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Int. J. Financial Stud., Volume 7, Issue 2 (June 2019) – 17 articles

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Open AccessArticle
The Predictive Power of the User Cost Spread for Economic Recession in China and the US
Int. J. Financial Stud. 2019, 7(2), 34; https://doi.org/10.3390/ijfs7020034 - 18 Jun 2019
Cited by 2 | Viewed by 948
Abstract
The predictive power of the yield curve slope, or the yield spread is well established in the United States (US) and European Union (EU) countries since 1998. However, there exists a gap in the literature on the predictive power of the yield spread [...] Read more.
The predictive power of the yield curve slope, or the yield spread is well established in the United States (US) and European Union (EU) countries since 1998. However, there exists a gap in the literature on the predictive power of the yield spread on the Chinese economy. This paper provides a different leading recession indicator using the Chinese and US economy as comparative examples: the user cost spread, being the difference of the opportunity costs of holding government securities of different maturities. We argue that the user cost spread, based on the Divisia monetary aggregate data like the ones produced by the Center for Financial Stability, provides improved predictive ability and a better intuitive explanation based on changes in the user cost price of holding bonds. Full article
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Open AccessArticle
Heavy Metals: Might as Well Jump
Int. J. Financial Stud. 2019, 7(2), 33; https://doi.org/10.3390/ijfs7020033 - 17 Jun 2019
Viewed by 752
Abstract
Financial times series, and commodity prices in particular, are known to exhibit fat tails in the distribution of prices. As with many natural resources price series, the arrival of new information can lead to unexpectedly rapid changes—or jump—in prices. This suggests that natural [...] Read more.
Financial times series, and commodity prices in particular, are known to exhibit fat tails in the distribution of prices. As with many natural resources price series, the arrival of new information can lead to unexpectedly rapid changes—or jump—in prices. This suggests that natural resource commodity prices should follow a more complex process than geometric Brownian motion (GBM), which is linked to the Gaussian distribution. The presence of jumps (discontinuities) in several heavy metal price series is investigated, as well as time-varying volatility. The results demonstrate that allowing for jumps and time-varying volatility provides statistically important improvements in the modelling or prices, relative to GBM. These complex processes contributed to the fatness of the tails in the distribution of heavy metal price returns. Full article
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Open AccessArticle
Effect of Speculators’ Position Changes on the LME Futures Market
Int. J. Financial Stud. 2019, 7(2), 32; https://doi.org/10.3390/ijfs7020032 - 14 Jun 2019
Cited by 1 | Viewed by 950
Abstract
This paper employs Granger causality tests to analyze the role of speculators using weekly COTR (commitment of traders reports) data covering the period of August 2014 to July 2017. The paper presents statistically significant evidence that the position changes of speculators, such as [...] Read more.
This paper employs Granger causality tests to analyze the role of speculators using weekly COTR (commitment of traders reports) data covering the period of August 2014 to July 2017. The paper presents statistically significant evidence that the position changes of speculators, such as hedge funds and CTAs (commodity trading advisors), unidirectionally Granger-cause the prices of base metals, such as aluminum, copper, and zinc. This finding is a result of causality going from the levels of net futures positions of money managers to futures price changes on the London Metal Exchange (LME). However, producers’ and swap dealers’ speculative roles in price-formation are rejected in Granger causality tests. This paper presents clear results with important market implications. Full article
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Open AccessArticle
Two Investment Options for Bearish ETF Investors: Inverse ETF and Shorting ETF
Int. J. Financial Stud. 2019, 7(2), 31; https://doi.org/10.3390/ijfs7020031 - 13 Jun 2019
Viewed by 864
Abstract
A high liquidity, low expense ratio and the possibility to conduct arbitrage allow exchange-traded funds (ETFs) to be used for short sales. Bearish investors can also buy inverse ETFs. This paper aims to outline two investment approaches for bearish ETF investors and the [...] Read more.
