A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
The study reveals traders' risk aversion and a new risk premium from market volumes.
We introduce a model of super-exponential financial bubbles with two assets (risky and risk-free), in which rational investors and noise traders co-exist. Rational investors form expectations on the return and risk of a risky asset and maximize their constant relative risk aversion expected utility with respect to thei…
It is essential to incorporate the impact of investor behavior when modeling the dynamics of asset returns. In this paper, we reconcile behavioral finance and rational finance by incorporating investor behavior within the framework of dynamic asset pricing theory. To include the views of investors, we employ the method…
LLMs mimic human traders in finance, but not as much as expected.
In this paper, we address one of the main puzzles in finance observed in the stock market by proponents of behavioral finance: the stock predictability puzzle. We offer a statistical model within the context of rational finance which can be used without relying on behavioral finance assumptions to model the predictabil…
We present a plausible micro-founded model for the previously postulated power law finite time singular form of the crash hazard rate in the Johansen-Ledoit-Sornette model of rational expectation bubbles. The model is based on a percolation picture of the network of traders and the concept that clusters of connected tr…
The Sornette-Ide differential equation of herding and rational trader behaviour together with very small random noise is shown to lead to crashes or bubbles where the price change goes to infinity after an unpredictable time. About 100 time steps before this singularity, a few predictable roughly log-periodic oscillati…
Episodes of market crashes have fascinated economists for centuries. Although many academics, practitioners and policy makers have studied questions related to collapsing asset price bubbles, there is little consensus yet about their causes and effects. This review and essay evaluates some of the hypotheses offered to …
Study examines how traders with asymmetric information and adaptive learning strategies affect market efficiency.
Model explains periodic trading in financial markets through game theory.
The modelling of financial markets presents a problem which is both theoretically challenging and practically important. The theoretical aspects concern the issue of market efficiency which may even have political implications \cite{Cuthbertson}, whilst the practical side of the problem has clear relevance to portfolio…
The paper explains financial volatility using simple news-driven models.
A Kyle-inspired model with adaptive agents explains excess volatility and volatility clustering.
The goal of developing a firmer theoretical understanding of inhomogenous temporal processes -- in particular, the waiting times in some collective dynamical system -- is attracting significant interest among physicists. Quantifying the deviations in the waiting-time distribution away from one generated by a random pro…
The study examines how backrun auctions can protect traders from price manipulation.
We investigate activities that have different periods of duration. We define the profit intensity as a measure of this economic category. The profit intensity in a repeated trading has a unique property of attaining its maximum at a fixed point regardless of the shape of demand curves for a wide class of probability di…
We present a simple model of a non-equilibrium self-organizing market where asset prices are partially driven by investment decisions of a bounded-rational agent. The agent acts in a stochastic market environment driven by various exogenous "alpha" signals, agent's own actions (via market impact), and noise. Unlike tra…
Study shows HFT benefits large traders under certain conditions.
Investigates market dynamics with informed traders and high-frequency traders.
The paper extends option pricing theory for markets with informed traders.
An informed broker optimizes trading strategies in a market influenced by many traders.
Study Nash equilibrium between broker and trader in a lit exchange with price impact.
PRZI traders adapt their quote-prices based on a strategy parameter s, affecting market dynamics.
Study shows unique linear equilibrium in market with constrained trader.
Modeling market dynamics with informed and uninformed traders and fads.
Solves a game between brokers and informed traders using stochastic differential equations.
High-frequency traders can act as either small informed traders or round-trippers, affecting price discovery and liquidity.
Traders underestimated risk-free rates, leading to poor investments.
Model shows how multiple markets can coexist or fragment based on trader behavior.
We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stoc…
Brokers and an informed trader compete for liquidity, affecting trading costs and inventory risk.
Study a market with uncertain informed traders, finding price impact depends on both asset value and informed trader count distribution.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
Maximizing trading volume in online learning framework between traders.
This paper improves robot traders' market impact sensitivity.
We introduce the concept of "negative bubbles" as the mirror image of standard financial bubbles, in which positive feedback mechanisms may lead to transient accelerating price falls. To model these negative bubbles, we adapt the Johansen-Ledoit-Sornette (JLS) model of rational expectation bubbles with a hazard rate de…
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…
This study models AI traders' impact on financial markets using a multi-agent framework.
Trading strategy advantage based on information asymmetry.
A combination of a priority queueing model and mean field theory shows the emergence of traders' swarm behavior, even when each has a subjective prediction of the market driven by a limit order book. Using a nonlinear Markov model, we analyze the dynamics of traders who select a favorable order price taking into accoun…
Study reveals patterns in trader clusters over time, improving investment predictions.
Bitcoin option prices reflect both market maker supply and trader demand, especially from 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…
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…
Traders buy and sell financial instruments in hopes of making profit, and brokers are responsible for the transaction. There are several hypotheses and conspiracy theories arguing that in some situations, brokers want their traders to lose money. For instance, a broker may want to protect the positions of a privileged …
Honest traders can outperform insiders in a Black-Scholes market with positive probability.
Neuro-symbolic traders suppress market prices, highlighting risks to stability.
We consider thin incomplete financial markets, where traders with heterogeneous preferences and risk exposures have motive to behave strategically regarding the demand schedules they submit, thereby impacting prices and allocations. We argue that traders relatively more exposed to market risk tend to submit more elasti…