The hottest Quantitative Substack posts right now

Using Autoencoder architectures in Statistical Arbitrage can simplify strategy development and improve returns compared to traditional methods.
A new method, Causal-NECOVaR, provides reliable risk predictions for financial risk analysis regardless of market shocks and systemic changes.
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.

The Combinatorial Purged Cross-Validation (CPCV) method is superior in financial analytics for reducing overfitting risks.
SPX options data analysis finds limitations in accurately capturing implied volatility using Volterra Bergomi models.
Incorporating Risk premia strategies in portfolios can lessen left-tail exposure, but diversification within options requires maximizing volatility parameters.

The blog post discusses various research papers on topics like financial risk modeling, interest rate models, and credit risk stress testing.
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.
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.

Recent research is exploring innovative methods for quantitative investing, such as using deep learning algorithms and new portfolio optimization models.
There are profitable opportunities in the ETF lending market due to cost differences between borrowing ETFs and stocks, creating room for cross-ETF arbitrage.
Studies are showcasing the importance of adaptive investment strategies focused on resilience, active ownership, and broader financial models to navigate fast-changing environments.

Large trades have a smaller impact than predicted by linear models due to concavity, following a 'square-root law'.
Price dislocations gradually dissipate over time, influencing statistical arbitrage strategies.
Algorithms are used for in-depth analysis of earnings call transcripts by investment funds for comprehensive insights.

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Research papers on SSRN cover various topics like volatility modeling, portfolio asset selection, and sentiment analysis using machine learning.
In the field of quantitative finance, there have been recent advancements in areas such as optimal portfolio selection, volatility forecasting, and financial sentiment analysis.

Weekly quantitative finance newsletter discussing 'Informed Trading Intensity' using ML indicators in asset management.
Machine learning techniques in finance include diversifying portfolios, tabular learning, and predicting fund performance.
Research in financial markets covers topics like bond fund performance, equity premia, thematic investing, and corporate bond returns prediction.

The newsletter discusses a study on intraday stock predictability with a large dataset.
The author asks for suggestions about papers they might have overlooked in the comment section.
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