Machine Learning’s Role in the Hunt for Alpha

Identifying patterns in complex, noisy financial data

Financial practitioners never have enough clearly actionable data, said Orosco, head of alpha equity investments. “Markets are incredibly noisy, so extracting the signal from the noise is quite the challenge.”

Through dogged research and testing, however, a QEG project team and members of Vanguard’s “AI Garage”—a community of artificial intelligence (AI)/machine learning practitioners from across the company—have found that using ML consistently added value in simulated portfolios. Complex relationships among economic and financial market conditions and the performance of individual companies and their stocks were clarified—if only a bit.

To be sure, the introduction of machine learning to a portfolio management process is no guarantee of stronger results, and ML-based recommendations remain imperfect. “But we are a systematic manager,” said Orosco. “We diversify widely, and in our business, being right 55%–60% of the time over extended periods can add significant long-term value for investors.”

Integrating machine learning into an established investment process

QEG’s U.S. alpha team employs an ensemble model—really, a collection of dozens of models tracking tens of millions of underlying economic and financial market interactions. Overall, half the inputs come from the team’s traditional approach, refined over decades, and half from ML.

The ML-driven output doesn't rely on entirely new signals, however. Said Rodemer: “From the beginning we engrained fundamental reasoning into the model, an understanding of what adds value to a company and influences future returns—concepts like valuations, growth, and profitability—and embedded this intuition into a proprietary deep neural net architecture.” A key new element is ML’s continual interpretation of the macroeconomic environment, as context for company- and stock-specific changes.

While the PMs naturally remain responsible for all investment choices, they lean heavily on model signals to make virtually all buy, sell, and rebalancing decisions for three U.S. equity portfolios that are managed entirely by QEG—Vanguard Market Neutral Fund, Vanguard Strategic Equity Fund, and Vanguard Strategic Small-Cap Equity Fund. They also rely on the signals in co-managing their portions of Vanguard Explorer Fund and VVIF–Small Company Growth Portfolio.

Making sense of ML-based investment recommendations

Before integrating machine learning into the management of Vanguard fund assets, Orosco and Rodemer and their colleagues set a high bar: They had to be able to understand why the algorithms would recommend any given investment.

“We won’t trade on signals that we don’t understand,” said Rodemer. “Everything has to tie back to a fundamental rationale.”

That’s why the team built an “interpretability model” that gives PMs—as well as Vanguard’s independent portfolio review and risk-management teams—explanations of:

• The drivers of individual stock predictions.
• The aggregate-level interpretations of model behavior (such as the sensitivity of the signals to macroeconomic changes).
• The model health analytics (such as the volatility of scores and outliers in daily model changes), which help the team assess model inputs and outputs.

“We believe the foundations are solid, the experimentation was solid, and the improvements so far continue to be in line with expectations,” said Orosco. “So as long as that continues, we're going to be comfortable moving forward.”

Notes:

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Used with permission from The Vanguard Group, Inc.

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