ARL and Hawkes processes improve market-making strategies with variable volatility.
problem Enhancing market-making strategies to adapt to varying volatility levels and self-exciting behaviors.
method Integrates ARL, Hawkes processes, and variable volatility levels; shifts from Poisson to Hawkes process.
result 4-action MM trained in low-volatility environment adapts to high-volatility conditions, providing stable performance.
Proposes a deep RL approach for high-frequency market making using tick data and periodic signals.
problem Challenges in high-frequency market making due to tick-level data complexity and high trading volume.
method Integrates tick-level data with periodic signals using deep reinforcement learning.
result The proposed framework outperforms existing methods in profitability and risk management.
A new high-frequency market making strategy using Deep Hawkes process.
problem Optimizing high-frequency trading in volatile markets.
method Developed a Deep Hawkes process to model order arrivals and their effects on the limit order book.
result The new strategy outperforms traditional methods in market making.
Market making is one of the most important aspects of algorithmic trading, and it has been studied quite extensively from a theoretical point of view. The practical implementation of so-called "optimal strategies" however suffers from the failure of most order book models to faithfully reproduce the behaviour of real m…
IMM uses imitation learning and predictive representation learning to improve market making strategies.
problem Challenges in training RL agents for multi-price level market making strategies.
method IMM combines RL and imitation learning, introducing effective state and action representations and a representation learning unit.
result IMM outperforms existing RL-based market making strategies in financial criteria.
Optimal market making strategy for electronic markets with persistent order flows.
problem Market making on electronic markets with persistent order flows.
method Formulated as a stochastic control problem, characterized by viscosity solutions, and implemented numerically.
result Characterization of an optimal market making strategy.
Modeling market makers' quoting strategies to understand price impact.
problem Understanding how price impact arises from market makers' quoting strategies.
method Modeling market making as a dynamic auction using Stochastic Differential Games and finding Nash Equilibrium.
result The price impact function derived from market makers' strategies matches the Almgren-Chriss model.
This paper introduces a new market making approach using scaled beta distributions.
problem Inventory management challenges faced by market makers.
method Scaled beta distribution policies for flexible market making actions.
result Flexibility in volume distribution across price intervals improves market making performance.
Adaptive market-making strategy improves profit by adjusting to order flow.
problem Optimizing market-making profits in a dynamic market environment.
method Closed-form solutions for optimal bid-ask spreads, modeling demand randomness, and adapting to market order behavior.
result Adaptive strategies outperform fixed and non-adaptive strategies.
Investigates optimal strategies for market makers using internal liquidity.
problem Optimizing strategies for market makers with internal liquidity.
method Investigates optimal multi-objective strategy for market makers with internal liquidity.
result Draws important qualitative insights for real-world trading.
AMSAs adaptively manage crypto-currency trading by selecting multiple strategies based on market conditions.
problem Maximizing gains in volatile crypto-currency markets with high uncertainty.
method AMSAs use multiple sub-agents with different strategies, dynamically selecting them based on market conditions.
result AMSAs can achieve high positive alpha in long-term crypto-currency trading.
Study finds users mostly use recent market and decision information to guess market direction.
problem Limited ability to model and predict human decision-making in stock markets.
method Used networks inference with stochastic block models (SBM) to find most predictive model of unobserved decisions.
result Users mostly use recent information to guess market direction, and their decision-making strategies are analogous to behaviors in other contexts.
Optimal market making strategy with price forecasts reduces inventory costs and spreads.
problem Optimal market making strategy with price forecasts reduces inventory costs and spreads.
method Modeling market making strategy with linear price impact, random slope and intercept, and simultaneous order arrivals.
result Simultaneous order arrivals and price forecasts reduce inventory costs and spreads.
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.
We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be \emph{efficient with respect to resources S} (e.g., time, memory) if no strategy using resources S can make a profit. As a first step, we consider memory-m strategies whose a…
Investment strategies in financial markets can lead to instability due to market impacts.
problem Market impacts make it impossible for investors to accurately optimize their strategies.
method Built an agent-based model with technical analysis strategy agents to investigate optimization instability.
result Investment strategies' parameters never converged but continued to change, leading to unstable market price evolution.
