Special Issue "Empirical Finance"

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

Deadline for manuscript submissions: 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 (5 papers)

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Research

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
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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,
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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
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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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Graphical abstract

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
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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
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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
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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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