Asian Journal of Economics and Banking (AJEB)
Tạp chí của Trường Đại học Ngân hàng Tp. Hồ Chí Minh
Stability analysis of solutions to equilibrium problems and applications in economics
Purpose – The purpose of this paper is to study the Hölder calmness of solutions to equilibrium problems and apply it to economics. Design/methodology/approach – The authors obtain the Hölder calmness by using an effective approach. More precisely, under the key assumption of strong convexity, sufficient conditions for the Hölder continuity of solution maps to equilibrium problems are established. Findings – Anewresult in stability analysis for equilibriumproblems and applications in economics is archived. Originality/value – The authors confirm that the paper has not been published previously, is not under consideration for publication elsewhere and is not being simultaneously submitted elsewhere.
Determinants of consumers’intention to use credit card: a perspective of multifaceted perceived risk
Purpose – The purpose of this study is to develop a theoretical model for consumer behavioral intention by integrating the technology acceptance model (TAM) and the theory of perceived risk, which is tested on the intended use of credit cards in Vietnam. Design/methodology/approach – The data were collected from 485 bank customers through a nationwide online survey. An exploratory and confirmatory factor analyzes were performed to validate the factor structure of the measurement items while structural equation modeling was used to validate the proposed model and testing the hypotheses. Findings – The results of structural equation modeling reveal that perceived risk, perceived usefulness, social influence and perceived ease of use were significant determinants of consumer intention to use a credit card. Of them, only perceived risk discouraged the intended use of a credit card, which was synthesized from psychological, financial, performance, privacy, time, social and security risk. Research limitations/implications – This study measured the first-order risk dimensions based on the payment function of the credit card only; these measurements missed potential losses relevant to credit function of credit cards. Practical implications – This study can be beneficial to banks enacting policies to attract more consumers and to help decide how to allocate resources to retain and expand their customer base. Originality/value – The study adds value to the literature on consumer behavior by confirming the impact of second-order perceived risk on the intended use of credit cards, which most previous studies have not demonstrated. The research also provides an empirical evidence to the academic research platform on e-banking services in Vietnam, especially related to the credit card industry.
Quantum-like modelling in game theory: Quo Vadis? A brief review
Purpose – The current paper is a brief review of the emerging field of quantum-like modelling in game theory. This paper aims to explore several quantum games, which are superior compared to their classical counterparts, which means either they give rise to superior Nash equilibria or they make the game fairer. For example, quantum Prisoners Dilemma generates Pareto superior outcomes as compared to defection outcome in the famous classical case. Again, a quantum-like version of cards game can make the game fairer, increasing the chance of winning of players who are disadvantaged in the classical case. This paper explores all the virtues of simple quantumgames, also highlighting some findings of the authors as regards Prisoners Dilemma game. Design/methodology/approach – As this is a general review paper, the authors have not demonstrated any specific mathematical method, rather explored the well-known quantum probability framework, used for designing quantum games. They have a short appendix which explores basic structure of Hilbert space representation of human decision-making. Findings – Along with the review of the extant literature, the authors have also highlighted some new findings for quantum Prisoners Dilemma game. Specifically, they have shown in the earlier studies (which are referred to here) that a pure quantum entanglement set up is not needed for designing better games, even a weaker condition, which is classical entanglement is sufficient for producing Pareto improved outcomes. Research limitations/implications – Theoretical research, with findings and implications for future game designs, it has been argued that it is not always needed to have true quantum entanglement for superior Nash Equilibria. Originality/value – The main purpose here is to raise awareness mainly in the social science community about the possible applications of quantum-like game theory paradigm. The findings related to Prisoners Dilemma game are, however, original.