AI simplifies trading strategies, potentially making markets more efficient.
problem Efficient market hypothesis (EMH) relies on traders optimising trading strategies based on information.
method Generalised notion of market efficiency, distinguishing model complexity through investor beliefs and trading strategies.
result Increased availability of low-cost AI systems may push towards more advanced trading strategies, potentially harder for inefficient traders.
Adaptive robust strategy improves online portfolio selection by managing market trends and costs.
problem Optimizing sequential investment decisions in volatile markets.
method Robust optimization with adaptive parameter adjustment.
result Adaptive scheme outperforms existing strategies in cumulative returns and Sharpe ratios.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
Novel method reconstructs liquidity data for CLMMs, optimizing dynamic liquidity strategies.
problem Challenges in evaluating and optimizing CLMMs due to lack of historical liquidity data.
method Reconstructs historical liquidity states from swap transaction data using machine learning.
result Identifies outperformance of dynamic liquidity strategies over uniform allocation benchmarks.
Study solves DREs for trading strategies using signals and past prices.
problem Solving DREs for optimal trading strategies.
method Analyzes DREs with indefinite matrix coefficients and applies to trading problems.
result Derives optimal trading strategies using signals and past prices.
Paper proposes a new framework for combining investment strategies without market-specific assumptions.
problem Lack of a distribution-free and consistent preference framework for decision-making in combining investment strategies.
method Introduces a novel framework for decision-making in combining strategies, free from market conditions and statistical assumptions.
result Proposed strategies outperform individual component strategies in long-term wealth accumulation, with small tradeoffs in Sharpe ratios.
New method detects heuristics in complex game strategies.
problem Understanding decision-making in games with infinite strategy spaces.
method Introducing decoupled strategies to detect convergence towards Nash equilibria.
result Predictive measure ΔD reveals participants' actions with high success rate.
Market confidence is essential for successful investing. By incorporating multi-market into the evolutionary minority game, we investigate the effects of investor beliefs on the evolution of collective behaviors and asset prices. When there exists another investment opportunity, market confidence, including overconfide…
This paper optimizes cryptocurrency portfolios by integrating sentiment analysis with technical indicators.
problem Effective portfolio management in volatile cryptocurrency markets.
method Dynamic portfolio strategy using technical indicators and sentiment analysis.
result The integrated approach outperforms traditional benchmarks and achieves stronger risk-adjusted returns.
Study evaluates discretized arbitrage strategies in fractional financial markets.
problem Serial correlation in financial markets with fractional Brownian motion.
method Revisit and transfer Shiryaev and Salopek's strategies to a real-world setting, distretizing dynamics and introducing transaction costs.
result Both strategies are promising with respect to terminal portfolio values and loss probabilities.
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
Optimal market making improves liquidity in prediction markets.
problem Efficient price discovery in prediction markets.
method Stochastic control framework for optimal market making.
result Optimal market quotes improve downside protection and profit.
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.
Paper explores MM strategies that can refuse to quote or provide single-sided quotes.
problem Overcoming risks in market making due to changing market conditions.
method Adversarial reinforcement learning with new MM agent designs.
result Refusal to quote or providing single-sided quotes can improve MM performance.
Study optimizes market making in Chinese stock market with stochastic control and scenario analysis.
problem Limited research on market making in Chinese stock market.
method Optimal market making framework with exponential CARA utility function, accounting for market conditions and risks.
result Impact of volatility and stamp duty on market maker's profit and liquidity.
An arbitrage strategy allows a financial agent to make certain profit out of nothing, i.e., out of zero initial investment. This has to be disallowed on economic basis if the market is in equilibrium state, as opportunities for riskless profit would result in an instantaneous movement of prices of certain financial ins…
Investment strategy for NYSE stocks minimizes market correlation.
problem Minimizing market correlation for steady returns.
method Combining momentum, fundamentals, and analyst recommendations; feature selection; backtesting various portfolio construction methods.
result Risk parity outperformed other methods, offering higher Sharpe ratio and lower beta.