A high liquidity, low expense ratio and the possibility to conduct arbitrage allow exchange-traded funds (ETFs) to be used for short sales. Bearish investors can also buy inverse ETFs. This paper aims to outline two investment approaches for bearish ETF investors and the differences between these two approaches; it also aims to examine the relationship between price and an indicator of volume and evaluate the final positions in selected ETFs in selected periods. Short ETFs dominate in simplicity, flexibility, paying out dividends and especially in the limited size of the loss. On the other hand, their structure, which demands daily rebalancing, causes substantial deviation from the benchmark in the long-term and leads to a higher expense ratio, and lower liquidity increases bid-ask spreads. Negative aspects of ETF short selling lie in unlimited loss, high borrowing costs, the need for margin accounts, variability of loan fees and the possibility of a transaction recall by the lender. On the contrary, margin operations enable potentially higher appreciation of capital by generating rebate rates. Our results show that with the decrease in value of the most used ETFs, short interest is growing for those funds where there is a very strong negative correlation implying hedging tendencies. Short selling proved to be a more advantageous strategy in the observed period of market downturn, as well as in 2011–2017, due to negative returns, however, by applying margin trading inverse ETFs turned out to make less losses. Sector-oriented inverse ETFs are the exception, where the largest differences between these two strategies are recorded. However, the final conclusion of the suitability of one of the analyzed strategies depends on the market volatility and the direction of the market itself. Full article
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Open AccessArticle
Investor Attention and Stock Market Activities: New Evidence from Panel Data
Int. J. Financial Stud. 2019, 7(2), 30; https://doi.org/10.3390/ijfs7020030 - 12 Jun 2019
Cited by 1 | Viewed by 1124
Abstract
Using the panel vector autoregression (VAR) method, this paper documents relationships between investor attention and stock market activities; i.e., return, volatility, and trading volume, respectively. In sum, bidirectional dynamic interdependence of the SVI–stock market activities relationship exists, in which the SVI–trading volume relationship [...] Read more.
Using the panel vector autoregression (VAR) method, this paper documents relationships between investor attention and stock market activities; i.e., return, volatility, and trading volume, respectively. In sum, bidirectional dynamic interdependence of the SVI–stock market activities relationship exists, in which the SVI–trading volume relationship shows the strongest evidence. This is consistent with prior literature using trading volume as a proxy of investor attention. However, the relationships in the developed and developing markets are statistically significantly different. The stock markets in the developed markets over-react more to the search volume than those in the developing markets. We postulate that investor attention is one of the key elements in asset pricing in stock markets. Full article
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Open AccessArticle
Impacts of Financial Market Shock on Bank Asset Allocation from the Perspective of Financial Characteristics of Banks
Int. J. Financial Stud. 2019, 7(2), 29; https://doi.org/10.3390/ijfs7020029 - 12 Jun 2019
Viewed by 767
Abstract
Given ongoing financial disintermediation and the need for central banks to establish interest rate corridors, commercial banks have increasingly enriched their asset allocation choices, forming an allocation pattern that combines traditional credit assets (loans) and financial assets (interbank and securities investment). Due to [...] Read more.
Given ongoing financial disintermediation and the need for central banks to establish interest rate corridors, commercial banks have increasingly enriched their asset allocation choices, forming an allocation pattern that combines traditional credit assets (loans) and financial assets (interbank and securities investment). Due to the long-standing dual interest rate system in China, the yields of credit assets and financial assets have differed, which means the latter has greater volatility. Using the quarterly panel data of 23 listed commercial banks in China from 2002 to 2017, the empirical results of this paper show that the fluctuation of the return rate of the two types of assets will affect the asset allocation of banks. Specifically, on the one hand, when the price of financial assets falls, which leads to the narrowing of the credit spread between the two types of assets, banks reduce transaction demand to prevent loss and reduce their holdings of financial assets, thus increasing the ratio of their credit assets to financial assets. On the other hand, rising benchmark lending rates leads to the increase in the credit financing cost of demanders, reducing the willingness of demanders to lend, forcing the demander to obtain funds through other channels. This results in the decrease in the ratio of credit assets to financial assets. Furthermore, the financial characteristics of banks also influence the dynamic adjustment range of asset allocation. That is, the lower the reserve ratio and capital adequacy ratio, the smaller the impact of financial asset yield volatility on bank asset allocation. Full article
(This article belongs to the Special Issue Financial Economics)
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Open AccessArticle
On Unbalanced Sampling in Bankruptcy Prediction
Int. J. Financial Stud. 2019, 7(2), 28; https://doi.org/10.3390/ijfs7020028 - 05 Jun 2019
Viewed by 913
Abstract
The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of unbalanced samples: (a) when bankrupt [...] Read more.