The impact of foreign capital shifts on economic activities and asset prices: a threshold VAR approach
Purpose – The purpose of this paper is to investigate the impact of foreign capital shifts on economic activities and asset prices in South Korea. Design/methodology/approach – The authors in this paper apply the Bayesian threshold vector autoregressive (TVAR) model to estimate the regimes of large and low inflows of foreign capital. Then, structural impulse-response analysis is used to check whether the responses of the variables differ across the estimated regimes. The model is estimated using quarterly data of foreign capital inflows, gross domestic product (GDP), consumer price index, credit to the private non-financial sector, real effective exchange rate (REER), stock returns and house prices. Findings – The main findings suggest that large inflows of gross foreign capital, foreign direct investments (FDI) and foreign portfolio investments (FPI) are ineffective to boost economic growth, but large inflows of other foreign investments (OFIs) significantly contribute to GDP. The decreases in the foreign capital inflows are associated with larger depreciation of REER. The large inflows of gross foreign capital, FDI and OFIs are associated with further expansion of credit supply to private nonfinancial sectors. Research limitations/implications – The policy implications of foreign capital inflows are of particular importance to all the emerging markets alike. However, the empirical analysis is limited to the case of South Korea due to various reasons. The experience with international capital inflows among emerging markets is heterogeneous. Therefore, it would be better to take each case of emerging market individually. In addition, TVAR analysis requires a long data sample, which unfortunately is not available for most of the emerging markets. Originality/value – The foreign capital inflows are shown to be procyclical and notoriously volatile in many studies. Nevertheless, this topic has commonly been studied using linear VAR models, which do not properly deal with the cyclical characteristics of foreign capital inflows. This study attempts to resolve these methodological limitations by examining a non-linear VAR model that is capable of capturing the structural breaks associated with the cyclical behaviors of foreign capital inflows.
Are you a good borrower? Mining interpretable pattern structures in credit scoring
Purpose – The purpose of this study is to show that closure-based classification and regression models provide both high accuracy and interpretability. Design/methodology/approach – Pattern structures allow one to approach the knowledge extraction problem in case of partially ordered descriptions. They provide a way to apply techniques based on closed descriptions to non-binary data. To provide scalability of the approach, the author introduced a lazy (querybased) classification algorithm. Findings – The experiments support the hypothesis that closure-based classification and regression allow one to both achieve higher accuracy in scoring models as compared to results obtained with classical banking models and retain interpretability of model results, whereas black-box methods grant better accuracy for the cost of losing interpretability. Originality/value – This is an original research showing the advantage of closure-based classification and regression models in the banking sphere.
How to choose a fuzzy similarity measure in decision-making?
Purpose – The paper is dedicated to the analysis of fuzzy similarity measures in uncertainty analysis in general, and in economic decision-making in particular. The purpose of this paper is to explain how a similarity measure can be chosen to quantify a qualitative description of similarities provided by experts of a given domain, in the case where the objects to compare are described through imprecise or linguistic attribute values represented by fuzzy sets. The case of qualitative dissimilarities is also addressed and the particular case of their representation by distances is presented. Design/methodology/approach – The approach is based on measurement theory, following Tversky’s well-known paradigm. Findings – A list of axioms which may or may not be satisfied by a qualitative comparative similarity between fuzzy objects is proposed, as extensions of axioms satisfied by similarities between crisp objects. They enable to express necessary and sufficient conditions for a numerical similarity measure to represent a comparative similarity between fuzzy objects. The representation of comparative dissimilarities is also addressed by means of specific functions depending on the distance between attribute values. Originality/value – Examples of functions satisfying certain axioms to represent comparative similarities are given. They are based on the choice of operators to compute intersection, union and difference of fuzzy sets. A simple application of this methodology to economy is given, to show how a measure of similarity can be chosen to represent intuitive similarities expressed by an economist by means of a quantitative measure easily calculable. More detailed and formal results are given in Coletti and Bouchon-Meunier (2020) for similarities and Coletti et al. (2020) for dissimilarities.
How can econometrics help fight the COVID-19 pandemic?
Purpose – The current pandemic is difficult to model – and thus difficult to control. In contrast to the previous epidemics, whose dynamics were smooth and well described by the existing models, the statistics of the current pandemic are highly oscillating. The purpose of this paper is to explain these oscillations and to see how this explanation can be used to fight the epidemic. Design/methodology/approach – The authors use an analogy with economic systems. Findings – The authors show that these oscillations can be explained if we take into account the disease’s long incubation period – as a result of which our control measures are determined by outdated data, showing number of infected people two weeks ago. To better control the pandemic, the authors propose to use the experience of economics, where also the effect of different measures can be observed only after some time. In the past, this led to wild oscillations of the economy, with rapid growth periods followed by devastating crises. In time, economists learned how to smooth the cycles and thus to drastically decrease the corresponding negative effects. The authors hope that this experience can help fight the pandemic. Originality/value – To the best of our knowledge, this is the first explanation of the highly oscillatory nature of this epidemic’s dynamics.
