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J. Risk Financial Manag., Volume 12, Issue 1 (March 2019)

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Open AccessArticle Insomnia: An Important Antecedent Impacting Entrepreneurs’ Health
J. Risk Financial Manag. 2019, 12(1), 44; https://doi.org/10.3390/jrfm12010044 (registering DOI)
Received: 20 February 2019 / Revised: 7 March 2019 / Accepted: 12 March 2019 / Published: 16 March 2019
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Abstract
Insomnia (and sleep deprivation) has an important impact on multiple outcomes such as individuals’ cognitive abilities, decision-making, and affect. In this paper, drawing from sleep research, we focus on entrepreneurs’ insomnia–health relationship and test a serial mediation model that considers entrepreneurs’ insomnia as [...] Read more.
Insomnia (and sleep deprivation) has an important impact on multiple outcomes such as individuals’ cognitive abilities, decision-making, and affect. In this paper, drawing from sleep research, we focus on entrepreneurs’ insomnia–health relationship and test a serial mediation model that considers entrepreneurs’ insomnia as an important predictor of their poor health. More specifically, we hypothesize that insomnia heightens entrepreneurs’ stress, which leads to increased negative affect, which ultimately undermines their health conditions. Using a sample of 152 Iranian entrepreneurs, we found support for our hypotheses as our results suggest that insomnia has a positive (and detrimental) effect on poor health (via more stress and negative affect). Contrary to research calls focused on stress reduction as one performance improvement mechanism, our results suggest sleep quality as a more effective mechanism for entrepreneurs to reduce their stress and to improve their health. Theoretical and practical implications, limitations, and directions for future research are also discussed. Full article
(This article belongs to the Special Issue Financing and Facilitating Entrepreneurship)
Open AccessArticle Effects of Global Oil Price on Exchange Rate, Trade Balance, and Reserves in Nigeria: A Frequency Domain Causality Approach
J. Risk Financial Manag. 2019, 12(1), 43; https://doi.org/10.3390/jrfm12010043
Received: 2 January 2019 / Revised: 16 February 2019 / Accepted: 25 February 2019 / Published: 13 March 2019
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Abstract
This study investigated the relative Granger causal effects of oil price on exchange rate, trade balance, and foreign reserve in Nigeria. We used seasonally adjusted quarterly data from 1986Q4 to 2018Q1 to remove predictable changes in the series. Given the non-stationarity of our [...] Read more.
This study investigated the relative Granger causal effects of oil price on exchange rate, trade balance, and foreign reserve in Nigeria. We used seasonally adjusted quarterly data from 1986Q4 to 2018Q1 to remove predictable changes in the series. Given the non-stationarity of our variables, we found cointegration to exist only between oil price and foreign reserve. The presence of cointegration implied the existence of long run relationship between the variables. The Granger causality result showed that oil price strongly Granger caused foreign reserve in the short period. However, no Granger causal relationships were found between oil price and trade balance and for oil price and exchange rate. The implication of the result is that Nigerian government should not rely solely on oil price to sustain her reserve but to diversify the economy towards non-resource production and export for foreign exchange generation. Full article
(This article belongs to the Special Issue Applied Econometrics)
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Open AccessArticle Determining Distribution for the Quotients of Dependent and Independent Random Variables by Using Copulas
J. Risk Financial Manag. 2019, 12(1), 42; https://doi.org/10.3390/jrfm12010042
Received: 21 February 2019 / Revised: 4 March 2019 / Accepted: 8 March 2019 / Published: 12 March 2019
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Abstract
Determining distributions of the functions of random variables is a very important problem with a wide range of applications in Risk Management, Finance, Economics, Science, and many other areas. This paper develops the theory on both density and distribution functions for the quotient [...] Read more.
Determining distributions of the functions of random variables is a very important problem with a wide range of applications in Risk Management, Finance, Economics, Science, and many other areas. This paper develops the theory on both density and distribution functions for the quotient Y = X 1 X 2 and the ratio of one variable over the sum of two variables Z = X 1 X 1 + X 2 of two dependent or independent random variables X 1 and X 2 by using copulas to capture the structures between X 1 and X 2 . Thereafter, we extend the theory by establishing the density and distribution functions for the quotients Y = X 1 X 2 and Z = X 1 X 1 + X 2 of two dependent normal random variables X 1 and X 2 in the case of Gaussian copulas. We then develop the theory on the median for the ratios of both Y and Z on two normal random variables X 1 and X 2 . Furthermore, we extend the result of median for Z to a larger family of symmetric distributions and symmetric copulas of X 1 and X 2 . Our results are the foundation of any further study that relies on the density and cumulative probability functions of ratios for two dependent or independent random variables. Since the densities and distributions of the ratios of both Y and Z are in terms of integrals and are very complicated, their exact forms cannot be obtained. To circumvent the difficulty, this paper introduces the Monte Carlo algorithm, numerical analysis, and graphical approach to efficiently compute the complicated integrals and study the behaviors of density and distribution. We illustrate our proposed approaches by using a simulation study with ratios of normal random variables on several different copulas, including Gaussian, Student-t, Clayton, Gumbel, Frank, and Joe Copulas. We find that copulas make big impacts from different Copulas on behavior of distributions, especially on median, spread, scale and skewness effects. In addition, we also discuss the behaviors via all copulas above with the same Kendall’s coefficient. The approaches developed in this paper are flexible and have a wide range of applications for both symmetric and non-symmetric distributions and also for both skewed and non-skewed copulas with absolutely continuous random variables that could contain a negative range, for instance, generalized skewed-t distribution and skewed-t Copulas. Thus, our findings are useful for academics, practitioners, and policy makers. Full article
(This article belongs to the Special Issue Mathematical Finance with Applications)
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Open AccessArticle The Role of Entrepreneurial Strategy, Network Ties, Human and Financial Capital in New Venture Performance
J. Risk Financial Manag. 2019, 12(1), 41; https://doi.org/10.3390/jrfm12010041
Received: 16 January 2019 / Revised: 26 February 2019 / Accepted: 4 March 2019 / Published: 11 March 2019
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Abstract
In the current era of globalization and competitive edge, the survival of newly established ventures has become a big challenge. Numerous studies have been carried out to discover factors that are essential for newly initiated ventures but the results are still fragmented. This [...] Read more.
