We report on the occurrence of an anomaly in the price impacts of small transaction volumes following a change in the fee structure of an electronic market. We first review evidence for the existence of a master curve for price impact on the Johannesburg Stock Exchange (JSE). On attempting to re-estimate a master curve…
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48 results for “impact curves”
Paper introduces impact curves for evaluating binarized regression models with varying costs.
problem Evaluating binarized regression models with varying costs and instance-specific utility.
method Proposes impact curves to optimize binary decisions across different utilities.
result Impact curves identify conditions where one model is favored over another and quantify model improvement.
New model predicts neural network performance from early training epochs, incorporating architecture impact.
problem Predicting neural network performance from early training epochs, neglecting architecture impact.
method Architecture-aware graph ordinary differential equation model.
result Model outperforms state-of-the-art methods for MLP and CNN learning curves.
The paper uses regression models to predict COVID-19 spread and its stock market impact.
problem Predicting and understanding the impact of COVID-19 on stock markets.
method Logistic curve model with Bayesian regression for predictive analytics.
result Different crises have different impacts on the same stocks.
Silkswap models stablecoin trading with minimal price impact.
problem Efficient trading of fiat-pegged stablecoins with minimal price impact.
method Silkswap uses an invariant price impact curve for asymmetric trading, derived from a hybrid function.
result Silkswap outperforms Curve Finance in price impact for stablecoin trading.
Model explains yield curve dynamics using order flow shocks.
problem Understanding the yield curve's fluctuations and their relation to order flows.
method Relates exogenous shocks to order flow surprises, creating a microstructural model that incorporates price and order flow dynamics.
result The model explains yield curve dynamics with fewer parameters and generates liquidity-dependent correlations.
We model a nonlinear price curve quoted in a market as the utility indifference curve of a representative liquidity supplier. As the utility function we adopt a g-expectation. In contrast to the standard framework of financial engineering, a trader is no more price taker as any trade has a permanent market impact via a…
The impact of trades on asset prices is a crucial aspect of market dynamics for academics, regulators and practitioners alike. Recently, universal and highly nonlinear master curves were observed for price impacts aggregated on all intra-day scales [1]. Here we investigate how well these curves, their scaling, and the …
This paper investigates the market impact of passive orders.
problem Understanding the market impact of passive orders executed through limit orders.
method Developed a microstructure model linking liquidity dynamics and price moves, replacing the constant information content assumption with a function dependent on available volume.
result Derived useful approximations for market impact curves, leading to closed-form formulas.
Study develops efficient algorithm for probabilistic penetration response of composite plates.
problem Probabilistic modeling of discrete structural response, focusing on binary events like buckling.
method Adaptive domain-based decomposition, sparse grid sampling, assumption of monotonic behavior.
result Efficient computational framework for probabilistic penetration response of composite plates.
How and why stock prices move is a centuries-old question still not answered conclusively. More recently, attention shifted to higher frequencies, where trades are processed piecewise across different timescales. Here we reveal that price impact has a universal non-linear shape for trades aggregated on any intra-day sc…
A simple learning agent learns to trade in an agent-based market model.
problem Optimal execution of trades in an agent-based financial market model.
method Asynchronous trading through a matching engine, varying initial order sizes and state spaces, calibration of empirical stylized facts and price impact curves.
result Smaller state space agents converge faster in learning and can trade intuitively using spread and volume states.
The work deals with the risk assessment theory. An unitary risk algorithm is elaborated. The algorithm is based on parallel curves. The basic curve of risk is a hyperbolic curve, obtained as a multiplication between the probability of occurrence of certain event and its impact. Section 1 contains the problem formulatio…
This paper is devoted to the important yet unexplored subject of crowding effects on market impact, that we call "co-impact". Our analysis is based on a large database of metaorders by institutional investors in the U.S. equity market. We find that the market chiefly reacts to the net order flow of ongoing metaorders, …
Motivated by the analogies between the projective and the almost quaternionic geometries, we study the generalized planar curves and mappings. We follow, recover, and extend the classical approach as developed by Mikes and Sinyukov. Then we exploit the impact of the general results in the almost quaternionic geometry. …
Paper defines feature impact and importance from data, not models.
problem Misinterpretation of feature importance as impact leads to flawed insights.
method Mathematical definitions of feature impact and importance derived from partial dependence curves.
result Feature rankings by these definitions are competitive with existing techniques.
