Toward a scenario with complementary stochastic and deterministic information in financial fluctuations

Chen, Cyun-Wei; Ma, Wen-Jong

doi:10.1016/j.cjph.2018.03.020

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Financial scenarios that use a joint distribution of losses for assessing the stability of the banking sector in the EU, based on the Basel II capital rules and micro-simulations of portfolio structures, has been researched in [22]. [23] focus on the development of scenarios using stochastic and deterministic information to describe the financial fluctuations of a set of 345 stocks listed in the SP500 index that allow, in a general way, identifying the behaviour of a stock market. This trend is characterized by the application of financial models in different areas of knowledge and the integration of aspects that are important to describe the environment of an organization.…”

Section: Previous Workmentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios.

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Section: Previous Workmentioning

confidence: 99%

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

González-Ruiz

Peña

Grisales

et al. 2019

Applied Soft Computing

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“…This terminology has been introduced and invented by those researchers who are working in the field of simulating financial and economic systems. This branch deals with some of the physical theories and real-world applications and techniques applied to financial fields [2][3][4]. The memory effects in the study based on these financial models play an important role to understand these complex systems [5].…”

Section: Introductionmentioning

confidence: 99%

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

Weera¹,

Botmart²,

Chantawat³

et al. 2023

Computers, Materials &Amp; Continua

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The main purpose of the study is to present a numerical approach to investigate the numerical performances of the fractional 4-D chaotic financial system using a stochastic procedure. The stochastic procedures mainly depend on the combination of the artificial neural network (ANNs) along with the Levenberg-Marquardt Backpropagation (LMB) i.e., ANNs-LMB technique. The fractional-order term is defined in the Caputo sense and three cases are solved using the proposed technique for different values of the fractional order α. The values of the fractional order derivatives to solve the fractional 4-D chaotic financial system are used between 0 and 1. The data proportion is applied as 73%, 15%, and 12% for training, testing, and certification to solve the chaotic fractional system. The acquired results are verified through the comparison of the reference solution, which indicates the proposed technique is efficient and robust. The 4-D chaotic model is numerically solved by using the ANNs-LMB technique to reduce the mean square error (MSE). To authenticate the exactness, and consistency of the technique, the obtained performances are plotted in the figures of correlation measures, error histograms, and regressions. From these figures, it can be witnessed that the provided technique is effective for solving such models to give some new insight into the physical behavior of the model.

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Transport properties of coupled Brownian particles confined to a microchannel in an asymmetric periodic potential

Deng

Luo

et al. 2019

Chinese Journal of Physics

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Toward a scenario with complementary stochastic and deterministic information in financial fluctuations

Cited by 5 publications

References 25 publications

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

An Intelligence Computational Approach for the Fractional 4D Chaotic Financial Model

Transport properties of coupled Brownian particles confined to a microchannel in an asymmetric periodic potential

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