میثاق آمریکایی: چگونه قانون اساسی ملت ما را متحد کرد - و می‌تواند دوباره

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. "Quantitative trading of financial securities is a multi-billion dollar business employing thousands of portfolio managers and quantitative analysts ("quants") trained in mathematics, physics, or other "hard" sciences. The quants trade stocks and other instruments creating liquidity for investors and competing, as best they can, at finding and exploiting any mispricings. The result is highly efficient financial markets not immune to occasional events of crowding, bubbling, and liquidation panic. This book covers all the major parts of the quantitative trading process starting with sourcing financial data, learning future asset returns from historical data, generating and combining multiple forecasts, dealing with risk, building optimal portfolio of stocks subject to risk preferences and trading costs, and executing trades. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual events and thoughts "from the trenches", as observed by a quantitative portfolio manager, and even actual questions asked at countless quant interviews. The intended audience includes practicing quants who will encounter things both familiar and novel (such lesser known ML algorithms or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), and the general public interested in quantitative and algorithmic trading from a broad scientific, and occasionally ironic, standpoint"-- Provided by publisher **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