Mathematics

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15 pages, 521 KiB

Open AccessArticle

by Joan Carles Ferrer-Comalat, Dolors Corominas-Coll and Salvador Linares-Mustarós

Mathematics 2021, 9(8), 826; https://doi.org/10.3390/math9080826 - 10 Apr 2021

Cited by 2 | Viewed by 1489

In the study presented here, fuzzy logic was used to analyze the behavior of a model of economic dynamics that assumes income to be in equilibrium when it is composed of consumption and investment, that is, when savings and investment are equal. The [...] Read more.

In the study presented here, fuzzy logic was used to analyze the behavior of a model of economic dynamics that assumes income to be in equilibrium when it is composed of consumption and investment, that is, when savings and investment are equal. The study considered that consumption and savings depend on the income of the previous period through uncertain factors, and, at the same time, that investment is an uncertain magnitude across various periods, represented as a fuzzy number with a known membership function. Under these conditions, the model determines the factor of income growth and investments required to maintain equilibrium, as well as the uncertain values of income for the different periods, expressed through fuzzy numbers. The study also analyzes the conditions for their convergence and the fuzzy value that income represents in equilibrium. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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10 pages, 5623 KiB

Open AccessArticle

Can Artificial Neural Networks Predict the Survival Capacity of Mutual Funds? Evidence from Spain

by Laura Fabregat-Aibar, Maria-Teresa Sorrosal-Forradellas, Glòria Barberà-Mariné and Antonio Terceño

Mathematics 2021, 9(6), 695; https://doi.org/10.3390/math9060695 - 23 Mar 2021

Viewed by 1626

Abstract

Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the [...] Read more.

Recently, the total net assets of mutual funds have increased considerably and turned them into one of the main investment instruments. Despite this increment, every year a considerable number of funds disappear. The main purpose of this paper is to determine if the neural networks can be a valid instrument to detect the survival capacity of a fund, using the traditional variables linked to the literature of disappearance funds: age, size, performance and volatility. This paper also incorporates annualized variation in return and the Sharpe ratio as variables. The data used is a sample of Spanish mutual funds during 2018 and 2019. The results show that the network correctly classifies funds into surviving and non-surviving with a total error of 13%. Moreover, it shows that not all variables are significant to determine the survival capacity of a fund. The results indicate that surviving and non-surviving funds differ in variables related to performance and its variation, volatility and the Sharpe ratio. However, age and size are not significant variables. As a conclusion, the neural network correctly predicts the 87% of survival capacity of mutual funds. Therefore, this methodology can be used to classify this financial instrument according to its survival or disappearance. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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18 pages, 1239 KiB

Open AccessArticle

The Reliability of Spanish and German Electricity Forward Prices. Databases and Price Discovery Process

by Ángela Coronado, Francisco Climent and Dolores Furió

Mathematics 2021, 9(6), 623; https://doi.org/10.3390/math9060623 - 15 Mar 2021

Viewed by 1395

Abstract

Given the existence of different databases from different sources that offer information on forward electricity prices, the need to compare them arises to guarantee that research results and trading decisions based on them are not sensitive to the database used. We worked with [...] Read more.

Given the existence of different databases from different sources that offer information on forward electricity prices, the need to compare them arises to guarantee that research results and trading decisions based on them are not sensitive to the database used. We worked with forward electricity prices traded over the counter, closest month to maturity, covering the period from 2010 to 2016 for the Spanish over the counter (OTC) market, and from 2008 to 2016 for the German OTC market. The goal of this paper was to test whether there were significant discrepancies between the price series provided by two of the main agencies of financial information (Thomson Reuters and Bloomberg), as well as to analyze the existence of causality relationships between them, both in the long-term and in the short-term. As a first step, we obtained the data availability and the distributional characteristics of each of the price series offered by the mentioned financial information providers for the Spanish and the German electricity OTC market. Then we studied the lead-lag relationship between two price series, previously chosen as representative of those provided by Thomson Reuters and Bloomberg, to ascertain if there are any leading databases that may systematically anticipate information with respect to the others. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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14 pages, 327 KiB

Open AccessArticle

Proposal for a Fuzzy Model to Assess Cost Overrun in Healthcare Due to Delays in Treatment

by José M. Brotons-Martínez and Manuel E. Sansalvador-Sellés

Mathematics 2021, 9(4), 408; https://doi.org/10.3390/math9040408 - 19 Feb 2021

Cited by 1 | Viewed by 1462

Abstract

Apart from the effects of treating those infected with COVID-19, the pandemic has also affected treatment for other diseases, which has been either interrupted or canceled. The aim of this paper is to provide a financial model for obtaining the cost overrun resulting [...] Read more.

Apart from the effects of treating those infected with COVID-19, the pandemic has also affected treatment for other diseases, which has been either interrupted or canceled. The aim of this paper is to provide a financial model for obtaining the cost overrun resulting from the worsening of illnesses and deaths for each of the causes considered. To achieve this, first deaths have been classified into causes of death and for each of these causes, an estimation has been made of the worsening condition of patients due to delay in treatment. Through these data, a fuzzy relation between deaths and the worsening condition of patients can be obtained. Next, the expertise process has been used to estimate cost overrun in relation to patients’ pathologies. The experts’ opinions have been aggregated using ordered weighted average (OWA). Lastly, using fuzzy logic again, a correction coefficient has been determined, which optimizes the future implementation of the proposed model without the need for a new estimation of inputs. The paper concludes with a numerical example for a better comprehension of the proposed theoretical model. Ultimately, it provides the scientific community in general and in particular managers of public administration entities with a novel tool for improving the efficiency of the healthcare system. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

15 pages, 755 KiB

Open AccessArticle

Exchange Market Liquidity Prediction with the K-Nearest Neighbor Approach: Crypto vs. Fiat Currencies

by Klender Cortez, Martha del Pilar Rodríguez-García and Samuel Mongrut

Mathematics 2021, 9(1), 56; https://doi.org/10.3390/math9010056 - 29 Dec 2020

Cited by 8 | Viewed by 3922

Abstract

In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor [...] Read more.

