arXiv research

Extended model ensures long-term survival of traders in limited stock market participation.

Proves existence of equilibrium in limited participation economy.

The paper proves an equilibrium in a limited stock market participation model with power utilities.

Paper models limit order book with informed traders and market makers.

Flexible device participation improves federated learning convergence.

Federated Learning tackles limited user participation with a new risk-aware approach.

Artemis framework improves distributed learning with bidirectional compression and partial participation.

FedAvg converges linearly to global minimum in federated learning with partial participation.

DNI recovers missing brain data from corrupted recordings.

Thanks to the access to labeled orders on the Cac40 index future provided by Euronext, we are able to quantify market participants contributions to the volatility in the diffusive limit. To achieve this result we leverage the branching properties of Hawkes point processes. We find that fast intermediaries (e.g., market…

ClusterLOB clusters market events to identify different trading behaviors.

Financial exchanges provide incentives for limit order book (LOB) liquidity provision to certain market participants, termed designated market makers or designated sponsors. While quoting requirements typically enforce the activity of these participants for a certain portion of the day, we argue that liquidity demand t…

FedSGM tackles constrained federated learning with unified framework.

This paper is split in three parts: first we use labelled trade data to exhibit how market participants accept or not transactions via limit orders as a function of liquidity imbalance; then we develop a theoretical stochastic control framework to provide details on how one can exploit his knowledge on liquidity imbala…

New methods improve subgroup analysis in trials with limited data.

FedSyn generates synthetic data from multiple organizations' datasets.

Optimizes capital structure for life insurance companies with surplus participation.

This paper analyzes tokenized U.S. Treasuries, revealing patterns and roles in blockchain transactions.

Paper proposes MMVFL for multi-class VFL with multiple participants.

The paper uncovers two key laws of market impact influenced by volume and participation rate.

AHEAD improves financial market efficiency through ad-hoc auctions.

This paper optimizes portfolio compression by reducing excess notional in market contracts.

Local adaptation improves federated learning models.

Unified analysis of FL with arbitrary client participation.

Traditionally, psychophysical experiments are conducted by repeated measurements on a few well-trained participants under well-controlled conditions, often resulting in, if done properly, high quality data. In recent years, however, crowdsourcing platforms are becoming increasingly popular means of data collection, mea…

Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to …

A small investor provides liquidity at the best bid and ask prices of a limit order market. For small spreads and frequent orders of other market participants, we explicitly determine the investor's optimal policy and welfare. In doing so, we allow for general dynamics of the mid price, the spread, and the order flow, …

In this paper we develop a new form of agent-based model for limit order books based on heterogeneous trading agents, whose motivations are liquidity driven. These agents are abstractions of real market participants, expressed in a stochastic model framework. We develop an efficient way to perform statistical calibrati…

FedAMD framework improves federated learning with partial client participation.

Paper tackles unknown participation in FL, proposing FedAU for better performance.

This paper analyzes and improves convergence in federated learning with biased client selection.

Methods for automatic analysis of clinical data are usually targeted towards a specific modality and do not make use of all relevant data available. In the field of male human reproduction, clinical and biological data are not used to its fullest potential. Manual evaluation of a semen sample using a microscope is time…

This paper analyzes error feedback in compressed federated learning for non-convex optimization problems.

The purpose of this article is to introduce, analyze and compare two performance participation methods based on a portfolio consisting of two risky assets: Option-Based Performance Participation (OBPP) and Constant Proportion Performance Participation (CPPP). By generalizing the provided guarantee to a participation in…

Data poisoning attacks can severely degrade FL models, especially targeting specific classes.

In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand …

In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), multiple types of data are collected on a single set of healthy participants at multiple timepoints in order to characterize and optimize wellness. One way to do this is to identify clusters, or subgroups, among the participants, and then to tailor pe…

New framework UIFV reconstructs private features in VFL without model details.

This paper poses a few fundamental questions regarding the attributes of the volume profile of a Limit Order Books stochastic structure by taking into consideration aspects of intraday and interday statistical features, the impact of different exchange features and the impact of market participants in different asset s…

Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli. Rarely do researchers attempt to model and examine how individual participants vary from each other -- a question that should be addressable even in small samples given the right statistical tools. We propose …

Gamification enhances law enforcement training on terrorism financing.

Large trades in a financial market are usually split into smaller parts and traded incrementally over extended periods of time. We address these large trades as hidden orders. In order to identify and characterize hidden orders we fit hidden Markov models to the time series of the sign of the tick by tick inventory var…

Federated learning leaks participant dataset quality even with secure aggregation.

ISP improves GNN expressivity by stratifying nodes based on graph invariants.

Due to the limited predictability of wind power and other stochastic generation, trading this energy in competitive electricity markets is challenging. This paper derives revenue-maximising and risk-constrained strategies for stochastic generators participating in electricity markets with a single-price balancing mecha…

We build an agent-based model for the order book with three types of market participants: informed trader, noise trader and competitive market makers. Using a Glosten-Milgrom like approach, we are able to deduce the whole limit order book (bid-ask spread and volume available at each price) from the interactions between…

Paper proposes decentralized annuities for better retirement security.

A new federated learning framework ensures fairness and robustness.