Special Issue "Empirical Finance"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074).

Deadline for manuscript submissions: closed (31 December 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Shigeyuki Hamori

Graduate School of Economics, Kobe University, Rokkodai, Nada-Ku, Kobe 657-8504, Japan
Website | E-Mail
Interests: applied time series analysis; empirical finance; data science; international finance

Special Issue 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 Special Issue focuses on the broad topic of “Empirical Finance” and includes novel empirical research associated with financial data. Articles on application of novel empirical techniques such as copula analysis, wavelet transform, machine learning, and analysis of tick data are welcome.

The Special Issue could include contributions on empirical finance, such as market efficiency, market microstructure, event study, portfolio theory and asset allocation, asset pricing models, stock return predictability, and volatility modeling.

Prof. Dr. Shigeyuki Hamori
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Risk and Financial Management is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Copula
  • Wavelet transform
  • Machine learning
  • Tick data
  • Market efficiency
  • Market microstructure
  • Event study
  • Portfolio theory and asset allocation
  • Asset pricing models
  • Stock return reducibility
  • Volatility modeling

Published Papers (14 papers)

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Open AccessArticle Is Window-Dressing around Going Public Beneficial? Evidence from Poland
J. Risk Financial Manag. 2019, 12(1), 18; https://doi.org/10.3390/jrfm12010018
Received: 13 December 2018 / Revised: 8 January 2019 / Accepted: 18 January 2019 / Published: 21 January 2019
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Abstract
The informativeness of financial reports has been of a great importance to both investors and academics. Earnings are crucial for evaluating future prospects and determining company value, especially around milestone events such as initial public offerings (IPO). If investors are misled by manipulated
[...] Read more.
The informativeness of financial reports has been of a great importance to both investors and academics. Earnings are crucial for evaluating future prospects and determining company value, especially around milestone events such as initial public offerings (IPO). If investors are misled by manipulated earnings, they could pay too high a price and suffer losses in the long-term when prices adjust to real value. We provide new evidence on the relationship between earnings management and the long-term performance of IPOs as we test the issue with a methodology that has not been applied so far for issues in Poland. We use a set of proxies of earnings management and test the long-term IPO performance under several factor models (CAPM, and three extensions of the Fama-French model). Aggressive IPOs perform very poorly later and earn severe negative stock returns up to three years after going public. The difference in returns in accrual quantiles is statistically significant in almost half of methodology settings. The results seem to suggest that investors might not be able to discount pre-IPO abnormal accruals and could be overoptimistic. Once the true earnings performance is revealed over time, the market makes downward price corrections. Full article
(This article belongs to the Special Issue Empirical Finance)
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Open AccessArticle Expectations for Statistical Arbitrage in Energy Futures Markets
J. Risk Financial Manag. 2019, 12(1), 14; https://doi.org/10.3390/jrfm12010014
Received: 7 December 2018 / Revised: 8 January 2019 / Accepted: 12 January 2019 / Published: 15 January 2019
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 21 November 2018 / Revised: 25 December 2018 / Accepted: 2 January 2019 / Published: 8 January 2019
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 7 December 2018 / Revised: 2 January 2019 / Accepted: 3 January 2019 / Published: 8 January 2019
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 15 November 2018 / Revised: 25 December 2018 / Accepted: 27 December 2018 / Published: 4 January 2019
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 2 November 2018 / Revised: 30 November 2018 / Accepted: 11 December 2018 / Published: 15 December 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 1 November 2018 / Revised: 27 November 2018 / Accepted: 1 December 2018 / Published: 4 December 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 30 September 2018 / Revised: 25 October 2018 / Accepted: 25 October 2018 / Published: 28 October 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 23 August 2018 / Revised: 14 September 2018 / Accepted: 28 September 2018 / Published: 29 September 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 22 August 2018 / Revised: 14 September 2018 / Accepted: 17 September 2018 / Published: 19 September 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 24 March 2018 / Revised: 6 April 2018 / Accepted: 6 April 2018 / Published: 26 April 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 19 January 2018 / Revised: 24 February 2018 / Accepted: 28 February 2018 / Published: 5 March 2018
Cited by 1 | PDF Full-text (4488 KB) | HTML Full-text | XML Full-text
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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 31 December 2017 / Revised: 23 January 2018 / Accepted: 25 January 2018 / Published: 31 January 2018
PDF Full-text (1016 KB) | HTML Full-text | XML Full-text | Supplementary Files
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
(This article belongs to the Special Issue Empirical Finance)
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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
Received: 22 October 2018 / Accepted: 24 October 2018 / Published: 25 October 2018
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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
(This article belongs to the Special Issue Empirical Finance)
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