A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Dark blockchain venues increase miners' profits but raise users' execution risk.
A new sequencing rule prevents miners from front-running transactions in decentralized exchanges.
This paper explores BTC-denominated prediction markets to avoid stablecoin opportunity costs.
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interact…
This paper develops a method to select a reference contract for multi-contract quoting to minimize execution risk.
Paper uses DRL to optimize trade execution, outperforming VWAP and TWAP.
Unified theory for optimal execution through signal-adaptive quotes in limit order books.
A risk of small defined-benefit pension schemes is that there are too few members to eliminate idiosyncratic mortality risk, that is there are too few members to effectively pool mortality risk. This means that when there are few members in the scheme, there is an increased risk of the liability value deviating signifi…
Paper analyzes how latency affects optimal order execution in markets.
The volume weighted average price (VWAP) execution strategy is well known and widely used in practice. In this study, we explicitly introduce a trading volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We then show that the VWAP strategy is the optimal execution strategy for…
When executing their orders, investors are proposed different strategies by brokers and investment banks. Most orders are executed using VWAP algorithms. Other basic execution strategies include POV (also called PVol) -- for percentage of volume --, IS -- implementation shortfall -- or Target Close. In this article ded…
RL optimizes trading algorithms to reduce market impact and costs.
With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally …
HRT uses bi-level reinforcement learning to optimize stock selection and execution in multi-asset equity markets.
The classical literature on optimal liquidation, rooted in Almgren-Chriss models, tackles the optimal liquidation problem using a trade-off between market impact and price risk. Therefore, it only answers the general question of the optimal liquidation rhythm. The very question of the actual way to proceed with liquida…
In this paper we derive the optimal execution trajectory for a trader who wishes to buy or sell a large position of shares which evolve as a geometric Brownian process in contrast to the arithmetic model which prevails in the existing literature, and with a general temporary impact . We provide a couple of examples …
The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning fr…
Develops a new model to optimize trading in markets.
Optimal execution of portfolio transactions is the essential part of algorithmic trading. In this paper we present in simple analytical form the optimal trajectory for risk-averse trader with the assumption of exponential market recovery and short-time investment horizon.
Trading large volumes of a financial asset in order driven markets requires the use of algorithmic execution dividing the volume in many transactions in order to minimize costs due to market impact. A proper design of an optimal execution strategy strongly depends on a careful modeling of market impact, i.e. how the pr…
Model shows how Ethereum can capture MEV from block construction, but centralization remains a concern.
In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station …
Optimal order execution strategies for brokers under reference benchmarks.
Short-term incentives lead to riskier trading strategies.
Enhances cryptocurrency pair trading with DRL, outperforming classical methods.
Derives metrics for DeFi vaults, addressing credit risk.
This paper optimizes trading strategies to minimize risk and maximize profit while accounting for market uncertainty.
MPC framework reduces execution costs and schedule deviations in trading.
FlowOE learns from experts to optimize financial trades.
Modeling liquidity risk in financial markets using agent-based simulation.
This study measures liquidity risks in Aave, a blockchain lending protocol.
TensorOpt finds optimal parallelization strategies for DNN training.
A new reinforcement learning framework separates users into risk-tolerant and risk-averse groups for better performance.
Intelligent Personal Assistants (IPAs) have become widely popular in recent times. Most of the commercial IPAs today support a wide range of skills including Alarms, Reminders, Weather Updates, Music, News, Factual Questioning-Answering, etc. The list grows every day, making it difficult to remember the command structu…
In the seminal paper on optimal execution of portfolio transactions, Almgren and Chriss (2001) define the optimal trading strategy to liquidate a fixed volume of a single security under price uncertainty. Yet there exist situations, such as in the power market, in which the volume to be traded can only be estimated and…
Faster Ethereum slots boost CEX-DEX arbitrage by 535% and 203%.
This research simplifies lending pools in decentralized finance for better understanding and security.
Uniswap V3 requires more decisions from liquidity providers, making it complex and risky.
We devise an optimal allocation strategy for the execution of a predefined number of stocks in a given time frame using the technique of discrete-time Stochastic Control Theory for a defined market model. This market structure allows an instant execution of the market orders and has been analyzed based on the assumptio…
Federated Learning tackles limited user participation with a new risk-aware approach.
This work analyzes how users and services adapt to reduce risk, leading to specialization.
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits like Theano, CNTK, and TensorFlow, the user first defines a computation graph (a symbolic representation of the computation), and then exampl…
Compound examines decentralized lending users and their short loan durations.
The paper develops a hybrid model for optimal order execution in markets with heterogeneous market makers.
Enhances cooperative multi-task SemCom for distributed users.
IllinoisSL is a Java library for learning structured prediction models. It supports structured Support Vector Machines and structured Perceptron. The library consists of a core learning module and several applications, which can be executed from command-lines. Documentation is provided to guide users. In Comparison to …
We solve a version of the optimal trade execution problem when the mid asset price follows a displaced diffusion. Optimal strategies in the adapted class under various risk criteria, namely value-at-risk, expected shortfall and a new criterion called "squared asset expectation" (SAE), related to a version of the cost v…
Due to recent technological developments, Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been successfully used to process and extract knowledge from a variety of complex problems. However, a thorough ML approach is complex and highly dependent on the problem at hand. Additionally, implementing …