A closer look at stochastic frontier analysis in economics
Purpose – While there exist many surveys on the use stochastic frontier analysis (SFA), many important issues and techniques in SFA were not well elaborated in the previous surveys, namely, regular models, copula modeling, nonparametric estimation by Grenander’s method of sieves, empirical likelihood and causality issues in SFA using regression discontinuity design (RDD) (sharp and fuzzy RDD). The purpose of this paper is to encourage more research in these directions. Design/methodology/approach – A literature survey. Findings – While there are many useful applications of SFA to econometrics, there are also many important open problems. Originality/value – This is the first survey of SFA in econometrics that emphasizes important issues and techniques such as copulas.
Asymmetric Impact of Exchange Rate on Trade Balance: How Do Country Characteristics Imply?
The study investigates the asymmetric impact of the exchange rate on aggregate trade balance of 45 countries taking account of country characteristics, including development, exchange rate system, openness, and export diversification. Employing non-linear autoregressive distributed lag (ARDL) methodology; the study comes to several findings. First, the exchange rate does have an asymmetric impact on aggregate trade balance across countries in both the short and long-run. However, either appreciation or depreciation shows a consistent superior effect on the trade balance in comparison with the other. Second, although results among country groups of development, exchange rate regime or openness are not obviously different, developed countries show the more powerful impact of exchange rate than developing countries on the average and insignificant effects of exchange rate on trade balance are found only in low openness countries. Finally, currency depreciation to improve trade balance widely works in higher diversified exporters but does not work in lower diversified exporters.
Posterior Summary of Bayes Error Using Monte-Carlo Sampling and Its Application in Credit Scoring
Bayesian classifier is one of the data classification methods that are of interest. In the Bayesian classifier, Bayes error, Pe is an important measure because it can estimate the error of the model built through the calculation of the posterior probability function's overlapping area. The exact calculation of Pe depends on the exact calculation of likelihood functions and the prior probability of each type. In previous studies, the prior probability has been considered as a fixed value only, hence, the Bayes error is usually a fixed value. This sometimes leads to unreasonable results. To fill the mentioned research gap, this paper considers the prior probability q in Bayesian classifier as a distribution, and looks insight the posterior distribution of Bayes error, using Monte-Carlo simulation. Finally, the proposed method is applied to credit scoring data of a bank in Vietnam. Based on the results, we can determine whether the Bayesian classifier is suitable for data or not. In addition, the prior parameter setting can be tested through sensitivity analysis.
Extending the A Priori Procedure to One-way Analysis of Variance Model with Skew Normal Random Effects
The a priori procedure (APP) was designed as a pre-data procedure whereby researchers could find the sample sizes necessary to ensure that sample statistics to be obtained are within particular ranges of corresponding population parameters with known probabilities. Although the APP has been devised for a variety of experimental paradigms, these have all been simple. The present work extends a priori thinking to an important case not addressed previously, where the researcher is interested in one-way ANOVA models with skew normal random effects. Computer simulations support the equations presented, along with a real data example for illustration of our main results.
Why Mean, Variance, Moments, Correlation, Skewness etc. - Invariance-Based Explanations
In principle, we can use many difierent characteristics of a probability distribution. However, in practice, a few of such characteristics are mostly used: mean, variance, moments, correlation, etc. Why these characteristics and not others? The fact that these characteristics have been successfully used indicates that there must be some reason for their selection. In this paper, we show that the selection of these characteristics can be explained by the fact that these characteristics are invariant with respect to natural transformations - while other possible characteristics are not invariant.
