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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.

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6131925 · May 202619922001200920172026
48 results for herding cycles

Agent-based market shows herding cycles with square-root price impact.

problem Understanding herding cycles in agent-based markets.
method Agent-based model with 20,000 retail traders interacting with a single institutional agent.
result Agent discovers multi-cycle predatory strategy with 8-11 complete cycles over 2000 trading days.

Herding defines a deterministic dynamical system at the edge of chaos. It generates a sequence of model states and parameters by alternating parameter perturbations with state maximizations, where the sequence of states can be interpreted as "samples" from an associated MRF model. Herding differs from maximum likelihoo…

2016-02-09abs ↗pdf ↗

The Gibbs sampler is one of the most popular algorithms for inference in statistical models. In this paper, we introduce a herding variant of this algorithm, called herded Gibbs, that is entirely deterministic. We prove that herded Gibbs has an O(1/T)O(1/T) convergence rate for models with independent variables and for ful…

2013-01-17abs ↗pdf ↗

Study examines herding behavior in stocks, US ETFs, and cryptocurrencies.

problem Understanding herding behavior in different types of investment vehicles.
method Cross-sectional Absolute Deviation model, Minimum Spanning Tree, Louvain community detection.
result Herding behavior exists at all times across all types of investment vehicles at a subset level.

Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals using Bayesian quadrature. We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior varianc…

2014-08-09abs ↗pdf ↗

Herding and kernel herding are deterministic methods of choosing samples which summarise a probability distribution. A related task is choosing samples for estimating integrals using Bayesian quadrature. We show that the criterion minimised when selecting samples in kernel herding is equivalent to the posterior varianc…

2012-04-07abs ↗pdf ↗

We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert …

2012-03-15abs ↗pdf ↗

Investment herding can reduce household consumption, a phenomenon called crowding-out effect.

problem Investment herding's impact on household consumption.
method Optimal control theory to model and solve for household investment and consumption decisions.
result Existence of crowding-out effect due to investment herding.

Generative AI reduces herd behavior in trading, but can also lead to optimal herding.

problem Impact of generative AI on financial stability and herd behavior.
method Laboratory experiments with large language models replicating human trading behavior.
result AI agents make more rational decisions than humans, reducing herd behavior but also potentially leading to optimal herding.

We show that the herding procedure of Welling (2009) takes exactly the form of a standard convex optimization algorithm--namely a conditional gradient algorithm minimizing a quadratic moment discrepancy. This link enables us to invoke convergence results from convex optimization and to consider faster alternatives for …

2012-03-20abs ↗pdf ↗

Collective behavior of the complex socio-economic systems is heavily influenced by the herding, group, behavior of individuals. The importance of the herding behavior may enable the control of the collective behavior of the individuals. In this contribution we consider a simple agent-based herding model modified to inc…

2013-09-24abs ↗pdf ↗

There are no solid arguments to sustain that digital currencies are the future of online payments or the disruptive technology that some of its former participants declared when used to face critiques. This paper aims to solve the cryptocurrency puzzle from a behavioral finance perspective by finding the parallelism be…

2018-06-29abs ↗pdf ↗

Study reveals how investor flows impact stock prices, especially during herding episodes.

problem Understanding how information transmits through prices and why it breaks down.
method Combining regularized deconvolution with Hawkes process analysis.
result Institutional price impact deteriorates sharply during herding episodes in small-cap stocks, while large-cap stocks maintain resilience.

We investigate the herd behavior of returns for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution P(R)P(R) of returns RR satisfies the power-law behavior P(R)RβP(R) \simeq R^{-β} with the exponents β=3.11 β=3.11(the time interval τ=τ= one minute) and 3.36(τ=τ= one da…

2004-05-09abs ↗pdf ↗

We derive a system of stochastic differential equations simulating the dynamics of the three agent groups with herding interaction. Proposed approach can be valuable in the modeling of the complex socio-economic systems with similar composition of the agents. We demonstrate how the sophisticated statistical features of…

2013-05-25abs ↗pdf ↗

Study optimal investment with herd behavior using rational decision decomposition.

problem Optimal investment problem considering herd behavior between two agents.
method Introduce average deviation term, use variational method, rational decision decomposition, investment opinion.
result Quantitative analysis of herd behavior impact on investment decisions.

The herd behavior of returns is investigated in Korean futures exchange market. It is obtained that the probability distribution of returns for three types of herding parameter scales as a power law RβR^{-β} with the exponents β=3.6 β=3.6(KTB203) and 2.9(KTB209) in two kinds of Korean treasury bond. For our case since the…

2003-04-07abs ↗pdf ↗

Improved kernel herding algorithm for faster quadrature rule convergence.

problem Slow convergence speed of standard kernel herding algorithm.
method Improved gradient approximation to obtain sparser solutions.
result The cosine of the angle between negative gradient and approximate gradient determines convergence speed.

GeomHerd predicts herding behavior before market prices move, using Ricci curvature of agent interaction graphs.

problem Quantifying herding behavior in markets that lags behind actual price movements.
method Develops a geometric framework to track coordination on agent interaction graphs, bypassing lag in price-correlation statistics.
result GeomHerd anticipates herding long before market baselines, with significant lead times in predictions.

The study categorizes Korean Exchange member firms into three types and analyzes their trading behavior.

problem Understanding the trading behavior of member firms on the Korea Exchange.
method Member firms were categorized into three types based on investor type. Trading dynamics were examined through directionality and trend analysis.
result Member firms exhibit distinct trading behaviors: FRMs trade one-way, DIMs and DSMs herd in opposite directions, and FRMs herd in the same direction.

