Topical Collection "Empirical Finance Research"

Editor

Prof. Dr. Shigeyuki Hamori
E-Mail Website
Guest Editor
Graduate School of Economics, Kobe University, 2-1, Rokkodai, Nada-Ku, Kobe 657-8501, Japan
Tel. +81788036832
Interests: applied time-series analysis; empirical finance; data science; international finance
Special Issues and Collections in MDPI journals

Topical Collection Information

Dear Colleagues,

There is no denying the role of empirical research in finance and the remarkable progress of empirical techniques in this research field. This Topical Collection focuses on the broad topic of “Empirical Finance.” It includes novel empirical research associated with financial data. Some examples include the application of novel empirical techniques, such as machine learning, data mining, algorithm trading, multivariate GARCH models, wavelet transform, copula, time-varying VAR, and high-frequency trading to financial data. The Topical Collection includes contributions on empirical finance, such as asset pricing models, volatility modeling, market efficiency, market microstructure, portfolio theory and asset allocation, return predictability, liquidity risk premium, systemic risk, financial crisis, contagion, cryptocurrencies, and financialization of commodity markets.

Prof. Dr. Shigeyuki Hamori
Guest Editor

Manuscript Submission Information

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Keywords

  • Machine learning
  • Data mining
  • Algorithmic trading
  • Multivariate GARCH
  • Copula
  • Wavelet transform
  • High-frequency trading
  • Asset pricing models
  • Volatility modeling
  • Market efficiency
  • Market microstructure
  • Portfolio theory and asset allocation
  • Return predictability
  • Liquidity risk premium
  • Systemic risk
  • Financial crisis
  • Contagion
  • Cryptocurrencies
  • Financialization of commodity markets

Related Special Issues

Published Papers (28 papers)

2020

Jump to: 2019, 2018

Open AccessEditorial
Empirical Finance
J. Risk Financial Manag. 2020, 13(1), 6; https://doi.org/10.3390/jrfm13010006 - 02 Jan 2020
Abstract
The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. [...] Read more.
The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six articles on machine learning, five articles based on traditional econometric analysis, and five articles on emerging markets. The large share of articles on the application of machine learning is in line with recent trends in finance research. This special issue provides a state-of-the-art overview of empirical finance from economic, financial, and technical points of view. Full article