When is cross impact relevant?q-fin.TR
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.
We present a detailed analysis of interest rate derivatives valuation under credit risk and collateral modeling. We show how the credit and collateral extended valuation framework in Pallavicini et al (2011), and the related collateralized valuation measure, can be helpful in defining the key market rates underlying th…
Optimizes bidding in hourly and quarter-hourly electricity markets to reduce price impact.
problem Maximizing profit in two consecutive electricity markets with market impact and transaction costs.
method Examined multiple price scenarios, estimated market impact, used trading strategies, provided theoretical results.
result Minimizing price impact is more profitable than maximizing arbitrage in the German EPEX market.
Active nematodynamics on deformable surfacescond-mat.soft
Study active nematic forces on curved surfaces, revealing new coupling mechanisms.
problem Understanding active nematic forces on curved surfaces.
method Developed a thermodynamically consistent surface model with nematic activity, analyzed topological defects.
result Active defects contribute both tangential and normal forces on curved surfaces.
Simulates financial market orders using anomalous diffusion models.
problem Anomalous diffusion in financial market order dynamics.
method Discrete Time Random Walk with Sibuya waiting times, non-uniform sampling, and cubic spline interpolation.
result Demonstrates price impact for different forcing functions and model parameters.
Agent-based model simulates market dynamics with real-time order matching.
problem Realistic simulation of market dynamics with realistic price impact.
method Agent-based model with asynchronous, event-time order matching.
result Realistic price impact curves and stylized facts presented.
Single Curve Collapse of the Price Impact Function for the New York Stock Exchangecond-mat.stat-mech
We study the average price impact of a single trade executed in the NYSE. After appropriate averaging and rescaling, the data for the 1000 most highly capitalized stocks collapse onto a single function, giving average price shift as a function of trade size. This function increases as a power that is the order of 1/2 f…
DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learningcs.LG
DeepCausalMMM models marketing impacts using deep learning and causal inference.
problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.
method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.
result Captures non-linear dynamics and temporal patterns in marketing impacts.
Paper analyzes constant-product market making protocols.
problem Understanding and optimizing constant-product market making.
method Mathematical analysis of trade splitting and fee recompounding.
result Splitting trades does not affect final exchange rate.
This study models Burundi's bond market yield curve using Nelson-Siegel and Svensson models.
problem Modeling the yield curve of Burundian bond market for financial analytics.
method Collected treasury securities auction reports, computed zero-coupon rates, and applied Nelson-Siegel and Svensson models.
result Nelson-Siegel model is optimal for Burundian yield curve modeling.
Investor optimizes utility in a market with endogenous pricing.
problem Maximizing utility in an incomplete market with endogenous pricing.
method Characterized optimality via FBSDEs and BSPDEs using generalized subgradients.
result Existence and smoothness of solutions for optimal investment and FBSDEs.
Modeling trading volume curves using hierarchical Poisson processes.
problem Predicting trading volume curves for financial instruments.
method Hierarchical Poisson process model based on hierarchical Dirichlet process with MCMC algorithm.
result Demonstrated scalability on NASDAQ stocks, including Apple.
The purpose of this paper relies on the study of long term affine yield curves modeling. It is inspired by the Ramsey rule of the economic literature, that links discount rate and marginal utility of aggregate optimal consumption. For such a long maturity modelization, the possibility of adjusting preferences to new ec…
We empirically study the trading activity in the electronic on-book segment and in the dealership off-book segment of the London Stock Exchange, investigating separately the trading of active market members and of other market participants which are non-members. We find that (i) the volume distribution of off-book tran…
Study improves prediction of commodity futures using multi-factor model.
problem Improving accuracy in predicting commodity futures prices.
method State-space functional regression model incorporating yield curve dynamics.
result Functional regression model outperforms Schwartz-Smith model in estimating short-end of futures curve.