Contextual Behaviour in a ComplexWorld: from Voting Preferences to Financial Expectations
Recent years were characterized by an increasing of interest for applications of the mathematical formalism of quantum theory and its methodology to information processing and human choice behaviour in cognition, decision making, artificial intelligence, economics and finance and also social and political science. Contextual expectations and choices in real economic and financial decision making settings, non-neutral attitudes to ambiguity and problems of complete non-knowledge are posing a challenge to standard decision theories that utilize the calculus of classical probability theory. A new area that showed a potential to cope with the non-classical decision making statistics of humans is known as quantum-like modeling. Its basic tool is quantum calculus of probabilities, which is based on operation with complex probability amplitudes and the usage of Born's rule to convert complex probability amplitudes into objective probabilities. In this paper we expose a brief review introducing the core axiomatic difierences between classical and quantum probability as well as discuss the decision-making settings in which quantum probability can capture agents' non-classical beliefs, superposition of ambiguous economic and financial events, and other instances of non-classical information processing.
Decision Making versus Testing: A Changing of the Guards in Empirical Research?
In this paper we will elaborate and give support to Manski's central message in “Statistical decision theory should supplant hypothesis tesing" (Manksi [15]; see also Manski [16]). Decisions should take the place of testing. This review should benefit practioners in empirical research of econometrics and in statistics in general. We emphasize thatWald's Statistical Decision Theory should be used in empirical research instead of “traditional" hypothesis testing (both frequentist and Bayesian testing), since hypothesis testing is only a one-sized-fits-all way to make decisions. The goal of empirical research is to make optimal decisions, and it is possible to make decisions without using testing.
Economic Growths of ASEAN-5 Countries Impacted by Global and Domestic Credit Risks
The five founding members of ASEAN - Indonesia, Malaysia, the Philippines, Singapore, and Thailand (a.k.a. ASEAN-5) have maintained moderate economic growths over the past decades. The role of the credit risk cycle in predicting the economic growths of the ASEAN-5 countries is the focus of this paper. Our analysis suggests that the aggregate credit risks of Malaysia and Singapore co-move strongly with the global credit risk cycle while those of Indonesia, Malaysia and the Philippines are more sensitive to regional/domestic credit risk shocks. By comparing to a benchmark growth forecasting model with typical macroeconomic indicators, we find that information on credit risk environments improves the model's explanatory power considerably. The economic growths of all except for Indonesia respond significantly to either global or domestic credit risk movements, or both. Not surprisingly, inclusion of more updated and readily available credit risk assessments arriving intra-quarter can, in a spirit similar to nowcasting, further boost the model's forecasting performance on economic growths.
Social Existence Determines Consciousness: How the Economy Matters for Cultural Changes? A Study of Selected Asian Countries
Based on economic determinism and Hofstede's cultural dimensions theory, applying the Bayesian approach throughout the study, the paper confirms the nonlinear relationship between economic performance and cultural changes. First, the results of this paper are diffierent from the previous studies, i.e., individualism and economic performance have a positive relationship; masculinity (MAS) and uncertainty avoidance index (UAI) first decrease, and then increase with economic development. The possible explanation for this is based on the cultural features of the studied Asian countries. In addition, the study found evidence that the English language and economic openness draw Eastern cultural values near to the Western culture, but there are some conservative straits of the Asian culture that hinder the convergence to the Western culture.
Two Cultures are Merging and Some Applications in Finance
Nearly two decades ago, prominent statistician Leo Breiman wrote an inuential paper titled \Statistical Modeling: The Two Cultures" [17] where he presented fundamental ideas of generative models (statistical) and algorithmic model in studying complex datasets. Breiman urged statisticians to adopt a more diverse set of tools for data modeling. As of 2019, the algorithmic model has witnessed a rapid development for more than 20 years and this trend will be continuing for years to come. Nowadays, it is usually referred to as machine learning, or in particular, neural network, deep learning etcetera. The good news is that with substantial investments from leading technology companies like Google, Facebook, Uber, the Breiman's goal of diversifying data modeling tools can be pushed forward even further: the infrastructure now supports getting the best of the two worlds, namely, researchers, practitioners are able to exibly incorporate various components of both generative model and algorithmic model into a hybrid framework which allows to prioritize between predictive power or interpretability of the models. In this paper, first, the two cultures are briey surveyed. Second, basic ideas of integration techniques are discussed together with supporting programming languages. Third, some exemplary implementations of integration with concrete applications are presented. Finally, certain recent relevant work in business and finance are introduced.
