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

@article{Neri2021HowTI,
  title={How to Identify Investor's types in real financial markets by means of agent based simulation},
  author={Filippo Neri},
  journal={2021 6th International Conference on Machine Learning Technologies},
  year={2021}
}
  • F. Neri
  • Published 31 December 2020
  • Economics
  • 2021 6th International Conference on Machine Learning Technologies
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, the learning system L-FABS was chosen which combines simulated… 
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References

SHOWING 1-10 OF 32 REFERENCES

A Comparative Study of a Financial Agent Based Simulator Across Learning Scenarios

TLDR
An experimental study of the learning system L-FABS is reported in this paper to show how it can discover models for approximating time series working in partial knowledge and full knowledge learning scenarios.

Combining Machine Learning and Agent Based Modeling for Gold Price Prediction

TLDR
A computational approach combining machine learning (simulated annealing) and agent based simulation is shown to approximate financial time series and the methodology can be applied under several meta-learning conditions and its experimentation on the real world SPDR Gold Trust (GLD) timeseries.

An Adaptive Agent Based Economic Model

TLDR
Results of adaptive and non-adaptive simulations over a period of ten years of real data of a specific stock are presented and it is shown that the artificial agents, by displaying different and rich behaviours evolved throughout the simulations, are able to discover and refine novel and successful sets of market strategies that can outperform baseline strategies.

Learning Predictive Models for Financial Time Series by Using Agent Based Simulations

  • F. Neri
  • Computer Science
    Trans. Comput. Collect. Intell.
  • 2012
TLDR
This work discusses a computational technique to model financial time series combining a learning component with a simulation one and shows that its approach requires a simple input, the time series for which a model has to be learned, versus the complex and feature rich input to be given to other systems thanks to the ability of the system to adjust its parameters by learning.

Agent-based modeling under partial and full knowledge learning settings to simulate financial markets

  • F. Neri
  • Computer Science
    AI Commun.
  • 2012
TLDR
L-FABS combines agent-based simulation with machine learning to model the behavior of financial time series and can be applied in a partial Knowledge learning scenario or a full knowledge learning scenario to approximate financial timeseries.

Learning and Predicting Financial Time Series by Combining Natural Computation and Agent Simulation

  • F. Neri
  • Computer Science
    EvoApplications
  • 2011
TLDR
This work investigates how, by combining natural computation and agent based simulation, it is possible to model financial time series by using the DJIA time series.

Simulating and modelling the DAX index and the USO Etf financial time series by using a simple agent‐based learning architecture

This work presents an extensive case study on modelling the DAX (Deutscher Aktienindex) index and United States Oil Fund (USO) exchange‐traded fund (Etf) time series with the financial agent‐based

Case Study on Modeling the Silver and Nasdaq Financial Time Series with Simulated Annealing

TLDR
It is shown here how adding financial information to the modeling system can significantly improve the modeling results.

Computational learning techniques for intraday FX trading using popular technical indicators

TLDR
It is found that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs.

Agent-Based Computational Economics: Growing Economies From the Bottom Up

TLDR
The main objectives and defining characteristics of the ACE methodology are outlined and similarities and distinctions between ACE and artificial life research are discussed and some potential difficulties are discussed.