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A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Applied Economics and Finance".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 30365

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Special Issue Editors

Prof. Dr. S. Ejaz Ahmed

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Guest Editor

Department of Mathematics and Statistics, Brock University, St. Catharines, ON L2S 3A1, Canada
Interests: model selection; post-estimation and prediction; shrinkage and empirical Bayes; Bayesian data analysis; machine learning; business; information science; statistical genetics; image analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Nataša Šarlija

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Co-Guest Editor

J.J. Strossmayer University of Osijek, Faculty of Economics in Osijek, 31000 Osijek, Croatia
Interests: credit risk; insolvency risk; prediction of companies growth
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I am pleased to be serving as Guest Editor of the Special Issue “Predictive Modeling for Economic and Financial Data” to be published in JRFM and support related research in this area.

In today’s data-centric world, there is a host of buzzwords appearing everywhere in digital and print media. We encounter data in every walk of life, and the information it contains can be used to improve business, finance, fraud protection and, ultimately, society. This presents a substantial opportunity for analytically and objectively minded researchers. Making sense of the financial data and extracting meaningful information is not always a trivial task. The rapid growth in the size and scope of financial datasets from a host of disciplines has created the need for innovative statistical strategies for analyzing and visualizing such data. Regression analysis has been proven to be a useful and efficient strategy for decades and still plays an important role in sparse regression models.

The contributions to this Special Issue will present new and original research in statistical methods and applications in regression analysis with an emphasis on analysis and prediction of financial data. Financial time series analysis and prediction problems present many challenges for the development of statistical methodology and computational strategies for streaming data. The arena of financial research has drawn much attention from researchers worldwide. This Special Issue aims to provide a platform for a deep discussion of novel statistical methods developed for the analysis of financial data. Contributions can either have an applied or theoretical perspective and emphasize different statistical and econometrical problems specifically using data analytics and statistical methodologies. Manuscripts summarizing the most recent state-of-the-art on these topics are welcome and up-to-date review papers will be also considered for publication.

Prof. Dr. Syed Ejaz Ahmed
Prof. Dr. Nataša Šarlija
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Modeling and parameter estimation
High dimensional data
Bias and prediction
Correlated data
Sparse regression
Penalized likelihood
Submodel and full model estimation
Monte Carlo methods
Financial and economic data

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Published Papers (12 papers)

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Research

15 pages, 431 KiB

Open AccessArticle

A Threshold GARCH Model for Chilean Economic Uncertainty

by Diego Chávez, Javier E. Contreras-Reyes and Byron J. Idrovo-Aguirre

J. Risk Financial Manag. 2023, 16(1), 20; https://doi.org/10.3390/jrfm16010020 - 28 Dec 2022

Cited by 7 | Viewed by 3081

Abstract

In this paper, an autoregressive moving average (ARMA) model with threshold generalized autoregressive conditional heteroscedasticity (TGARCH) innovations is considered to model Chilean economic uncertainty time series. Uncertainty is measured through the Business Confidence Index (BCI) and Consumer Perception Index (CPI). The BCI time series provide useful information about industry; commerce; the finance, mining, construction, and agricultural sectors; and the global economic situation and the general business situation. As a counterpart, the CPI time series measure the perception of consumers regarding the state of the Chilean economy, evaluating their economic situation and expectations. The ARMA-TGARCH model is compared with the classical seasonal ARIMA and threshold AR ones. The results show that the ARMA-TGARCH model explains the regime changes in economic uncertainty better than the others, given that negative shocks are associated with statistically significant and quantitatively larger levels of volatility produced by the COVID-19 pandemic. In addition, a diagnostic analysis and prediction performance illustrates the suitability of the proposed model. Using a cross-validation analysis for the forecasting performance, a proposed heteroscedastic model may effectively help improve the forecasting accuracy for observations related to pessimism periods like the social uprising and the COVID-19 crisis which produced volatility in the Chilean uncertainty indexes. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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19 pages, 1956 KiB

