Michael Isichenko, является автором книги "Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage". В ней представлен систематизированный обзор количественной торговли акциями, или статистического арбитража. Эта книга научит вас получать финансовые данные, анализировать исторические данные о доходности активов, генерировать и объединять множественные прогнозы, управлять рисками, создавать портфель акций, оптимизированный с учетом рисков и торговых издержек, и осуществлять сделки.
Электронная Книга «Quantitative Portfolio Management» написана автором Michael Isichenko в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9781119821212
Описание книги от Michael Isichenko
Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage , distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.