FX Modelling in Collateralized Markets: foreign measures, basis curves, and pricing formulaeq-fin.PR
We present a general derivation of the arbitrage-free pricing framework for multiple-currency collateralized products. We include the impact on option pricing of the policy adopted to fund in foreign currency, so that we are able to price contracts with cash flows and/or collateral accounts expressed in foreign currenc…
Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii) to evaluate impact of construction details on the structural performance of installations under se…
We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correl…
The study improves the assessment of fairness in face recognition using ROC curves and statistical guarantees.
problem Improving the assessment of fairness in face recognition systems.
method Proves asymptotic guarantees for empirical ROC curves and fairness metrics, and introduces a recentering technique to avoid bootstrap pitfalls.
result Demonstrates the practical relevance of the methods for assessing fairness in face recognition systems.
Confidence bands for tuning curves improve hyperparameter comparison in NLP.
problem Ambiguity in comparing hyperparameter tuning methods.
method Constructs exact, simultaneous, and distribution-free confidence bands for tuning curves.
result Confidence bands provide a robust basis for comparing methods rigorously.
The utility-based pricing of defaultable bonds in the case of stochastic intensity models of default risk is discussed. The Hamilton-Jacobi- Bellman (HJB) equations for the value functions is derived. A finite difference method is used to solve this problem. The yield-spreads for both buyer and seller are extracted. Th…
Study shows annotation instrument design affects model performance in hate speech detection.
problem Impact of annotation instrument design on model performance in hate speech detection.
method Collected annotations from five experimental conditions of an annotation instrument, fine-tuned BERT models on each dataset, evaluated performance on holdout portion.
result Significant differences in model performance and annotations across conditions.
Over-parameterized CNNs show U-shaped test risk with depth increase.
problem Understanding the impact of depth on test risk in over-parameterized CNNs.
method Empirical image classification experiments and linear regression framework.
result Test risk is U-shaped with increasing depth in over-parameterized CNNs.
Extensive empirical evidence reveals that, for a wide range of different learning methods and datasets, the risk curve exhibits a double-descent (DD) trend as a function of the model size. In a recent paper [Zeyu,Kammoun,Thrampoulidis,2019] the authors studied binary linear classification models and showed that the tes…
This work proposes a model for geodesic distances and flows on manifolds.
problem Geodesic distances and flows on differentiable manifolds.
method Manifold-augmented Eikonal equation solutions.
result Geodesic flow provides globally length-minimizing curves.
The trade size has direct impact on the price formation of the stock traded. Econophysical analyses of transaction data for the US and Australian stock markets have uncovered market-specific scaling laws, where a master curve of price impact can be obtained in each market when stock capitalization is included a…
When using active learning, smaller batch sizes are typically more efficient from a learning efficiency perspective. However, in practice due to speed and human annotator considerations, the use of larger batch sizes is necessary. While past work has shown that larger batch sizes decrease learning efficiency from a lea…
Develops method to assess feature importance in black-box models for unconditional distribution.
problem Lack of methods to analyze feature importance in black-box models for unconditional distribution.
method Approximation method to compute feature importance curves for unconditional distribution.
result Produces sparse and faithful results, computationally efficient.
We consider a price-maker company which generates electricity and sells it in the spot market. The company can increase its level of installed power by irreversible installations of solar panels. In absence of the company's economic activities, the spot electricity price evolves as an Ornstein-Uhlenbeck process, and th…
We develop a fundamentally different stochastic dynamic programming model of trading costs. Built on a strong theoretical foundation, our model provides insights to market participants by splitting the overall move of the security price during the duration of an order into the Market Impact (price move caused by their …
Study optimal liquidation with incomplete trend information and multiplicative price impact.
problem Optimal liquidation of assets with incomplete trend information and multiplicative price impact.
method Singular stochastic control problem with finite-fuel constraint and partial observation. Equivalent three-dimensional degenerate problem under full information. Two-dimensional optimal stopping problem with belief-dependent free boundary.
result Optimal execution rule and value function expressed in terms of a nonlinear integral equation, solved through Monte-Carlo method.
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications. However, learning the impact of a treatment on a continuous temporal response, when the covariates suffer extensively from measurement error and even the timing of the treatments is uncertain…