Open AccessArticle

Old but Resilient Story: Impact of Decentralization on Social Welfare

by José Luis Alberto Delgado, Dilek Demirbaş and Ahmet Faruk Aysan

J. Risk Financial Manag. 2022, 15(12), 584; https://doi.org/10.3390/jrfm15120584 - 6 Dec 2022

Cited by 1 | Viewed by 1749

Abstract

This paper analyzes the fiscal performance of Turkey and Argentina during the period 2000–2021, when both countries faced rapid economic growth with the consequent impact on social welfare. This work explored two different systems: Centralization in Turkey and Federalism in Argentina and, in general, studied the decentralization impact of both the systems on social welfare. This study intended to create new social welfare indexes in other regions to analyze the resource allocation in different regions of these countries. As a first step, we built a regional human development index (HDI) for each region. This attempt is considered a new contribution to the literature and intended to fill the gap in this field. Afterward, this index was compared with the fiscal resources allocation (FRA), used as a proxy of fiscal decentralization in an econometric panel data model. By using this method, we concluded that the social welfare indexes have a positive relationship with the fiscal resource allocation in the Federal system, such as in Argentina, but not in the centralized system such as in Turkey during the period analyzed from 2000 to 2020. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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20 pages, 954 KiB

Open AccessArticle

BWM—RAPS Approach for Evaluating and Ranking Banking Sector Companies Based on Their Financial Indicators in the Saudi Stock Market

by Mohammed H. Alamoudi and Omer A. Bafail

J. Risk Financial Manag. 2022, 15(10), 467; https://doi.org/10.3390/jrfm15100467 - 17 Oct 2022

Cited by 11 | Viewed by 2442

Abstract

Seeking the greatest possible return on long-term investments, investors naturally seek equities of the best-performing companies that fit their investment timeframe. Long-term investment success rests on selecting the best companies, which requires a challenging analysis reviewing voluminous and often-conflicting data about companies and understanding broader economic forecasts. This paper undertook a case study deployment of MCDM methodologies to examine the suitability and effectiveness of Multi-Criteria Decision-Making (MCDM) methods in assessing and ranking the best stocks for portfolio inclusion. A combination of MCDM techniques comprised a methodology to evaluate and rank Saudi Arabian banking stocks based on their performance in the Saudi stock market. Specifically, the paper combined the Best–Worst Method (BWM) and Ranking Alternatives by Perimeter Similarity (RAPS) for the analysis. BWM calculated each criterion’s relative impact (weight) in selecting a stock. RAPS then used the weighting to rank the results of the investigation. The study’s findings yielded encouraging results regarding using an integrated MCDM technique to derive optimal banking sector securities in the expansive Saudi stock market. The novel application of the robust RAPS technique combined with BWM encourages continued and increased use of MCDM techniques in financial matters and broader application in evaluating equities. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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21 pages, 814 KiB

Open AccessArticle

An Alternative to Coping with COVID-19—Knowledge Management Applied to the Banking Industry in Taiwan

by Chih-Hsiung Chang, Wu-Hua Chang, Hsiu-Chin Hsieh and Yi-Yu Shih

J. Risk Financial Manag. 2022, 15(9), 405; https://doi.org/10.3390/jrfm15090405 - 12 Sep 2022

Cited by 1 | Viewed by 1979

Abstract

This study seeks to find an alternative strategy to cope with the impact of COVID-19. Though various measures have been adopted to respond to the threat of the pandemic, the problem remains unchanged. Undoubtedly, COVID-19 is also a crisis of knowledge, so this study explores whether the banking industry in Taiwan can apply knowledge management (KM) and fight the catastrophe of the century successfully and effectively. This study adopts an actual case to analyze the relationship between KM implementation and the banking industry; applies consistent fuzzy preference relations (CFPRs) to evaluate influential criteria including computational simplicity and guarantee the consistency of decision matrices; illustrates a decision-making model with seven criteria; and conducts pairwise comparisons, which are utilized to determine the priority weights of influential criteria amongst the outcome rankings and to formulate accurate KM strategies. The results show that predictions of success probabilities are higher than those of failure probabilities among the seven influential criteria and, in particular, the headquarters system and human resources are the most important priority indicators for implementing KM successfully during the pandemic or post-pandemic. The conclusion suggests significant policy implications for policymakers within other industries or countries in coping with COVID-19. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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16 pages, 1373 KiB

Open AccessArticle

Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators?

by Sabyasachi Mohapatra, Rohan Mukherjee, Arindam Roy, Anirban Sengupta and Amit Puniyani