In the current era of globalization and competitive edge, the survival of newly established ventures has become a big challenge. Numerous studies have been carried out to discover factors that are essential for newly initiated ventures but the results are still fragmented. This study focuses on measuring the effect of entrepreneurial strategy, network ties, human capital and financial capital on new venture performance. A structured questionnaire was used to collect data from 196 registered firms located in the emerging market Pakistan. The results indicate that entrepreneurial strategy, network ties and financial capital have a significant positive effect, while human capital showed an insignificant effect on new venture performance. This research recommends owners and managers of new firms build effective entrepreneurial strategies, expand their networks with external bodies (other firms, government and financial institutions) to acquire useful resources that in turn can spur their performance. Further implications are discussed. Policy makers and responsible authorities are advised to encourage and support new ventures which in turn can contribute to GDP and economic development. Practical implications and suggestions are also discussed. Full article
(This article belongs to the Special Issue Financing and Facilitating Entrepreneurship)
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Open AccessArticle What Factors Affect Income Inequality and Economic Growth in Middle-Income Countries?
J. Risk Financial Manag. 2019, 12(1), 40; https://doi.org/10.3390/jrfm12010040
Received: 9 December 2018 / Revised: 19 February 2019 / Accepted: 19 February 2019 / Published: 8 March 2019
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Abstract
Income inequality in many middle-income countries has increased at an alarming level. While the time series relationship between income inequality and economic growth has been extensively investigated, the causal and dynamic link between them, particularly for the middle-income countries, has been largely ignored [...] Read more.
Income inequality in many middle-income countries has increased at an alarming level. While the time series relationship between income inequality and economic growth has been extensively investigated, the causal and dynamic link between them, particularly for the middle-income countries, has been largely ignored in the current literature. This study was conducted to fill in this gap on two different samples for the period from 1960 to 2014: (i) a full sample of 158 countries; and (ii) a sample of 86 middle-income countries. The Granger causality test and a system generalized method of moments (GMM) are utilized in this study. The findings from this study indicate that causality is found from economic growth to income inequality and vice versa in both samples of countries. In addition, this study also finds that income inequality contributes negatively to the economic growth in the middle-income countries in the research period. Full article
(This article belongs to the Special Issue Applied Econometrics)
Open AccessArticle The Global Legal Entity Identifier System: How Can It Deliver?
J. Risk Financial Manag. 2019, 12(1), 39; https://doi.org/10.3390/jrfm12010039
Received: 17 December 2018 / Revised: 5 February 2019 / Accepted: 19 February 2019 / Published: 7 March 2019
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Abstract
We examine the global legal entity identifier (LEI) system for the identification of participants in financial markets. Semi-structured interviews with data professionals revealed the many ways in which the LEI can improve both business process efficiency, and counterparty and credit risk management. Larger [...] Read more.
We examine the global legal entity identifier (LEI) system for the identification of participants in financial markets. Semi-structured interviews with data professionals revealed the many ways in which the LEI can improve both business process efficiency, and counterparty and credit risk management. Larger social benefits, including the monitoring of systemic financial risk, are achievable if it becomes the accepted universal standard for legal entity identification. Our interviews also review the substantial co-ordination and investment barriers to LEI adoption. To address these, a clear regulatory-led road map is needed for its future development, with widespread application in regulatory reporting. Full article
(This article belongs to the Special Issue Financial Crises, Macroeconomic Management, and Financial Regulation)
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Open AccessArticle Asymmetric Mean Reversion in Low Liquid Markets: Evidence from BRVM
J. Risk Financial Manag. 2019, 12(1), 38; https://doi.org/10.3390/jrfm12010038
Received: 31 December 2018 / Revised: 28 February 2019 / Accepted: 4 March 2019 / Published: 6 March 2019
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Abstract
This paper analyzes the mean reversion property on the west African stock market (in French, Bourse Régionale des Valeurs Mobilières BRVM). For this purpose, we use two daily indices: (i) the composite index (BRVMC) and (ii) the index of the 10 most liquid [...] Read more.
