The paper analyzes trading strategies in a competitive market with incomplete information.
problem Strategic trading under uncertainty when firms lack full knowledge of competitors' strategies.
method Bayesian games framework to incorporate uncertainty and derive optimal trading strategies.
result Uncertainty significantly impacts trading strategies compared to complete information scenarios.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
problem Strategic interactions and information leakage in broker-mediated markets.
method Study of strategic trading behavior and information leakage in a broker-mediated market.
result Brokers hold a strategic advantage over informed traders due to information leakage in trading flows.
Model predicts trading strategies based on latent demand and price impact.
problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.
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.
problem Optimizing market-making strategies in a strategic trading environment.
method Mean-field game approach to model strategic market-takers and derive optimal controls.
result Derivation of optimal controls for market-maker and market-taker.
New paper finds strategic trade centralization benefits firms, while naive centralization often harms them.
problem Finding optimal trading strategies in competitive markets.
method Complete solution to finding equilibrium strategies in competition using Fourier Series methods.
result Firms that strategically centralize trades generally benefit, while naive centralization often harms them.
Two strategic agents track their portfolios, influencing each other's trading targets.
problem Strategic competition in portfolio tracking with price impact.
method Stochastic linear quadratic differential game with terminal state constraints.
result Unique open-loop Nash equilibrium strategies emerge based on price impact types.
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.
problem Optimization criteria on learning platforms do not reflect users' priorities.
method Formalized level-k thinking and contrasted collective and selfish behavior.
result Coordination benefits users but does not offer higher-level reasoning advantages in the long run.
This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.
problem Wash trading in cryptocurrency markets to inflate volume and manipulate market conditions.
method Analysis of 18 million Mt. Gox transactions, exogenous demand shock study.
result Wash trading intensifies in low legitimate trading volume and responds to demand shocks, indicating strategic behavior.
This study analyzes global oil trade networks to assess their efficiency and robustness.
problem Dynamic monitoring and warning of international trade risks in global oil trade.
method Constructing unweighted and weighted global oil trade networks (OTNs) using UN Comtrade data from 1988 to 2017, and applying complex network theories.
result Efficiency of oil flows increases with complexity of OTNs, and weighted efficiency indicators highlight major events.
Model predicts option movements using residual transactions for better market timing.
problem Predicting option movements using standard metrics like open interest and trading volume.
method Analyzes residual transactions, integrates machine learning and regression techniques.
result Identifies early indicators of market trends for better option price forecasting.
Investors can enhance their portfolios by strategically using LETFs, especially with dynamic strategies.
problem Unsuitability of passive or static approaches to LETFs leads to undesirable risk-return profiles.
method Demonstrated the effectiveness of simple dynamic strategies in exploiting favorable Omega ratio dynamics.
result Dynamic strategies can exploit the compounding effect of LETFs, improving risk-return profiles.
Investors can achieve optimal risk-reward trade-offs with bonds and stocks under mean-reverting stock returns.
problem Optimizing investment strategies with mean-reverting stock returns.
method Calculus of variations to derive the entire family of extremal strategies, not just the optimal ones.
result The value of the portfolio is effectively bounded from below, providing a 'guarantee' on the horizon.
Informed traders strategically reveal noisier signals, making prices less responsive to public information.
problem How informed traders strategically reveal signals impacts market prices and utility.
method Modeling a market with an informed trader, an uninformed trader, and liquidity providers, proving equilibrium existence.
result In equilibrium, the insider strategically reveals a noisier signal, 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.
problem Investors' trading rates influence asset prices in dynamic markets.
method Model as a non-zero sum singular stochastic differential game, establishing equivalence between best-response and auxiliary control problems.
result Unique Nash equilibrium is deterministic with a closed-form solution.
AI algorithms outperform traditional trading methods in stock markets.
problem Traditional trading methods struggle with risk management and edge over classical approaches.
method Used Deep Reinforcement Learning (DRL) algorithms (DDQN and PPO) to compare with Buy and Hold benchmark.
result DRL algorithms provide a substantial edge over classical approaches in terms of risk-adjusted returns.
This paper combines LLMs with RL for better trading strategies.
problem Myopic behavior and opaque policies in RL for trading.
method LLMs generate strategic trading advice to guide RL agents.
result LLM-guided RL agents outperform unguided RL in return and risk metrics.
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.
Optimizes trade execution with reinforcement learning for limit orders.
problem Maximizing revenue in a limit order book with market and limit orders.
method Formulated as a dynamic allocation task, uses multivariate logistic-normal distributions for efficient training.
result Outperforms traditional strategies in simulated environments.
Optimal trading strategies identified in electricity markets with a major player.
problem Price formation and optimal trading in intraday electricity markets with strategic interactions.
method Stochastic control theory and mean field games with a major player.
result Nash equilibrium identified in closed form for the asymptotic case.
Study Nash equilibrium between broker and trader in a lit exchange with price impact.
problem Optimizing trading strategies between informed and uninformed traders with broker's inventory penalties.
method Characterized Nash equilibrium through FBSDEs, solved explicitly.
result Explicit solution to trading strategies of broker and informed trader.
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.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
Endogenous business cycles explain higher comovement across countries.
problem Standard models struggle to explain high comovement in business cycles across countries.
method Developed a demand-driven reduced-form model with strategic complementarities and international trade linkages.
result Combining endogenous business cycles with exogenous shocks matches empirical comovement levels.
