Automated and Personalized Asset Portfolio Optimization Integrating ESG and Financial Data on Amazon FinSpace

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As discussed in our previous post, Environmental, Social, and Governance (ESG) data has become an essential component for evaluating a company’s risk and performance alongside traditional financial and alternative data. Institutional investors and asset managers are increasingly incorporating ESG data into their investment decisions as sustainable investing transitions from a niche practice to mainstream acceptance. Notably, 40% of the $110 trillion in global professionally managed financial assets now consider ESG factors.

However, incorporating ESG data into investment strategies is not straightforward. The processes of sourcing, cleaning, processing, integrating, and analyzing ESG data can be labor-intensive, particularly for firms lacking the appropriate technology. This challenge is underscored by the fact that 59% of asset managers and institutional investors view data as a significant barrier to ESG integration.

A specific challenge lies in the absence of standardization, which continues to contribute to the proliferation of ESG data, affecting everything from reported metrics to data structure. Furthermore, the evolving regulatory landscape around ESG means that factors deemed material today may not hold the same status in the future.

For asset managers, aggregating various data sources is crucial in overcoming standardization issues. We have observed customers utilizing anywhere from a few to over 40 data sources that encompass thousands of entities.

Greenwashing is another pervasive challenge in the industry. Global financial regulators are responding to this issue by implementing regulations that necessitate more detailed ESG product reporting and labeling. This is evident in Europe with the Sustainable Finance Disclosure Regulation (SFDR) and the Sustainable Finance Roadmap, as well as in the U.S. as announced by the SEC.

AWS is assisting financial services clients as they adapt to this shifting landscape. With the cloud, it has never been easier to consolidate multiple ESG data sources, regardless of their format (both structured and unstructured), for comprehensive analysis.

Two AWS Services for ESG Data Challenges

• Amazon FinSpace: A fully-managed data management and analytics service that simplifies the storage, cataloging, preparation, and analysis of financial industry data at scale. This significantly decreases the time financial services customers spend finding, accessing, and analyzing diverse types of financial and ESG data.
• AWS Data Exchange: This service enables AWS customers to easily find, subscribe to, and utilize third-party data in the AWS Cloud. Subscribers can access thousands of products from verified data providers.

Amazon FinSpace and AWS Data Exchange are tailored to streamline and expedite financial evaluations and decisions based on data analysis. Their user-friendly capabilities, combined with the diverse data products available through AWS Data Exchange, enhance the process.

In this post, we will illustrate how to utilize AWS Data Exchange and FinSpace for a specific case: automatically identifying optimized asset portfolios based on user preferences (such as minimum and maximum percentage allocations per stock in a portfolio) while integrating various ESG datasets from AWS Data Exchange with financial data from third-party sources.

Dataset Description

In this discussion, we will use the following datasets:

• ESG Safeguard – Transcripts Sentiment Dataset: This dataset, provided by Amenity Analytics via AWS Data Exchange, features industrial-scale natural language processing applied to earnings call transcripts, enabling real-time scoring on ESG issues at the company level. It offers scoring for 12,000 companies globally, facilitating tracking of portfolio and company exposures.
• RepRisk ESG Data Feed: Dow Jones Industrial Average Company Universe: This customizable data export includes RepRisk ESG metrics and analytics that can be easily integrated into internal or external systems. It systematically screens and monitors ESG and business conduct issues related to clients and investments.

Additionally, we will utilize historical stock prices from the Yahoo Finance dataset for demonstration purposes, though this can be substituted with any market dataset of your choice.

To execute this post, you can also use trial versions of the Amenity Analytics and RepRisk datasets; however, note that the smaller datasets may yield less significant results.

Architecture and Workflow

1. We begin by importing the two ESG datasets mentioned above from AWS Data Exchange into the FinSpace data catalog.
2. Next, we access the FinSpace environment, loading the two ESG datasets from the catalog and the Yahoo Finance dataset. Since the ESG scores from the two providers are calculated using different criteria, we must normalize these scores for integration.

Personalization Option: You can define arbitrary thresholds for normalization. Feel free to adjust the provided values to suit your needs or to experiment with different scenarios.

3. After obtaining the normalized ESG scores and historical stock prices, we combine these datasets into a single working dataset.
4. For each stock, we now have the percentage return and two ESG scores. We apply a specific business logic to determine the final ESG score that will be utilized in the portfolio optimization process.

Personalization Option: Users might want to apply their unique methodology here. For instance, if a user places greater emphasis on Corporate Governance scores from a particular data provider, they can select that provider’s ‘G’ score while sourcing ‘E’ and ‘S’ from others. Another user may opt to average different ESG scores with varying weights. The logic provided here can be modified to fit individual preferences or to explore alternative scenarios.

In conclusion, understanding and utilizing ESG data effectively can significantly enhance investment strategies. For further insights into engagement strategies, check out this resource from SHRM, which is an authority on the topic. Also, for those interested in onboarding processes, this Reddit post serves as an excellent resource.

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