Empirical Finance

• Format: eBook
• Language: English
• Published: MDPI - Multidisciplinary Digital Publishing Institute 2019
• Subjects: algorithmic trading; ARDL; asset pricing model; asymmetric dependence; ATR; bagging; bank credit; bankruptcy prediction; boosting; causality-in-variance; city banks; cointegration; convolutional neural networks; copula; credit risk; cross-correlation function; crude oil futures prices forecasting; currency crisis; data mining; deep learning; deep neural network; dependence structure; earnings management; earnings manipulation; earnings quality; ensemble learning; exchange rate; exports; financial and non-financial variables; financial market stress; flight to quality; futures market; global financial crisis; gold return; housing and stock markets; housing loans; housing price; inertia; initial public offering; institutional investors' shareholdings; IPO; Japanese yen; latency; liquidity risk premium; LSTM; MACD; machine learning; market microstructure; n/a; natural gas; neural network; panel data model; piecewise regression model; predictive accuracy; price discovery; quantile regression; random forest; random forests; real estate development loans; robust regression; short-term forecasting; spark spread; statistical arbitrage; stop loss; structural break; SVM; take profit; text mining; text similarity; TVP-VAR model; US dollar; utility of international currency; Vietnam; volatility; wavelet transform; wholesale electricity
• Online Access: Open Access: DOAB: description of the publication; Open Access: DOAB, download the publication
• Physical Description: 1 online resource (276 p.)
• ISBN: 9783038977063; books978-3-03897-707-0
• Access: Open Access

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. One example includes the application of novel empirical techniques, such as machine learning, data mining, wavelet transform, copula analysis, and TV-VAR, to financial data. The Special Issue includes contributions on empirical finance, such as algorithmic trading, market efficiency, market microstructure, portfolio theory and asset allocation, asset pricing models, liquidity risk premium, currency crisis, return predictability, and volatility modeling.