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Book
Empirical Finance
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Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

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.

Keywords

short-term forecasting --- wavelet transform --- IPO --- volatility --- US dollar --- institutional investors’ shareholdings --- neural network --- financial market stress --- market microstructure --- text similarity --- TVP-VAR model --- Japanese yen --- convolutional neural networks --- global financial crisis --- deep neural network --- cross-correlation function --- boosting --- causality-in-variance --- flight to quality --- bagging --- earnings quality --- algorithmic trading --- stop loss --- statistical arbitrage --- ensemble learning --- liquidity risk premium --- gold return --- futures market --- take profit --- currency crisis --- spark spread --- city banks --- piecewise regression model --- financial and non-financial variables --- exports --- data mining --- latency --- crude oil futures prices forecasting --- random forests --- wholesale electricity --- SVM --- random forest --- bank credit --- deep learning --- Vietnam --- inertia --- MACD --- initial public offering --- text mining --- bankruptcy prediction --- exchange rate --- asset pricing model --- LSTM --- panel data model --- structural break --- credit risk --- housing and stock markets --- copula --- ARDL --- earnings manipulation --- machine learning --- natural gas --- housing price --- asymmetric dependence --- real estate development loans --- earnings management --- cointegration --- predictive accuracy --- robust regression --- quantile regression --- dependence structure --- housing loans --- price discovery --- utility of international currency --- ATR --- short-term forecasting --- wavelet transform --- IPO --- volatility --- US dollar --- institutional investors’ shareholdings --- neural network --- financial market stress --- market microstructure --- text similarity --- TVP-VAR model --- Japanese yen --- convolutional neural networks --- global financial crisis --- deep neural network --- cross-correlation function --- boosting --- causality-in-variance --- flight to quality --- bagging --- earnings quality --- algorithmic trading --- stop loss --- statistical arbitrage --- ensemble learning --- liquidity risk premium --- gold return --- futures market --- take profit --- currency crisis --- spark spread --- city banks --- piecewise regression model --- financial and non-financial variables --- exports --- data mining --- latency --- crude oil futures prices forecasting --- random forests --- wholesale electricity --- SVM --- random forest --- bank credit --- deep learning --- Vietnam --- inertia --- MACD --- initial public offering --- text mining --- bankruptcy prediction --- exchange rate --- asset pricing model --- LSTM --- panel data model --- structural break --- credit risk --- housing and stock markets --- copula --- ARDL --- earnings manipulation --- machine learning --- natural gas --- housing price --- asymmetric dependence --- real estate development loans --- earnings management --- cointegration --- predictive accuracy --- robust regression --- quantile regression --- dependence structure --- housing loans --- price discovery --- utility of international currency --- ATR


Book
Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
Authors: ---
ISBN: 3039212168 303921215X Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

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Abstract

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been rapidly maturing. Moreover, over the last few decades, machine learning techniques including artificial neural network (ANN), deep learning, decision tree, and support vector machine (SVM) have been successfully applied to geospatial science and engineering research fields. The machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards, and natural resources. This book is a collection representing novel contributions detailing machine learning techniques as applied to geoscience information systems and remote sensing.

Keywords

artificial neural network --- n/a --- model switching --- sensitivity analysis --- neural networks --- logit boost --- Qaidam Basin --- land subsidence --- land use/land cover (LULC) --- naïve Bayes --- multilayer perceptron --- convolutional neural networks --- single-class data descriptors --- logistic regression --- feature selection --- mapping --- particulate matter 10 (PM10) --- Bayes net --- gray-level co-occurrence matrix --- multi-scale --- Logistic Model Trees --- classification --- Panax notoginseng --- large scene --- coarse particle --- grayscale aerial image --- Gaofen-2 --- environmental variables --- variable selection --- spatial predictive models --- weights of evidence --- landslide prediction --- random forest --- boosted regression tree --- convolutional network --- Vietnam --- model validation --- colorization --- data mining techniques --- spatial predictions --- SCAI --- unmanned aerial vehicle --- high-resolution --- texture --- spatial sparse recovery --- landslide susceptibility map --- machine learning --- reproducible research --- constrained spatial smoothing --- support vector machine --- random forest regression --- model assessment --- information gain --- ALS point cloud --- bagging ensemble --- one-class classifiers --- leaf area index (LAI) --- landslide susceptibility --- landsat image --- ionospheric delay constraints --- spatial spline regression --- remote sensing image segmentation --- panchromatic --- Sentinel-2 --- remote sensing --- optical remote sensing --- materia medica resource --- GIS --- precise weighting --- change detection --- TRMM --- traffic CO --- crop --- training sample size --- convergence time --- object detection --- gully erosion --- deep learning --- classification-based learning --- transfer learning --- landslide --- traffic CO prediction --- hybrid model --- winter wheat spatial distribution --- logistic --- alternating direction method of multipliers --- hybrid structure convolutional neural networks --- geoherb --- predictive accuracy --- real-time precise point positioning --- spectral bands --- naïve Bayes


Book
Empirical Finance
Author:
Year: 2019 Publisher: MDPI - Multidisciplinary Digital Publishing Institute

Loading...
Export citation

Choose an application

Bookmark

Abstract

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.

Keywords

n/a --- short-term forecasting --- wavelet transform --- IPO --- volatility --- US dollar --- institutional investors’ shareholdings --- neural network --- financial market stress --- market microstructure --- text similarity --- TVP-VAR model --- Japanese yen --- convolutional neural networks --- global financial crisis --- deep neural network --- cross-correlation function --- boosting --- causality-in-variance --- flight to quality --- bagging --- earnings quality --- algorithmic trading --- stop loss --- statistical arbitrage --- ensemble learning --- liquidity risk premium --- gold return --- futures market --- take profit --- currency crisis --- spark spread --- city banks --- piecewise regression model --- financial and non-financial variables --- exports --- data mining --- latency --- crude oil futures prices forecasting --- random forests --- wholesale electricity --- SVM --- random forest --- bank credit --- deep learning --- Vietnam --- inertia --- MACD --- initial public offering --- text mining --- bankruptcy prediction --- exchange rate --- asset pricing model --- LSTM --- panel data model --- structural break --- credit risk --- housing and stock markets --- copula --- ARDL --- earnings manipulation --- machine learning --- natural gas --- housing price --- asymmetric dependence --- real estate development loans --- earnings management --- cointegration --- predictive accuracy --- robust regression --- quantile regression --- dependence structure --- housing loans --- price discovery --- utility of international currency --- ATR

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