2019

Jump to: 2020, 2018

Open AccessArticle
The Impacts of Selling Expense Structure on Enterprise Growth in Large Enterprises: A Study from Vietnam
J. Risk Financial Manag. 2020, 13(1), 4; https://doi.org/10.3390/jrfm13010004 - 28 Dec 2019
Abstract
This study intends to examine the impact of selling expense structure on the business growth of 255 Vietnamese large-scale enterprises in three different industries (Consumer Staples, Industrials, and Manufacture) listed on the Vietnamese Stock Exchange over four years from 2015 to 2018. By [...] Read more.
This study intends to examine the impact of selling expense structure on the business growth of 255 Vietnamese large-scale enterprises in three different industries (Consumer Staples, Industrials, and Manufacture) listed on the Vietnamese Stock Exchange over four years from 2015 to 2018. By using STATA software (StataCorp LLC, 4905 Lakeway Drive, College Station, Texas 77845-4512, USA), the research outcomes indicate that both labour expense and depreciation expense have a negative influence on revenue growth and firm size growth but positive influence on profit growth while materials and tools expenses negatively affect all three dependent variables. Furthermore, an increase in the proportion of outsourcing expenses and other selling expenses would result in a significant increase in revenue but a decline in the profit of these companies. From this research results, large-scale enterprises should consider changing the selling expense structure as they spend too much on outsourcing and other selling expenses (60%–70% total selling expense) but too little on labour, which plays an important role in upgrading the profitability of these enterprises. Full article
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Open AccessArticle
Skewness Preference and Asset Pricing: Evidence from the Japanese Stock Market
J. Risk Financial Manag. 2019, 12(3), 149; https://doi.org/10.3390/jrfm12030149 - 12 Sep 2019
Abstract
Previous studies have shown that investor preference for positive skewness creates a potential premium on negatively skewed assets. In this paper, we attempt to explore the connection between investors’ skewness preferences and corresponding demand for a risk premium on asset returns. Using data [...] Read more.
Previous studies have shown that investor preference for positive skewness creates a potential premium on negatively skewed assets. In this paper, we attempt to explore the connection between investors’ skewness preferences and corresponding demand for a risk premium on asset returns. Using data from the Japanese stock market, we empirically study the significance of risk aversion with skewness preference that potentially delivers a premium. Compared to studies on other stock markets, our finding suggests that Japanese investors exhibit preference for positively skewed assets, but do not display dislike for ones that are negatively skewed. This implies that investors from different countries having dissimilar attitudes toward risk may possess different preferences toward positive skewness, which would result in a different magnitude of expected risk premium on negatively skewed assets. Full article
Open AccessArticle
Conditional Dependence between Oil Prices and Exchange Rates in BRICS Countries: An Application of the Copula-GARCH Model
J. Risk Financial Manag. 2019, 12(2), 99; https://doi.org/10.3390/jrfm12020099 - 09 Jun 2019
Abstract
We studied the dependence structure between West Texas Intermediate (WTI) oil prices and the exchange rates of BRICS1 countries, using copula models. We used the Normal, Plackett, rotated-Gumbel, and Student’s t copulas to measure the constant dependence, and we captured the dynamic [...] Read more.
We studied the dependence structure between West Texas Intermediate (WTI) oil prices and the exchange rates of BRICS1 countries, using copula models. We used the Normal, Plackett, rotated-Gumbel, and Student’s t copulas to measure the constant dependence, and we captured the dynamic dependence using the Generalized Autoregressive Score with the Student’s t copula. We found that negative dependence and significant tail dependence exist in all pairs considered. The Russian Ruble (RUB)–WTI pair has the strongest dependence. Moreover, we treated five exchange rate–oil pairs as portfolios and evaluated the Value at Risk and Expected Shortfall from the time-varying copula models. We found that both reach low values when the oil price falls sharply. Full article
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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 - 14 Feb 2019
Cited by 1
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
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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 - 14 Feb 2019
Cited by 1
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
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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 - 10 Feb 2019
Cited by 3
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
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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 - 06 Feb 2019
Cited by 1
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
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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 - 21 Jan 2019
Cited by 1
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
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Open AccessArticle
Expectations for Statistical Arbitrage in Energy Futures Markets