This paper analyzes the mean reversion property on the west African stock market (in French, Bourse Régionale des Valeurs Mobilières BRVM). For this purpose, we use two daily indices: (i) the composite index (BRVMC) and (ii) the index of the 10 most liquid assets (BRVM10) collected from 3 January 2005 to 29 June 2018. We estimate an asymmetric nonlinear autoregressive model with an EGARCH innovation to account for heteroskedasticity. The results suggest the existence of a mean reversion property for both indices. The half-life time is 7 days for the composite index and 2 days for the BRVM 10 index. Furthermore, using a rolling regression technique, we show that the estimated half-life time declines slightly for the composite index. Full article
(This article belongs to the Special Issue Financial Time Series: Methods & Models)
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Open AccessArticle Developments in Risk Management in Islamic Finance: A Review
J. Risk Financial Manag. 2019, 12(1), 37; https://doi.org/10.3390/jrfm12010037
Received: 4 January 2019 / Revised: 4 February 2019 / Accepted: 12 February 2019 / Published: 20 February 2019
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Abstract
The purpose of this study is to review recent developments pertaining to risk management in Islamic banking and finance literature. The study explores the fundamental features of risks associated with Islamic banks (IBs) as compared to those associated with conventional banks (CBs) in [...] Read more.
The purpose of this study is to review recent developments pertaining to risk management in Islamic banking and finance literature. The study explores the fundamental features of risks associated with Islamic banks (IBs) as compared to those associated with conventional banks (CBs) in order to determine the extent to which IBs engage in effective risk mitigation. The study includes a consideration of the major studies in which the fundamental features of Islamic banks and finance (IBF) and the main characteristics of risk management in IBs are analyzed in comparison with those of CBs. Specifically, these two kinds of banks are compared in relation to the types of risks faced, the characteristics of those risks, and the nature and extent of exposure to those risks. A tabular methodology approach is used in concert with a comparative literature review approach for the analysis. The results show that there is weak support for Shariah-based product development due to the lack of risk mitigation expertise in IBs. The conclusion presented is that in comparison with CBs, IBs are more risk-sensitive due to the nature of their products, contract structure, legal costing, governance practices, and liquidity infrastructure. Furthermore, the determinants of the credit risk of Islamic banks in Malaysia (MIBs) are examined. Overall, bank capital and financing expansion have a significant negative impact on the credit risk level of IBs in Malaysia. Full article
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Open AccessArticle Bitcoin at High Frequency
J. Risk Financial Manag. 2019, 12(1), 36; https://doi.org/10.3390/jrfm12010036
Received: 12 December 2018 / Revised: 23 January 2019 / Accepted: 30 January 2019 / Published: 15 February 2019
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Abstract
This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and [...] Read more.
This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle The Importance of the Financial Derivatives Markets to Economic Development in the World’s Four Major Economies
J. Risk Financial Manag. 2019, 12(1), 35; https://doi.org/10.3390/jrfm12010035
Received: 28 December 2018 / Revised: 29 January 2019 / Accepted: 1 February 2019 / Published: 14 February 2019
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Abstract
Over the past three decades, China and India have attained economic power close to that of Japan and the U.S. During this period, the importance of the derivatives market within the financial market has been widely recognized. However, little supporting evidence is available [...] Read more.
Over the past three decades, China and India have attained economic power close to that of Japan and the U.S. During this period, the importance of the derivatives market within the financial market has been widely recognized. However, little supporting evidence is available on its economic effects. This paper investigates the dynamic relationship between the derivatives markets and economic development in these four large economies, which we consider together as the CIJU (China, India, Japan, and the U.S.) group. We use a Granger-causality test in the framework of a vector error correction model (VECM) to examine this causal and dynamic relation with data for the period 1998Q1 to 2017Q4. Derivative markets are found to positively contribute to economic development in the short run in the U.S., Japan, and India, but the effect disappears in the long run. In China, the derivatives market has a negative effect on economic development in the short run. However, in the long run, we observe a positive effect from the derivatives market on economic development based on two long-run estimation techniques, namely, dynamic ordinary least squares and fully modified ordinary least squares. Also, the development of derivative markets causes growth volatility in India, both in the short run and long run. Full article
(This article belongs to the Special Issue Applied Econometrics)
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Open AccessArticle Tax Competitiveness of the New EU Member States
J. Risk Financial Manag. 2019, 12(1), 34; https://doi.org/10.3390/jrfm12010034
Received: 7 December 2018 / Revised: 21 January 2019 / Accepted: 21 January 2019 / Published: 14 February 2019
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Abstract
This paper investigates tax competitiveness among the EU member countries. The tax competition of countries causes both positive and negative effects on macroeconomic processes such as the effectiveness of government spending, the rationality of supply of externalities, and the length and amplitudes of [...] Read more.
