Special Issue "Application of Time Series Analyses in Business"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic, Business and Management Aspects of Sustainability".

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editor

Prof. Zabihollah Rezaee
E-Mail Website
Guest Editor
Thompson-Hill Chair of Excellence and Professor of Accounting, Fogelman College of Business and Economics, the University of Memphis, Memphis, TN 38152, USA
Interests: auditing; business sustainability; corporate governance; data analytics; financial and managerial accounting; forensic accounting; professional ethics

Special Issue Information

Dear Colleagues,

The application of, and time series analyses in, business is currently at an early stage. This Special Issue of Sustainability is devoted to the publication of high-quality papers in time series analyses. This themed issue welcomes all views on the application of time series analyses in business with all types of papers (empirical, theoretical, and analytical) and case studies. Using time series models and analyses, millions of transactions can be searched to spot patterns and detect abnormalities and irregularities. The emergence of big data creates an opportunity to further investigate the application of time series models in business, accounting, and auditing using financial performance information and non-financial sustainability information on environmental, social, and governance performance. The ever-increasing business complexity, corporate governance reforms, risk management, and globalization, along with the growing demand for high-quality financial and non-financial information, necessitate the use of time series analyses to modernize the financial reporting and audit processes. Information and insight that once were not publicly available now extend far beyond traditional financial transactions and reports and expand into data from social media, e-mail, audio, video, and text files.

Submissions should be original work that investigate an aspect of time series and its application in business. The submitted manuscripts for this Special Issue are expected to address the following topics of interest, but they are not an exhaustive list:

  • The relevance and use of time series analyses for big data and business analytics.
  • How time series models can be efficiently and effectively applied in business, accounting, and auditing.
  • Presentation of policy, practical, educational, and research implications of time series analyses.
  • The use of time series analyses by businesses and management in their predictive models of managerial strategies, decisions, and actions.
  • Integration of time series analyses into the curricula of business schools and accounting programs.
  • The use of time series models and data analytics in the age of Big Data by businesses to enable them to make more informed strategic and operational decisions.
  • The use of time series models to transform unstructured and semi-structured data into structured information in improving the quality of financial and non-financial information.
  • Application of time series analyses in advancing business sustainability by presenting an example of the integrated big data and time series analyses into environmental, social and governance dimensions of business sustainability.
  • The use of time series analyses in detecting patterns in unstructured data and generates testable research hypotheses in future business, accounting, and auditing research
  • The application of time series in evaluating the feasibility, cost efficiency, and effectiveness of new rules, regulations, as well as accounting and auditing standards.
  • The use of time series in data science algorithms to capture all relevant information for decision making.

Prof. Zabihollah Rezaee
Guest Editor

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 papers will be 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. Sustainability is an international peer-reviewed open access semimonthly 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 1700 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

  • Big data
  • time series
  • data analytics
  • business analytics
  • business sustainability
  • financial information
  • non-financial information

Published Papers (15 papers)

