Pub Date : 2020-07-09DOI: 10.1177/0972652720930586
A. Sampath, A. Gopalaswamy
In this article, we investigate patterns in returns, volume and volatility and analyse the volume–return relationship using tick-by-tick data from the Indian equity market. Based on descriptive measures and regression frameworks, we document three important findings. First, we report unusually high volatility, trading volume and number of trades during the opening and closing minutes of the market depicting a ‘U’-shaped curve, implying high market activity during these periods. Second, while accounting for trading volume, we observe that volatility is not significantly different between mid-day period and evening period as compared to the normal ‘U’ curve. Finally, we document a significant positive relationship between intraday volume and price movements controlling for microstructure effects. The impact of positive returns on trading volume is higher than the impact of negative returns, implying the presence of return–volume asymmetry in the Indian market. JEL Codes: G12, G15
{"title":"Intraday Variability and Trading Volume: Evidence from National Stock Exchange","authors":"A. Sampath, A. Gopalaswamy","doi":"10.1177/0972652720930586","DOIUrl":"https://doi.org/10.1177/0972652720930586","url":null,"abstract":"In this article, we investigate patterns in returns, volume and volatility and analyse the volume–return relationship using tick-by-tick data from the Indian equity market. Based on descriptive measures and regression frameworks, we document three important findings. First, we report unusually high volatility, trading volume and number of trades during the opening and closing minutes of the market depicting a ‘U’-shaped curve, implying high market activity during these periods. Second, while accounting for trading volume, we observe that volatility is not significantly different between mid-day period and evening period as compared to the normal ‘U’ curve. Finally, we document a significant positive relationship between intraday volume and price movements controlling for microstructure effects. The impact of positive returns on trading volume is higher than the impact of negative returns, implying the presence of return–volume asymmetry in the Indian market. JEL Codes: G12, G15","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652720930586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46049918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-14DOI: 10.1177/0972652720913478
P. Viswanathan, S. Srinivasan, N. Hariharan
While earlier studies have focused excessively on bankruptcy prediction of banks, this study classifies banks based on their financial strength from the perspective of retail depositors who currently do not have an authentic guiding framework that helps them identify banks with higher risk profiles. Using machine learning techniques, we classify 44 Indian banks into distinct categories of financial health based on 12-year data from 2005 to 2017. We first use unsupervised learning to identify a pattern leading to logical groups in terms of financial health and then move to supervised learning for prediction. Using linear discriminant analysis (LDA), Classification and Regression Tree (CART) and Random Forest methods, we predict the cluster membership with the associated explanatory power alongside. We also compare our classification with the credit ratings awarded by rating agencies and highlight certain discrepancies that exist between what is predicted by our models and the credit rating awards. JEL Codes: C53; M10
{"title":"Predicting Financial Health of Banks for Investor Guidance Using Machine Learning Algorithms","authors":"P. Viswanathan, S. Srinivasan, N. Hariharan","doi":"10.1177/0972652720913478","DOIUrl":"https://doi.org/10.1177/0972652720913478","url":null,"abstract":"While earlier studies have focused excessively on bankruptcy prediction of banks, this study classifies banks based on their financial strength from the perspective of retail depositors who currently do not have an authentic guiding framework that helps them identify banks with higher risk profiles. Using machine learning techniques, we classify 44 Indian banks into distinct categories of financial health based on 12-year data from 2005 to 2017. We first use unsupervised learning to identify a pattern leading to logical groups in terms of financial health and then move to supervised learning for prediction. Using linear discriminant analysis (LDA), Classification and Regression Tree (CART) and Random Forest methods, we predict the cluster membership with the associated explanatory power alongside. We also compare our classification with the credit ratings awarded by rating agencies and highlight certain discrepancies that exist between what is predicted by our models and the credit rating awards. JEL Codes: C53; M10","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652720913478","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43778375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-05-09DOI: 10.1177/0972652719890750
Eric Fischer
This article examines the effect of Federal Reserve announcements on global financial flows to Latin America since the Global Financial Crisis. The Federal Reserve announcements are classified using daily measures of expectations from a shadow rate term structure model as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). This classification is then used for an event study on daily global financial flows classified by asset class (debt, equity), currency (all currencies, hard currency, local currency), and region (Latin America, Brazil, and Mexico). The results suggest easing (unexpected) and tightening (unexpected) announcements cause debt outflows but have no effect on equity flows to Latin America. Local currency debt flows to Latin America are more sensitive than the hard currency debt flows and Brazil is the country in Latin America that responds most to these announcements. JEL Classification: F32, G14, G15, N26