This paper investigates tax competitiveness among the EU member countries. The tax competition of countries causes both positive and negative effects on macroeconomic processes such as the effectiveness of government spending, the rationality of supply of externalities, and the length and amplitudes of business cycles. A considerable reduction of corporate tax in the EU is related to increased tax competition after new members entered the EU. Multiple criteria methods were chosen for the quantitative evaluation of EU countries from different regions of the EU. Criteria of evaluation were chosen and structured into a hierarchy. The convergence process of the new members of the EU is reinforced with the increasing tax competitiveness of such countries. Results of the multiple criteria evaluation revealed both the factors that increased the tax competitiveness of new members of the EU, and outlined the factors that hampered such competition. Full article
(This article belongs to the Special Issue Quantitative Finance)
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Open AccessArticle Clarifying the Response of Gold Return to Financial Indicators: An Empirical Comparative Analysis Using Ordinary Least Squares, Robust and Quantile Regressions
J. Risk Financial Manag. 2019, 12(1), 33; https://doi.org/10.3390/jrfm12010033
Received: 17 December 2018 / Revised: 31 January 2019 / Accepted: 7 February 2019 / Published: 14 February 2019
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Abstract
In this study, I apply a quantile regression model to investigate how gold returns respond to changes in various financial indicators. The model quantifies the asymmetric response of gold return in the tails of the distribution based on weekly data over the past [...] Read more.
In this study, I apply a quantile regression model to investigate how gold returns respond to changes in various financial indicators. The model quantifies the asymmetric response of gold return in the tails of the distribution based on weekly data over the past 30 years. I conducted a statistical test that allows for multiple structural changes and find that the relationship between gold return and some key financial indicators changed three times throughout the sample period. According to my empirical analysis of the whole sample period, I find that: (1) the gold return rises significantly if stock returns fall sharply; (2) it rises as the stock market volatility increases; (3) it also rises when general financial market conditions tighten; (4) gold and crude oil prices generally move toward the same direction; and (5) gold and the US dollar have an almost constant negative correlation. Looking at each sample period, (1) and (2) are remarkable in the period covering the global financial crisis (GFC), suggesting that investors divested from stocks as a risky asset. On the other hand, (3) is a phenomenon observed during the sample period after the GFC, suggesting that it reflects investors’ behavior of flight to quality. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Effect of Corporate Governance on Institutional Investors’ Preferences: An Empirical Investigation in Taiwan
J. Risk Financial Manag. 2019, 12(1), 32; https://doi.org/10.3390/jrfm12010032
Received: 31 December 2018 / Revised: 3 February 2019 / Accepted: 10 February 2019 / Published: 14 February 2019
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Abstract
This study discusses the institutional investors’ shareholding base on corporate governance system in Taiwan. The sample was 4760 Taiwanese companies from 2005 to 2012. Then, this study established six hypotheses to investigate the effects of corporate governance on institutional investors’ shareholdings. The panel [...] Read more.
This study discusses the institutional investors’ shareholding base on corporate governance system in Taiwan. The sample was 4760 Taiwanese companies from 2005 to 2012. Then, this study established six hypotheses to investigate the effects of corporate governance on institutional investors’ shareholdings. The panel data regression model and piecewise regression model were adopted to determine whether six hypotheses are supported. For sensitive analysis, additional consideration was given on the basis of industrial category (electronics or nonelectronics), and the 2008–2010 global financial crises. This study discovered that a nonlinear relationship exists between the domestic institutional investors’ shareholdings. The managerial ownership ratio and blockholder ownership ratio have positive effects both on domestic and foreign institutional investors. However, domestic and foreign institutional investors have distinct opinions regarding independent director ratios. Finally, the corporate governance did not improve institutional investors’ shareholdings during financial crisis periods; instead, they paid more attention to firm profits or other characteristics. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Statistical Arbitrage in Cryptocurrency Markets
J. Risk Financial Manag. 2019, 12(1), 31; https://doi.org/10.3390/jrfm12010031
Received: 30 December 2018 / Revised: 3 February 2019 / Accepted: 7 February 2019 / Published: 13 February 2019
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Abstract
Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. ( [...] Read more.
Machine learning research has gained momentum—also in finance. Consequently, initial machine-learning-based statistical arbitrage strategies have emerged in the U.S. equities markets in the academic literature, see e.g., Takeuchi and Lee (2013); Moritz and Zimmermann (2014); Krauss et al. (2017). With our paper, we pose the question how such a statistical arbitrage approach would fare in the cryptocurrency space on minute-binned data. Specifically, we train a random forest on lagged returns of 40 cryptocurrency coins, with the objective to predict whether a coin outperforms the cross-sectional median of all 40 coins over the subsequent 120 min. We buy the coins with the top-3 predictions and short-sell the coins with the flop-3 predictions, only to reverse the positions after 120 min. During the out-of-sample period of our backtest, ranging from 18 June 2018 to 17 September 2018, and after more than 100,000 trades, we find statistically and economically significant returns of 7.1 bps per day, after transaction costs of 15 bps per half-turn. While this finding poses a challenge to the semi-strong from of market efficiency, we critically discuss it in light of limits to arbitrage, focusing on total volume constraints of the presented intraday-strategy. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle Predicting Micro-Enterprise Failures Using Data Mining Techniques
J. Risk Financial Manag. 2019, 12(1), 30; https://doi.org/10.3390/jrfm12010030
Received: 30 December 2018 / Revised: 27 January 2019 / Accepted: 3 February 2019 / Published: 10 February 2019
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Abstract
Research analysis of small enterprises are still rare, due to lack of individual level data. Small enterprise failures are connected not only with their financial situation abut also with non-financial factors. In recent research we tend to apply more and more complex models. [...] Read more.
