The study predicts trader actions and price movements in forex markets using lead-lag networks.
problem Predicting trader behavior and price movements in foreign exchange markets.
method Infer lead-lag networks from trader-resolved data in the foreign exchange market.
result Trader actions and price movements can be predicted from past prices and trader behavior.
We present a model of predatory traders interacting with each other in the presence of a central reserve (which dissipates their wealth through say, taxation), as well as inflation. This model is examined on a network for the purposes of correlating complexity of interactions with systemic risk. We suggest the use of s…
Method infers trader lead-lag networks to reveal market dynamics.
problem Understanding herding and liquidity in financial markets.
method Kinetic Ising model and inference algorithm to reconstruct trader opinions.
result Identifies leading players and links herding to liquidity.
Deep learning mimics successful traders in financial markets.
problem Tackling profitable trading behavior in financial markets.
method Using deep learning neural networks to replicate adaptive trading behavior.
result Deep learning can outperform human traders and even improve performance.
This study analyzes stock trading networks to quantify price impacts based on trader positions.
problem Quantifying the immediate price impact of trades in stock markets.
method Constructed stock trading networks using k-shell decomposition to classify traders and compare different market segments. result Institutional traders have lower price impacts compared to individuals at the same positions in the trading network.
Study reveals asymmetry in market dynamics across different timescales.
problem Understanding asymmetry in market dynamics across different timescales.
method Infer lead-lag networks for multiple timescales to capture causal structure.
result Strong and complex asymmetric influence of timescales on lead-lag networks.
Study reveals patterns in trader clusters over time, improving investment predictions.
problem Managing diverse trader risk in financial services.
method Clustered trader data analyzed using Ewens' Sampling Distribution and Aggregating Algorithm (AA). Statistically Validated Networks (SVN) applied for improved results.
result Temporal distributions of trader clusters follow Ewens' Sampling Distribution, and AA can be improved with SVN.
Building on similarities between earthquakes and extreme financial events, we use a self-organized criticality-generating model to study herding and avalanche dynamics in financial markets. We consider a community of interacting investors, distributed on a small-world network, who bet on the bullish (increasing) or bea…
Model predicts crashes in rational expectation bubbles using percolation theory.
problem Predicting crashes in rational expectation bubbles.
method Micro-founded model based on percolation theory of trader networks.
result Estimates crash hazard rate via percolation clusters and power law.
We discuss how minimal financial market models can be constructed by bridging the gap between two existing, but incomplete, market models: a model in which a population of virtual traders make decisions based on common global information but lack local information from their social network, and a model in which the tra…
DeepTrader learns to mimic a successful trader from market data.
problem Creating an algorithmic trader that performs as well as a human one.
method Trains a deep learning neural network on Level-2 LOB data to mimic a trader's quotes.
result DeepTrader can match or outperform existing algorithmic trading systems.
Traders adopt different trading strategies to maximize their returns in financial markets. These trading strategies not only results in specific topological structures in trading networks, which connect the traders with the pairwise buy-sell relationships, but also have potential impacts on market dynamics. Here, we pr…
We study trade-based manipulation of stock prices from the perspective of complex trading networks constructed by using detailed information of trades. A stock trading network consists of nodes and directed links, where every trader is a node and a link is formed from one trader to the other if the former sells shares …
Study shows HFT benefits large traders under certain conditions.
problem Influence of high-frequency traders (HFTs) on large traders.
method Analyzes the impact of HFT front-running on large traders under different conditions.
result HFT benefits large traders when there is high-speed noise trading and vague HFT predictions.
Investigates market dynamics with informed traders and high-frequency traders.
problem Trading large orders in a market with multiple high-frequency traders.
method Analyzes a three-period Kyle's model with a normal-speed informed trader and multiple anticipatory high-frequency traders under different inventory pressures.
result Surprising results: improving HFTs' speed or prediction can harm them but benefit the informed trader.
Framework explains deep learning candlestick recognition.
problem Deep learning models explain candlestick patterns in a black box.
method Local search adversarial attacks to explain model reasoning.
result Model perceives candlestick patterns similarly to human traders.
