【2019专辑】Empirical Finance

5056

收藏 2019-06-01

Empirical Finance
by Shigeyuki Hamori (Editor)

About the author
Shigeyuki Hamori is a Professor of Economics, Graduate School of Economics, Kobe University, Japan. He holds a Ph.D. in Economics from Duke University, the United States. He is a Distinguished Fellow, International Engineering and Technology Institute (DFIETI), and Honorary Chair Professor, Asia University, Taiwan. His main research interests are applied time series analysis, empirical finance, data science, and international finance. He has published approximately 200 articles in international peer-reviewed journals, and he is presently a member of the editorial boards of International Review of Financial Analysis, Singapore Economic Review, AGING AND HEALTH, Advances in Decision Sciences, Journal of Risk and Financial Management, Annals of Financial Economics, Journal of Management Information and Decision Sciences, International Economics and Finance Journal, Journal of Reviews on Global Economics, and Accounting and Finance Research. He is also the Vice President of the International Research Institute for Economics and Management (IRIEM).

About this book
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.

Brief contents

Estimation of Cross-Lingual News Similarities Using Text-Mining Methods
What Determines Utility of International Currencies?
Can We Forecast Daily Oil Futures Prices? Experimental Evidence from Convolutional Neural Networks
Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System
Predicting Currency Crises: A Novel Approach Combining Random Forests and Wavelet Transform
Predicting Micro-Enterprise Failures Using Data Mining Techniques
Ensemble Learning or Deep Learning? Application to Default Risk Analysis
Price Discovery and the Accuracy of Consolidated Data Feeds in the U.S. Equity Markets
Expectations for Statistical Arbitrage in Energy Futures Markets
Clarifying the Response of Gold Return to Financial Indicators: An Empirical ComparativeAnalysis Using Ordinary Least Squares, Robust and Quantile Regressions
Testing for Causality-In-Mean and Variance between the UK Housing and Stock Markets
Modeling the Dependence Structure of Share Prices among Three Chinese City Banks
Bank Credit and Housing Prices in China: Evidence from a TVP-VAR Model with Stochastic Volatility
Is Window-Dressing around Going Public Beneficial? Evidence from Poland
Effect of Corporate Governance on Institutional Investors’ Preferences: An Empirical Investigation in Taiwan
The Impact of Exchange Rate Volatility on Exports in Vietnam: A Bounds Testing Approach
Book Review for “Credit Default Swap Markets in the Global Economy”