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Ridge regression (Statistics)

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Mathematical statistics --- Estimation theory --- Ridge regression (Statistics) --- 519.233.5 --- Regression, Ridge (Statistics) --- Multicollinearity --- Regression analysis --- Estimating techniques --- Least squares --- Stochastic processes --- Correlation analysis. Regression analysis --- Estimation theory. --- Ridge regression (Statistics). --- 519.233.5 Correlation analysis. Regression analysis

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.

Capital assets pricing model. --- Machine learning --- Finance --- Economic aspects. --- Mathematical models. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Capital asset pricing model --- CAPM (Capital assets pricing model) --- Pricing model, Capital assets --- Capital --- Investments --- Mathematical models --- Advances in Financial Learning. --- Bayesian estimation. --- Bayesian regression. --- Igor Halperin. --- Machine Learning in Finance. --- Marcos Lopez de Prado. --- Matthew Dixon. --- Paul Bilokon. --- Supervised learning. --- asset prices. --- cross-section of stock returns. --- data-driven methods of tuning. --- elastic-net estimator. --- factor models. --- firm fundamentals. --- high-dimensional prediction. --- market efficiency. --- mean-variance optimization framework. --- neural networks. --- out-of-sample performance. --- regularization. --- return predictability. --- ridge regression. --- risk premia estimation. --- trees and random forests. --- Machine learning. --- Financial applications.

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Mathematical finance plays a vital role in many fields within finance and provides the theories and tools that have been widely used in all areas of finance. Knowledge of mathematics, probability, and statistics is essential to develop finance theories and test their validity through the analysis of empirical, real-world data. For example, mathematics, probability, and statistics could help to develop pricing models for financial assets such as equities, bonds, currencies, and derivative securities.

Coins, banknotes, medals, seals (numismatics) --- cluster analysis --- equity index networks --- machine learning --- copulas --- dependence structures --- quotient of random variables --- density functions --- distribution functions --- multi-factor model --- risk factors --- OLS and ridge regression model --- python --- chi-square test --- quantile --- VaR --- quadrangle --- CVaR --- conditional value-at-risk --- expected shortfall --- ES --- superquantile --- deviation --- risk --- error --- regret --- minimization --- CVaR estimation --- regression --- linear regression --- linear programming --- portfolio safeguard --- PSG --- equity option pricing --- factor models --- stochastic volatility --- jumps --- mathematics --- probability --- statistics --- finance --- applications --- investment home bias (IHB) --- bivariate first-degree stochastic dominance (BFSD) --- keeping up with the Joneses (KUJ) --- correlation loving (CL) --- return spillover --- volatility spillover --- optimal weights --- hedge ratios --- US financial crisis --- Chinese stock market crash --- stock price prediction --- auto-regressive integrated moving average --- artificial neural network --- stochastic process-geometric Brownian motion --- financial models --- firm performance --- causality tests --- leverage --- long-term debt --- capital structure --- shock spillover

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Modern financial management is largely about risk management, which is increasingly data-driven. The problem is how to extract information from the data overload. It is here that advanced statistical and machine learning techniques can help. Accordingly, finance, statistics, and data analytics go hand in hand. The purpose of this book is to bring the state-of-art research in these three areas to the fore and especially research that juxtaposes these three.

Index parameter --- estimation --- wrapped stable --- Hill estimator --- characteristic function-based estimator --- asymptotic --- efficiency --- GARCH model --- HARCH model --- PHARCH model --- Griddy-Gibs --- Euro-Dollar --- safe-haven assets --- gold price --- Swiss Franc exchange rate --- oil price --- generalized Birnbaum–Saunders distributions --- ACD models --- Box-Cox transformation --- high-frequency financial data --- goodness-of-fit --- banking competition --- credit risk --- NPLs --- Theil index --- convergence analysis --- interest rates --- yeld curve --- no-arbitrage --- bonds --- B-splines --- time series --- multifractal processes --- fractal scaling --- heavy tails --- long range dependence --- financial models --- Bitcoin --- capital asset pricing model --- estimation of systematic risk --- tests of mean-variance efficiency --- t-distribution --- generalized method of moments --- multifactor asset pricing model --- Lerner index --- stochastic frontiers --- shrinkage estimator --- seemingly unrelated regression model --- multicollinearity --- ridge regression --- financial incentives --- public service motivation --- job performance --- job satisfaction --- intention to leave