The rise of ML in the investment industry

The investment industry has evolved dramatically over the last several decades and continues to do so amid increased competition, technological advances, and a challenging economic environment. This section reviews key trends that have shaped the overall investment environment overall and the context for algorithmic trading and the use of ML more specifically.

The trends that have propelled algorithmic trading and ML to current prominence include: – Changes in the market microstructure, such as the spread of electronic trading and the integration of markets across asset classes and geographies – The development of investment strategies framed in terms of risk-factor exposure, as opposed to asset classes – The revolutions in computing power, data generation and management, and statistical methods, including breakthroughs in deep learning – The outperformance of the pioneers in algorithmic trading relative to human, discretionary investors

In addition, the financial crises of 2001 and 2008 have affected how investors approach diversification and risk management. One outcome is the rise to low-cost passive investment vehicles in the form of exchange-traded funds (ETFs). Amid low yields and low volatility following the 2008 crisis that triggered large-scale asset purchases by leading central banks, cost-conscious investors shifted over $3.5 trillion from actively managed mutual funds into passively managed ETFs.

Competitive pressure is also reflected in lower hedge fund fees that dropped from the traditional 2 percent annual management fee and 20 percent take of profits to an average of 1.48 percent and 17.4 percent, respectively, in 2017.