The paper discusses methodological topics of bankruptcy prediction modelling—unbalanced sampling, sample bias, and unbiased predictions of bankruptcy. Bankruptcy models are typically estimated with the use of non-random samples, which creates sample choice biases. We consider two types of unbalanced samples: (a) when bankrupt and non-bankrupt companies enter the sample in unequal numbers; and (b) when sample composition allows for different ratios of bankrupt and non-bankrupt companies than those in the population. An imbalance of type (b), being more general, is examined in several sections of the paper. We offer an extended view of the relationship between the biased and unbiased estimated probabilities of bankruptcy—probability of default (PD). A common error in applications is neglecting the possibility of calibrating the PD obtained from a bankruptcy model to the unbiased PD that is population adjusted. We show that Skogsviks’ formula of 2013 coincides with prior correction known for the logit model. This, together with solutions for other binomial models, serves as practical advice for obtaining the calibration of unbiased PDs from popular bankruptcy models. In the final section, we explore sample bias effects on classification. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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Open AccessArticle
Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul
Int. J. Financial Stud. 2019, 7(2), 27; https://doi.org/10.3390/ijfs7020027 - 04 Jun 2019
Viewed by 1403
Abstract
Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) [...] Read more.
Market efficiency has been analyzed through many studies using different linear methods. However, studies on financial econometrics reveal that financial time series exhibit nonlinear patterns because of various reasons. This paper examines market efficiency at Borsa Istanbul using a smooth transition autoregressive (STAR) type nonlinear model. I develop nonlinear ARCH and STAR models, a linear AR model and random walk model for 10 years’ weekly data and then out-of-sample forecast next 12 weeks’ return. Comparing forecast performance powers, I find that the STAR model outperforms random walk, that is Borsa Istanbul returns are predictable at the given period. The results show that the shareholders may earn abnormal return and identify the direction of the return change for the next week with at least 66% accuracy. Contrary to the linear level studies, these findings show that the Borsa Istanbul is not weak form efficient at nonlinear level within the studied period. Full article
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Open AccessReview
Stock Market Analysis: A Review and Taxonomy of Prediction Techniques
Int. J. Financial Stud. 2019, 7(2), 26; https://doi.org/10.3390/ijfs7020026 - 27 May 2019
Cited by 7 | Viewed by 2689
Abstract
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in [...] Read more.
Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stock price analysis and forecasting is an area that shows great promise. In this paper, we first provide a concise review of stock markets and taxonomy of stock market prediction methods. We then focus on some of the research achievements in stock analysis and prediction. We discuss technical, fundamental, short- and long-term approaches used for stock analysis. Finally, we present some challenges and research opportunities in this field. Full article
(This article belongs to the Special Issue New Trends in Algorithmic Trading)
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Open AccessArticle
SME Steeplechase: When Obtaining Money Is Harder Than Innovating
Int. J. Financial Stud. 2019, 7(2), 25; https://doi.org/10.3390/ijfs7020025 - 21 May 2019
Cited by 2 | Viewed by 919
Abstract
In this paper, we analyze the main characteristics of European Small and Medium Enterprises (SMEs), related to the demand for and access to external financial resources. We use microdata from an extensive database, elaborated by the European Central Bank and the European Commission: [...] Read more.
In this paper, we analyze the main characteristics of European Small and Medium Enterprises (SMEs), related to the demand for and access to external financial resources. We use microdata from an extensive database, elaborated by the European Central Bank and the European Commission: the Survey on the Access to Finance of Enterprises. Firstly, we consider a set of variables as determinants to the decision to apply for different financial instruments. Secondly, we use the same set of variables to analyze the actual access to these instruments. For each regression, several SMEs profiles were created, in order to detect SMEs archetypes according to their decisions. The results are thought-provoking, and highlight that differences in firms characteristics (size, innovative activities, etc.), influence not only the access to, but also the demand for external finance. Full article
Open AccessArticle
Country of Origin Effects on the Average Annual Values of NHL Player Contracts
Int. J. Financial Stud. 2019, 7(2), 24; https://doi.org/10.3390/ijfs7020024 - 17 May 2019
Viewed by 822
Abstract
Using data from 2005 to 2016, this paper examines if players in the National Hockey League (NHL) are being paid a positive differential for their services due to the competition from the Kontinental Hockey League (KHL) and the Swedish Hockey League (SHL). In [...] Read more.