Quantum-like Modeling: from Economics to Social Laser
This is a brief review of the recent development of quantum-like modeling, in decision making, finance, social and political science. We point to the recent quantum information revolution elevating the role of the information dimension of quantum theory. Then the essentials of quantum probability are briey presented with emphasis on the formula of total probability with the interference term as a tool for amplication of probability (as is done in quantum computing). We stress that this amplication is the essence of the quantum probability update, non-Bayesian update. We show that contextuality is the basic reason for the applicability of quantum probability to modeling of behavior of human agents - the quantum-like paradigm. We then discuss semiclassical modeling in economics and fanance. Finally, we present the model of “social laser" for stimulated amplication of social actions (SASA). This is a new field of research. We formulate a number of open problems, both in theory and social engineering. One of the main intriguing problems is the mathematical formalization of the notion of social energy (with its possible generalizations such as, e.g., fanancial energy).
Quantum (and More General) Models of Research Collaboration
In the last decades, several papers have shown that quantum techniques can be successful in describing not only events in the micro-scale physical world - for which they were originally invented - but also in describing social phenomena, e.g., different economic processes. In our previous paper, we provide an explanation for this somewhat surprising successes. In this paper, we extend this explanation and show that quantum (and more general) techniques can also be used to model research collaboration.
How to Choose Tuning Parameters in Lasso and Ridge Regression?
This paper gives a literature review of choosing tuning parameters for ridge regression and lasso. These regularized regressions introduce a little bias in return for a considerable decrease in the variance of the predicted values, thus increasing prediction accuracy. We can use AIC or BIC to select the tuning parameter for linear predictive models. However, for general predictive models, cross-validation and bootstrap work better because they directly estimate prediction error. Though, cross-validation is more widely used than bootstrap. An empirical example is employed to illustrate how cross-validated lasso works. It shows that lasso may solve the multicollinearity problem.
On the Calculus of Subjective Probability in Behavioral Economics
In elaborationg upon the recent thought-provoking paper\Subjective probability in behavioral economics and _nance: A radical reformulation" by H. Joel Jeffrey and Anthony O. Putman [5], we proceed to specify the calculus of their \probability (uncertainty) appraisals" as possiblity measures, i.e., the \radical" reformulation of the usual calculus of subjective probabilities is that of idempotent uncertainty. With possibility measures as quantitative uncertainty for subjective probabilities, we discuss the necessary mariage of possibility measures and Kolmogorov probability measures in a new Bayesian analysis for economic applications.
Predictably (Boundedly) Rational: Examples of Seemingly Irrational Behavior Can Be Quantitatively Explained by Bounded Rationality
Traditional economics is based on the simplifying assumption that people behave perfectly rationally, that before making any decision, a person thoroughly analyzes all possible situations. In reality, we often do not have enough time to thoroughly analyze all the available information, as a result of which we make decisions of bounded rationality { bounded by our inability to perform a thorough analysis of the situation. So, to predict human behavior, it is desirable to study how people actually make decisions. The corresponding area of economics is known as behavioral economics. It is known that many examples of seemingly irrational behavior can be explained, on the qualitative level, by this idea of bounded rationality. In this paper, we show that in many case, this qualitative explanation can be expanded into a quantitative one, that enables us to explain the numerical characteristics of the corresponding behavior.
Understanding the Economic Impact of Oil Price Shocks
There is a rich literature on the link between oil price uctuations and macroeconomic fundamentals that emerged after the oil shocks of the 1970s. This paper reviews this research and provides an overview of key historical developments in the global crude oil market and their implications for the oil price-acroeconomy relationship. We discuss theoretical and empirical studies on this theme with a focus on empirical issues researchers have had to overcome. We speculate that, as the world transitions to a low-carbon economy, oil is likely to continue losing market share to other energy sources and that this will lead to a secular decline in the importance of oil for the macroeconomy. But even in the most optimistic scenarios, this transition will take decades and, in the meantime, understanding oil price shocks will continue to be an area of active research.