J. Risk Financial Manag. 2022, 15(8), 350; https://doi.org/10.3390/jrfm15080350 - 7 Aug 2022

Cited by 10 | Viewed by 3541

Abstract

This paper develops ensemble machine learning models (XGBoost, Gradient Boosting, and AdaBoost in addition to Random Forest) for predicting stock returns of Indian banks using technical indicators. These indicators are based on three broad categories of technical analysis: Price, Volume, and Turnover. Various error metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), Root-Mean-Squared-Error (RMSE) have been used to check the performance of the models. Results show that the XGBoost algorithm performs best among the four ensemble models. The mean of absolute error and the root-mean-square -error vary around 3–5%. The feature importance plots generated by the models depict the importance of the variables in predicting the output. The proposed machine learning models help traders, investors, as well as portfolio managers, better predict the stock market trends and, in turn, the returns, particularly in banking stocks minimizing their sole dependency on macroeconomic factors. The techniques further assist the market participants in pre-empting any price-volume action across stocks irrespective of their size, liquidity, or past turnover. Finally, the techniques are incredibly robust and display a strong capability in predicting trend forecasts, particularly with any large deviations. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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14 pages, 1184 KiB

Open AccessArticle

The Role of Coefficient Drivers of Time-Varying Coefficients in Estimating the Total Effects of a Regressor on the Dependent Variable of an Equation

by Paravastu Ananta Venkata Bhattanatha Swamy, I-Lok Chang, Peter von zur Muehlen and Amit Achameesing

J. Risk Financial Manag. 2022, 15(8), 331; https://doi.org/10.3390/jrfm15080331 - 27 Jul 2022

Viewed by 1751

Abstract

Typically, the explanatory variables included in a regression model, in conjunction with the omitted relevant regressors implied by the usual error term, have both direct and indirect effects on the dependent variable. Attempts to obtain their separate estimates have been plagued with simultaneity issues. To circumvent these problems, this paper defines their sum as “total effects”, develops a time-varying coefficients methodology for their estimation without simultaneity bias, and applies these techniques to estimate the total effects of commercial bank credit per-capita on real GDP per-capita in Mauritius. An innovation is the introduction of extraneous variables that act as “coefficient drivers” chosen on the basis of best predictive performance, as measured by the smallest value of Theil’s U-statistic we were able to locate in the estimation. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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20 pages, 4133 KiB

Open AccessArticle

Modelling Seasonal Short-Run Effects in Time-Series Tourism Prices

by Sergej Gricar and Stefan Bojnec

J. Risk Financial Manag. 2022, 15(5), 212; https://doi.org/10.3390/jrfm15050212 - 6 May 2022

Viewed by 2084

Abstract

The paper’s primary purpose is to better monitor shocks; therefore, reliable scientific methods should be used to predict, monitor, and implement those events. In this paper, tourism prices are studied as an economic,

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and social phenomenon for better performance. [...] Read more.

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and social phenomenon for better performance. The selection of inadequacies in price time series is analysed. The state-of-the-art proposed methodology step of nominal to real prices is based on monthly data using the cointegrated-vector-autoregressive model (CVAR). This is the key feature selection on time-series properties in the economy and supported software(s). An attempt at a CVAR model with five seasonally unadjusted macroeconomic variables is developed. It introduces a meaningful, genuine and indispensable new data vector of transformed variables, and this stepwise process is more appropriate against the wrong model specification. The results for the period of economic crises show that the proposed model is reliable from nominal to real prices, and the researchers implement normality to price modelling in its econometric mock-up phase. Overall, the proposed model predicts testable events for up to 48-months. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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17 pages, 416 KiB

Open AccessArticle

Model Selection and Post Selection to Improve the Estimation of the ARCH Model

by Marwan Al-Momani and Abdaljbbar B. A. Dawod

J. Risk Financial Manag. 2022, 15(4), 174; https://doi.org/10.3390/jrfm15040174 - 10 Apr 2022

Cited by 2 | Viewed by 2366

Abstract

The Autoregressive Conditionally Heteroscedastic (ARCH) model is useful for handling volatilities in economical time series phenomena that ARIMA models are unable to handle. The ARCH model has been adopted in many applications that contain time series data such as financial market prices, options, commodity prices and the oil industry. In this paper, we propose an improved post-selection estimation strategy. We investigated and developed some asymptotic properties of the suggested strategies and compared with a benchmark estimator. Furthermore, we conducted a Monte Carlo simulation study to reappraise the relative characteristics of the listed estimators. Our numerical results corroborate with the analytical work of the study. We applied the proposed methods on the S&P500 stock market daily closing prices index to illustrate the usefulness of the developed methodologies. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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12 pages, 521 KiB

