Читать книгу Quantitative Portfolio Management - Michael Isichenko - Страница 6

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 securities creating liquidity for investors and competing, as best they can, at finding and exploiting any mispricings with their systematic data-driven trading algorithms. The result is highly efficient financial markets, which nonetheless are not immune to events of crowding, bubbling, occasional liquidation panic, and “cobra effects” including the high-frequency trading (HFT) arms race. This book attempts a systematic description of the quant trading process by covering all its major parts including sourcing financial data, “learning” future asset returns from historical data, generating and combining forecasts, diversification and its limitations, risk and leverage management, building optimal portfolios of stocks subject to risk preferences and trading costs, and executing trades. The book highlights the difficulties of financial forecasting due to quantitative competition, the curse of dimensionality, and the propensity to overfitting. Some of the topics included in the book have not been previously discussed in the literature. The exposition seeks a balance between financial insight, mathematical ideas of statistical and machine learning, practical computational aspects, actual stories and thoughts “from the trenches,” as observed by a physicist turned a quant, and even tough or funny questions asked at countless quant interviews. The intended audience includes practicing quants, who will encounter things both familiar and novel (such as lesser-known ML algorithms, combining multiple alphas, or multi-period portfolio optimization), students and scientists thinking of joining the quant workforce (and wondering if it's worth it), financial regulators (mindful of the unintended cobra effects they may create), investors (trying to understand their risk-reward tradeoff), and the general public interested in quantitative and algorithmic trading from a broad scientific, social, and occasionally ironic standpoint.