Using data from 2005 to 2016, this paper examines if players in the National Hockey League (NHL) are being paid a positive differential for their services due to the competition from the Kontinental Hockey League (KHL) and the Swedish Hockey League (SHL). In order to control for performance, we use two different large datasets, (N = 4046) and (N = 1717). In keeping with the existing literature, we use lagged performance statistics and dummy variables to control for the type of NHL contract. The first dataset contains lagged career performance statistics, while the performance statistics are based on the statistics generated during the years under the player’s previous contract. Fixed effects least squares (FELS) and quantile regression results suggest that player production statistics, contract status, and country of origin are significant determinants of NHL player salaries. Full article
(This article belongs to the Special Issue Sports Finance 2018) Printed Edition available
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Open AccessArticle
Estimation of Effects of Recent Macroprudential Policies in a Sample of Advanced Open Economies
Int. J. Financial Stud. 2019, 7(2), 23; https://doi.org/10.3390/ijfs7020023 - 08 May 2019
Viewed by 942
Abstract
We used a time-series cross-section dataset to test several hypotheses pertaining to the role of macroprudential policy instruments in the management of the financial cycle in advanced open economies. The short-run effects are most significant for caps on loan to value and income [...] Read more.
We used a time-series cross-section dataset to test several hypotheses pertaining to the role of macroprudential policy instruments in the management of the financial cycle in advanced open economies. The short-run effects are most significant for caps on loan to value and income (LTV and LTI) and risk weights (RW). The long-run coefficients of credit growth with respect to the indicators of amortisation requirements (Amort) and RW are also significant. The estimation results when house price growth is the dependent variable are consistent with these results. Our findings do not support that Basel III type countercyclical buffer (CCyB) has affected credit growth, and we suggest that the variable is mainly a control in our dataset. In that interpretation, it is interesting that the estimated coefficients of the other instruments are robust with respect to exclusion of CCyB from the empirical models. The main results are also robust to controls in the form of impulse indicator saturation (IIS), which we employed as a novel estimation method for macro panels. Full article
Open AccessArticle
Flobsion—Flexible Option with Benefit Sharing
Int. J. Financial Stud. 2019, 7(2), 22; https://doi.org/10.3390/ijfs7020022 - 19 Apr 2019
Cited by 1 | Viewed by 1043
Abstract
Global environmental goals and the Paris agreement declared the need to avoid dangerous climate change by reducing emissions of greenhouse gases with an ultimate goal to transform today’s policies and reach climate neutrality before the end of the century. In the medium to [...] Read more.
Global environmental goals and the Paris agreement declared the need to avoid dangerous climate change by reducing emissions of greenhouse gases with an ultimate goal to transform today’s policies and reach climate neutrality before the end of the century. In the medium to long-term, climate policies imply rising CO 2 price and consequent financial risk for carbon-intensive producers. In this context, there is a need for tools to buffer CO 2 prices within the period of transition to greener technologies when the emission offsetting markets expose high volatility. Contracts for optional future purchase of carbon credits could provide emitters with a cost-efficient solution to address existing regulatory risks. At the same time, this would help to create much needed financing for the projects generating carbon credits in the future. This work presents the concept of a flobsion—a flexible option with benefit sharing—and demonstrates its advantages in terms of risk reduction for both seller and buyer as compared to both a “do nothing” strategy (offsetting at future market price) and a traditional option with a fixed strike price. The results are supported analytically and numerically, employing as a benchmark the dataset on historical CO 2 prices from the European Emission Trading Scheme. Flobsion has the potential to extend the traditional option in financial applications beyond compliance markets. Full article
(This article belongs to the Special Issue Financial Economics)
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Open AccessArticle
Cross-Border Lending, Government Capital Injection, and Bank Performance
Int. J. Financial Stud. 2019, 7(2), 21; https://doi.org/10.3390/ijfs7020021 - 09 Apr 2019
Cited by 2 | Viewed by 1002
Abstract
In this paper, we develop a contingent claim model to examine the optimal bank interest margin, i.e., the spread between the domestic loan rate and the deposit market rate of an international bank in distress. The framework is used to evaluate the cross-border [...] Read more.
In this paper, we develop a contingent claim model to examine the optimal bank interest margin, i.e., the spread between the domestic loan rate and the deposit market rate of an international bank in distress. The framework is used to evaluate the cross-border lending efficiency for a bank that participates in a government capital injection program, a government intervention used in response to the 2008 financial crisis. This paper suggests that government capital injection is an appropriate way to recapitalize the distressed bank, enhancing the bank interest margin and survival probability. Nevertheless, the government capital injection lacks efficiency when the bank’s cross-border lending is high. Stringent capital regulation, suggested to prevent future crises by literature, leads to superior lending efficiency when the government capital injection is low. Full article
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Open AccessArticle
Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence
Int. J. Financial Stud. 2019, 7(2), 20; https://doi.org/10.3390/ijfs7020020 - 08 Apr 2019
Cited by 1 | Viewed by 1217
Abstract
Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for [...] Read more.
