Augmented Investment Management

Our approach: Transforming information into alpha signals

Successful active management, in our view, relies on the premise that investors can gain an informational advantage over other market participants to generate excess returns. Systematic investors codify information sources into predictive investment signals that serve as building blocks in an investment strategy. For example, we can apply text analysis algorithms to analysts reports to gain insight into whether analysts have a positive or negative view.

In an increasingly data-rich world, there are now potentially hundreds or even thousands of signals available to investors. This raises a new challenge for systematic investors, which is how to optimally combine investment signals.

Advances in machine learning offer powerful tools to help address the challenges faced by investment managers in designing alpha models. Starting in 2014, we set out to design a system that can train machine learning models for alpha forecasts. We named the system Augmented Investment Management (“AIM”). AIM is a system for building systematic alpha models from a large collection of signals.

Our framework: modular machine learning for portfolio managers

AIM relies on two key ingredients. The first is a comprehensive signal library with substantial historical data available for model training. The library includes a wide range of signals from different sources, such as financial statements, market indicators, news and analyst reports, and an array of alternative data sources. Figure 1 illustrates the growth in the number of stock-selection signals developed by our team over the past 40 years.

The second ingredient is a high-fidelity, high-performance portfolio optimizer and performance simulator. The portfolio optimizer is used to transform forecasts into portfolio positions by solving a constrained optimization problem accounting for portfolio risk. The performance simulator then evaluates portfolio holdings over a historical backtest period, and model parameters are adjusted with the goal of maximizing a specific performance metric. The simulator accounts for transaction costs, borrow costs, and fund constraints to produce evaluation results that approximate realized portfolio performance with high fidelity.

At its core, AIM is designed as a modular machine learning pipeline that can be configured and customized by portfolio managers. AIM allows managers to create models that are sophisticated enough to capture complex signal interactions yet general enough to seek to perform well on unseen data, balancing model capacity with versatility.

Customizing alpha models

A key benefit of machine learning systems is the low marginal cost of designing custom alpha models. Thus, portfolio managers can use AIM to design regime-aware models that are trained to be more resilient during negative regimes.

A simple and intuitive approach to training resilient models is to overstate the likelihood of negative regimes in the historical data used to train the model. This is akin to training the model on an alternate history where the prevalence of negative states is significantly higher. As a result, the model learns to find investment patterns that perform well during negative regimes.

These methods are also used to help safeguard against the pitfall of over-reliance on a dominant market regime in the training data. Through a training regime that emphasizes a higher frequency of negative states, the model explores diverse investment patterns for robust performance across market variations. This technique insulates AIM models against over-fitting to a particular market regime.

Ongoing research directions

Signals can lose efficacy over time as a source of data becomes commoditized or underlying mechanisms become widely understood by market participants. BlackRock’s Systematic investment team has been working to continuously improve AIM by researching new approaches to address this challenge. One research direction the team is exploring focuses on developing adaptive learning algorithms. These algorithms attempt to identify and adjust to changes in the distribution of financial data over time. Another promising research direction involves the incorporation of domain-specific knowledge and economic indicators into the model to better capture the underlying dynamics of financial markets. By incorporating information related to market cycles, macroeconomic factors, and other relevant data, the model may be better equipped to adapt to changes in the relationships between different signals.

As the proliferation of new data results in the multiplication of innovative new investment signals, AIM seeks to help systematic investors automate ans better optimize the process of signal combination. The calibration and refinement of alpha models by expert portfolio managers brings this system to life - enabling models to be designed and optimized for navigating a wide range of investment objectives and market conditions.

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Risk warnings

Capital at risk. The value of investments and the income from them can fall as well as rise and are not guaranteed. Investors may not get back the amount originally invested.

Past performance is not a reliable indicator of current or future results and should not be the sole factor of consideration when selecting a product or strategy.

Changes in the rates of exchange between currencies may cause the value of investments to diminish or increase. Fluctuation may be particularly marked in the case of a higher volatility product or strategy and the value of an investment may fall suddenly and substantially. Levels and basis of taxation may change from time to time.

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Customizing Alpha Models

Predicting inflection points in global financial markets or the outcomes of macroeconomic shocks is a very difficult task, and portfolio managers are often guided by judgement and intuition when assessing regime changes and repositioning portfolios. By combining this human expertise with machine learning, we can enhance our approach for preparing for shifts in market regimes. A key benefit of machine learning systems is the low marginal cost of designing custom alpha models. Thus, portfolio managers can use AIM to design regime-aware models that are trained to be more resilient during negative regimes. This approach is analogous to earthquake management, where, due to the lack of reliable earthquake forecasting models, earthquake-resilient structures and contingency plans are developed to handle seismic events.

A simple and intuitive approach to training resilient models is to overstate the likelihood of negative regimes in the historical data used to train the model. This is akin to training the model on an alternate history where the prevalence of negative states (e.g., bear markets) is significantly higher. As a result, the model learns that to do well on its training data, it needs to find investment patterns that perform well during negative regimes.