J. Risk Financial Manag. 2019, 12(1), 14; https://doi.org/10.3390/jrfm12010014 - 15 Jan 2019
Cited by 3
Abstract
Energy futures have become important as alternative investment assets to minimize the volatility of portfolio return, owing to their low links with traditional financial markets. In order to make energy futures markets grow further, it is necessary to expand expectations of returns from [...] Read more.
Energy futures have become important as alternative investment assets to minimize the volatility of portfolio return, owing to their low links with traditional financial markets. In order to make energy futures markets grow further, it is necessary to expand expectations of returns from trading in energy futures markets. Therefore, this study examines whether profits can be earned by statistical arbitrage between wholesale electricity futures and natural gas futures listed on the New York Mercantile Exchange. On the assumption that power prices and natural gas prices have a cointegration relationship, as tested and supported by previous studies, the short-term deviation from the long-term equilibrium is regarded as an arbitrage opportunity. The results of the spark-spread trading simulations using historical data from 2 January 2014 to 29 December 2017 show about 30% yield at maximum. This study shows the possibility of generating earnings in energy futures market. Full article
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Open AccessArticle
Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks
J. Risk Financial Manag. 2019, 12(1), 9; https://doi.org/10.3390/jrfm12010009 - 08 Jan 2019
Cited by 3
Abstract
This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil [...] Read more.
This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We also provide strong evidence that CNN models with matrix inputs are better at short-term prediction than neural network (NN) models with single-vector input, which indicates that strengthening the dependence of inputs and providing more useful information can improve short-term forecasting performance. Full article
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Open AccessArticle
What Determines Utility of International Currencies?
J. Risk Financial Manag. 2019, 12(1), 10; https://doi.org/10.3390/jrfm12010010 - 08 Jan 2019
Cited by 1
Abstract
In previous studies, we estimated a time series of coefficients on five international currencies (the US dollar, the euro, the Japanese yen, the British pound, and the Swiss franc) in a utility function. We call the coefficients utilities of international currencies. The time [...] Read more.
In previous studies, we estimated a time series of coefficients on five international currencies (the US dollar, the euro, the Japanese yen, the British pound, and the Swiss franc) in a utility function. We call the coefficients utilities of international currencies. The time series show that the utility of the US dollar as an international currency has remained in the first position in the changing international monetary system despite of the fact that the euro was created as a single common currency for European countries. On one hand, the utility of the Japanese yen has been declining as an international currency. In this paper, we investigate what determines the utility of international currencies. We use a dynamic panel data model to analyze the issue with Generalized Method of Moments (GMM). Specifically, liquidity shortage in terms of an international currency means that it is inconvenient for economic agents to use the relevant currency for international economic transactions. In other words, liquidity shortages might reduce the utility of an international currency. In this analysis we focus on liquidity premium which represents a liquidity shortage in terms of an international currency. Our empirical results showed not only inertia in terms of change but also the impact of a liquidity shortage in an international currency on the utility of the relevant international currency. Full article
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Open AccessArticle
The Impact of Exchange Rate Volatility on Exports in Vietnam: A Bounds Testing Approach
J. Risk Financial Manag. 2019, 12(1), 6; https://doi.org/10.3390/jrfm12010006 - 04 Jan 2019
Cited by 2
Abstract
This paper investigates the impact of exchange rate volatility on exports in Vietnam using quarterly data from the first quarter of 2000 to the fourth quarter of 2014. The paper applies the autoregressive distributed lag (ARDL) bounds testing approach to the analysis of [...] Read more.
This paper investigates the impact of exchange rate volatility on exports in Vietnam using quarterly data from the first quarter of 2000 to the fourth quarter of 2014. The paper applies the autoregressive distributed lag (ARDL) bounds testing approach to the analysis of level relationships between effective exchange rate volatility and exports. Using the demand function of exports, the paper also considers the effect of depreciation and foreign income on exports of Vietnam. The results show that exchange rate volatility negatively affects the export volume in the long run, as expected. A depreciation of the domestic currency affects exports negatively in the short run, but positively in the long run, consistent with the J curve effect. Surprisingly, an increase in the real income of a foreign country actually decreases Vietnamese export volume. These findings suggest some policy implications in managing the exchange rate system and promoting exports of Vietnam. Full article
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2018

