by Ramesh Koovelimodham –
Machine learning (ML), a subset of artificial intelligence (AI) isn’t just for programming self-driving cars or sorting cat pictures. It’s entering the investment management space, we are now seeing its potential beginning to emerge.
From Siri and Alexa to Amazon and IBM’s Watson, computer programs driven by artificial intelligence draw on massive amounts of data to solve previously intractable problems. Machine learning gives computers the additional ability to learn without being explicitly programmed. This type of AI enables computers to change and learn when exposed to new data.
Eric Schmidt, Executive Chairman of Alphabet Inc. (Google’s parent company) speaking earlier this year at Viva Technology conference in Paris where he was a headline speaker, acknowledged that the fast pace of innovation had made many wary of change. But he emphasized that machine learning and artificial intelligence hold opportunities for a broad range of sectors, including farming, energy, fashion, and healthcare, even if they operated very differently to today.
A report released during the conference by McKinsey Global Institute1 highlighted the global growth of the artificial intelligence (AI) sector.
The technology behind machine learning is being propelled by major algorithmic innovations that allow machines to synthesize extremely large data sets and reveal patterns, trends, and associations that are relevant to prediction problems. The increasing ubiquity of inexpensive parallel computation is making this technology accessible to even lean startups, who are spurring the activity on disruptive business models.
The technology has already transformed many industries, from the medical to the automotive. However, its adoption in investment management so far has been limited. Except for a few leading hedge funds, the industry has failed to recognize machine learning’s potential to drive investment decisions.
Algorithms that continuously improve
ML automates the discovery of predictive algorithms that can continuously improve as they get access to more data. Recently, the focus has been on automating many of the tasks traditionally performed by data scientists, including data cleaning, model selection, data clustering, automatic feature generation and dimensionality reduction.
One technique, deep learning, has been responsible for many recent breakthroughs. Deep learning is enabling image recognition that is on par with human abilities and is significantly improving speech recognition and language translation; it is also permitting better story and ad targeting at places like Google and Facebook. Part of what makes deep learning so powerful is that it can organize and aggregate large unlabeled data sets into abstracted forms, which are more useful for prediction. The results have been stunning, both in speed and accuracy.
What does this mean for investment management?
Machine learning can transform the way investment strategies are administered by all types of managers. Even the most fundamental, non-quantitative managers will be generating ideas from data that originally was sourced and synthesized via ML. For example, deep learning’s ability to create structured data could be used to extract topic and sentiment from text sources such as earnings calls, SEC filings, and social media; or for the analysis of satellite imagery for parking lot or crop data; or to evaluate location data from mobile phones.
Machine learning will become increasingly important for asset management and most firms will be utilizing either machine learning tools or data within the next few years. Human involvement will still be critical for risk management and framework selection, but increasingly the strategy innovation process will be automated.
The prospect of better outcomes from deeper analysis of vast amounts of newly available data is enticing. Portfolio managers can turn to artificial intelligence and its subset, machine learning, to fine-tune their investment processes. Recent advances in the investment arena, such as robo-advisers provide asset allocation advice to individual investors are based on machine-learning processes.
Neither the technology nor the math behind artificial intelligence and machine learning is new. What’s new is the vast amount of data that’s available now.
AI/ML tools can be used to evaluate diverse data sets by:
- evaluating credit card transactions to see which stocks might benefit from higher consumer spending;
- tapping satellite imagery of retail parking lots to gauge which stores are getting — or not getting — a lot of foot traffic, or to track the pace of shipbuilding in yards around the world;
- skimming data from location services to predict where people are shopping, eating or recreating, and
- applying filters to Twitter to check whether whatever’s trending could result in a spike or decline in the popularity of certain goods and services.
In the next decade, we will realize that computers are better than humans at allocating capital. Those forward-looking investment management firms that will incorporate more artificial intelligence and big-data analysis into their processes will have a distinctive edge.
Fiduciary Duty, the value of ESG, and the impact of machine learning
Fiduciary duty is the golden rule against which much of this capital is deployed. Fiduciary obligation establishes a relationship of “trust and confidence” and rigorous standards of conduct. Some see this as the money managers equivalent of the Hippocratic oath.