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.
problem Lack of interpretability and trust in AI for finance.
method Supervised fine-tuning and reinforcement learning with a curriculum.
result Improved risk-adjusted returns and lower drawdowns compared to other models.
XGBoost detects unlawful insider trading with high accuracy.
problem Detecting unlawful insider trading from large volumes of transactions.
method Applying eXtreme Gradient Boosting (XGBoost) for identifying and ranking key features.
result XGBoost achieves 97% accuracy in detecting unlawful transactions.
This paper combines RL with CPPI and TIPP for better trading strategies.
problem Challenges in quantitative trading due to swift dynamics and uncertainties.
method Fusion of CPPI and TIPP with MADDPG framework for multi-agent reinforcement learning.
result CPPI-MADDPG and TIPP-MADDPG outperform traditional strategies in real-market shares.
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
problem Analyzing trade execution strategies in a stochastic market with price impact.
method Formulated a Markov game model and used backward induction method of dynamic programming.
result Explicit closed-form execution strategy at Markov perfect equilibrium.
Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.
problem Effect of traders' strategic behavior on market efficiency and informational asymmetry.
method Examines a market with boundedly rational, asymmetrically informed traders using multiarmed bandit algorithms.
result Strategically acting traders can lead to more efficient markets than purely competitive ones under certain conditions.
European steel industry shifts to electric arc furnaces, reducing scrap imports and increasing competition.
problem Reducing CO2 emissions in the European steel industry through electric arc furnaces.
method Combining trade data with business intelligence to model the impact of EAF capacity on scrap trade.
result Scrap imports decrease as EAF capacity increases, highlighting the need for a new business ecosystem.
Model explains periodic trading in financial markets through game theory.
problem Understanding periodic trading activities in financial markets.
method Mean-field liquidation game with major-minor players.
result Existence and uniqueness of Nash equilibrium established.
Hybrid model combines VAR and neural network for OFI prediction.
problem Accurate prediction of Order Flow Imbalance (OFI) in high frequency trading.
method Combines Vector Auto Regression (VAR) and a simple feedforward neural network (FNN).
result Hybrid model achieves superior predictive accuracy compared to standalone models.
New framework for robust uncertainty quantification in strategic settings.
problem Machine learning model predictions can be strategically altered by informed agents.
method Strategic Conformal Prediction framework
result Theoretical guarantees and experimental validation show remarkable effectiveness.
Deep RL algorithm trades high-dimensional stock portfolios.
problem Trading high-dimensional stock portfolios with data gaps and non-unique history lengths.
method Deep Q-learning algorithm, sequentially setting up environments, rewarding based on asset returns and cash reservation.
result Algorithm outperforms all passive and active benchmarks by a large margin.
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.
problem Strategic manipulation of testing data to fool classifiers.
method Unified framework for strategic classification, strategic VC-dimension (SVC).
result Characterizes the learnability and computational tractability of linear classifiers.
Study strategic dynamic pricing for buyers with unknown manipulation costs.
problem Strategic buyers manipulate their features to get lower prices, hindering profit maximization.
method Proposes a strategic dynamic pricing policy that incorporates strategic behavior and binary response data.
result Achieves sublinear regret bound of O ( T ) O(\sqrt{T}) O ( T ) compared to linear Ω ( T ) Ω(T) Ω ( T ) regret of non-strategic policies. Develops a mathematical model for CLMM dynamics in DeFi.
problem Analyzing CLMMs in continuous time trading.
method Modeling CLMM dynamics as measure-valued processes, examining three arbitrage models.
result Trading fees limit admissible price processes, impacting CLMM design.
Optimal trading strategy between CEXs and DEXs with priority fees and stochastic delays.
problem Managing latency risk in trading between centralized and decentralized exchanges.
method Developed a mixed control framework combining absolutely continuous controls with impulse interventions, allowing for stochastic execution delays and multiple pending orders.
result Optimal priority fee selection significantly outperforms non-strategic fee selection.
A game-theoretic analysis of DEX competition through dynamic trading fees.
problem Competition between decentralized exchanges (DEXs) and their impact on trading fees and slippage.
method Characterization of an approximate Nash equilibrium via coupled system of partial differential equations and closed-form expressions for equilibrium fees.
result The equilibrium trading fees shift from the oracle price to a weighted average of the oracle and competitors' exchange rates under competition.
New framework reduces strategic manipulation cost for minority groups in fair classification.
problem Strategic manipulation disparities in fair classification.
method Constrained optimization framework that constructs classifiers to reduce strategic manipulation cost for minority groups.
result Empirically, the approach reduces strategic manipulation cost for minority groups over multiple real-world datasets.
DRL automates stock market trading with a 2.68 Sharpe Ratio.
problem Automating profitable trades in the stock market.
method Formulated as a POMDP, solved with TD3 algorithm.
result 2.68 Sharpe Ratio on unseen data.
Study compares employers with and without anticipating strategic labor force responses.
problem Understanding and optimizing strategic interactions in labor markets.
method Formulation of causal strategic classification, theory, and experiments.
result Performatively optimal hiring policies improve employer and labor outcomes, but can also harm labor force utility.