Research analysis of small enterprises are still rare, due to lack of individual level data. Small enterprise failures are connected not only with their financial situation abut also with non-financial factors. In recent research we tend to apply more and more complex models. However, it is not so obvious that increasing complexity increases the effectiveness. In this paper the sample of 806 small enterprises were analyzed. Qualitative factors were used in modeling. Some simple and more complex models were estimated, such as logistic regression, decision trees, neural networks, gradient boosting, and support vector machines. Two hypothesis were verified: (i) not only financial ratios but also non-financial factors matter for small enterprise survival, and (ii) advanced statistical models and data mining techniques only insignificantly increase the prediction accuracy of small enterprise failures. Results show that simple models are as good as more complex model. Data mining models tend to be overfitted. Most important financial ratios in predicting small enterprise failures were: operating profitability of assets, current assets turnover, capital ratio, coverage of short-term liabilities by equity, coverage of fixed assets by equity, and the share of net financial surplus in total liabilities. Among non-financial factors only two of them were important: the sector of activity and employment. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle The Determinants of Sovereign Risk Premium in African Countries
J. Risk Financial Manag. 2019, 12(1), 29; https://doi.org/10.3390/jrfm12010029
Received: 4 January 2019 / Revised: 22 January 2019 / Accepted: 30 January 2019 / Published: 9 February 2019
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Abstract
This paper investigates the determinants of the sovereign risk premium in African countries. We employ the dynamic fixed effects model to determine the key drivers of sovereign bond spreads. Country-specific effects are fixed and the inclusion of dummy variables using the Bai–Perron multiple [...] Read more.
This paper investigates the determinants of the sovereign risk premium in African countries. We employ the dynamic fixed effects model to determine the key drivers of sovereign bond spreads. Country-specific effects are fixed and the inclusion of dummy variables using the Bai–Perron multiple structural break test is significant at a 5% level. For robustness, the time-series generalized method of moments (GMM) is used where the null hypothesis of the Sargan Test of over-identifying restrictions (OIR) and the Arellano–Bond Test of no autocorrelation are not rejected. This implies that the instruments used are valid and relevant. In addition, there is no autocorrelation in the error terms. Our results show that the exchange rate, Money supply/GDP (M2/GDP) ratio, and trade are insignificant. Furthermore, our findings indicate that public debt/GDP ratio, GDP growth, inflation rate, foreign exchange reserves, commodity price, and market sentiment are significant at a 5% and 10% level. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling)
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Open AccessArticle Multivariate Student versus Multivariate Gaussian Regression Models with Application to Finance
J. Risk Financial Manag. 2019, 12(1), 28; https://doi.org/10.3390/jrfm12010028
Received: 29 December 2018 / Revised: 24 January 2019 / Accepted: 31 January 2019 / Published: 9 February 2019
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Abstract
To model multivariate, possibly heavy-tailed data, we compare the multivariate normal model (N) with two versions of the multivariate Student model: the independent multivariate Student (IT) and the uncorrelated multivariate Student (UT). After recalling some facts about these distributions and models, known but [...] Read more.
To model multivariate, possibly heavy-tailed data, we compare the multivariate normal model (N) with two versions of the multivariate Student model: the independent multivariate Student (IT) and the uncorrelated multivariate Student (UT). After recalling some facts about these distributions and models, known but scattered in the literature, we prove that the maximum likelihood estimator of the covariance matrix in the UT model is asymptotically biased and propose an unbiased version. We provide implementation details for an iterative reweighted algorithm to compute the maximum likelihood estimators of the parameters of the IT model. We present a simulation study to compare the bias and root mean squared error of the ensuing estimators of the regression coefficients and covariance matrix under several scenarios of the potential data-generating process, misspecified or not. We propose a graphical tool and a test based on the Mahalanobis distance to guide the choice between the competing models. We also present an application to model vectors of financial assets returns. Full article
(This article belongs to the Special Issue Applied Econometrics)
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Open AccessArticle Contribution to the Valuation of BRVM’s Assets: A Conditional CAPM Approach
J. Risk Financial Manag. 2019, 12(1), 27; https://doi.org/10.3390/jrfm12010027
Received: 29 July 2018 / Revised: 27 November 2018 / Accepted: 5 December 2018 / Published: 6 February 2019
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Abstract
The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk (β) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems [...] Read more.
The conditional capital asset pricing model (CAPM) theory postulates that the systematic risk ( β ) of an asset or portfolio varies over time. Several dynamics are thus given to systematic risk in the literature. This article looks for the dynamic that seems to best explain the returns of the assets of the Regional Stock Exchange of West Africa (BRVM) by comparing two dynamics: one by the Kalman filter (assuming that the β follow a random walk) and the other by the Markov switching (MS) model (assuming that β varies according to regimes) for four portfolios of the BRVM. Having found a link between the beta of the market portfolio and the size criterion (measured by capitalization), the two previous models were re-estimated with the addition of the SMB (Small Minus Big) variable. The results show according to the RMSE criterion that the estimation by the Kalman filter fits better than MS, which suggests that investors cannot anticipate systematic risk because of its high volatility. Full article
(This article belongs to the Special Issue Stock Market Volatility Modelling and Forecasting)
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Open AccessArticle Geometric No-Arbitrage Analysis in the Dynamic Financial Market with Transaction Costs
J. Risk Financial Manag. 2019, 12(1), 26; https://doi.org/10.3390/jrfm12010026
Received: 16 December 2018 / Revised: 17 January 2019 / Accepted: 1 February 2019 / Published: 6 February 2019
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Abstract
The present paper considers a class of financial market with transaction costs and constructs a geometric no-arbitrage analysis frame. Then, this paper arrives at the fact that this financial market is of no-arbitrage if and only if the curvature 2-form of a specific [...] Read more.
