Modeling price formation in intraday electricity markets with renewable generation.
problem Price formation and optimal trading strategies in intraday electricity markets with intermittent renewable generation.
method Developed a tractable equilibrium model using stochastic control theory to identify optimal strategies and exhibit Nash equilibrium.
result Identified optimal trading strategies and exhibited Nash equilibrium in closed form for a finite number of agents and in the asymptotic framework of mean field games.
We seek to utilize the nonextensive statistics to the microscopic modeling of the interacting many-investor dynamics that drive the price changes in a market. The statistics of price changes are known to be fit well by the Students-T and power-law distributions of the nonextensive statistics. We therefore derive models…
Study on price formation in a market with a major player and minor firms.
problem Equilibrium price formation in a market with a major financial firm and many minor firms.
method Analyzes the equilibrium price process in both finite and mean field models, considering idiosyncratic and common noises.
result Derives the functional form of price impact for the major firm in both market sizes.
Model asset pricing with habit formation in a large market.
problem Understanding asset pricing in large heterogeneous markets with habit formation.
method Mean field game theory and quadratic-growth mean field BSDEs.
result Derives a semi-analytic solution for asset pricing model.
Deep model predicts Bitcoin price movements without retraining.
problem Stationary modelling of high-frequency Bitcoin price movements.
method Deep recurrent model based on order flow.
result Model maintains stability during volatile periods.
Analyzes how order flow affects price formation in financial markets.
problem Understanding how prices are formed by order flow in financial markets.
method Critical discussion of modeling approaches and empirical observations, focusing on market impact and transaction costs.
result Algorithmic trading impacts the quality and cost of trading.
MiFID II impacts European stock liquidity and price formation.
problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.
Bayesian framework explains price formation with learning and market impact.
problem Understanding how prices form in markets with informed participants.
method Introduces a Bayesian model for updating priors on efficient prices.
result Exponential intensities for aggressive order arrivals are a natural outcome.
We explore nature of price formation in financial markets and develop a theory of bid and ask price dynamics in which the two prices form due to quantum-chaotic interaction between buy and sell orders. In this model bid and ask prices are represented by eigenvalues of a 2x2 price operator corresponding to 'bid' and 'as…
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.
This paper studies how relative performance concerns affect stock prices in a tree-like market model.
problem The impact of relative performance concerns on stock prices in a tree-like market model.
method Mean-field equilibrium analysis in a binomial tree framework with exponential utility.
result Existence and uniqueness of market-clearing mean-field equilibrium in both single- and multi-population settings.
Game-theoretic models predict asset prices in financial markets.
problem Understanding price formation in financial markets with limited liquidity.
method Developed game-theoretic models for many-person and mean-field games, derived analytical formulas, and numerically assessed results.
result The derived price converges to the mean-field counterpart under specific conditions.
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
We develop a theory of securities price formation and dynamics based on quantum approach and without presuming any similarities with quantum mechanics. Disorder introduced by trading environment leads to probability distribution of returns that is not a smooth curve, but a speckle-pattern fluctuating in both price coor…
Study on stock price formation on trees with multi-population and non-rational agents.
problem Equilibrium price formation for risky stock with multi-population and non-rational agents.
method Combining mean-field game theory with binomial tree framework, proving existence of unique equilibrium, deriving explicit formula for transition probabilities.
result Existence of unique mean-field market-clearing equilibrium with explicit analytic formula for stock price transition probabilities.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
XGB-Chiarella model generates realistic intra-day financial price data using agent-based models.
problem Generating accurate intra-day financial price data for research and risk management.
method Agent-based financial market simulation with XGBoost machine learning calibration.
result XGB-Chiarella model accurately reflects real market behaviours and generates realistic price time series.
The study identifies features making cross-impact relevant in explaining price variance of US assets.
problem Understanding the relevance of cross-impact in explaining price variance of US assets.
method Using tick-by-tick data spanning 5 years for 500 US assets, the study investigates the features making cross-impact relevant.
result Price formation is endogenous within highly liquid assets, influencing less liquid correlated products with a constrained impact velocity.
A simple Ising spin model which can describe the mechanism of price formation in financial markets is proposed. In contrast to other agent-based models, the influence does not flow inward from the surrounding neighbors to the center site, but spreads outward from the center to the neighbors. The model thus describes th…
Study on price formation among investors with exponential utility and liabilities.
problem Equilibrium price formation among investors with heterogeneous risk-averseness and liabilities.
method Mean-field game theory and mean-field backward stochastic differential equations (BSDE).
result Existence of equilibrium risk-premium process and market clearing in the large population limit.
Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of electronic market quotes and transactions for US equities, we uncover nonparametric evidence for the existence of a universal and stationary price formation mechanism relating the dynamics of supply and demand for a s…
Distributions of assets returns exhibit a slight skewness. In this note we show that our model of endogenous price formation \cite{Reimann2006} creates an asymmetric return distribution if the price dynamics are a process in which consecutive trading periods are dependent from each other in the sense that opening price…
We consider an auction market in which market makers fill the order book during a given time period while some other investors send market orders. We define the clearing price of the auction as the price maximizing the exchanged volume at the clearing time according to the supply and demand of each market participants.…
New method identifies informed traders in prediction markets.
problem How information is incorporated into market prices is unknown.
method Kyle model applied to field experiment prediction market data.
result Traders with significant price impact are identified as informed.
Study optimal auction formats for maximizing MEV on Ethereum.
problem Maximizing extractable value from Ethereum auctions.
method Empirical analysis of 2.2 million transactions, modeling affiliation among bidders.
result English and second-price sealed-bid auctions dominate other formats, with significant revenue losses.