Study shows market makers can cooperate without communication.
problem Concerns of collusion in AI-driven market-making.
method Formulated as a repeated game, studied with Q-learning.
result Market makers can learn cooperative strategies without communication.
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
Optimal stock trading strategy with market orders and limit orders in a risky market.
problem Finding the best time and amount to place market and limit orders to minimize costs.
method Analyzes single and multi-period models with limit and market orders, considering liquidity risk.
result Optimal placement of market and limit orders can be determined under different market conditions.
A new macroscopic market making model connects market making and optimal execution.
problem Connecting market making and optimal execution problems.
method Using continuous processes for orders, the model bridges the gap between market making and optimal execution.
result Demonstrates the model's effectiveness through various noise and intensity function scenarios.
In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold…
This thesis studies CPMMs with CL, developing strategies for LTs and LPs.
problem Trading mechanisms and strategies for CPMMs with CL.
method Formalizes CPMMs with CL, develops strategies using market data and models.
result Derives optimal strategies for LTs and LPs in CPMMs with CL.
Paper uses conformal prediction for solar power forecasting in electricity markets.
problem Enhancing participation in electricity markets through accurate day-ahead PV power predictions.
method Combines machine learning for point predictions and conformal prediction for uncertainty quantification.
result CP with k-nearest neighbors and Mondrian binning outperforms linear quantile regressors in predicting PV power.
This paper explores how RL enhances HFT strategies in volatile markets.
problem Adapting to changing market dynamics in HFT.
method Deep Q-Learning applied to statistical arbitrage strategies.
result RL improves adaptability and profitability in HFT.
Paper analyzes liquidity for everlasting options in DeFi, offering strategies to reduce costs.
problem Challenges of perpetual derivatives in decentralized finance markets.
method Dynamic proactive market maker model, simulations, hedging strategies.
result Liquidity providers can achieve net positive PnL with effective strategies.
Modeling option market making with hedging-induced price impact.
problem Tackles the challenge of market making in options markets with price impact.
method Models option order flow using Cox processes and studies the dynamics of inventory and price under hedging-induced impact.
result Establishes the well-posedness of the mixed control problem involving quoting and hedging.
Investment strategies involving cryptocurrencies and VIX INDEX show positive impact in market performance.
problem Investment strategies involving cryptocurrencies and VIX INDEX.
method Parameter estimation on raw data, comparison of two different portfolios, and analysis of different market conditions.
result VIX INDEX positively impacts the investment portfolio of cryptocurrencies in both standard and downward markets.
This study optimizes trading and arbitrage in decentralized finance's CPMs, revealing convexity costs and developing efficient strategies.
problem Optimizing trading and arbitrage in decentralized finance's constant product markets (CPMs).
method Developed models for CPMs in competing centralised exchanges, CPMs, and both venues. Derived computationally efficient strategies.
result Accurately estimated convexity costs in CPMs, which are linear in trade size and nonlinear in liquidity depth and exchange rate.
We propose a microstructural modeling framework for studying optimal market making policies in a FIFO (first in first out) limit order book (LOB). In this context, the limit orders, market orders, and cancel orders arrivals in the LOB are modeled as Cox point processes with intensities that only depend on the state of …
Market makers face a trade-off between fill probability and post-fill returns, requiring contrarian strategies.
problem Navigating the trade-off between fill probability and post-fill returns in market making.
method Analysis of live trading data from Binance Bitcoin perpetual.
result A negative correlation between maker fill likelihood and post-fill returns, necessitating contrarian strategies.
The portfolio optimisation problem, first raised by Harry Markowitz in 1952, has been a fundamental and central topic to understanding the stock market and making decisions. There has been plenty of works contributing to development of the mean-variance optimisation (MVO) so far. In this paper, one kind of them, namely…