{"title":"Monetary Surprises and Global Financial Flows: A Case Study of Latin America","authors":"Eric Fischer","doi":"10.1177/0972652719890750","DOIUrl":"https://doi.org/10.1177/0972652719890750","url":null,"abstract":"This article examines the effect of Federal Reserve announcements on global financial flows to Latin America since the Global Financial Crisis. The Federal Reserve announcements are classified using daily measures of expectations from a shadow rate term structure model as easing (unexpected), tightening (unexpected), easing (expected), and tightening (expected). This classification is then used for an event study on daily global financial flows classified by asset class (debt, equity), currency (all currencies, hard currency, local currency), and region (Latin America, Brazil, and Mexico). The results suggest easing (unexpected) and tightening (unexpected) announcements cause debt outflows but have no effect on equity flows to Latin America. Local currency debt flows to Latin America are more sensitive than the hard currency debt flows and Brazil is the country in Latin America that responds most to these announcements. JEL Classification: F32, G14, G15, N26","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719890750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42057019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1177/0972652719880153
M. Eissa, M. Elgammal
This article explores the determinants of foreign direct investment (FDI) in oil-dependent economies and revisits the role of natural resources in attracting FDI to countries of this kind. Panel data from the six Gulf Cooperation Council (GCC) countries, namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates, have been employed, covering the period from 1990 to 2015. First, we investigate the FDI determinants during the entire sample period, and then run another investigation starting from the beginning of 2000, when the FDI in the GCC region increased substantially. The results show that there is a positive nexus between market growth, trade openness, inflation, infrastructure, oil price and FDI. Interestingly, oil reserves have a negative impact on FDI; this may be because countries with large reserves of oil like the GCC countries have enough financial resources to finance their economic development. This leads these governments to set up restrictions to protect their resources, thus reducing the amount of resource-seeking FDI. JEL Codes: E22, F21, F23, F43, O13
{"title":"Foreign Direct Investment Determinants in Oil Exporting Countries: Revisiting the Role of Natural Resources","authors":"M. Eissa, M. Elgammal","doi":"10.1177/0972652719880153","DOIUrl":"https://doi.org/10.1177/0972652719880153","url":null,"abstract":"This article explores the determinants of foreign direct investment (FDI) in oil-dependent economies and revisits the role of natural resources in attracting FDI to countries of this kind. Panel data from the six Gulf Cooperation Council (GCC) countries, namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates, have been employed, covering the period from 1990 to 2015. First, we investigate the FDI determinants during the entire sample period, and then run another investigation starting from the beginning of 2000, when the FDI in the GCC region increased substantially. The results show that there is a positive nexus between market growth, trade openness, inflation, infrastructure, oil price and FDI. Interestingly, oil reserves have a negative impact on FDI; this may be because countries with large reserves of oil like the GCC countries have enough financial resources to finance their economic development. This leads these governments to set up restrictions to protect their resources, thus reducing the amount of resource-seeking FDI. JEL Codes: E22, F21, F23, F43, O13","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719880153","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43233587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1177/0972652719877475
A. Sahasranaman, V. Prasad, Aditi Balachander
The design of housing solutions for low-income populations has been one of the most pressing policy concerns in developing countries like India. In this work, we explore the effect of risks confronting low-income households—unemployment, health and mortality—on their choice of housing arrangements. We use simulations to study the evolution of long-term wealth of a stylised low-income household faced with these risks and find that, on average, rental housing significantly reduces the risk of undesirable wealth fluctuations over time. From a policy perspective, this means greater focus and incentives for the development of low-income rental markets using strategies such as provision of rental vouchers, rent-to-own models or long-term leases, in addition to the traditional ownership-based housing strategies. The development of housing solutions encompassing a range of rental and ownership models will be critical to ensuring the availability of safe and affordable housing for all urban residents. JEL Codes: C63, O18
