November 1, 2023
We explore the popularity of investable indexes and the use of NLP and Machine Learning in building successful index products.
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Investable indexes have become a major force in the financial industry, attracting substantial investments from both retail and institutional investors. We delve into the reasons behind the growing popularity of investable indexes and explore how they are constructed, the current trends shaping the landscape, and the integration of alternative data, particularly driven by Natural Language Processing (NLP) and Machine Learning, in building successful index products.
The financial world has witnessed an intense debate between active and passive investment managers. Critics argue that passive indexing merely replicates benchmark performance, making it a less attractive investment strategy. However, the data paints a different picture. According to the U.S. Year-End 2022 report, only 11% of U.S. active portfolio managers outperformed their benchmark over the past 10 years. This stark contrast has contributed to the increasing popularity of investable indexes.
Another compelling reason for the rise of investable indexes is the cost-saving benefit they offer. The same S&P Dow Jones report revealed that passive investing has saved investors nearly $300 billion in cumulative management fees over the last two decades. This substantial reduction in expenses has further incentivized investors to opt for passive index-based strategies.
Investable indexes are meticulously constructed, adhering to several key principles:
These indexes follow a predefined set of rules outlined in an index's rulebook, ensuring transparency and consistency.
Investable indexes are built systematically, relying on data-driven processes rather than subjective decision-making.
These indexes provide clear insights into their composition and methodology, allowing investors to make informed decisions.
The components of these indexes are chosen to be easily accessible and tradable by investors.
To achieve these objectives, investable indexes require a calculation agent and an index administrator to oversee their maintenance and operation.
Several trends are shaping the landscape and driving innovation in investable indexes:
These indexes focus on trends and narratives, providing exposure to specific emerging themes and megatrends such as artificial intelligence, sustainability, or supply chain;
To navigate business cycles effectively, some indexes shift assets between different equity sectors in response to regime shifts in the business cycle. Equity sector rotation can help portfolio managers to time the equity risk premia across defensive or growth sectors
This approach to passive investing takes a holistic view of an investor's portfolio, considering both asset classes and global macro indicators. Global asset allocation can help portfolio managers to diversify their portfolios while timing risk premia across asset classes.
This approach to passive investing seeks to augment traditional and crowded factors, such as value, size, growth or momentum, with alternative sources of information. Enhanced factor investing can help portfolio managers to time the risk premia across investment styles.
With growing regulatory constraints in Europe, indexes incorporating Environmental, Social, and Governance (ESG) criteria have gained traction.
Incorporating alternative data into passive indexing has become a critical source of product differentiation in a competitive market. Structured product specialists seek unique datasets to underpin indexes, aiming for alpha generation and captivating storytelling.
Traditional sources of alpha are becoming overused, complicating alpha discovery with increasing speed and complexity. Hence, the growing need for alternative data. Data points such as news sentiment, credit card or transaction data, website traffic data, job data, satellite imagery, and other unconventional financial indicators are becoming invaluable for constructing innovative indexes.
NLP is a Language AI technique that enables the collection, analysis, and categorization of textual data, at scale. Amid this shift towards alternative data, the role of NLP has emerged as a pivotal factor in improving the accuracy, efficiency, and transparency of index products.
Powered by news sentiment, the Credit Suisse RavenPack AI Sentiment Index is based on a simple concept - create a sector rotation strategy across US large caps that will rely on earnings news sentiment data, which has been proven to have predictive power over long periods of time.
NLP informs the MSCI Future Mobility Index by scraping data from diverse sources, including news, social media, and financial reports. It dissects sentiment, offering insights into market reactions. Named Entity Recognition identifies key players, while topic modeling unveils overarching themes. This approach ensures companies' roles in future mobility technologies are accurately categorized and weighted, reflecting their influence in this dynamic sector.
The J.P. Morgan QUEST Cloud Computing Index utilizes RavenPack’s NLP technology to construct its portfolio of companies associated with the cloud computing industry. The Index aims to offer exposure to companies strongly connected to the cloud computing sector based on news coverage prominence and recency, with weights determined using an optimization model.
Investable indexes have transformed the investment landscape by offering cost-effective, transparent, and innovative strategies for both retail and institutional investors. As the financial industry continues to evolve, the integration of alternative data and NLP-driven insights is likely to further fuel growth and differentiation in investable indexes.
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