The present paper considers a class of financial market with transaction costs and constructs a geometric no-arbitrage analysis frame. Then, this paper arrives at the fact that this financial market is of no-arbitrage if and only if the curvature 2-form of a specific connection is zero. Furthermore, this paper derives the fact that the no-arbitrage condition for the one-period financial market is equivalent to the geometric no-arbitrage condition. Finally, an example states the equivalence between the geometric no-arbitrage condition and the existence of the solutions for a maximization problem of expected utility. Full article
Open AccessArticle Testing Stylized Facts of Bitcoin Limit Order Books
J. Risk Financial Manag. 2019, 12(1), 25; https://doi.org/10.3390/jrfm12010025
Received: 21 December 2018 / Revised: 17 January 2019 / Accepted: 30 January 2019 / Published: 5 February 2019
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Abstract
The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on [...] Read more.
The majority of electronic markets worldwide employ limit order books, and the recently emerging exchanges for cryptocurrencies pose no exception. With this work, we empirically analyze whether commonly observed empirical properties from established limit order exchanges transfer to the cryptocurrency domain. Based on the literature, we establish a structured methodological framework to conduct analyses in a systematic and comprehensive way. We then present results from a unique and extensive limit order data set acquired from major cryptocurrency exchanges for the currency pair Bitcoin to US Dollar. We recover many observations from mature markets, such as a symmetry between the average ask and the average bid side of the order book, autocorrelation in returns on the smallest time scales only, volatility clustering and the timing of large trades. We also observe some idiosyncrasies: The distributions of trade size and limit order prices deviate from commonly observed patterns. Also, we find limit order books to be relatively shallow and liquidity costs to be relatively high when compared to established markets. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle Has ‘Too Big To Fail’ Been Solved? A Longitudinal Analysis of Major U.S. Banks
J. Risk Financial Manag. 2019, 12(1), 24; https://doi.org/10.3390/jrfm12010024
Received: 29 November 2018 / Revised: 16 January 2019 / Accepted: 25 January 2019 / Published: 1 February 2019
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Abstract
In the wake of the global financial crisis that erupted in 2008, there has been extensive commentary and regulatory focus on the ‘Too Big to Fail’ issue. In this paper, we survey the proposed solutions and regulatory initiatives that have been undertaken. We [...] Read more.
In the wake of the global financial crisis that erupted in 2008, there has been extensive commentary and regulatory focus on the ‘Too Big to Fail’ issue. In this paper, we survey the proposed solutions and regulatory initiatives that have been undertaken. We conduct a longitudinal analysis of major U.S. banks in four discrete time periods: pre-crisis (2005–2007), crisis (2008–2010), post-crisis (2011–2013) and normalcy (2014–2016). We find that risk metrics such as leverage and volatility which spiked during the crisis have reverted to pre-crisis levels and there has been improvement in the proportion of equity capital available to cushion against asset value deterioration. However, banks have grown in size and it does not appear as if their business models have been redirected toward more traditional lending activities. We believe that it is premature to conclude that ‘Too Big to Fail” has been solved, but macro-prudential regulation is now much more effective and, consequently, banks are on a considerably sounder footing since the depths of the crisis. Full article
(This article belongs to the Special Issue Financial Crises, Macroeconomic Management, and Financial Regulation)
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Open AccessArticle Growth and Debt: An Endogenous Smooth Coefficient Approach
J. Risk Financial Manag. 2019, 12(1), 23; https://doi.org/10.3390/jrfm12010023
Received: 21 January 2019 / Revised: 25 January 2019 / Accepted: 25 January 2019 / Published: 1 February 2019
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Abstract
The new growth theories with an emphasis on fundamental determinants such as institutions suggest a non-linear cross-country growth process. In this paper, we investigate the public debt and economic growth relationship using the semi-parametric smooth coefficient approach that allows democracy to influence this [...] Read more.
The new growth theories with an emphasis on fundamental determinants such as institutions suggest a non-linear cross-country growth process. In this paper, we investigate the public debt and economic growth relationship using the semi-parametric smooth coefficient approach that allows democracy to influence this relationship and parameter heterogeneity in the unknown functional form and addresses the endogeneity of variables. We find results consistent with the previous literature that identified a significant adverse effect of public debt on growth for the countries below a particular democracy level. However, we also find conclusive evidence that countries with high institutional quality have an adverse effect of public debt on growth for the period 1980–2009, as well as for the extended period including the years 2010–2014. A 10-percentage point increase in the debt-to-GDP ratio is associated with a 0.12% and 0.07% decrease in the subsequent 10-year period real GDP growth rate for the zero democracy countries and for the countries with a democracy score of 10, respectively. Full article
(This article belongs to the Special Issue Nonparametric Econometric Methods and Application)
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Open AccessArticle Does the Misery Index Influence a U.S. President’s Political Re-Election Prospects?