Open AccessArticle

Machine-Learning-Based Semiparametric Time Series Conditional Variance: Estimation and Forecasting

by Justin Dang and Aman Ullah

J. Risk Financial Manag. 2022, 15(1), 38; https://doi.org/10.3390/jrfm15010038 - 17 Jan 2022

Cited by 1 | Viewed by 2642

Abstract

This paper proposes a new combined semiparametric estimator of the conditional variance that takes the product of a parametric estimator and a nonparametric estimator based on machine learning. A popular kernel-based machine learning algorithm, known as the kernel-regularized least squares estimator, is used to estimate the nonparametric component. We discuss how to estimate the semiparametric estimator using real data and how to use this estimator to make forecasts for the conditional variance. Simulations are conducted to show the dominance of the proposed estimator in terms of mean squared error. An empirical application using S&P 500 daily returns is analyzed, and the semiparametric estimator effectively forecasts future volatility. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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18 pages, 388 KiB

Open AccessArticle

Super RaSE: Super Random Subspace Ensemble Classification

by Jianan Zhu and Yang Feng

J. Risk Financial Manag. 2021, 14(12), 612; https://doi.org/10.3390/jrfm14120612 - 17 Dec 2021

Cited by 1 | Viewed by 2503

Abstract

We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success of RaSE largely depends on the proper choice of the base classifier, which is unfortunately unknown to us. In this work, we show that Super RaSE avoids the need to choose a base classifier by randomly sampling a collection of classifiers together with the subspace. As a result, Super RaSE is more flexible and robust than RaSE. In addition to the vanilla Super RaSE, we also develop the iterative Super RaSE, which adaptively changes the base classifier distribution as well as the subspace distribution. We show that the Super RaSE algorithm and its iterative version perform competitively for a wide range of simulated data sets and two real data examples. The new Super RaSE algorithm and its iterative version are implemented in a new version of the R package RaSEn. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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21 pages, 1690 KiB

Open AccessArticle

Efficient Variance Reduction for American Call Options Using Symmetry Arguments

by François-Michel Boire, R. Mark Reesor and Lars Stentoft

J. Risk Financial Manag. 2021, 14(11), 504; https://doi.org/10.3390/jrfm14110504 - 20 Oct 2021

Cited by 2 | Viewed by 2131

Abstract

Recently it was shown that the estimated American call prices obtained with regression and simulation based methods can be significantly improved on by using put-call symmetry. This paper extends these results and demonstrates that it is also possible to significantly reduce the variance of the estimated call price by applying variance reduction techniques to corresponding symmetric put options. First, by comparing performance for pairs of call and (symmetric) put options for which the solution coincides, our results show that efficiency gains from variance reduction methods are different for calls and symmetric puts. Second, control variates should always be used and is the most efficient method. Furthermore, since control variates is more effective for puts than calls, and since symmetric pricing already offers some variance reduction, we demonstrate that drastic reductions in the standard deviation of the estimated call price is obtained by combining all three variance reduction techniques in a symmetric pricing approach. This reduces the standard deviation by a factor of over 20 for long maturity call options on highly volatile assets. Finally, we show that our findings are not particular to using in-sample pricing but also hold when using an out-of-sample pricing approach. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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19 pages, 1013 KiB

Open AccessArticle

Second-Order Least Squares Method for Dynamic Panel Data Models with Application

by Mustafa Salamh and Liqun Wang

J. Risk Financial Manag. 2021, 14(9), 410; https://doi.org/10.3390/jrfm14090410 - 1 Sep 2021

Cited by 2 | Viewed by 2286

Abstract

Management of financial risks and sound decision making rely on the accurate information and predictive models. Drawing useful information efficiently from big data with complex structures and building accurate models are therefore crucial tasks. Most commonly used methods for statistical inference in dynamic panel data models are based on the differencing transformation of data. However, differencing data may cause substantial loss of information, and therefore the subsequent analysis may fail to capture important features in the original level data. This point is demonstrated by a real data example where we use a semiparametrically efficient estimation method on the level data to reach a more favorable model. In particular, we study a second-order least squares approach which is based on the first two conditional moments of the response variable given the explanatory variables. This estimator is root-N consistent and its asymptotic variance reaches a lower bound semiparametric efficiency. Monte Carlo simulations show that this estimator performs favorably in finite sample situations compared to the first-differenced GMM and the random effects pseudo ML estimators. We also propose a new diagnostic test to check the working moments assumption based on the proposed estimator. A real data application is presented to further demonstrate the usage of this method. Full article

(This article belongs to the Special Issue Predictive Modeling for Economic and Financial Data)

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