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Research

Open AccessArticle
Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information
Sustainability 2019, 11(14), 3906; https://doi.org/10.3390/su11143906 - 18 Jul 2019
Cited by 1
Abstract
This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between [...] Read more.
This paper investigates the linear/nonlinear long-run and short-run dynamic relationships between oil prices and two implied volatilities, oil price volatility index (OVX) and stock index options volatility index (VIX), representing panic gauges. The results show that there is a long-run equilibrium relationship between oil prices and OVX (VIX) using the linear autoregressive distributed lag (ARDL)-bounds test. Likewise, while using the nonlinear autoregressive distributed lag (NARDL)-bounds test, not only does a long-run equilibrium relationship exist, but also the rising OVX (VIX) has a greater negative influence on oil prices than the declining OVX (VIX), thus indicating that a long-run, asymmetric cointegration exists between the variables. Furthermore, OVX (VIX) oil prices have a linear Granger causality, while for the nonlinear Granger causality test, oil prices have a bidirectional relation with OVX (VIX). In addition, we find that once major international political and economic events occur, structural changes in oil prices change the behavior of oil prices, and thus panic indices, thereby switching from a linear relationship to a nonlinear one. The empirical results of this study provide market participants with more valuable information. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Determinants of Banks’ Net Interest Margin: Evidence from the Euro Area during the Crisis and Post-Crisis Period
Sustainability 2019, 11(14), 3785; https://doi.org/10.3390/su11143785 - 10 Jul 2019
Cited by 1
Abstract
This paper analyses the determinants of net interest margin during the period 2008–2014 in the Euro Area. The starting point of the analysis is the premise that this variable is a gauge of financial institutions’ health and stability. In particular, since the outbreak [...] Read more.
This paper analyses the determinants of net interest margin during the period 2008–2014 in the Euro Area. The starting point of the analysis is the premise that this variable is a gauge of financial institutions’ health and stability. In particular, since the outbreak of the global financial crisis, difficulties in achieving sustainable levels of profitability, mainly due to the vulnerable margins from the banks’ traditional activity, have significantly increased the fragility of the European banking system. Besides considering the main bank-level drivers affecting the net interest margin such as market power, capitalization, interest risk and the level of efficiency, we explicitly account for the effects of regulatory and institutional settings. The results show a persistence in the vulnerability of the banks’ sustainable profitability, even though this negative trend has been partly mitigated by the European Central Bank (ECB)’s recent monetary policies. The increase in non-traditional activities as well as the heterogeneous efficiency levels characterizing banking systems across the Euro Area, where operating costs remain generally high, have significantly contributed to the slowdown in bank margins from traditional activity. Finally, the regulatory environment is an important driver of the net interest margin, which remained lower in countries with higher capital requirements and greater supervisory power. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Sustainable Local Currency Debt: An Analysis of Foreigners’ Korea Treasury Bonds Investments Using a LA-VARX Model
Sustainability 2019, 11(13), 3603; https://doi.org/10.3390/su11133603 - 30 Jun 2019
Cited by 1
Abstract
Foreign investors’ interest in Korean local currency bonds, and especially in Korea Treasury Bonds (KTBs) has increased significantly since the mid-2000s. This paper examines the determinants of foreign investors’ KTB investments by means of a lag-augmented vector autoregressive model with exogenous variables (LA-VARX). [...] Read more.
Foreign investors’ interest in Korean local currency bonds, and especially in Korea Treasury Bonds (KTBs) has increased significantly since the mid-2000s. This paper examines the determinants of foreign investors’ KTB investments by means of a lag-augmented vector autoregressive model with exogenous variables (LA-VARX). The model specification includes variables capturing the domestic, international, and risk factors. The risk factors are especially important in the context of South Korea since geopolitical tensions and economic policy uncertainty might adversely affect all investment decisions by foreigners. We find that expected return rates, country default risks, and global economic conditions have a significant impact on foreign investors’ KTB investment, but geopolitical risks have only a short-term negative impact. Our findings not for only provide a better understanding of the determinants of financial investments in South Korean financial markets, but they have broader implications in terms of the economic and social aspects of sustainability in South Korea. This is because KTBs provide a source of funding for the South Korean government for social projects and that KTBs are also held largely by long-term investors such as pension funds and insurers which require stable and sustainable investments. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis
Sustainability 2019, 11(12), 3489; https://doi.org/10.3390/su11123489 - 25 Jun 2019
Abstract
Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; [...] Read more.
Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Policy Reforms and Productivity Change in the Dutch Drinking Water Industry: A Time Series Analysis 1980–2015
