arXiv research

The paper analyzes trading strategies in a competitive market with incomplete information.

Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.

Model predicts trading strategies based on latent demand and price impact.

This paper investigates the equilibrium interactions between trading targets and private information in a multi-period Kyle (1985) market. There are two investors who each follow dynamic trading strategies: A strategic portfolio rebalancer who engages in order splitting to reach a cumulative trading target and an uncon…

Market-maker optimizes quotes based on strategic market-takers' behavior.

New paper finds strategic trade centralization benefits firms, while naive centralization often harms them.

This paper analyzes the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can invest both in low-emitting production technologies and trade permits. In the model, technology adoption and allowance price are generated…

Look-ahead reasoning helps predict strategic user behavior on learning platforms.

This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.

This study analyzes global oil trade networks to assess their efficiency and robustness.

Model predicts option movements using residual transactions for better market timing.

Investors can enhance their portfolios by strategically using LETFs, especially with dynamic strategies.

Investors can achieve optimal risk-reward trade-offs with bonds and stocks under mean-reverting stock returns.

Informed traders strategically reveal noisier signals, making prices less responsive to public information.

We propose a design for schedule-based execution trading strategies based on uncertainty bands. This formulation: 1) simplifies strategy specification and implementation; 2) provides for flexible allocation among passive, opportunistic, aggressive, and dark pool crossing execution tactics; 3) allows for rapid enhanceme…

This article is a prologue to the article "Why Markets are Inefficient: A Gambling 'Theory' of Financial Markets for Practitioners and Theorists." It presents important background for that article --- why gambling is important, even necessary, for real-world traders --- the reason for the superiority of the strategic/g…

The dynamics of many socioeconomic systems is determined by the decision making process of agents. The decision process depends on agent's characteristics, such as preferences, risk aversion, behavioral biases, etc.. In addition, in some systems the size of agents can be highly heterogeneous leading to very different i…

Investors' strategic trading affects asset prices, modeled as a game.

AI algorithms outperform traditional trading methods in stock markets.

This paper combines LLMs with RL for better trading strategies.

The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.

Optimizes trade execution with reinforcement learning for limit orders.

Optimal trading strategies identified in electricity markets with a major player.

Study Nash equilibrium between broker and trader in a lit exchange with price impact.

Trade networks, across which countries distribute their products, are crucial components of the globalized world economy. Their structure is strongly heterogeneous across products, given the different features of the countries which buy and sell goods. By using a diversified pool of indicators from network science and …

DeltaHedge uses AI to optimize portfolio options trading.

Endogenous business cycles explain higher comovement across countries.

In illiquid markets, option traders may have an incentive to increase their portfolio value by using their impact on the dynamics of the underlying. We provide a mathematical framework within which to value derivatives under market impact in a multi-player framework by introducing strategic interactions into the Almgre…

Trading-R1 uses LLMs for financial trading, improving risk-adjusted returns.

XGBoost detects unlawful insider trading with high accuracy.

This paper combines RL with CPPI and TIPP for better trading strategies.

The paper analyzes trade execution strategies for large traders in a stochastic market environment.

Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.

We study the competition of two strategic agents for liquidity in the benchmark portfolio tracking setup of Bank, Soner, Voß (2017). Specifically, both agents track their own stochastic running trading targets while interacting through common aggregated temporary and permanent price impact à la Almgren and Chriss (2001…

European steel industry shifts to electric arc furnaces, reducing scrap imports and increasing competition.

Model explains periodic trading in financial markets through game theory.

Hybrid model combines VAR and neural network for OFI prediction.

New framework for robust uncertainty quantification in strategic settings.

Deep RL algorithm trades high-dimensional stock portfolios.

An ability to postpone one's execution without penalty provides an important strategic advantage in high-frequency trading. To elucidate competition between traders one has to formulate to a quantitative theory of formation of the execution price from market expectations and quotes. This theory was provided in 2005 by …

Paper generalizes strategic classification framework and introduces SVC for PAC-learning.

Study strategic dynamic pricing for buyers with unknown manipulation costs.

Develops a mathematical model for CLMM dynamics in DeFi.

Optimal trading strategy between CEXs and DEXs with priority fees and stochastic delays.

A game-theoretic analysis of DEX competition through dynamic trading fees.

New framework reduces strategic manipulation cost for minority groups in fair classification.

DRL automates stock market trading with a 2.68 Sharpe Ratio.

Study compares employers with and without anticipating strategic labor force responses.