We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream.
However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies.
I will touch upon some key themes in this blog, but will go into more detail in a series to follow.
Quantamental investing
Active equity managers have now underperformed passive managers for 10 years in a row. Finding alpha is therefore an imperative, even though this is proving harder through traditional methods of finding alpha.
For many portfolio managers, the answer may lie in quantamental investing – the combination of traditional fundamental techniques and quantitative techniques.
Increasingly, we are seeing fundamental teams leveraging fundamental and quantitative datasets to correlate and predict equity performance, draw out factors driving equity and sector performance, hedge positions more effectively, minimize volatility and risk, and enhance portfolio yields.
This growth of quantitative techniques in fundamental investing is also being driven by the ability of today’s technology and market data platforms to offer and integrate many of these capabilities in an easy-to-use and seamless manner.
In our case, Refinitiv’s Eikon Data API, which provides access to various Refinitiv fundamental datasets, our Starmine quantitative models, our Eikon Portfolio analytics capabilities, QA Point backtesting tool with Eikon integration are all mechanisms that enable fundamental investment managers to incorporate quantamental techniques into their investment process. More on these and others in upcoming blogs.
Watch: Eikon & MSCI risk analytics
Data science and AI
The application of data science and various AI techniques, in particular machine learning, to the investment process has now become a major priority for asset managers.
The availability and the need to analyze large fundamental and alternative datasets, requires the use of data science and machine learning. The traditional techniques of collation and human analyst interpretation simply prove to be ineffective, or do not scale. Only with the use of AI and machine learning techniques can these datasets be analyzed, classified, ranked, correlations and anomalies understood, predictive models built and ideas generated.
Refinitiv’s Eikon leverages machine learning in various areas, including our News monitor, Screeners, Advanced Research/Events Search, Search & Discover, Social Media monitor, forthcoming Research and Event Discovery apps and other core applications. Machine learning is behind what helps us surface the most relevant news, research, and various analytics for the analyst or portfolio manager.
Sustainable investing
2020 is without a doubt the year of ESG, and in particular ESG for investing.
Davos, Blackrock’s climate change letter to company CEOs, and the steady growth of ESG focused funds have all ensured this. Sustainable investment decisions require good data, and where disclosure and data exist, we see clients adding ESG criteria to their stock selection and portfolio management processes.
We expect ESG data to improve, assessment techniques to solidify, and new aggregate fund level scoring and factor model-based approaches to gain acceptance.
Refinitiv’s Eikon has one of the most comprehensive ESG data sets and connected workflows, to help analysts and PMs analyze companies and industries.
In addition, Eikon’s portfolio management tools allow portfolio ESG characteristics to be assessed, and portfolios to also be optimized, for aggregate ESG scores with the help of the MSCI Barra optimizer.
Talent
Ultimately, it is the combination of data, technology and human talent that will lead to successful incorporation of these techniques into an asset manager’s strategies.
Understanding and leveraging all this data and these techniques requires a team with skillsets spanning data engineering, data science, quantitative techniques, portfolio and risk management techniques, and traditional financial analysis.
In conclusion, data and technology have now created a formidable challenge for active managers: how to leverage data, various quantitative and data science techniques, and technologies to uncover alpha for their investors.
This will require asset managers to develop a deeper understanding of these capabilities and, in parallel, upskill their teams to fully take advantage of them.