Sustainability 2019, 11(12), 3463; https://doi.org/10.3390/su11123463 - 24 Jun 2019
Abstract
In the last four decades, the Dutch drinking water industry has undergone two major policy reforms, namely the consolidation of the industry by stimulating mergers and the introduction of yardstick competition by applying benchmarks. This paper addresses the question of whether these two [...] Read more.
In the last four decades, the Dutch drinking water industry has undergone two major policy reforms, namely the consolidation of the industry by stimulating mergers and the introduction of yardstick competition by applying benchmarks. This paper addresses the question of whether these two instruments have improved productivity. Productivity changes are derived from an estimated cost function. The effects of average scale as well as the introduction of a form of yardstick competition on productivity are formally tested. Estimation is conducted on the basis of time series data in the period 1980–2015. Industry consolidation has taken place over a long period of time. Yardstick competition was introduced in 1997 on a voluntary basis. It shows that total factor productivity was rather stable in the period 1980–1998. Since 1998, annual productivity growth has been substantial (about 0.6% on average). There was an obvious break point in 1998, providing clear evidence that the introduction of the benchmark instrument has affected productivity change. Moreover, there are various indications that benchmarking has also contributed to improving quality and sustainability. We could not find any empirical evidence for the hypothesis that consolidation of the industry has improved productivity. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
On the Relationship between Economic Policy Uncertainty and the Implied Volatility Index
Sustainability 2019, 11(6), 1628; https://doi.org/10.3390/su11061628 - 18 Mar 2019
Cited by 1
Abstract
This article examines the effects of economic policy uncertainty (EPU) on the implied volatility index. The implied volatility index of various markets has been analyzed in relation to scheduled macroeconomic announcements, such as EPU and equity market policy uncertainty (EMPU) indices. The study [...] Read more.
This article examines the effects of economic policy uncertainty (EPU) on the implied volatility index. The implied volatility index of various markets has been analyzed in relation to scheduled macroeconomic announcements, such as EPU and equity market policy uncertainty (EMPU) indices. The study highlights that EPU contains important information to explain the diverse market effects of the U.S., which is gauged into the volatility index. Estimates obtained in an autoregressive conditional heteroscedasticity framework indicate the persistence of volatility during spikes in the EPU. More importantly, the lagged values of the policy uncertainty index also contains market-related information to explain the markets’ future volatility. Major political and economic events have also contributed positively in that a presidential election contains information to explain various asset classes. Commodities, such as crude oil, gold, corn, and soybean, have been impacted significantly followed by EPU. Moreover, interest rate market volatility has also been moved adversely due to tight monetary policy. The Markov regime switching regression manifests that the implied volatility index (VIX) behaves abruptly in two different regimes followed by EPU. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Estimating Residential and Industrial City Gas Demand Function in the Republic of Korea—A Kalman Filter Application
Sustainability 2019, 11(5), 1363; https://doi.org/10.3390/su11051363 - 05 Mar 2019
Cited by 2
Abstract
This paper analyzes the city gas demand function in Korea from 1998 to 2018. The demand function of city gas is derived by a Kalman filter method, and price and income elasticities varying with time are estimated. In the case of residential city [...] Read more.
This paper analyzes the city gas demand function in Korea from 1998 to 2018. The demand function of city gas is derived by a Kalman filter method, and price and income elasticities varying with time are estimated. In the case of residential city gas, the price elasticity gradually decreased to a value of approximately 0.57, while income elasticity increased to approximately 1.48 from 1998 to 2018. Alternatively, industrial city gas demand’s price and income elasticities have been estimated as inelastic, as their absolute values were less than unity over time. The absolute values of price and income elasticities are estimated to be larger for residential than industrial city gas, and thus, city gas consumers are more likely to respond to changes in price and income for residential than industrial city gas. There is a substantial income effect on demand for residential city gas in Korea, whereas industrial city gas is found to have relatively small income and price effects. The results of this study provide policy makers with a Kalman filter method to access more accurate information on the city gas demand function’s elasticities, which change with time. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Heterogeneous Causal Relationships between Spot and Futures Oil Prices: Evidence from Quantile Causality Analysis
Sustainability 2019, 11(5), 1359; https://doi.org/10.3390/su11051359 - 05 Mar 2019
Abstract
The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. [...] Read more.
