Search Results for author:
Found 28 papers, 4 papers with code
Why Are Learned Indexes So Effective?
A recent trend in algorithm design consists of augmenting classic data structures with machine learning models, which are better suited to reveal and exploit patterns and trends in the input data so to achieve outstanding practical improvements in space occupancy and time efficiency.
Dimensionality reduction techniques to support insider trading detection
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets.
Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying
Optimal execution is an important problem faced by any trader.
Online Learning of Order Flow and Market Impact with Bayesian Change-Point Detection Methods
behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.
A machine learning approach to support decision in insider trading detection
The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers.
How Covid mobility restrictions modified the population of investors in Italian stock markets
There are thus indications that the lockdown, and more generally the Covid pandemic, created a sort of regime change in the population of financial investors.
Transient impact from the Nash equilibrium of a permanent market impact game
We show that an implied transient impact arises from the Nash equilibrium between a directional trader and one arbitrageur in a market impact game with fixed and permanent impact.
From Zero-Intelligence to Queue-Reactive: Limit Order Book modeling for high-frequency volatility estimation and optimal execution
Building on this approach, in this paper we introduce three main innovations: (i) we use as data-generating process the Queue-Reactive model of the limit order book (Huang et al. (2015)), which - compared to the Zero-Intelligence model - generates more realistic microstructure dynamics, as shown here by using an Hausman test; (ii) we consider not only estimators of the integrated volatility but also of the spot volatility; (iii) we show the relevance of the estimator in the prediction of the variance of the cost of a simulated VWAP execution.
Information dynamics of price and liquidity around the 2017 Bitcoin markets crash
We study the information dynamics between the largest Bitcoin exchange markets during the bubble in 2017-2018.
Order flow and price formation
I present an overview of some recent advancements on the empirical analysis and theoretical modeling of the process of price formation in financial markets as the result of the arrival of orders in a limit order book exchange.
Analysis of bank leverage via dynamical systems and deep neural networks
We find that the parameters of a substantial fraction of banks lie in the dynamical core, and their leverage time series are consistent with a chaotic behavior.
Estimating the Total Volume of Queries to a Search Engine
The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data).
On the equivalence between the Kinetic Ising Model and discrete autoregressive processes
Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity.
Statistical Mechanics Econometrics Data Analysis, Statistics and Probability
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model
A common issue when analyzing real-world complex systems is that the interactions between the elements often change over time: this makes it difficult to find optimal models that describe this evolution and that can be estimated from data, particularly when the driving mechanisms are not known.
Temporal mixture ensemble models for intraday volume forecasting in cryptocurrency exchange markets
We study the problem of the intraday short-term volume forecasting in cryptocurrency exchange markets.
Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages
Identifying risk spillovers in financial markets is of great importance for assessing systemic risk and portfolio management.
Instabilities in Multi-Asset and Multi-Agent Market Impact Games
We consider the general problem of a set of agents trading a portfolio of assets in the presence of transient price impact and additional quadratic transaction costs and we study, with analytical and numerical methods, the resulting Nash equilibria.
A tale of two sentiment scales: Disentangling short-run and long-run components in multivariate sentiment dynamics
We propose a novel approach to sentiment data filtering for a portfolio of assets.
Unveiling the relation between herding and liquidity with trader lead-lag networks
We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade.
Clusters of investors around Initial Public Offering
The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored.
Crossover from linear to square-Root market impact
Using a large database of 8 million institutional trades executed in the U. S. equity market, we establish a clear crossover between a linear market impact regime and a square-root regime as a function of the volume of the order.
Trading and Market Microstructure Statistical Mechanics
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links.
Corporate payments networks and credit risk rating
Aggregate and systemic risk in complex systems are emergent phenomena depending on two properties: the idiosyncratic risks of the elements and the topology of the network of interactions among them.
Social and Information Networks Risk Management 91D30, 05C82, 91G40, 68T05 91D30
Disentangling group and link persistence in Dynamic Stochastic Block models
We analytically and numerically characterize the detectability transitions of such algorithm as a function of the memory parameters of the model and we make a comparison with a full dynamic inference.
Modeling FX market activity around macroeconomic news: a Hawkes process approach
We present a Hawkes model approach to foreign exchange market in which the high frequency price dynamics is affected by a self exciting mechanism and an exogenous component, generated by the pre-announced arrival of macroeconomic news.
Trading and Market Microstructure
How markets slowly digest changes in supply and demand
In this article we revisit the classic problem of tatonnement in price formation from a microstructure point of view, reviewing a recent body of theoretical and empirical work explaining how fluctuations in supply and demand are slowly incorporated into prices.
Trading and Market Microstructure Statistical Mechanics Physics and Society
Cluster analysis for portfolio optimization
We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio.
Physics and Society Other Condensed Matter Statistical Finance
Topology of correlation based minimal spanning trees in real and model markets
We present here a topological characterization of the minimal spanning tree that can be obtained by considering the price return correlations of stocks traded in a financial market.
Statistical Mechanics