J. Risk Financial Manag. 2019, 12(1), 22; https://doi.org/10.3390/jrfm12010022
Received: 12 December 2018 / Revised: 27 January 2019 / Accepted: 28 January 2019 / Published: 1 February 2019
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Abstract
We seek to determine whether a United States President’s job approval rating is influenced by the Misery Index. This hypothesis is examined in two ways. First, we employ a nonlinear model that includes several macroeconomic variables: the current account deficit, exchange rate, unemployment, [...] Read more.
We seek to determine whether a United States President’s job approval rating is influenced by the Misery Index. This hypothesis is examined in two ways. First, we employ a nonlinear model that includes several macroeconomic variables: the current account deficit, exchange rate, unemployment, inflation, and mortgage rates. Second, we employ probit and logit regression models to calculate the probabilities of U.S. Presidents’ approval ratings to the Misery Index. The results suggest that Layton’s model does not perform well when adopted for the United States. Conversely, the probit and logit regression analysis suggests that the Misery Index significantly impacts the probability of the approval of U.S. Presidents’ performances. Full article
(This article belongs to the Special Issue Applied Econometrics)
Open AccessArticle Valuation of Environmental Management Standard ISO 14001: Evidence from an Emerging Market
J. Risk Financial Manag. 2019, 12(1), 21; https://doi.org/10.3390/jrfm12010021
Received: 28 December 2018 / Revised: 22 January 2019 / Accepted: 25 January 2019 / Published: 29 January 2019
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Abstract
ISO 14001 (Environmental Management Standard) helps corporations to build legitimacy and goodwill, and can be also viewed as an organizational response to institutional pressure to act proactively towards the environment. The purpose of this paper is to investigate how investors in the emerging [...] Read more.
ISO 14001 (Environmental Management Standard) helps corporations to build legitimacy and goodwill, and can be also viewed as an organizational response to institutional pressure to act proactively towards the environment. The purpose of this paper is to investigate how investors in the emerging country value voluntary environmental management standard ISO 14001 certification. The impact of voluntary environmental management standard ISO 14001 on market performance is still not clear. By using event study methodology, this study matched ISO-certified firms with non-certified ones based on three different matching principles that include return on assets, size, and industry. The findings indicated that investors negatively valued ISO 14001 in both the short and long run. The study recommended policy implications for managers, policy makers, and non-government organizations. Full article
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Open AccessArticle Finance and Jobs: How Financial Markets and Prudential Regulation Shape Unemployment Dynamics
J. Risk Financial Manag. 2019, 12(1), 20; https://doi.org/10.3390/jrfm12010020
Received: 30 November 2018 / Revised: 8 January 2019 / Accepted: 17 January 2019 / Published: 24 January 2019
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Abstract
This article explores the impact of financial market regulation on jobs. It argues that understanding the impact of finance on labor markets is key to an understanding of the trade-off between economic stability and financial sector growth. The article combines information on labor [...] Read more.
This article explores the impact of financial market regulation on jobs. It argues that understanding the impact of finance on labor markets is key to an understanding of the trade-off between economic stability and financial sector growth. The article combines information on labor market flows with indicators of financial market development and reforms to assess the implications of financial markets on employment dynamics directly, using information from the International Labour Organization (ILO) datatabse on unemployment flows. On the basis of a matching model of the labor market, it analyses the economic, institutional, and policy determinants of unemployment in- and out-flows. Against a set of basic controls, we present evidence regarding the relationship between financial sector development and reforms and their impact on unemployment dynamics. Using scenario analysis, the article demonstrates the importance of broad financial sector re-regulation to stabilize unemployment inflows and to promote faster employment growth. In particular, we find that encompassing financial sector regulation, had it been in place prior to the global financial crisis in 2008, would have helped a faster recovery in jobs. Full article
(This article belongs to the Special Issue Financial Crises, Macroeconomic Management, and Financial Regulation)
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Open AccessArticle Determinants and Impacts of Financial Literacy in Cambodia and Viet Nam
J. Risk Financial Manag. 2019, 12(1), 19; https://doi.org/10.3390/jrfm12010019
Received: 1 December 2018 / Revised: 11 January 2019 / Accepted: 21 January 2019 / Published: 24 January 2019
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Abstract
Our paper extends the literature on the determinants and impacts of financial literacy by conducting the OECD/INFE survey in two relatively low-income Asian economies—Cambodia and Viet Nam—and analyzing the determinants of financial literacy and the effects of financial literacy on savings and financial [...] Read more.
Our paper extends the literature on the determinants and impacts of financial literacy by conducting the OECD/INFE survey in two relatively low-income Asian economies—Cambodia and Viet Nam—and analyzing the determinants of financial literacy and the effects of financial literacy on savings and financial inclusion. Generally, our study corroborates the findings of studies of other countries, but uncovers some differences as well. The main determinants of financial literacy are found to be educational level, income, age, and occupational status. Both financial literacy and general education levels are found to be positively and significantly related to savings behavior and financial inclusion, and these results generally hold even when correcting for possible endogeneity of financial literacy. Full article
(This article belongs to the collection Trends in Emerging Markets Finance, Institutions and Money)
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Open AccessArticle Is Window-Dressing around Going Public Beneficial? Evidence from Poland
J. Risk Financial Manag. 2019, 12(1), 18; https://doi.org/10.3390/jrfm12010018
Received: 13 December 2018 / Revised: 8 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
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Abstract
The informativeness of financial reports has been of a great importance to both investors and academics. Earnings are crucial for evaluating future prospects and determining company value, especially around milestone events such as initial public offerings (IPO). If investors are misled by manipulated [...] Read more.
