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Found 14 papers, 2 papers with code
Equity auction dynamics: latent liquidity models with activity acceleration
In this study, we adapt the latent/revealed order book framework to the specifics of equity auctions.
Can ChatGPT Compute Trustworthy Sentiment Scores from Bloomberg Market Wraps?
We used a dataset of daily Bloomberg Financial Market Summaries from 2010 to 2023, reposted on large financial media, to determine how global news headlines may affect stock market movements using ChatGPT and a two-stage prompt approach.
Covariance matrix filtering and portfolio optimisation: the Average Oracle vs Non-Linear Shrinkage and all the variants of DCC-NLS
The Average Oracle, a simple and very fast covariance filtering method, is shown to yield superior Sharpe ratios than the current state-of-the-art (and complex) methods, Dynamic Conditional Covariance coupled to Non-Linear Shrinkage (DCC+NLS).
Recurrent Neural Networks with more flexible memory: better predictions than rough volatility
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or with highly disparate time scales.
When is cross impact relevant?
Trading pressure from one asset can move the price of another, a phenomenon referred to as cross impact.
Price impact in equity auctions: zero, then linear
Using high-quality data, we report several statistical regularities of equity auctions in the Paris stock exchange.
Statistical inference of lead-lag at various timescales between asynchronous time series from p-values of transfer entropy
On the other hand, the asymptotic distribution of Transfer Entropy between two time series is known.
Dissecting the explanatory power of ESG features on equity returns by sector, capitalization, and year with interpretable machine learning
We systematically investigate the links between price returns and Environment, Social and Governance (ESG) scores in the European equity market.
Non-linear shrinkage of the price return covariance matrix is far from optimal for portfolio optimisation
Portfolio optimization requires sophisticated covariance estimators that are able to filter out estimation noise.
Financial factors selection with knockoffs: fund replication, explanatory and prediction networks
We apply the knockoff procedure to factor selection in finance.
Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning
We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices.
Nonparametric sign prediction of high-dimensional correlation matrix coefficients
We introduce a method to predict which correlation matrix coefficients are likely to change their signs in the future in the high-dimensional regime, i. e. when the number of features is larger than the number of samples per feature.
Deep Prediction of Investor Interest: a Supervised Clustering Approach
We propose a novel deep learning architecture suitable for the prediction of investor interest for a given asset in a given time frame.
Sharper asset ranking from total drawdown durations
The total duration of drawdowns is shown to provide a moment-free, unbiased, efficient and robust estimator of Sharpe ratios both for Gaussian and heavy-tailed price returns.