{"title":"Housing Choice as a Function of Risks Confronting Low-income Households","authors":"A. Sahasranaman, V. Prasad, Aditi Balachander","doi":"10.1177/0972652719877475","DOIUrl":"https://doi.org/10.1177/0972652719877475","url":null,"abstract":"The design of housing solutions for low-income populations has been one of the most pressing policy concerns in developing countries like India. In this work, we explore the effect of risks confronting low-income households—unemployment, health and mortality—on their choice of housing arrangements. We use simulations to study the evolution of long-term wealth of a stylised low-income household faced with these risks and find that, on average, rental housing significantly reduces the risk of undesirable wealth fluctuations over time. From a policy perspective, this means greater focus and incentives for the development of low-income rental markets using strategies such as provision of rental vouchers, rent-to-own models or long-term leases, in addition to the traditional ownership-based housing strategies. The development of housing solutions encompassing a range of rental and ownership models will be critical to ensuring the availability of safe and affordable housing for all urban residents. JEL Codes: C63, O18","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719877475","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44810142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1177/0972652719877472
K. Krishnan, Sankarshan Basu, Ashok Thampy
This article analyses the differential market response to credit rating revisions in the pre- and post-global financial crisis (GFC) period using data from India. By reviewing the stock price reaction to the announcement of long-term rating changes during the period 1996–2015, the study finds evidence that the stock price reacted less to rating announcements after the GFC of 2008. However, the difference in the cumulative abnormal returns before the GFC and after the GFC is not statistically significant. JEL codes: G240, G010, G140
{"title":"Has the Global Financial Crisis Changed the Market Response to Credit Ratings? Evidence from an Emerging Market","authors":"K. Krishnan, Sankarshan Basu, Ashok Thampy","doi":"10.1177/0972652719877472","DOIUrl":"https://doi.org/10.1177/0972652719877472","url":null,"abstract":"This article analyses the differential market response to credit rating revisions in the pre- and post-global financial crisis (GFC) period using data from India. By reviewing the stock price reaction to the announcement of long-term rating changes during the period 1996–2015, the study finds evidence that the stock price reacted less to rating announcements after the GFC of 2008. However, the difference in the cumulative abnormal returns before the GFC and after the GFC is not statistically significant. JEL codes: G240, G010, G140","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719877472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41680719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1177/0972652719878597
B. Kusi, Lydia Dzidzor Adzobu, Alex Kwame Abasi, Kwadjo Ansah-Adu
In this study, the effect of sectoral loan portfolio concentration on bank stability is investigated in the Ghanaian banking sector between 2007 and 2014. Specifically, we investigate the linearity and non-linearity effects of sectoral loan concentration on bank stability given the limited exploration of this nexus. Employing a two-step generalized method of moments (GMM) robust random and fixed effects panel models of 30 banks, the study provides evidence showing that sectoral loan concentration weakens the stability of banks. This confirms the concentration-fragility hypothesis and the diversification theory of traditional banking but may promote bank stability beyond a certain threshold point. This implies that bank sectoral loan concentrate has a direct non-linear U-shape effect on bank stability in Ghana. We argue that although sectoral loan concentration may weaken stability of banks in the short run, it may however enhance the stability of banks in the long run through prolonged expert knowledge, experience and understanding of sectors. From these findings, policymakers, regulators and bank managers must not only develop and design policies and regulations that prohibit sectoral loan concentration but should also incorporate plans and policies that encourage banks to develop core competence and competitive advantage to take advantage of advancing bank stability through sectoral loan concentration. JEL Codes: G10; G18; G41
{"title":"Sectoral Loan Portfolio Concentration and Bank Stability: Evidence from an Emerging Economy","authors":"B. Kusi, Lydia Dzidzor Adzobu, Alex Kwame Abasi, Kwadjo Ansah-Adu","doi":"10.1177/0972652719878597","DOIUrl":"https://doi.org/10.1177/0972652719878597","url":null,"abstract":"In this study, the effect of sectoral loan portfolio concentration on bank stability is investigated in the Ghanaian banking sector between 2007 and 2014. Specifically, we investigate the linearity and non-linearity effects of sectoral loan concentration on bank stability given the limited exploration of this nexus. Employing a two-step generalized method of moments (GMM) robust random and fixed effects panel models of 30 banks, the study provides evidence showing that sectoral loan concentration weakens the stability of banks. This confirms the concentration-fragility hypothesis and the diversification theory of traditional banking but may promote bank stability beyond a certain threshold point. This implies that bank sectoral loan concentrate has a direct non-linear U-shape effect on bank stability in Ghana. We argue that although sectoral loan concentration may weaken stability of banks in the short run, it may however enhance the stability of banks in the long run through prolonged expert knowledge, experience and understanding of sectors. From these findings, policymakers, regulators and bank managers must not only develop and design policies and regulations that prohibit sectoral loan concentration but should also incorporate plans and policies that encourage banks to develop core competence and competitive advantage to take advantage of advancing bank stability through sectoral loan concentration. JEL Codes: G10; G18; G41","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719878597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65337889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-14DOI: 10.1177/0972652719877474
A. Agoba, J. Abor, K. A. Osei, J. Sa-Aadu