It is a straightforward principle: asset managers (fiduciary agents or trustees), have the legal obligation to act in the best interests of asset owners (principals) or beneficiaries (in the case of pension funds).
The challenge, however, is that some institutional investors have interpreted their fiduciary duties narrowly, arguing that this was sufficient to meet their fiduciary duties. Investment decisions have often been derived only from traditional financial indicators to gain insight on an asset’s current and future performance. This was based on the belief that managing capital in a way that also considered environmental, social and governance issues (ESG), could compromise returns, and therefore was not in alignment with fiduciary duties.
This is rapidly changing for three reasons:
- ESG is quickly moving to the mainstream.
ESG factors represent risks and opportunities that may have financial effects on a financial asset’s performance, but may not be reflected in traditional financial data. ESG practices can help make a company less vulnerable to reputational, political and regulatory risks, leading to lower volatility of cash flows and stock performance. As a result, a lack of integration of ESG factors into investment processes may cause the miss-pricing of risk, and poor asset allocation decisions. Besides, regulators around the world are requiring investment managers to consider ESG factors in their regulatory reports. The drive for advancing ESG is often less about new regulation than it is about better regulation.
- Long term investing, such as for pension funds, require the analysis of ESG factors.
There is a growing consensus in the wake of the 2008 financial crisis that “seeking immediate high returns without accounting for long-term implications may lead to underperformance for long-term investors, and, by extension, underperformance for the economy as a whole,” as pointed out by a Harvard Kennedy School brief2.
- Machine learning usage is on the rise.
Despite the importance attached to long-term value creation, investors are also increasingly using big data, AI and machine learning to inform asset allocation and investment decision-making, and funds are beginning to incorporate machine learning into ESG analysis and measuring impact.
A clear indicator of interest in ESG is evidenced by a leading shareholder advisory firm, Institutional Shareholder Services (ISS)3, buying up environmental data and analytics companies because it says investors are increasingly concerned about the impact climate change will have on their portfolios.
“We see asset managers who care about things like board diversity and CEO pay alignment, which are more traditional, but increasingly we see some who care also about the potential impact of climate change, and whether companies are taking a sustainable approach to growing.”, according to Stephen Harvey, COO of ISS.
The firm is targeting asset owners, such as pension funds, investment managers and alternative investors, such as hedge funds.
The data can also help inform investment managers of environmental issues that might arise at a company’s annual general meeting. In some cases, for example, managers may even use a shareholder meeting to raise issues around a company’s environmental footprint, Harvey said.
There is reason to believe that machine learning driven funds might be enjoying success in the present because they have done what the first hedge funds were able to do; exploit inefficiencies. Once more and more firms start moving into the same space, the competitive advantage that firms such as Rebellion Research4 and Two Sigma5 enjoy today could be dramatically reduced or destroyed. It is not an implausible idea that several similar algorithms finding the best, undervalued investments, will quickly dry up all the lucrative investment opportunities, and all assets will be priced at fair value, much like the Efficient Market Hypothesis suggests. With the avalanche of data available, those that use AI and machine learning have the potential to drive greater returns in the next several years. Even the managers of the quantitative and AI based funds don’t discount the role that human talent plays in the management of successful funds. It seems like the in the world of finance, intuition still has a place on the stage, but sitting back and not engaging with this technology is no longer an option.
With the avalanche of data available, those that use AI and machine learning have the potential to drive greater returns in the next several years. Even the managers of the quantitative and AI based funds don’t discount the role that human talent plays in the management of successful funds. It seems like the in the world of finance, intuition still has a place on the stage, but sitting back and not engaging with this technology is no longer an option.
1. McKinsey (2021, June 15) Retrieved from https://www.mckinsey.com/capabilities/quantumblack/our-insights/how-artificial-intelligence-can-deliver-real-value-to-companies
2. Initiative for Responsible Investment https://iri.hks.harvard.edu/files/iri/files/tlf-note-on-long-term-investing.pdf
3. CNBC (2017, June 21) Retrieved from https://www.cnbc.com/2017/06/21/why-this-leading-shareholder-advisory-firm-is-now-studying-climate-change.html
4. Rebellion Research AI Asset Management https://www.rebellionresearch.com/
5. Two Sigma https://www.twosigma.com/