We study the phase transition of dynamical herd behaviors for the yen-dollar exchange rate in the Japanese financial market. It is obtained that the probability distribution of returns satisfies the power-law behavior with three different values of the scaling exponent 3.11 (one time lag ττ = 1 minute), 2.81 (30 minut…

2004-08-28abs ↗pdf ↗

Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.

problem Early onset of herd immunity contradicts R value estimates from early stage growth.
method Utilizes Vasicek's bond pricing formula and de Finetti's Theorem approach.
result Reduces modeling discrepancy to simple convexity formulas.

We study the statistics of earning forecasts of US, EU, UK and JP stocks during the period 1987-2004. We confirm, on this large data set, that financial analysts are on average over-optimistic and show a pronounced herding behavior. These effects are time dependent, and were particularly strong in the early nineties an…

2004-10-04abs ↗pdf ↗

A new method compresses conditional distributions of labelled data.

problem No existing method directly compresses the conditional distribution of labelled data.
method Introduce Average Maximum Conditional Mean Discrepancy (AMCMD), derive a closed form estimator, and extend Kernel Herding (KH) to Average Conditional Kernel Herding (ACKH).
result Directly compressing conditional distributions outperforms joint distribution compression and greedy selection.

In this work, we propose an order book model with herd behavior. The proposed model is built upon two distinct approaches: a recent empirical study of the detailed order book records by Kanazawa et al. [Phys. Rev. Lett. 120, 138301] and financial herd behavior model. Combining these approaches allows us to propose a mo…

2018-09-08abs ↗pdf ↗

Analyzed a generalized voter model with power-law herding intensity, revealing anomalous diffusion and long-range memory.

problem Anomalous diffusion and long-range memory in a generalized voter model.
method Derived analytical expressions for moments and first passage time distribution, confirmed numerically.
result The model exhibits long-range memory indicators despite being a Markov model.

The herd behaviors of returns for the won-dollar exchange rate and the KOSPI are analyzed in Korean financial markets. It is shown that the probability distribution P(R)P(R) of price returns RR for three values of the herding parameter tends to a power-law behavior P(R)RβP(R) \simeq R^{-β} with the exponents β=2.2 β=2.2(the wo…

2003-04-21abs ↗pdf ↗

Model explains herding and volatility in urban housing prices.

problem Understanding non-linear price dynamics in urban housing markets.
method Agent-based model with rational households and trend-following behavior.
result Model accurately predicts price variability and herding behavior.

The paper improves probabilistic herding methods using Gibbs distributions.

problem Improving integration accuracy over Monte Carlo quadrature in infinite-dimensional RKHS.
method Developed a Gibbs distribution over quadrature nodes to minimize MMD.
result The Gibbs distribution outperforms i.i.d. Monte Carlo in integration accuracy.

Order flow in equity markets is remarkably persistent in the sense that order signs (to buy or sell) are positively autocorrelated out to time lags of tens of thousands of orders, corresponding to many days. Two possible explanations are herding, corresponding to positive correlation in the behavior of different invest…

2011-08-08abs ↗pdf ↗

Machine learning improves diagnostic test accuracy for bovine tuberculosis.

problem Improving diagnostic test sensitivity for bovine tuberculosis.
method Machine learning to assess risk landscapes and predict infection.
result Test sensitivity improved, detecting 240 more infected herds per year.

Our analysis of financial data, in terms of super-exponential growth, suggests that the seed of the 2002/03 crisis of the Dutch supermarket giant AHOLD was planted in 1996. It became quite visible in 1999 when the post-bubble destabilization regime was well-developed and acted as the precursor of an inevitable collapse…

2004-03-22abs ↗pdf ↗

Stock markets can be characterized by fat tails in the volatility distribution, clustering of volatilities and slow decay of their time correlations. For an explanation models with several mechanisms and consequently many parameters as the Lux-Marchesi model have been used. We show that a simple herding model with only…

2002-07-11abs ↗pdf ↗

Model shows liquidity stress crossover in market dynamics.

problem Identifying genuine market instabilities in agent-based models.
method Applied Bouchaud's phase-diagram method to a continuous-double-auction model.
result Emergent liquidity-stress crossover with specific tipping point parameters.

Study uses deep learning to predict stock trends with superior performance.

problem Predicting short-term equity trends with high accuracy.
method Dual-task multilayer perceptron (MLP) integrating technical signals and deep learning.
result Deep learning model outperforms linear baselines in multi-factor stock selection.

Learning the parameters of a (potentially partially observable) random field model is intractable in general. Instead of focussing on a single optimal parameter value we propose to treat parameters as dynamical quantities. We introduce an algorithm to generate complex dynamics for parameters and (both visible and hidde…

2012-05-09abs ↗pdf ↗

Study analyzes market co-movements in critical mineral investments using change point detection and cross-sectional analysis.

problem Market dynamics in critical mineral investments during significant global events.
method Combines change-point detection (PELT algorithm) with cross-sectional analysis on ESG-ranked ETFs.
result Investors herded during market downturns and shifted to anti-herding after positive news and geopolitical shocks.

A growing part of the behavioral finance literature has addressed some of the stylized facts of financial time series as macroscopic patterns emerging from herding interactions among groups of agents with heterogeneous trading strategies and a limited rationality. We extend a stochastic herding formalism introduced for…

2013-02-26abs ↗pdf ↗

Individual choices are either based on personal experience or on information provided by peers. The latter case, causes individuals to conform to the majority in their neighborhood. Such herding behavior may be very efficient in aggregating disperse private information, thereby revealing the optimal choice. However if …

2005-06-17abs ↗pdf ↗