Central Bank Independence (CBI) as a mechanism for achieving lower inflation and effective regulation and supervision of the financial sector should promote financial sector development. Though there is not much difference in CBI legal provisions, it seems to be more effective in developed countries than in African countries. There are suggestions that this could be due to differences in political institutional quality. Using panel data from 1970 to 2012, we find that CBI does not promote financial development in Africa. The impact of CBI is dependent on the level of development of a country. CBI promotes financial development more in countries with strong political institutions. JEL codes: E02; E44; E58
{"title":"The Independence of Central Banks, Political Institutional Quality and Financial Sector Development in Africa","authors":"A. Agoba, J. Abor, K. A. Osei, J. Sa-Aadu","doi":"10.1177/0972652719877474","DOIUrl":"https://doi.org/10.1177/0972652719877474","url":null,"abstract":"Central Bank Independence (CBI) as a mechanism for achieving lower inflation and effective regulation and supervision of the financial sector should promote financial sector development. Though there is not much difference in CBI legal provisions, it seems to be more effective in developed countries than in African countries. There are suggestions that this could be due to differences in political institutional quality. Using panel data from 1970 to 2012, we find that CBI does not promote financial development in Africa. The impact of CBI is dependent on the level of development of a country. CBI promotes financial development more in countries with strong political institutions. JEL codes: E02; E44; E58","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2020-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719877474","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44305872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-21DOI: 10.1177/0972652719846315
G. Sharma, P. Kayal, P. Pandey
In this article, we examine the information linkages of the forward-looking measure of volatility, the volatility index (VIX), for underlying equity market indices of BRICS countries—Brazil, Russia, India, China and South Africa. A study of the information transmission process confirmed a long-run equilibrium relationship between pairs of BRICS countries. The multivariate generalised autoregressive conditional heteroscedasticity (MGARCH) model revealed strong intertemporal linkages between sample VIX. Return and volatility spill-over matrix show the varying degree of connectedness of BRICS VIX across the study period. This study contributes to the international finance literature and has important implications for investors, portfolio managers, policymakers and academia. JEL Classification: C58, F36, G11, G14, G15
{"title":"Information Linkages Among BRICS Countries: Empirical Evidence from Implied Volatility Indices","authors":"G. Sharma, P. Kayal, P. Pandey","doi":"10.1177/0972652719846315","DOIUrl":"https://doi.org/10.1177/0972652719846315","url":null,"abstract":"In this article, we examine the information linkages of the forward-looking measure of volatility, the volatility index (VIX), for underlying equity market indices of BRICS countries—Brazil, Russia, India, China and South Africa. A study of the information transmission process confirmed a long-run equilibrium relationship between pairs of BRICS countries. The multivariate generalised autoregressive conditional heteroscedasticity (MGARCH) model revealed strong intertemporal linkages between sample VIX. Return and volatility spill-over matrix show the varying degree of connectedness of BRICS VIX across the study period. This study contributes to the international finance literature and has important implications for investors, portfolio managers, policymakers and academia. JEL Classification: C58, F36, G11, G14, G15","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42242121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.1177/0972652719846317
Vandana Bhama, P. K. Jain, Surendra S. Yadav
The present study tests the pecking order of firms at varying debt levels. The findings indicate that deficit firms at low debt levels raise significant amounts of debt, thus indicating the adherence to the pecking order theory. Deficit firms (from both countries) at exceptionally high debt levels do not adjust their capital structure by issuing less debt. In a surplus situation, Chinese firms at very high level redeem the substantial debt because of the dominance of short-term debt in their capital structure. In contrast, Indian surplus firms hesitate to redeem more debt if their existing debt levels are extremely high. JEL Classification: Q14, G32
{"title":"Pecking Order Test at Varying Debt Levels: A Comparative Study of Indian and Chinese Firms","authors":"Vandana Bhama, P. K. Jain, Surendra S. Yadav","doi":"10.1177/0972652719846317","DOIUrl":"https://doi.org/10.1177/0972652719846317","url":null,"abstract":"The present study tests the pecking order of firms at varying debt levels. The findings indicate that deficit firms at low debt levels raise significant amounts of debt, thus indicating the adherence to the pecking order theory. Deficit firms (from both countries) at exceptionally high debt levels do not adjust their capital structure by issuing less debt. In a surplus situation, Chinese firms at very high level redeem the substantial debt because of the dominance of short-term debt in their capital structure. In contrast, Indian surplus firms hesitate to redeem more debt if their existing debt levels are extremely high. JEL Classification: Q14, G32","PeriodicalId":44100,"journal":{"name":"Journal of Emerging Market Finance","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/0972652719846317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44267013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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