Jump to: 2020, 2019

Open AccessArticle
Bank Credit and Housing Prices in China: Evidence from a TVP-VAR Model with Stochastic Volatility
J. Risk Financial Manag. 2018, 11(4), 90; https://doi.org/10.3390/jrfm11040090 - 15 Dec 2018
Cited by 2
Abstract
Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China’s housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses? [...] Read more.
Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China’s housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses? We aim to study the dynamic relationship between housing prices and bank credit in China from the second quarter of 2005 to the fourth quarter of 2017 by using a time-varying parameter vector autoregression (VAR) model with stochastic volatility. Furthermore, we study the relationships between housing prices and housing loans on the demand side and real estate development loans on the supply side, separately. Finally, we obtain several findings. First, the relationship between housing prices and bank credit shows significant time-varying features; second, the mutual effects of housing prices and bank credit vary between the demand side and supply side; third, influences of housing prices on all kinds of bank credit are stronger than influences in the opposite direction. Full article
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Open AccessArticle
Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform
J. Risk Financial Manag. 2018, 11(4), 86; https://doi.org/10.3390/jrfm11040086 - 04 Dec 2018
Cited by 2
Abstract
We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably [...] Read more.
We propose a novel approach that combines random forests and the wavelet transform to model the prediction of currency crises. Our classification model of random forests, built using both standard predictors and wavelet predictors, and obtained from the wavelet transform, achieves a demonstrably high level of predictive accuracy. We also use variable importance measures to find that wavelet predictors are key predictors of crises. In particular, we find that real exchange rate appreciation and overvaluation, which are measured over a horizon of 16–32 months, are the most important. Full article
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Open AccessArticle
Forecasting Volatility: Evidence from the Saudi Stock Market
J. Risk Financial Manag. 2018, 11(4), 84; https://doi.org/10.3390/jrfm11040084 - 28 Nov 2018
Abstract
The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries [...] Read more.
The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations. Full article
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Open AccessArticle
Stock Market Volatility and Trading Volume: A Special Case in Hong Kong With Stock Connect Turnover
J. Risk Financial Manag. 2018, 11(4), 76; https://doi.org/10.3390/jrfm11040076 - 31 Oct 2018
Cited by 1
Abstract
The cross-boundary Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect provides a special data set to study the dynamic relationships among volatility, trading volume and turnover among three stock markets, namely Shanghai, Shenzhen, and Hong Kong. We employ the Granger Causality test with the [...] Read more.
The cross-boundary Shanghai-Hong Kong and Shenzhen-Hong Kong Stock Connect provides a special data set to study the dynamic relationships among volatility, trading volume and turnover among three stock markets, namely Shanghai, Shenzhen, and Hong Kong. We employ the Granger Causality test with the vector autoregressive model (VAR) to examine whether Stock Connect turnover contributes to future realized volatility and market volume of these three markets. Our results support the evidence of causality from Stock Connect turnover to market volatility and trading volume. The finding of this causality is consistent with the implication of the sequential information arrival model in the literature. Full article
Open AccessArticle
Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
J. Risk Financial Manag. 2018, 11(4), 73; https://doi.org/10.3390/jrfm11040073 - 28 Oct 2018
Cited by 1
Abstract
Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor—the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor (SIP), we [...] Read more.
Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor—the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor (SIP), we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60% or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures, such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today’s stock market. Full article
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Open AccessArticle
Volatility Spillovers Arising from the Financialization of Commodities
J. Risk Financial Manag. 2018, 11(4), 72; https://doi.org/10.3390/jrfm11040072 - 27 Oct 2018
Abstract
This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results [...] Read more.
