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
17 implied HN points 21 Feb 24
  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.
12 implied HN points 06 Mar 24
  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.
30 implied HN points 09 Jan 24
  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.
17 implied HN points 14 Feb 24
  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.
21 implied HN points 23 Jan 24
  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.
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21 implied HN points 03 Jan 24
  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.
21 implied HN points 20 Dec 23
  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.
21 implied HN points 29 Nov 23
  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.
17 implied HN points 08 Nov 23
  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.
12 implied HN points 13 Dec 23
  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.
17 implied HN points 18 Oct 23
  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.
12 implied HN points 02 Nov 23
  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.
12 implied HN points 14 Sep 23
  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.
17 implied HN points 12 Jul 23
  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.
12 implied HN points 02 Aug 23
  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.
12 implied HN points 05 Jul 23
  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.
12 implied HN points 14 Jun 23
  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.
0 implied HN points 13 Mar 24
  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.