The formation of price in a financial market is modelled as a chain of Ising spin with three fundamental figures of trading. We investigate the time behaviour of the model, and we compare the results with the real EURO/USD change rate. By using the test of local Poisson hypothesis, we show that this minimal model leads…
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.
This paper develops a model of liquidity provision in financial markets by adapting the Madhavan, Richardson, and Roomans (1997) price formation model to realistic order books with quote discretization and liquidity rebates. We postulate that liquidity providers observe a fundamental price which is continuous, efficien…
We use standard physics techniques to model trading and price formation in a market under the assumption that order arrival and cancellations are Poisson random processes. This model makes testable predictions for the most basic properties of a market, such as the diffusion rate of prices, which is the standard measure…
When modelling stock market dynamics, the price formation is often based on an equilbrium mechanism. In real stock exchanges, however, the price formation is goverend by the order book. It is thus interesting to check if the resulting stylized facts of a model with equilibrium pricing change, remain the same or, more g…
Efficiently price high-dimensional Bermudan options using tensor compression.
problem High-dimensional option pricing with computational complexity.
method Hierarchical tensor compression for Monte Carlo and dual martingale methods.
result Tensor compression alleviates the curse of dimensionality for Bermudan option pricing.
New model predicts energy prices under different scenarios.
problem Complex causal relationships in energy markets with continuous regime changes.
method Augmented Time Series Structural Causal Models (ATSCM) integrating neural causal discovery.
result Enables novel counterfactual queries in energy markets.
Although behavioral economics has demonstrated that there are many situations where rational choice is a poor empirical model, it has so far failed to provide quantitative models of economic problems such as price formation. We make a step in this direction by developing empirical models that capture behavioral regular…
A deterministic trading strategy can be regarded as a signal processing element that uses external information and past prices as inputs and incorporates them into future prices. This paper uses a market maker based method of price formation to study the price dynamics induced by several commonly used financial trading…
The use of kinetic modelling based on partial differential equations for the dynamics of stock price formation in financial markets is briefly reviewed. The importance of behavioral aspects in market booms and crashes and the role of agents' heterogeneity in emerging power laws for price distributions is emphasized and…
Blockchain markets with paid-priority trading can lead to biased prices and reduced liquidity.
problem Discrete clearing and paid-priority in blockchain markets lead to biased prices and reduced liquidity.
method Developed a model to evaluate the viability of blockchain markets under discrete clearing and paid-priority.
result Paid-priority ordering induces endogenous selection, leading to biased prices and reduced liquidity.
Study on price formation in financial markets with a single default event.
problem Equilibrium price formation in financial markets with a single default risk.
method Characterized optimal strategies using quadratic-growth BSDEs, derived market-clearing condition, and established mean-field BSDE solvability.
result Characterized equilibrium risk premium and its dependence on default risk factors.
This article aims to present an elementary analytical solution to the question of the formation of a structure of differentiation of rates of return in a classical gravitation model and in a model of the dynamics of price-wage spirals.
This paper analyses the relationship between BitCoin price and supply-demand fundamentals of BitCoin, global macro-financial indicators and BitCoin attractiveness for investors. Using daily data for the period 2009-2014 and applying time-series analytical mechanisms, we find that BitCoin market fundamentals and BitCoin…
Study shows how macroeconomic news affects intraday price and order flow dynamics.
problem Understanding how macroeconomic news impacts intraday price and order flow dynamics.
method Structural VAR model identified through heteroskedasticity, estimated at one-second frequency for each 15-minute interval.
result Macroeconomic news announcements reshape price-flow dynamics, with significant impacts on price and flow impacts at the one-second horizon.
Opinions and beliefs determine the evolution of social systems. This is of particular interest in finance, as the increasing complexity of financial systems is coupled with information overload. Opinion formation, therefore, is not always the result of optimal information processing. On the contrary, agents are bounded…
Article examines NFT market microstructure and trading risks.
problem Difficulty in distinguishing genuine NFTs from fads and scams.
method Analyzes price formation, market structure, and transparency.
result Provides due-diligence pointers to mitigate NFT trading risk.
Modeling dynamic groundwater markets with price formation and trading strategies.
problem Understanding competitive effects in environmental markets with groundwater banking.
method Stochastic models and game theory with machine learning algorithms.
result Sub-game perfect Nash equilibria characterized by groundwater price processes.
Study examines Bitcoin market fragmentation and price formation, revealing market leader-lagger dynamics and trading strategies.
problem Understanding price formation in fragmented Bitcoin markets at sub-second time scales.
method Utilized granular orderbook and trades data, constructed features, and trained linear models to explain market returns.
result Fee regime determines market leadership and profitability of taker strategies, maker strategies tested in real-world trading.
In this chapter we review some recent results on the dynamics of price formation in financial markets and its relations with the efficient market hypothesis. Specifically, we present the limit order book mechanism for markets and we introduce the concepts of market impact and order flow, presenting their recently disco…
We analyze a controlled price formation experiment in the laboratory that shows evidence for bubbles. We calibrate two models that demonstrate with high statistical significance that these laboratory bubbles have a tendency to grow faster than exponential due to positive feedback. We show that the positive feedback ope…
We propose that predictability is a prerequisite for profitability on financial markets. We look at ways to measure predictability of price changes using information theoretic approach and employ them on all historical data available for NYSE 100 stocks. This allows us to determine whether frequency of sampling price c…
Proposes a neural network for efficient imbalance electricity price forecasting.
problem Accurate and efficient imbalance electricity price forecasting in industrial energy trading systems.
method Market-rule-informed neural network framework.
result The proposed model achieves competitive forecasting performance with fewer parameters and shorter training time.