Investors spot key AI applications in portfolio management

AI can add value in almost every part of the investment process, providing support in information gathering and analysis, sometimes from non-traditional and hard-to-access data sources for portfolio management purposes, according to a new CFA Institute Research and Policy Center report.

The report, titled Pensions in the Age of Artificial Intelligence, combed through selected academic literature and outlined the potential for AI enhancements up and down the pension value chain. It highlights to asset owners how AI can be used to modernise their operations as well as problems that may arise.

The report highlighted AI and machine learning (ML) as useful tools to increase the accuracy of manager selection and review for asset owners, using a case study from Japan’s Government Pension Investment Fund (GPIF).

GPIF, the world’s largest asset owner, uses a variety of external managers whose performance has been uneven over the past decade. It relied on a small number of internal human experts to select and evaluate active managers, but in 2017, GPIF trialled a multi-stage program to better identify and assess manager styles using techniques like deep learning.

“…An AI system would allow GPIF to more thoroughly, accurately, and efficiently evaluate fund manager investment style, providing quantitative metrics for what was previously available only as qualitative fund management descriptions,” the CFA Institute report said.

“These technologies demonstrate the potential for GPIF to access the benefits of a wider array of asset managers and funds by relying on internal data-driven analyses to judge fund manager performance, rather than relying on qualitative descriptions of performance or policies.

“This can serve to eliminate bias against fund managers with a history of working with GPIF and larger firms who are better placed to market their products.”

Information gathering and analysis is one of the most common ways asset owners are using AI, even from unconventional data sources when using programs like natural language processing (NLP) and “increase the range of information available”, the report said.

For example, NLP can screen earnings call transcripts and identify changes in company positioning, while sentiment analysis can be used to predict market and investor reactions to company events.

Similarly, AI can be used to scan and formulate ESG insights by collecting and analysing large amounts of structured and unstructured data from portfolio companies. The availability and consistency of company ESG data is by far the biggest challenge for pension funds that want to exercise stewardship, and a human-driven approach with AI assistance can greatly improve the efficiency of company analysis. [See also Can artificial intelligence (AI) help stewardship resourcing?]

Connecting the dots

Ultimately, the most valuable advantage for risk and liability aware fiduciary investors is to know what is on the horizon, and AI has proven use cases in both market and credit risk management.

The report explained that since “market behaviour is nonlinear and emergent from dynamic system-wide interactions”, AI is well-placed to identify correlations and offer predictions of market crashes. ML technologies such as random forests and artificial neural networks can effectively identify signs of recession.

AI and ML could also help improve the accuracy of credit risk assessment, the report said.

But the use of AI is not without risks. Just like humans, CFA Institute highlighted that AI models have biases and variances. “Model bias refers to discrepancies between model predictions and actual values, and variance refers to the model’s generalisability and sensitivity to variations in the training data,” it said.

“Ideally, models would have low bias and low variance, but this scenario is not always possible because there is generally a tradeoff between bias and variance.”

Strong bias suggests “underfitting”, meaning the model is not complex enough for the training data, while strong variance suggests “overfitting”, meaning the model is too complex.

“Because market data are often irregular over time… and the statistical properties of such data also change over time, it can be challenging to generate models that appropriately fit these data variations and produce accurate predictions,” CFA Institute said in the report.

“As with other applications of AI and ML, it is important to subject AI-generated outputs to appropriate testing and supervision.”