Overinvestment and Free Cash Flow: Empirical Evidence from Vietnamese Enterprises
The paper reports an investigation into Vietnamese enterprises’ optimal level of investment based on panel data to find out the relationship between overinvestment and free cash flow. The results show that Vietnamese enterprises have been largely overinvesting. Overinvestment is significantly negatively associated with the efficiency of the company. By following the statistical approach to measure overinvestment and free cash flow, the results show that there is a significantly positive association between overinvestment and free cash flow of enterprises. This completely corresponds to agency theory. Enterprises with free cash flow have strong incentives to engage in overinvestment.
A Technique to Predict Short-term Stock Trend Using Bayesian Classifier
In this paper, an application of Bayesian classifier for shortterm stock trend prediction, which is a popular field of study, is presented. In order to use Bayesian classifier effectively, we transform daily stock price time series object into data frame format where the dependent variable is stock trend label and the independent variables are the stock variations with respect to previous days. The numerical example using stock market data of individual firms demonstrates the potential of the proposed method in predicting the short-term stock trend. In addition, to reduce the risk for the investor, a method to adjust the probability threshold using the ROC curve is investigated. Also, it can be implied that the performance of the new technique mainly depends on the skill of investors, such as adjusting the threshold, identifying the suitable stock and the suitable time for trading, combining the proposed technique with other tools of fundamental analysis and technical analysis, etc.
How Trading the S&P 500 ETFs \Beats" the Market by an Average of 30% in Annual Returns
The market refers to the S&P 500 index, which is an important benchmark of U.S. stock performances, and\beating"the market means earning a return greater than the market. The historical average annual market return is approximately 10% since its inception in 1928. The purpose of this paper is to showcase a trading strategy that earns an average annual return of about 13%, which is 30% higher than the historical average annual market return. The strategy is contained in the website WaveletTrader.com, which is quite user-friendly, and no special skills or prior practice is needed. It has been generally believed that coming up with a scientific system to \beat" the market is impossible. But the results of this paper strongly suggest otherwise
A Survey of Fixed Point and Economic Game Theory
This paper surveys the development of fixed point theory regarding to game theory. Moreover, we focus on the theoretical results applied for economics. Many recent papers are also collected and summarized throughout a particular period of time.
Extending a Priori Procedure to Two Independent Samples under Skew Normal Settings
It is useful for researchers to be able to estimate the sample size necessary to have an impressive probability of obtaining a difference between sample locations of two independent groups that is close to the difference between corresponding population locations. The present manuscript provides the necessary equations. To increase generality, the derivations presented are not based on the typical assumption that the samples come from the family of normally distributed populations but rather from the much larger family of skew normal distributions. In addition, counter to many researchers’ intuitions, we demonstrate that greater sampling precision ensues from skewness than from normality, all else being equal, with simulation results. Finally a real data example on faculty salaries of New Mexico State University is given for the illustration of our main results.
Maximum Entropy Approach to Portfolio Optimization: Economic Justification of an Intuitive Diversity Idea
The traditional Markowitz approach to portfolio optimization assumes that we know the means, variances, and covariances of the return rates of all the financial instruments. In some practical situations, however, we do not have enough information to determine the variances and covariances, we only know the means. To provide a reasonable portfolio allocation for such cases, researchers proposed a heuristic maximum entropy approach. In this paper, we provide an economic justification for this heuristic idea.
Clarifying ASA’s View on P-Values in Hypothesis Testing
This paper aims at clarifying both the ASA’s Statements on Pvalues (2016) and the recent The American Statistician (TAS) special issue on \Statistical inference in the 21st century: Moving to a world beyond p < 0:05" (2019), as well as the US National Academy of Science’s recent \Reproducibility and Replicability in Science" (2019). These documents, as a worldwide announcement, put a final end to the use of the notion of P-values in frequentist testing of statistical hypotheses. Statisticians might get the impression that abandoning P-values only affects Fisher’s significance testing, and not NeymanPearson’s (N-P) hypothesis testing since these two \theories" of (frequentist) testing are different, although they are put in a combined testing theory called Null Hypothesis Significance Testing (NHST). Such an impression might be gained because the above documents were somewhat silent on N-P testing, whose main messages are \Don’t say statistically significant" and \Abandon statistical significance". They do not specifically declare \The final collapse of the Neyman-Pearson decision theoretic framework" (as previously presented in Hurlbert and Lombard [14]). Such an impression is dangerous as it might be thought that N-P testing is still valid because P-values are not used per se in it