Besides facilitating customization, these methods also help to safeguard against the pitfall of over-reliance on a dominant market regime in the training data. Through a training regime that emphasizes a higher frequency of negative states, the model explores diverse investment patterns for robust performance across market variations. This technique insulates AIM models against over-fitting to a particular market regime, leading to more stable and resilient portfolio strategies.

As a concrete example, we used AIM to train an alpha model that is more resilient to drawdowns of a broad equity index, in this case the MSCI World index. Starting with index returns, we resampled periods where the index return was consistently negative to overstate the likelihood of drawdowns, and trained a model on resampled historical data. Figure 4 summarizes the hypothetical historical performance of this defensive model relative to a baseline model trained on the same historical data without resampling. We find that the defensive model picks up many of the characteristics that we would expect a defensive strategy to have, including better risk-adjusted performance during market drawdowns relative to the benchmark, and lower overall drawdowns, particularly around periods of market stress like the Great Financial Crisis (“GFC”). This more resilient performance comes at a cost. Compared with the baseline model, the defensive model incurs a 20% performance haircut overall. We further found that the model intuitively discovers investment patterns that are consistent with a defensive strategy, by avoiding leveraged, high growth, high volatility names, and realizing an overall negative exposure to these risk factors (Figure 5).

Figure 4: The defensive model demonstrates better risk-adjusted performance during historical drawdowns, but weaker performance than the baseline model overall

Net cumulative returns of a baseline and defensive model

Figure 5: The defensive model reflects investment patterns that are consistent with expectations of a defensive strategy

Baseline and defensive model risk exposures

IMPORTANT: The projections or other information generated by the tool regarding the likelihood of various investment outcomes are hypothetical in nature, do not reflect actual investment results and are not guarantees of future results. Results may vary with each use and over time.

What’s Next?

Putting it all together

AIM seeks to allow portfolio managers to harness the power of machine learning to design alpha models. Managers can use their expertise to calibrate, customize, and scale alpha models—relying on AIM to automate the complex task of optimally combining and considering non-linear relationships across a large set of individual signals.

Portfolio managers configure the component pieces of AIM models to fit a specific function or investment mandate using their domain knowledge and historical training data. In the context of the previous example, a defensive alpha model may be used in a strategy with a conservative risk allocation or to limit portfolio drawdowns during periods of market stress. Portfolio managers can tailor models to capture opportunities as they arise, for example to target returns that aren’t explained by traditional risk factors. The combination of human expertise and machine learning is what we believe makes AIM such a powerful tool for constructing systematic portfolios while balancing risk and return considerations.

Ongoing Research Directions

A fundamental assumption common to most machine learning models is that data used for training the model is drawn from the same distribution as live data used for inference. When the two distributions diverge, a phenomenon referred to as non-stationarity or covariate shift, the ability of a machine learning model to generalize from training data to live data is hampered. Non-stationarity is common in financial data and manifests in different forms.

First, signals can lose efficacy over time as sources of data become commoditized or underlying mechanisms become well understood by market participants. Figure 6 illustrates the cumulative returns of a calendar seasonality signal over the past decade, demonstrating a sharp turning point in the predictive ability of the signal. This is a particularly acute example of a change in the predictive power of a signal. More often, signals tend to stagnate or flatline and their predictive power decays over time.

This can also occur as financial markets move with business cycles, causing fluctuations in market dynamics and shifts in the relationships between different signals. These cyclical patterns can create changes in the overall market structure, which in turn impacts the effectiveness of various signals. For example, during periods of economic expansion, certain signals related to growth-oriented companies or industries may exhibit stronger predictive power, whereas during periods of contraction, signals related to defensive or counter-cyclical sectors might become more valuable.

Figure 6: Historical returns of a seasonality signal

BlackRock’s Systematic Investment Team has been working to continuously improve AIM by researching new approaches to address this challenge. One research direction we’re exploring focuses on developing adaptive learning algorithms. These algorithms attempt to identify and adjust to changes in the distribution of financial data over time. By continuously monitoring the performance of the model and the characteristics of the input data, adaptive learning algorithms could detect shifts in the underlying distribution and update the model parameters accordingly. Techniques such as online learning, transfer learning, and domain adaptation could be explored in this context to develop more robust models that can better generalize from training data to live data.

Another promising research direction involves the incorporation of domain-specific knowledge and economic indicators into the model to better capture the underlying dynamics of financial markets. By incorporating information related to market cycles, macroeconomic factors, and other relevant data, the model may be better equipped to adapt to changes in the relationships between different signals.

Conclusion

As a proliferation of new data results in a multiplication of innovative new investment signals, AIM seeks to help systematic investors automate and better optimize the process of signal combination. AIM provides a customizable foundation for the creation of alpha models. The calibration and refinement of alpha models by expert portfolio managers brings this system to life—enabling models to be designed and optimized for navigating a wide range of investment objectives and market conditions.