 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: October 2023, Week 2

The Parlour • 21 implied HN points • 12 Oct 23
💰 Finance Quantitative finance Machine Learning Reinforcement Learning
  1. The post is about a quantitative finance newsletter for October 2023, Week 2.
  2. A recently published thesis discusses Deep RL for Portfolio Allocation, showing the potential of deep reinforcement learning in enhancing portfolio allocation methods.
  3. Readers can subscribe to Machine Learning & Quant Finance for more content and a 7-day free trial.

Quant Letter: March 2024, Week-1

The Parlour • 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: October 2023, Week 3

The Parlour • 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: November 2023, Week 1

The Parlour • 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: September 2023, Week 2

The Parlour • 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

The Parlour • 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: March 2024, Week-2

The Parlour • 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: December 2024, Week-2

The Parlour • 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.

The problem with regression betas

Musings on Markets • 0 implied HN points • 11 Feb 09
💰 Finance Investing Economics Risk management Market Analysis Statistical Methods Investments Risk management Market Analysis Economics Quantitative finance
  1. Regression betas can be unreliable because they come with a standard error, meaning the estimated beta can vary widely.
  2. Using different time frames or market indices can give you different beta values for the same company, and there's no one 'correct' beta.
  3. Regression betas are based on past data, so they may not accurately reflect a company's future risk as its business model or debt levels change.

Quant Letter: November 2024, Week-3

The Parlour • 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.