Evaluation of Statistical Forecast Method Efficiency in the Conditions of Dynamic Chaos

Yusupov, Rafael; Мусаев, Александр Азерович; Grigoriev, Dmitrij A.

doi:10.1109/cts53513.2021.9562780

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…It follows from this that as an interpretive model of the processes considered in this article, it is advisable to use a three-component scheme (3), in which a quasisystem component formed by a nonstationary random process is added. Tis model provides a visual representation of the structure of the observed processes.…”

Section: Discussionmentioning

confidence: 99%

“…In essence, the division of the observed series into a system process and a noise component in this case turns out to be ambiguous and requires additional defnition related to such subjective factors as the choice of the fltering algorithm and the criterion of its efectiveness. Te process of identifying the system component is decisively infuenced by strategic preferences of participants in trading operations [3], which is an exogenous factor.…”

Section: Structures With Large Scattermentioning

confidence: 99%

“…An efective forecast will be understood as a quantitative or qualitative prediction of the cost of a working instrument, which can be a basis for a winning management strategy. It is essential to keep in mind that, when assessing the efectiveness of a predictive model, you should not focus on its accuracy or reliability, but on the fnal outcome [3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The Genesis of Uncertainty: Structural Analysis of Stochastic Chaos in Finance Markets

2023

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The presented article is methodological in nature and is devoted to the analysis of observation series of financial asset quotation changes in capital markets. The most important feature of these processes is their instability, which manifests itself in high sensitivity to seemingly minor disturbing factors. This phenomenon is well-studied in the theory of nonlinear dynamical systems and is described by models of deterministic chaos. However, for the processes considered in the article, the dynamic instability of the immersion environment is exacerbated by stochastic uncertainty caused by random fluctuations in the pricing process. As a result, describing observation series of quotations of financial assets is difficult because it involves stochastic chaos. This article analyzes and classifies chaotic series of observations to help model and forecast related processes.

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Section: Discussionmentioning

confidence: 99%

Section: Structures With Large Scattermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Genesis of Uncertainty: Structural Analysis of Stochastic Chaos in Finance Markets

2023

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“…with a smoothing coefficient α = 0.01-0.3 was used. The observed process yk, k = 1, …, n does not conform to the efficient market hypothesis [7] and, as shown in [16,17], has almost no inertia. The latter statement leads to a complete failure of management strategies based on mechanistic prolongation of the detected trends.…”

Section: Data Modelmentioning

confidence: 99%

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

2022

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In this paper, the problem of estimating the current value of financial instruments using multidimensional statistical analysis is considered. The research considers various approaches to constructing regression computational schemes using quotes of financial instruments correlated to the data as regressors. An essential feature of the problem is the chaotic nature of its observation series, which is due to the instability of the probabilistic structure of the initial data. These conditions invalidate the constraints under which traditional statistical estimates remain non-biased and effective. Violation of experiment repeatability requirements obstructs the use of the conventional data averaging approach. In this case, numeric experiments become the main method for investigating the efficiency of forecasting and analysis algorithms of observation series. The empirical approach does not provide guaranteed results. However, it can be used to build sufficiently effective rational strategies for managing trading operations.

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Multi-regression Forecast in Stochastic Chaos

2023

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Evaluation of Statistical Forecast Method Efficiency in the Conditions of Dynamic Chaos

Cited by 7 publications

References 9 publications

The Genesis of Uncertainty: Structural Analysis of Stochastic Chaos in Finance Markets

The Genesis of Uncertainty: Structural Analysis of Stochastic Chaos in Finance Markets

Statistical Analysis of Current Financial Instrument Quotes in the Conditions of Market Chaos

Multi-regression Forecast in Stochastic Chaos

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