Real world markets display power-law features in variables such as price fluctuations in stocks. To further understand market behavior, we have conducted a series of market experiments on our web-based prediction market platform which allows us to reconstruct transaction networks among traders. From these networks, we …
The paper extends option pricing theory for markets with informed traders.
problem Discontinuity in option pricing for markets with informed traders.
method New models for option pricing in complete markets considering informed traders' information on stock price direction and return mean.
result The discontinuity puzzle in option pricing is resolved using continuous diffusion price processes.
An informed broker optimizes trading strategies in a market influenced by many traders.
problem Optimizing trading strategies for an informed broker in a market with many traders.
method Developed a mean-field game approach to derive equilibrium strategies for both the broker and traders.
result The broker's optimal strategy involves a Stackelberg equilibrium, leading and traders following.
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.
PRZI traders adapt their quote-prices based on a strategy parameter s, affecting market dynamics.
problem Understanding the dynamics of continuous double auction markets with adaptive traders.
method Introduced a new zero-intelligence trader PRZI that uses a parameterised probability distribution to generate quote-prices. Used a stochastic hill-climber algorithm to adapt strategies based on market conditions.
result The co-evolutionary dynamics of PRZI traders can lead to rich and complex market behaviors, including periods of stability and change.
Study shows unique linear equilibrium in market with constrained trader.
problem Unique equilibrium in financial market with constrained trader.
method Linear equilibrium model with competitive market makers and noise traders.
result Equilibrium uniquely determined by two state variables.
Model shows traders' swarm behavior in markets with subjective predictions.
problem Understanding swarm behavior in markets with traders having subjective market predictions.
method Combination of priority queueing model and mean field theory, nonlinear Markov model.
result Swarm behavior emerges due to traders' reactions to market conditions, not their subjective predictions.
Modeling market dynamics with informed and uninformed traders and fads.
problem Optimizing market making in a market with fads, informed, and uninformed traders.
method Characterizing the optimal liquidity provision problem in a market with fads, informed, and uninformed traders, considering both complete and partial information.
result The price of liquidity is a function of the proportion of informed traders, and strategies ignoring fads underperform.
Solves a game between brokers and informed traders using stochastic differential equations.
problem Optimizing wealth in a game between brokers and informed traders with private signals.
method Closed-form solutions to a mean-field game using forward-backward SDEs.
result Optimal trading strategies for both brokers and informed traders are found.
This paper optimizes brokers' manipulation of prices to maximize traders' losses.
problem Optimizing brokers' manipulation of prices to maximize traders' losses.
method Assuming total control over asset prices, the paper shows how brokers can find a maximum loss price movement in quadratic time.
result Brokers can find a maximum loss price movement in quadratic time given a set of trades.
High-frequency traders can act as either small informed traders or round-trippers, affecting price discovery and liquidity.
problem Effects of high-frequency trading on price discovery and liquidity.
method Extended Kyle's model with interactions between large informed traders and high-frequency traders.
result High-frequency traders can act as Small-IT or Round-Tripper, impacting price discovery and liquidity.
Endogenous randomness emerges from adversarial market learning.
problem Market randomness
method Deterministic adversarial market model
result Out-of-sample profitability collapses to zero.
Model shows how multiple markets can coexist or fragment based on trader behavior.
problem Understanding market competition and coexistence among multiple trading venues.
method Stylized model of traders making repeated decisions at three markets, analyzed numerically and analytically.
result Parameters like memory length and choice intensity determine whether markets coexist or fragment.
Traders underestimated risk-free rates, leading to poor investments.
problem Incorrect setting of risk-free rates by traders.
method Analysis of investment decisions and financial models.
result Underestimating risk-free rates led to flawed investment decisions.
Brokers and an informed trader compete for liquidity, affecting trading costs and inventory risk.
problem How brokers and an informed trader manage liquidity and trading costs.
method Sequential Stackelberg game, solving for trading strategies, numerical solutions.
result Equilibrium strategies and liquidity prices determined, not Pareto efficient.
Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.
problem Uncertain participation of informed traders in a market with limit orders.
method Characterized equilibrium by a fixed point integral equation, analyzed large order asymptotics, solved numerically.
result Equilibrium price impact depends on both asset value and distribution of informed traders, not just expected number of informed traders.
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.
Maximizing trading volume in online learning framework between traders.
problem Maximizing the total number of trades between traders with unknown valuations.
method Developed algorithms for brokers to maximize trading volume under different feedback scenarios.
result Achieved logarithmic and poly-logarithmic regret rates for different feedback models.
This paper improves robot traders' market impact sensitivity.
problem Market impact in automated trading systems.
method Critiqued existing methods, introduced MLOFI, and demonstrated new algorithms.
result New imbalance-sensitive trader-agents exhibit market impact effects.
This study models AI traders' impact on financial markets using a multi-agent framework.
problem Lack of a comprehensive model to assess AI traders' effects on market price formation and volatility.
method Developed a multi-agent market model with microfoundations of the GARCH model.
result Validated the model through simulations and analyzed AI traders' impact.
In a very simple stock market, made by only two \emph{initially equivalent} traders, we discuss how the information can affect the performance of the traders. More in detail, we first consider how the portfolios of the traders evolve in time when the market is \emph{closed}. After that, we discuss two models in which a…
The study reveals traders' risk aversion and a new risk premium from market volumes.
problem Understanding traders' rationality and risk aversion from market volumes.
method Optimal Merton dynamics model to estimate average risk aversion and price of risk.
result Validation of the proposed trading strategy model on real data.
Trading strategy advantage based on information asymmetry.
problem Trading advantage due to information disparity.
method Modeling market information, analyzing risk-neutral distribution, proving value difference.
result First trader's position is strictly more valuable than the second.
Deep neural network learns optimal trading controls for high-frequency finance.
problem Optimal trading on high-frequency data with market impact and limited data.
method Deep neural network, Monte-Carlo initialization, transfer learning, explainable controls.
result Neural network learns optimal controls for trader preferences.
Using a model of wealth distribution where traders are characterized by quenched random saving propensities and trade among themselves by bipartite transactions, we mimic the enhanced rates of trading of the rich by introducing the preferential selection rule using a pair of continuously tunable parameters. The biparti…
Hybrid model simulates market dynamics using neural stochastic background traders.
problem Lack of realistic LOB simulations that combine historical data and dynamic interactions.
method Neural stochastic background trader trained on historical LOB data, embedded in multi-agent simulation.
result Hybrid model recreates stylised market facts and financial herding behaviors.
Bitcoin option prices reflect both market maker supply and trader demand, especially from those with insider information.
problem Understanding how market prices of bitcoin options are influenced by both market makers and informed traders.
method Analysis of Deribit options tick-level data to identify supply and demand effects.
result At-the-money option prices are driven by volatility traders, while out-of-the-money options are influenced by both volatility traders and those with insider information.
We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor, we show that this allows the fast trader to obtain risk free profits, but that these profits cannot be scaled. We derive the fast trader's…
Deep learning predicts risky behavior in retail investors for financial risk management.
problem Predicting profitable trading behavior in retail investors.
method Developed a deep learning model to predict trader profitability.
result Deep learning outperforms conventional machine learning methods in predicting trader behavior.
We consider an ideal closed stock market, in which 100 traders have economic activities. The assets of the traders change through buying and selling stocks. We simulate the assets under conservation of both total currency and total number of stocks. If the traders are identical, then the assets are distributed as a sta…
This paper detects fraudulent trading in the NFT market.
problem Fraudulent activities like wash trading in the NFT market.
method Unsupervised learning using K-means clustering on market data.
result Identified groups of traders with suspicious behavior.
Paper proposes a new method to simulate realistic markets from data.
problem Lack of accurate market simulators leading to misleading conclusions.
method Proposes a world agent model trained on historical data without agent calibration.
result Models consistently outperform previous methods in realism and responsiveness.