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The Comprehensive Model of Using In-Depth Consolidated Multimodal Learning to Study Trading Strategies in the Securities Market

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Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

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Abstract

The paper describes the relevance of machine learning methods, namely training with reinforcement, to the problems of predicting financial time series. An overview of existing applications based on machine learning in the issues of financial market forecasting is presented. The reasons for the popularity of my research topic are highlighted both from the scientific (increasing the number of publications on issues relevant to the research topic over the past five years) and from the practical side. The analysis of scientific works, the subject and purpose of which are related to the issues and objectives of my research and their features are presented. The main problems associated with the problem of predicting stochastic time series are identified. According to the analysis, the purpose of work is defined, and also the list of tasks for the achievement of the set goal is made. The article is devoted to studying the use of the ensemble of neuro-learning networks with the strengthening of the securities trading market. The practical significance of the work is to use the model of efficient distribution of investments in the market. This paper will explore a set of models that use in-depth consolidated learning to explore trading strategies to maximize return on investment. The potential of using acting-critical models as an ensemble has been studied. Models such as Proximal Policy Optimizer (PPO), Advantage Actor-Critic (A2C) and Deep Determinist Police Gradient (DDPG) were used to teach trading strategy. To adapt the model to different situations, analyzes are analyzed according to three algorithms: the Dow Jones average and a portfolio that minimizes fluctuations in the Charpy ratio by balancing risk and return. The article compares ensembles by the method of fixing deep neural networks. To optimize the balance of risk and profit, the indicators of the ensemble model are analyzed. The ensemble and three-component models that apply well to market collapse conditions are considered. The models have learned to use the turbulence index for early stock sales to minimize losses during a stock collapse. The turbulence index threshold is demonstrated using ensemble models to regulate risk avoidance.

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Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Nataliya Boyko

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  1. Nataliya Boyko
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Correspondence to Nataliya Boyko .

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Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

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Boyko, N. (2023). The Comprehensive Model of Using In-Depth Consolidated Multimodal Learning to Study Trading Strategies in the Securities Market. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_8

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