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Found 34 papers, 11 papers with code
Deep Limit Order Book Forecasting
We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange.
Topological Portfolio Selection and Optimization
Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance.
Unraveling the Enigma of Double Descent: An In-depth Analysis through the Lens of Learned Feature Space
Double descent presents a counter-intuitive aspect within the machine learning domain, and researchers have observed its manifestation in various models and tasks.
Homological Convolutional Neural Networks
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e. g., image, audio, and text data).
ESG Reputation Risk Matters: An Event Study Based on Social Media Data
We investigate the response of shareholders to Environmental, Social, and Governance-related reputational risk (ESG-risk), focusing exclusively on the impact of social media.
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability.
FTX's downfall and Binance's consolidation: The fragility of centralised digital finance
This paper investigates the causes of the FTX digital currency exchange's failure in November 2022.
Topological Feature Selection
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations.
Regime-based Implied Stochastic Volatility Model for Crypto Option Pricing
The increasing adoption of Digital Assets (DAs), such as Bitcoin (BTC), rises the need for accurate option pricing models.
Anatomy of a Stablecoin's failure: the Terra-Luna case
We quantitatively describe the main events that led to the Terra project's failure in May 2022.
Dependency structures in cryptocurrency market from high to low frequency
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange.
Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series
We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module.
Dynamic Portfolio Optimization with Inverse Covariance Clustering
Market conditions change continuously.
Heterogenous criticality in high frequency finance: a phase transition in flash crashes
Flash crashes in financial markets have become increasingly important attracting attention from financial regulators, market makers as well as from the media and the broader audience.
Self-organised criticality in high frequency finance: the case of flash crashes
With the rise of computing and artificial intelligence, advanced modeling and forecasting has been applied to High Frequency markets.
An Information Filtering approach to stress testing: an application to FTSE markets
We present a novel methodology to quantify the "impact" of and "response" to market shocks.
Portfolio Optimization with Sparse Multivariate Modelling
Portfolio optimization approaches inevitably rely on multivariate modeling of markets and the economy.
Deep Reinforcement Learning for Active High Frequency Trading
The training is performed on three contiguous months of high frequency Limit Order Book data, of which the last month constitutes the validation data.
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk
We observe long memory in the evolution of structures from correlation filtering, with a two regime power law decay in the number of persistent simplicial complexes.
Deep Learning modeling of Limit Order Book: a comparative perspective
The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading.
Topological regularization with information filtering networks
A methodology to perform topological regularization via information filtering network is introduced.
Stress testing and systemic risk measures using multivariate conditional probability
In the study of systemic risk in a financial system, the multivariate conditional probability distribution can be used for stress-testing by quantifying the propagation of losses from a set of `stressing' variables to another set of `stressed' variables.
Sector Neutral Portfolios: Long memory motifs persistence in market structure dynamics
We observe long-memory processes in these structures in the form of power law decays in the number of persistent motifs.
P2P Loan acceptance and default prediction with Artificial Intelligence
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans.
Risk Management General Finance
Learning Clique Forests
Through this move the decomposability and calculation of scores is performed incrementally at the variable (rather than edge) level, and this provides better computational performance and an intuitive application of multivariate statistical tests.
Cryptocurrency market structure: connecting emotions and economics
The study of the causality structure reveals a causality network that is consistently related with the correlation structures and shows that both prices cause sentiment and sentiment cause prices across currencies with the latter being stronger in size but smaller in number of significative interactions.
Statistical Finance Physics and Society Trading and Market Microstructure
Predicting future stock market structure by combining social and financial network information
We demonstrate that future market correlation structure can be predicted with high out-of-sample accuracy using a multiplex network approach that combines information from social media and financial data.
Statistical Finance
Forecasting market states
In another experiment, with again one hundred log-returns and two states, we demonstrate that this procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy.
The multiplex dependency structure of financial markets
We propose here a multiplex network approach to investigate simultaneously different types of dependency in complex data sets.
Physics and Society Computational Engineering, Finance, and Science Statistical Finance
Parsimonious modeling with Information Filtering Networks
We also discuss performances with sparse factor models where we notice that relative performances decrease with the number of factors.
A nonlinear impact: evidences of causal effects of social media on market prices
Online social networks offer a new way to investigate financial markets' dynamics by enabling the large-scale analysis of investors' collective behavior.
Statistical Finance Computers and Society Data Analysis, Statistics and Probability Computational Finance
Twitter Sentiment Analysis: Lexicon Method, Machine Learning Method and Their Combination
We present a new ensemble method that uses a lexicon based sentiment score as input feature for the machine learning approach.
Network Filtering for Big Data: Triangulated Maximally Filtered Graph
TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling.
Nested hierarchies in planar graphs
We construct a partial order relation which acts on the set of 3-cliques of a maximal planar graph G and defines a unique hierarchy.
Geometric Topology