The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produces heterogeneous investment behaviors, this study applied a quantile causality framework to capture different causalities across various quantile levels and found that the causal relationships between crude oil spot and futures prices significantly derive from tail quantile intervals and appear as heterogeneous effects. Before the Iraq War, crude oil spot and futures prices were mutually Granger-caused at lower quantile levels, and only futures prices led spot prices at upper quantile levels. Since the war, a clear bidirectional causality has existed at the upper quantile levels, but only in lower quantile levels have futures prices led spot prices. These results provide useful information to investors using crude spot or futures prices to hedge or manage downside or upside risks in their portfolios. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Ownership Concentration and Performance Recovery Patterns in the European Union
Sustainability 2019, 11(4), 953; https://doi.org/10.3390/su11040953 - 13 Feb 2019
Abstract
Our study addresses the link between ownership concentration and corporate performance in the manufacturing sector in the European Union in an economic environment stressed by the global financial and sovereign debt crises. This is, to our knowledge, the first attempt to tackle differences [...] Read more.
Our study addresses the link between ownership concentration and corporate performance in the manufacturing sector in the European Union in an economic environment stressed by the global financial and sovereign debt crises. This is, to our knowledge, the first attempt to tackle differences between companies with different origin-countries in EU from the perspective of ownership concentration and corporate performance in a period marked by the adverse impact of the global financial crisis. Ownership concentration is measured by the number of shareholders and the percentage of their individual and collective holdings, while performance is measured by accounting-based and market-based indicators. Our results, based on a detailed and methodical statistical analysis, show a clear division between Western and Eastern companies in terms of ownership concentration and performance, with an impact on businesses’ recovery patterns. Overall, there is a positive link between ownership concentration and corporate performance in the case of Western companies, but not for Eastern-based companies. Moreover, ownership concentration has supported business recovery in EU, but particularly for Western companies. On the other hand, our results suggest that market investors’ assessment of corporate performance is disconnected from business fundamentals and do not acknowledge the role of ownership concentration (either beneficial of detrimental) for performance assessment. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting
Sustainability 2019, 11(3), 793; https://doi.org/10.3390/su11030793 - 02 Feb 2019
Cited by 2
Abstract
Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ [...] Read more.
Receiving appropriate forecast accuracy is important in many countries’ economic activities, and developing effective and precise time series model is critical issue in tourism demand forecasting. In this paper, fuzzy rule-based system model for hotel occupancy forecasting is developed by analyzing 40 months’ time series data and applying fuzzy c-means clustering algorithm. Based on the values of root mean square error and mean absolute percentage error which are metrics for measuring forecast accuracy, it is defined that the model with 7 clusters and 4 inputs is the optimal forecasting model for hotel occupancy. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Risk Transmission between Chinese and U.S. Agricultural Commodity Futures Markets—A CoVaR Approach
Sustainability 2019, 11(1), 239; https://doi.org/10.3390/su11010239 - 05 Jan 2019
Cited by 1
Abstract
Commodity futures markets play an important role, through risk management and price discovery, in helping firms make sustainable production and marketing decisions. An important related issue is how pricing signals between futures exchanges impact traders’ risk. We address this issue by shedding light [...] Read more.
Commodity futures markets play an important role, through risk management and price discovery, in helping firms make sustainable production and marketing decisions. An important related issue is how pricing signals between futures exchanges impact traders’ risk. We address this issue by shedding light on risk transmission between the most mature (U.S.) and the fastest growing (Chinese) commodity futures markets. Gaining greater insight of risk transmission between these key markets is vitally important to firms engaged in the efficient and sustainable trade of commodities needed to feed the world. We examine the risk transmission between Chinese and U.S. agricultural futures markets for soybean, corn, and sugar with a Copula based conditional value at risk (CoVaR) approach. We find significant upside, and to a lesser extent downside risk transmission, between Chinese and U.S. markets. We confirm the dominant pricing role of U.S. agricultural futures markets while acknowledging the increasing price discovery role performed by Chinese markets. Our results highlight that soybean markets exhibit greater risk transmission than sugar and corn markets. We argue that our findings may be explained by Chinese government policy intervention, and by the large role played by U.S. firms in the underlying cash commodity markets–both in terms of production and trade. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example
Sustainability 2018, 10(11), 4330; https://doi.org/10.3390/su10114330 - 21 Nov 2018
Cited by 1
Abstract
Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of [...] Read more.
Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Export Diversification and Ecological Footprint: A Comparative Study on EKC Theory among Korea, Japan, and China
Sustainability 2018, 10(10), 3657; https://doi.org/10.3390/su10103657 - 12 Oct 2018
Cited by 5
Abstract