This paper examines whether the proliferation of new index products, such as commodity-tracking exchange-traded funds (ETFs), amplified the volatility transmission channel introduced by financialization. This paper focuses on the volatility spillover effects among crude oil, metals, agriculture, and non-energy commodity markets. The results show financialization has an impact on the volatility of commodity prices, predominantly for non-energy commodities. However, the impact on volatility is not symmetric across all commodities. The analysis of index investment and investors’ positions in futures markets shows that, when a relationship exists, it is generally negatively correlated with the realized volatility of non-energy commodities. Using realized volatility in the difference-in-difference model provides estimates that are inconsistent with other findings that non-energy commodities, traded as a part of indices, have experienced higher volatility. The results are similar to the index investment and futures market analysis, where increased participation by investors through new investment products has put download pressure on realized volatility. Full article
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Open AccessBook Review
Book Review for “Credit Default Swap Markets in the Global Economy” by Go Tamakoshi and Shigeyuki Hamori. Routledge: Oxford, UK, 2018; ISBN: 9781138244726
J. Risk Financial Manag. 2018, 11(4), 68; https://doi.org/10.3390/jrfm11040068 - 25 Oct 2018
Abstract
Credit default swaps (CDS) came into existence in 1994 when they were invented by JP Morgan, then it became popular in the early 2000s, and by 2007, the outstanding credit default swaps balance reached $62 trillion. [...] Full article
Open AccessFeature PaperArticle
Modeling the Dependence Structure of Share Prices among Three Chinese City Banks
J. Risk Financial Manag. 2018, 11(4), 57; https://doi.org/10.3390/jrfm11040057 - 29 Sep 2018
Cited by 3
Abstract
We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student’s t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two [...] Read more.
We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student’s t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two periods: one covering the global financial crisis and the other covering the domestic share market crash in China. We show that Beijing Bank is less dependent on the other two city banks than Nanjing Bank, which is dependent on the other two in share price extreme returns. We also observe a major decrease of dependency from 2007 to 2018 in three one-to-one dependence structures. Interestingly, contrary to recent literatures, Ningbo Bank and Nanjing Bank tend to be more dependent on each other in positive returns than in negative returns during the past decade. We also show the dynamic dependence structures among three city banks using time-varying copula. Full article
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Open AccessArticle
Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System
J. Risk Financial Manag. 2018, 11(3), 56; https://doi.org/10.3390/jrfm11030056 - 19 Sep 2018
Cited by 4
Abstract
A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and scrutinized over the years. In this paper, we examine various strategies added to a simple MACD automated trading system and used on selected assets from Forex, Metals, Energy, [...] Read more.
A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and scrutinized over the years. In this paper, we examine various strategies added to a simple MACD automated trading system and used on selected assets from Forex, Metals, Energy, and Cryptocurrencies categories and afterwards, we compare and contrast their results. We conclude that Take Profit strategies based on faster take profit signals on MACD are not better than a simple MACD strategy and of the different Stop Loss strategies based on ATR, the sliding and variable ATR window has the best results for a period of 12 and a multiplier of 6. For the first time, to the best of our knowledge, we implement a combination of an adaptive MACD Expert Advisor that uses back-tested optimized parameters per asset with price levels defined by the ATR indicator, used to set limits for Stop Loss. Full article
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Open AccessArticle
Dynamic Linkages between Japan’s Foreign Exchange and Stock Markets: Response to the Brexit Referendum and the 2016 U.S. Presidential Election
J. Risk Financial Manag. 2018, 11(3), 34; https://doi.org/10.3390/jrfm11030034 - 28 Jun 2018
Cited by 2
Abstract
In this paper, we analyse the response of Japan’s foreign exchange and stock markets to the outcomes of the Brexit referendum and the U.S. presidential election. We estimate the changes in returns of the daily exchange rates of the yen (JPY), the daily [...] Read more.
In this paper, we analyse the response of Japan’s foreign exchange and stock markets to the outcomes of the Brexit referendum and the U.S. presidential election. We estimate the changes in returns of the daily exchange rates of the yen (JPY), the daily closing price index of the Nikkei and the dynamic conditional correlation (DCC) coefficients between the JPY and the Nikkei caused by both events. The empirical findings showed a significant change in the daily logarithmic returns of exchange rates of the JPY and the closing price index of the Nikkei, as well as their time-varying comovement (DCC) after both events. In general, the impact of the U.S. elections on financial markets and their dynamic correlation was stronger than the impact of the Brexit referendum. Full article
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Open AccessArticle
Determinants of Stock Market Co-Movements between Pakistan and Asian Emerging Economies
J. Risk Financial Manag. 2018, 11(3), 32; https://doi.org/10.3390/jrfm11030032 - 21 Jun 2018
