PAMS: Platform for Artificial Market Simulations

This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users' modification. In this paper, we demonstrate PAMS effectiveness through a study using agents predicting future prices by deep learning.Comment: 7page

Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling

Deep hedging is a deep-learning-based framework for derivative hedging in incomplete markets. The advantage of deep hedging lies in its ability to handle various realistic market conditions, such as market frictions, which are challenging to address within the traditional mathematical finance framework. Since deep hedging relies on market simulation, the underlying asset price process model is crucial. However, existing literature on deep hedging often relies on traditional mathematical finance models, e.g., Brownian motion and stochastic volatility models, and discovering effective underlying asset models for deep hedging learning has been a challenge. In this study, we propose a new framework called adversarial deep hedging, inspired by adversarial learning. In this framework, a hedger and a generator, which respectively model the underlying asset process and the underlying asset process, are trained in an adversarial manner. The proposed method enables to learn a robust hedger without explicitly modeling the underlying asset process. Through numerical experiments, we demonstrate that our proposed method achieves competitive performance to models that assume explicit underlying asset processes across various real market data.Comment: 8 pages, 7 figure

Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study

Option pricing, a fundamental problem in finance, often requires solving non-linear partial differential equations (PDEs). When dealing with multi-asset options, such as rainbow options, these PDEs become high-dimensional, leading to challenges posed by the curse of dimensionality. While deep learning-based PDE solvers have recently emerged as scalable solutions to this high-dimensional problem, their empirical and quantitative accuracy remains not well-understood, hindering their real-world applicability. In this study, we aimed to offer actionable insights into the utility of Deep PDE solvers for practical option pricing implementation. Through comparative experiments, we assessed the empirical performance of these solvers in high-dimensional contexts. Our investigation identified three primary sources of errors in Deep PDE solvers: (i) errors inherent in the specifications of the target option and underlying assets, (ii) errors originating from the asset model simulation methods, and (iii) errors stemming from the neural network training. Through ablation studies, we evaluated the individual impact of each error source. Our results indicate that the Deep BSDE method (DBSDE) is superior in performance and exhibits robustness against variations in option specifications. In contrast, some other methods are overly sensitive to option specifications, such as time to expiration. We also find that the performance of these methods improves inversely proportional to the square root of batch size and the number of time steps. This observation can aid in estimating computational resources for achieving desired accuracies with Deep PDE solvers.Comment: 11 pages, 6 figure

Vortex state in double transition superconductors

Novel vortex phase and nature of double transition field are investigated by two-component Ginzburg-Landau theory in a situation where fourfold-twofold symmetric superconducting double transition occurs. The deformation from 60 degree triangular vortex lattice and a possibility of the vortex sheet structure are discussed. In the presence of the gradient coupling, the transition changes to a crossover at finite fields. These characters are important to identify the multiple superconducting phase in PrOs_4_Sb_12.Comment: 4 pages, 4 figures, to appear in Phys. Rev. Let

Model-Free Idealization: Adaptive Integrated Approach for Idealization of Ion Channel Currents (AI2)

Single-channel electrophysiological recordings provide insights into transmembrane ion permeation and channel gating mechanisms. The first step in the analysis of the recorded currents involves an "idealization" process, in which noisy raw data are classified into two discrete levels corresponding to the open and closed states of channels. This provides valuable information on the gating kinetics of ion channels. However, the idealization step is often challenging in cases of currents with poor signal-to-noise ratios (SNR) and baseline drifts, especially when the gating model of the target channel is not identified. We report herein on a highly robust model-free idealization method for achieving this goal. The algorithm, called AI2 (Adaptive Integrated Approach for the Idealization of Ion Channel Currents), is composed of Kalman filter and Gaussian Mixture Model (GMM) clustering and functions without user input. AI2 automatically determines the noise reduction setting based on the degree of separation between the open and closed levels. We validated the method on pseudo-channel-current datasets which contain either computed or experimentally recorded noise. The AI2 algorithm was then tested on actual experimental data for biological channels including gramicidin A, a voltage-gated sodium channel, and other unidentified channels. We compared the idealization results with those obtained by the conventional methods, including the 50%-threshold-crossing method

Juvenile Bow Hunter’s Stroke without Hemodynamic Changes

Bow hunter’s stroke (BHS) is a cerebrovascular disease caused by occlusion of the vertebral artery (VA) on head rotation. BHS is generally associated with hemodynamic changes, often leading to vertebrobasilar insufficiency symptoms, such as vertigo and faintness. Although artery-to-artery embolism has also been proposed as an underlying mechanism, it remains controversial. This report documents a case of BHS without hemodynamic changes. We describe a 26-year-old male patient who had VA occlusion on head rotation and repetitive infarction of thalami. He had an anomalous bypass of the VA and therefore no symptomatic hemodynamic changes. Thus, non-hemodynamic BHS should be considered in juvenile patients with vertebrobasilar stroke