How to Identify Investor's types in real financial markets by means of agent based simulation

• URL: http://arxiv.org/abs/2101.03127v1
• Date: Thu, 31 Dec 2020 16:22:30 GMT
• Title: How to Identify Investor's types in real financial markets by means of agent based simulation
• Authors: Filippo Neri
• Abstract summary: The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation. The proposed methodology is to find a reduced set of investor s models which is able to approximate or explain a target financial time series.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The paper proposes a computational adaptation of the principles underlying principal component analysis with agent based simulation in order to produce a novel modeling methodology for financial time series and financial markets. Goal of the proposed methodology is to find a reduced set of investor s models (agents) which is able to approximate or explain a target financial time series. As computational testbed for the study, we choose the learning system L FABS which combines simulated annealing with agent based simulation for approximating financial time series. We will also comment on how L FABS s architecture could exploit parallel computation to scale when dealing with massive agent simulations. Two experimental case studies showing the efficacy of the proposed methodology are reported.

Related papers

• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim. Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts. To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• Experimental Analysis of Deep Hedging Using Artificial Market Simulations for Underlying Asset Simulators [0.7329727526222747]
  This study proposes a new approach using artificial market simulations for underlying asset simulations in deep hedging. We investigate the effectiveness of the proposed approach by comparing its results with those of the traditional approach. The results show that the proposed approach can achieve almost the same level of performance as the traditional approach.
  arXiv  Detail & Related papers  (2024-04-15T05:11:07Z)
• Optimal simulation-based Bayesian decisions [0.0]
  We present a framework for the efficient computation of optimal Bayesian decisions under intractable likelihoods. We develop active learning schemes to choose where in parameter and action spaces to simulate. The resulting framework is extremely simulation efficient, typically requiring fewer model calls than the associated posterior inference task alone.
  arXiv  Detail & Related papers  (2023-11-09T20:59:52Z)
• Towards a Prediction of Machine Learning Training Time to Support Continuous Learning Systems Development [5.207307163958806]
  We present an empirical study of the Full. Time Complexity (FPTC) approach by Zheng et al. We study the formulations proposed for the Logistic Regression and Random Forest classifiers. We observe how, from the conducted study, the prediction of training time is strictly related to the context.
  arXiv  Detail & Related papers  (2023-09-20T11:35:03Z)
• A Modular Framework for Reinforcement Learning Optimal Execution [68.8204255655161]
  We develop a modular framework for the application of Reinforcement Learning to the problem of Optimal Trade Execution. The framework is designed with flexibility in mind, in order to ease the implementation of different simulation setups.
  arXiv  Detail & Related papers  (2022-08-11T09:40:42Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Adaptive Learning on Time Series: Method and Financial Applications [0.0]
  We use Adaptive Learning to forecast S&P 500 returns across multiple forecast horizons. We find that Adaptive Learning models are on par with, if not better than, the best of the parametric models a posteriori. We present a financial application of the learning results and an interpretation of the learning regime during the 2020 market crash.
  arXiv  Detail & Related papers  (2021-10-21T13:59:54Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Interpretable ML-driven Strategy for Automated Trading Pattern Extraction [2.7910505923792646]
  We propose a volume-based data pre-processing method for financial time series analysis. We use a statistical approach for assessing the performance of the method. Our analysis shows that the proposed method allows successful classification of the financial time series patterns.
  arXiv  Detail & Related papers  (2021-03-23T09:55:46Z)
• On Exploiting Hitting Sets for Model Reconciliation [53.81101846598925]
  In human-aware planning, a planning agent may need to provide an explanation to a human user on why its plan is optimal. A popular approach to do this is called model reconciliation, where the agent tries to reconcile the differences in its model and the human's model. We present a logic-based framework for model reconciliation that extends beyond the realm of planning.
  arXiv  Detail & Related papers  (2020-12-16T21:25:53Z)
• Gaussian process imputation of multiple financial series [71.08576457371433]
  Multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market. We focus on learning the relationships among financial time series by modelling them through a multi-output Gaussian process.
  arXiv  Detail & Related papers  (2020-02-11T19:18:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.