Source: Securities Lending Times | January 8, 2019 Author: Stephanie Lo and Jian Wu Stephanie Lo and Jian Wu of State Street discuss the intersection of machine learning applications and equity markets in portfolio management, exploring the potential market segmentation that will arise Artificial intelligence (AI) and machine learning (ML) are poised to change the way financial institutions operate.
Yet the transformation will not impact all market players equally.
In this piece, we examine the intersection of ML applications and equity markets in portfolio management and explore the potential market segmentation that will arise due to different adoption strategies of ML driven by each players’ mandates and goals.
The potential use cases for ML in portfolio management are broad, and in our view, likely segmented across different types of portfolio managers, with some representative considerations shown in figure one.
Factors driving resistance to machine learning in institutional investors For most institutional investors, ML may be used in the research stages of portfolio construction, but are unlikely to enter the decision-making function.
This is driven by several factors: Transparency: Industry surveys have found that respondents view communication/transparency as one of the top considerations for hedge fund allocation decisions.
Overfitting: With fast-changing financial markets, overfitting is often a concern, particularly for investors that do not develop the underlying models themselves.
Lack of historical data: An estimated 90 percent of data in existence today—much of it alternative data—was created in the past two years; without crisis-relevant data, ML might not behave in a suitable way if another Great Recession were to happen.
Indeed, many researchers cannot wait for full validation, as it could require decades of data to develop confidence that an algorithm is robust in a statistical sense.
Several factors, such as regime shifts, higher-order interactions, and changing sources of data mean that there can be a leap of faith for researchers leveraging these methods.
Algorithm aversion: People tend to prefer human forecasters over statistical algorithms, and more quickly lose confidence in algorithms than human forecasters after seeing them make the same mistake.
Interestingly, giving individuals some control of the algorithm’s forecasts—even if extremely restricted—helps people overcome this “algorithm aversion”.
Mismatch of talent and needs: As quantitative methods have evolved quickly, many institutional investors may not have the necessary talent in-house to build or maintain ML models.
Case study State Street Global Advisors has introduced three exchange-traded funds (ETFs) built with proprietary index methodologies developed by Kensho Technologies.
The ETFs track companies that provide AI-relevant products and services; natural language processing (NLP) is used to parse through regulatory filings to understand company exposure to AI technologies.
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