In this paper, we compare the predictions on the market liquidity in crypto and fiat currencies between two traditional time series methods, the autoregressive moving average (ARMA) and the generalized autoregressive conditional heteroskedasticity (GARCH), and the machine learning algorithm called the k-nearest neighbor (KNN) approach. We measure market liquidity as the log rates of bid-ask spreads in a sample of three cryptocurrencies (Bitcoin, Ethereum, and Ripple) and 16 major fiat currencies from 9 February 2018 to 8 February 2019. We find that the KNN approach is better suited for capturing the market liquidity in a cryptocurrency in the short-term than the ARMA and GARCH models maybe due to the complexity of the microstructure of the market. Considering traditional time series models, we find that ARMA models perform well when estimating the liquidity of fiat currencies in developed markets, whereas GARCH models do the same for fiat currencies in emerging markets. Nevertheless, our results show that the KNN approach can better predict the log rates of the bid-ask spreads of crypto and fiat currencies than ARMA and GARCH models. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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19 pages, 2133 KiB

Open AccessArticle

The Financial Valuation Risk in Pepper Production: The Use of Decoupled Net Present Value

by Josefa López-Marín, Amparo Gálvez, Francisco M. del Amor and Jose M. Brotons

Mathematics 2021, 9(1), 13; https://doi.org/10.3390/math9010013 - 23 Dec 2020

Cited by 6 | Viewed by 2587

Abstract

Greenhouse peppers are one of the most important crops globally. However, as in any production activity, especially agricultural, they are subject to important risk factors such as price fluctuations, pests, or the use of bad quality water. This article aims to evaluate the [...] Read more.

Greenhouse peppers are one of the most important crops globally. However, as in any production activity, especially agricultural, they are subject to important risk factors such as price fluctuations, pests, or the use of bad quality water. This article aims to evaluate the viability of these types of crops by using discounted cash flows. Risk evaluation has been carried out through the analysis of pepper plantations for 2016 and 2017. The traditional application of this tool has significant limitations, such as the discount rate to be used or the estimation of future cash flows. However, by using discount functions that decrease over time in combination with decoupled net present value, these limitations are expected to improve. The use of decoupled net present value has permitted an increase in the accuracy and quantification of risks, isolating the main risks such as price drops (EUR 3720 ha⁻¹ year⁻¹) and structural risks (EUR 1622 € ha⁻¹ year⁻¹). The use of decreasing discount functions has permitted a more realistic investment estimation. Finally, the sensitivity analysis shows that decoupled net present value (DNPV) is little affected by changes in interest rates in contrast to traditional net present value (NPV). Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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14 pages, 773 KiB

Open AccessArticle

Extended Fuzzy Analytic Hierarchy Process (E-FAHP): A General Approach

by Javier Reig-Mullor, David Pla-Santamaria and Ana Garcia-Bernabeu

Mathematics 2020, 8(11), 2014; https://doi.org/10.3390/math8112014 - 12 Nov 2020

Cited by 18 | Viewed by 2380

Abstract

Fuzzy analytic hierarchy process (FAHP) methodologies have witnessed a growing development from the late 1980s until now, and countless FAHP based applications have been published in many fields including economics, finance, environment or engineering. In this context, the FAHP methodologies have been generally [...] Read more.

Fuzzy analytic hierarchy process (FAHP) methodologies have witnessed a growing development from the late 1980s until now, and countless FAHP based applications have been published in many fields including economics, finance, environment or engineering. In this context, the FAHP methodologies have been generally restricted to fuzzy numbers with linear type of membership functions (triangular numbers—TN—and trapezoidal numbers—TrN). This paper proposes an extended FAHP model (E-FAHP) where pairwise fuzzy comparison matrices are represented by a special type of fuzzy numbers referred to as (m,n)-trapezoidal numbers (TrN (m,n)) with nonlinear membership functions. It is then demonstrated that there are a significant number of FAHP approaches that can be reduced to the proposed E-FAHP structure. A comparative analysis of E-FAHP and Mikhailov’s model is illustrated with a case study showing that E-FAHP includes linear and nonlinear fuzzy numbers. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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16 pages, 460 KiB

Open AccessArticle

Feature Selection to Optimize Credit Banking Risk Evaluation Decisions for the Example of Home Equity Loans

by Agustin Pérez-Martín, Agustin Pérez-Torregrosa, Alejandro Rabasa and Marta Vaca

Mathematics 2020, 8(11), 1971; https://doi.org/10.3390/math8111971 - 06 Nov 2020

Cited by 5 | Viewed by 2131

Abstract

Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest [...] Read more.

Measuring credit risk is essential for financial institutions because there is a high risk level associated with incorrect credit decisions. The Basel II agreement recommended the use of advanced credit scoring methods in order to improve the efficiency of capital allocation. The latest Basel agreement (Basel III) states that the requirements for reserves based on risk have increased. Financial institutions currently have exhaustive datasets regarding their operations; this is a problem that can be addressed by applying a good feature selection method combined with big data techniques for data management. A comparative study of selection techniques is conducted in this work to find the selector that reduces the mean square error and requires the least execution time. Full article

(This article belongs to the Special Issue Mathematics of Financial Operations)

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