This study examines the Environmental Kuznets Curve (EKC) hypothesis by adopting a country’s ecological footprint as an indicator of environmental degradation in three East Asian countries: Japan, Korea, and China. During the development process, countries intend to balance between stabilizing export demand and [...] Read more.
This study examines the Environmental Kuznets Curve (EKC) hypothesis by adopting a country’s ecological footprint as an indicator of environmental degradation in three East Asian countries: Japan, Korea, and China. During the development process, countries intend to balance between stabilizing export demand and maintaining sustainable economic improvement in the context of deteriorating global warming and climate change. The Environmental Kuznets Curve (henceforth, EKC) was originally developed to estimate the correlation between environment condition and economic development. In this paper, we started from the EKC model and adopted an Error Correction Methodology (henceforth, ECM) to estimate the EKC relationships in Japan, Korea (two developed countries), and China (a developing country) over the period of 1990 to 2013. Besides this, instead of only using Gross Domestic Product (henceforth, GDP), two subdivisions of trade diversification—export product diversification and export market diversification—are introduced as proxy variables for economic development in rectification of the EKC. The results demonstrate that both Korea and Japan satisfy the EKC theory by demonstrating an inverted U-shaped relationship between economic development and ecological footprint, while analysis based on data from China does not display the same tendency. For both export product diversification and market diversification, the more diversified the country’s export is, the bigger its ecological footprint. The policy implications of this econometric outcome are also discussed. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
Open AccessArticle
Macroprudential Policy, Credit Cycle, and Bank Risk-Taking
Sustainability 2018, 10(10), 3620; https://doi.org/10.3390/su10103620 - 10 Oct 2018
Cited by 3
Abstract
This paper constructs a theoretical model to analyze the effect of macroprudential policies (MPPs) on bank risk-taking. We collect a data set of 231 commercial banks in China to empirically test whether macroprudential tools, including countercyclical capital buffers, reserve requirements, and caps on [...] Read more.
This paper constructs a theoretical model to analyze the effect of macroprudential policies (MPPs) on bank risk-taking. We collect a data set of 231 commercial banks in China to empirically test whether macroprudential tools, including countercyclical capital buffers, reserve requirements, and caps on loan-to-value, can affect bank risk-taking behaviors by using the dynamic unbalanced panel system generalized method of moment (SYS-GMM). The results provide further evidence on the important role of MPPs in maintaining financial stability, which helps mitigate financial system vulnerabilities. Bank risk-taking will be decreased with the strengthening of macroprudential supervision, which greatly benefits the resilience and the sustainability of bank sector. Moreover, the credit cycle has a magnifying role on MPPs’ effect on bank risk-taking. Reducing risks in bank loans requires a further slowing of credit growth, which is necessary to ensure sustainable growth in a bank system, or more ambitiously, to smooth financial booms and busts. The results survive robustness checks under alternative estimation methods and alternative proxies of bank risk-taking and MPPs. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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Open AccessArticle
Market Reaction to Other Comprehensive Income
Sustainability 2018, 10(6), 1837; https://doi.org/10.3390/su10061837 - 01 Jun 2018
Cited by 3
Abstract
The comprehensive income statement was adopted as the standard type of financial statement in 2011, and other comprehensive income (OCI) was included in the text of the financial statements. While OCI (unrealized income) is less sustainable than net income, it can help to [...] Read more.
The comprehensive income statement was adopted as the standard type of financial statement in 2011, and other comprehensive income (OCI) was included in the text of the financial statements. While OCI (unrealized income) is less sustainable than net income, it can help to assess a firm’s value. Therefore, testing the usefulness of OCI is important in analyzing whether persistence of earning information affects a firm’s value. The text of the financial statements enables market participants to access not only the realized net income (operating income, non-operating income), but also information on comprehensive income, which has not yet been realized. Although levels of realized and unrealized income indicate an increase in net worth, changes in realized and unrealized income differ in terms of uncertainty; it is, therefore, more important for market participants to judge information’s usefulness. This study examines whether OCI increases earnings response coefficients (ERC). We analyzed the information content of OCI before and after international financial reporting standards (IFRS) to verify whether the information content varies as the format of OCI reporting changes from a footnote to the main text of the financial statement. In addition, we analyzed dividing OCI into positive OCI and negative OCI. The analysis showed that under the condition in which the realized income is constant, OCI (which is unrealized earnings) has additional information effects. This means that differences might be observed in the decision-making process depending on whether or not the OCI information is used. Full article
(This article belongs to the Special Issue Application of Time Series Analyses in Business)
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