Cited by 1
Abstract
This study analyzes the determinants of stock market co-movement between Pakistan and Asian emerging economies for the period 2001 to 2015. Augmented Dickey and Fuller (ADF) and Philips-Perron (PP) tests are applied to check co-integration between their stock markets. Results of this study [...] Read more.
This study analyzes the determinants of stock market co-movement between Pakistan and Asian emerging economies for the period 2001 to 2015. Augmented Dickey and Fuller (ADF) and Philips-Perron (PP) tests are applied to check co-integration between their stock markets. Results of this study reveal that there is long-term integration between the stock market of Pakistan and the stock markets of China, India, Indonesia, Korea, Malaysia and Thailand. This study reports the driving forces of the co-movement between the Pakistan and Asian emerging markets where co-integration is found. Results of the panel data reveal that there are significant underlying forces of integration between Pakistan and each Asian emerging stock market. The findings of this study have significant implications for policy makers in Pakistan who are designing strategies for macroeconomic harmonization and stability of the country’s economy against financial shocks. Full article
Open AccessArticle
Investigation of the Financial Stability of S&P 500 Using Realized Volatility and Stock Returns Distribution
J. Risk Financial Manag. 2018, 11(2), 22; https://doi.org/10.3390/jrfm11020022 - 28 Apr 2018
Cited by 1
Abstract
In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency [...] Read more.
In this work, the financial data of 377 stocks of Standard & Poor’s 500 Index (S&P 500) from the years 1998–2012 with a 250-day time window were investigated by measuring realized stock returns and realized volatility. We examined the normal distribution and frequency distribution for both daily stock returns and volatility. We also determined the beta-coefficient and correlation among the stocks for 15 years and found that, during the crisis period, the beta-coefficient between the market index and stock’s prices and correlation among stock’s prices increased remarkably and decreased during the non-crisis period. We compared the stock volatility and stock returns for specific time periods i.e., non-crisis, before crisis and during crisis year in detail and found that the distribution behaviors of stock return prices has a better long-term effect that allows predictions of near-future market behavior than realized volatility of stock returns. Our detailed statistical analysis provides a valuable guideline for both researchers and market participants because it provides a significantly clearer comparison of the strengths and weaknesses of the two methods. Full article
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Open AccessArticle
Testing for Causality-In-Mean and Variance between the UK Housing and Stock Markets
J. Risk Financial Manag. 2018, 11(2), 21; https://doi.org/10.3390/jrfm11020021 - 26 Apr 2018
Cited by 1
Abstract
This paper employs the two-step procedure to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to [...] Read more.
This paper employs the two-step procedure to analyze the causality-in-mean and causality-in-variance between the housing and stock markets of the UK. The empirical findings make two key contributions. First, although previous studies have indicated a one-way causal relation from the housing market to the stock market in the UK, this paper discovered a two-way causal relation between them. Second, a causality-in-variance as well as a causality-in-mean was detected from the housing market to the stock market. Full article
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Open AccessArticle
Ensemble Learning or Deep Learning? Application to Default Risk Analysis
J. Risk Financial Manag. 2018, 11(1), 12; https://doi.org/10.3390/jrfm11010012 - 05 Mar 2018
Cited by 5
Abstract
Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy [...] Read more.
Proper credit-risk management is essential for lending institutions, as substantial losses can be incurred when borrowers default. Consequently, statistical methods that can measure and analyze credit risk objectively are becoming increasingly important. This study analyzes default payment data and compares the prediction accuracy and classification ability of three ensemble-learning methods—specifically, bagging, random forest, and boosting—with those of various neural-network methods, each of which has a different activation function. The results obtained indicate that the classification ability of boosting is superior to other machine-learning methods including neural networks. It is also found that the performance of neural-network models depends on the choice of activation function, the number of middle layers, and the inclusion of dropout. Full article
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Open AccessFeature PaperArticle
Estimation of Cross-Lingual News Similarities Using Text-Mining Methods
J. Risk Financial Manag. 2018, 11(1), 8; https://doi.org/10.3390/jrfm11010008 - 31 Jan 2018
Cited by 1
Abstract
In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and [...] Read more.
In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method. Full article
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