The informativeness of financial reports has been of a great importance to both investors and academics. Earnings are crucial for evaluating future prospects and determining company value, especially around milestone events such as initial public offerings (IPO). If investors are misled by manipulated earnings, they could pay too high a price and suffer losses in the long-term when prices adjust to real value. We provide new evidence on the relationship between earnings management and the long-term performance of IPOs as we test the issue with a methodology that has not been applied so far for issues in Poland. We use a set of proxies of earnings management and test the long-term IPO performance under several factor models (CAPM, and three extensions of the Fama-French model). Aggressive IPOs perform very poorly later and earn severe negative stock returns up to three years after going public. The difference in returns in accrual quantiles is statistically significant in almost half of methodology settings. The results seem to suggest that investors might not be able to discount pre-IPO abnormal accruals and could be overoptimistic. Once the true earnings performance is revealed over time, the market makes downward price corrections. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Trend Prediction Classification for High Frequency Bitcoin Time Series with Deep Learning
J. Risk Financial Manag. 2019, 12(1), 17; https://doi.org/10.3390/jrfm12010017
Received: 25 December 2018 / Revised: 15 January 2019 / Accepted: 17 January 2019 / Published: 21 January 2019
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Abstract
We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case [...] Read more.
We provide a trend prediction classification framework named the random sampling method (RSM) for cryptocurrency time series that are non-stationary. This framework is based on deep learning (DL). We compare the performance of our approach to two classical baseline methods in the case of the prediction of unstable Bitcoin prices in the OkCoin market and show that the baseline approaches are easily biased by class imbalance, whereas our model mitigates this problem. We also show that the classification performance of our method expressed as the F-measure substantially exceeds the odds of a uniform random process with three outcomes, proving that extraction of deterministic patterns for trend classification, and hence market prediction, is possible to some degree. The profit rates based on RSM outperformed those based on LSTM, although they did not exceed those of the buy-and-hold strategy within the testing data period, and thus do not provide a basis for algorithmic trading. Full article
(This article belongs to the Special Issue Alternative Assets and Cryptocurrencies)
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Open AccessArticle Time–Scale Relationship between Securitized Real Estate and Local Stock Markets: Some Wavelet Evidence
J. Risk Financial Manag. 2019, 12(1), 16; https://doi.org/10.3390/jrfm12010016
Received: 10 December 2018 / Revised: 6 January 2019 / Accepted: 15 January 2019 / Published: 20 January 2019
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Abstract
This study revisits the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries. Since securitized real estate market is an important capital component of the domestic stock market in the [...] Read more.
This study revisits the relationship between securitized real estate and local stock markets by focusing on their time-scale co-movement and contagion dynamics across five developed countries. Since securitized real estate market is an important capital component of the domestic stock market in the respective economies, it is linked to the stock market. Earlier research does not have satisfactory results, because traditional methods average different relationships over various time and frequency domains between securitized real estate and local stock markets. According to our novel wavelet analysis, the relationship between the two asset markets is time–frequency varying. The average long run real estate–stock correlation fails to outweigh the average short run correlation, indicating the real estate markets examined may have become increasingly less sensitive to the domestic stock markets in the long-run in recent years. Moreover, securitized real estate markets appear to lead stock markets in the short run, whereas stock markets tend to lead securitized real estate markets in the long run, and to a lesser degree medium-term. Finally, we find incomplete real estate and local stock market integration among the five developed economies, given only weaker long-run integration beyond crisis periods. Full article
(This article belongs to the Special Issue Risk Analysis and Portfolio Modelling)
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Open AccessArticle Limitation of Financial Health Prediction in Companies from Post-Communist Countries
J. Risk Financial Manag. 2019, 12(1), 15; https://doi.org/10.3390/jrfm12010015
Received: 29 November 2018 / Revised: 4 January 2019 / Accepted: 15 January 2019 / Published: 18 January 2019
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Abstract
The financial health of a company can be seen as the ability to maintain a balance against changing conditions in the environment and at the same time in relation to everyone participating in the business. In the evaluation of financial health and prediction [...] Read more.
The financial health of a company can be seen as the ability to maintain a balance against changing conditions in the environment and at the same time in relation to everyone participating in the business. In the evaluation of financial health and prediction of financial problems of the companies, various indexes are used that can serve as input for expert estimation or creation of various models using, for example, multi-dimensional statistical methods. The practical application of the proper method for evaluation of financial health has been analysed in post-communist countries, since they have common historic experiences and economic interests. During the research we followed up the following indexes: Altman model, Taffler model, Springate model, and the index IN, based on multi-dimensional discrimination analysis. From the research results there is obvious a necessity to combine available methods in post-communist countries and at least to eliminate their disadvantages partially. Experiences from prediction models have proved their relatively high prediction ability, but only in perfect conditions, which cannot be affirmed in post-communist countries. The task remains to modify existing indexes to concrete situations and problems of the individual industries in the chosen countries, which have unique conditions for business making. Full article
(This article belongs to the Special Issue Applied Econometrics)
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