Quantitative equity investing refers to the use of mathematical and statistical models to make investment decisions and construct portfolios. It relies heavily on analytics, data, and financial modeling rather than fundamental research about specific companies. Some key techniques and findings in quantitative equity investing research include:
Factor Models
Many quantitative equity strategies are based on factor models that aim to capture risk premia associated with certain characteristics like value, momentum, quality, and low volatility. Fama-French three factor model and Carhart four factor model are seminal works establishing importance of factors.
Alpha Models
Alpha models try to predict stock returns using statistical arbitrage, machine learning, deep learning etc. They extract signals from a diverse range of datasets including fundamentals, prices, alternative data etc. Advanced techniques like deep learning and natural language processing are being applied.
Portfolio Construction
Portfolio construction techniques like risk-based portfolio optimization, hierarchical risk parity, minimum variance can translate factor and alpha signals into portfolios. Goal is to balance risk-return tradeoff and constraints. Techniques from modern portfolio theory are foundational.
Execution Strategies
Effective trade execution is critical to minimize impact costs, manage liquidity and slippage. Execution algorithms, smart order routing, and optimal trade scheduling are some key techniques to manage the trading process.
Backtesting Framework
Rigorous backtesting over long time periods and market environments is essential to validate strategy performance. Technologies like Pyfolio in Python enable standardization and automation of backtesting process.
In summary, quantitative equity investing encompasses a wide range of techniques from factor models, alpha models, portfolio construction, execution, and backtesting. Advanced statistical and computational methods are being applied to drive returns and manage risks.