Despite the number of studies on bankruptcy prediction using financial ratios, very little is known about how external audit information can contribute to anticipating financial distress. A handful of papers have shown that a combination of ratios and audit data is significant for predictive purposes, but only one recent paper provided a predictive accuracy of 80% solely by using the disclosures contained in audit reports. This study was complemented by simplifying the analysis of audit reports for prediction purposes and the same predictive accuracy was achieved. By applying three artificial intelligence techniques (PART algorithm, random forest, and support vector machine), the predictive ability of more easily extracted information from the report was examined and a practical implication suggested for each user. Simply by (1) finding the audit opinion, (2) identifying if a matter section exists, and (3) the number of comments disclosed, any user may predict a bankruptcy situation with the same accuracy as if they had scrutinized the whole report. In addition, an extended literature review is included, on previous studies on the interaction between bankruptcy prediction and the external audit information. Full article
(This article belongs to the Special Issue Bankruptcy Prediction)
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Open AccessArticle
The Impact of College Athletic Success on Donations and Applicant Quality
Int. J. Financial Stud. 2019, 7(2), 19; https://doi.org/10.3390/ijfs7020019 - 01 Apr 2019
Viewed by 1209
Abstract
For the 65 colleges and universities that participate in the Power Five athletic conferences (Pac 12, Big 10, SEC, ACC, and Big 12), the football and men’s basketball teams are highly visible. While these programs generate tens of millions of dollars in revenue [...] Read more.
For the 65 colleges and universities that participate in the Power Five athletic conferences (Pac 12, Big 10, SEC, ACC, and Big 12), the football and men’s basketball teams are highly visible. While these programs generate tens of millions of dollars in revenue annually, very few of them turn an operating “profit.” Their existence is thus justified by the claim that athletic success leads to ancillary benefits for the academic institution, in terms of both quantity (e.g., more applications, donations, and state funding) and quality (e.g., stronger applicants, lower acceptance rates, higher yields). Previous studies provide only weak support for some of these claims. Using data from 2006–2016 and a multiple regression model with corrections for multiple testing, we find that while a successful football program is associated with more applicants, there is no effect on the composition of the student body or (with a few caveats) funding for the school through donations or state appropriations. Full article
(This article belongs to the Special Issue Sports Finance 2018) Printed Edition available
Open AccessArticle
The Impact of Macroeconomic Factors on the German Stock Market: Evidence for the Crisis, Pre- and Post-Crisis Periods
Int. J. Financial Stud. 2019, 7(2), 18; https://doi.org/10.3390/ijfs7020018 - 29 Mar 2019
Cited by 2 | Viewed by 1377
Abstract
Today we live in a post-truth and highly digitalized era characterized by a flow of (mis-) information around the world. Identifying the impact of this information on stock markets and forecasting stock returns and volatilities has become a much more difficult task, perhaps [...] Read more.
Today we live in a post-truth and highly digitalized era characterized by a flow of (mis-) information around the world. Identifying the impact of this information on stock markets and forecasting stock returns and volatilities has become a much more difficult task, perhaps almost impossible. This paper investigates the impact of macroeconomic factors, German government bond yields, sentiment and other leading indicators on the main German stock index, namely the DAX30, for the time period from 1991 to 2018. Using a dataset on 24 factors and over a timeframe of about 27 years, we found evidence that across most subsamples, the Composite Leading Indicator (OECD), the Institute for Economic Research (ifo) Export Expectations index, the ifo Export Climate index, exports, the Consumer Price Index CPI, as well as 3 y German government bonds yields show delayed impacts on stock returns. We further found that the delayed impact of the constituents of the monetary aggregate M2 on stock returns changed direction between the crisis and post-crisis periods. Overall, the results illustrate that in the crisis period a larger number of factors and economic indicators had significant impacts on the stock returns compared to the pre- and post-crisis periods. This implies that in the post-crisis period a macro-driven market prevails. Full article
(This article belongs to the Special Issue Macro News and Financial Variables)
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