An Alternative Investment Team Harnesses Textual Data Analytics to Find New Sources of Alpha

THE CLIENT: A Swiss-based multi-asset management firm with $95 billion in assets under management.

USERS: Alternative Style Premia Team

This Swiss-based investment management firm specializing in alternative investing sought to leverage natural language processing (NLP) to extract valuable insights from earnings call transcripts. Managed as an independent company, the firm has developed long-term partnerships with its clients largely due to its expertise in private markets, liquid alternatives, and multi-asset solutions. Being a principal investor in its own strategies, the company has become a leading global alternative investment specialist.

The investment firm manages various asset classes throughout international markets, including equities, fixed income, private assets, and liquid alternatives. The Alternative Style Premia team is a middle-size group consisting of portfolio managers and quantitative strategists responsible for creating and implementing rule-based investment strategies that exploit various economic, price-based, and fundamental factors driving the cross-section of asset returns. To boost the performance of their market-neutral equity strategies, the team decided to expand the spectrum of investment signals by adding numerical scores derived from quarterly earnings call transcripts. Pain Points

The investment team needs to make both tactical and strategic decisions. Its members saw the benefits of using natural language processing (NLP) but gathering and maintaining the information as well as developing algorithms would require an extensive amount of time. In addition, the team was concerned about coverage, quality, and reliable data delivery on a daily basis.
They wanted to outsource this task to a reputable provider that offered:

A comprehensive set of elaborated sentiment and behavioural metrics that would allow for generating low-correlated investment signals.

Machine-readable data enabling full integration into the existing strategy-building process on the trading platform and leveraging the information available on the transcript component level.

Integrated meta-data to set up sentiment monitoring on the aggregated level (e.g., sector, index, etc.)

The Solution

Solution engineers from S&P Global recommended enhancing the current strategy mix with numerical scores obtained from earnings call transcripts. As the investment firm has been using the CIQ Financial data for building and managing its equity strategies, the new dataset was integrated smoothly into the existing investment framework. The scores are delivered in a structured format facilitating an efficient strategy back test. The extensive meta-data allows for flexible score aggregation on the industry, geographical, and index levels. The scores are delivered within 90 minutes after the end of the earnings call which enables the investment team a timely reaction to any unexpected news.

• Members of the investment team saw various benefits from subscribing to the Textual

Data Analytics package, of which the most important are:

• Access to an extensive range of sentiment and behavioural scores for companies around the world, which are pre-tagged, structured, and organized.
• Flexibility of combining the TDA scores with other investment data by leveraging the meta-level tags included in the package.
• Operationally efficient enrichment of pre-existing strategies with new textual investment signals.
• Speedy, intraday update of scores allowing for a timely response to unexpected news.

• Easy access through a data feed.

• Hands-on technical and product support to address any issues as they arise.

In both domains, leveraging natural language processing (NLP) and textual data analytics can provide valuable insights and a competitive edge. Here’s how the case can be related to digital marketing:

• Data-Driven Decision-Making: In digital marketing, just as in investment, making informed decisions based on data is crucial. Marketers use data analytics to understand consumer behavior, market trends, and the performance of their campaigns. NLP can be employed to extract valuable insights from social media comments, customer reviews, and other textual data sources, helping marketers refine their strategies.

• Timely Reactions: The ability to react swiftly to market changes and customer feedback is vital in both investment and digital marketing. In the case of marketing, real-time sentiment analysis can help brands respond to customer comments, reviews, or trending topics on social media, allowing them to adapt their campaigns or customer engagement strategies quickly.

• Data Integration: Just as the investment team in the case sought to integrate NLP- derived insights with their existing data sources, digital marketers often combine textual data analytics with other data sets such as website analytics, customer demographics, and click-through rates. This integration enables a more comprehensive view of customer behavior.

• Operational Efficiency: The case highlighted the operational efficiency achieved by outsourcing textual data analytics. In digital marketing, companies can utilize third- party tools or services to gather and analyze textual data from various sources, reducing the time and resources required to build and maintain an in-house NLP system.

• Content and Campaign Optimization: Digital marketers can use textual data analytics to optimize their content and advertising campaigns. By analyzing customer sentiment and feedback, marketers can fine-tune their messaging and tailor it to specific audience segments, improving engagement and conversion rates.

• Customer Sentiment Analysis: Both investment teams and digital marketers can benefit from sentiment analysis. In marketing, sentiment analysis helps in understanding how customers perceive a brand, product, or campaign. Brands can proactively manage their online reputation by identifying and addressing negative sentiment.

• Competitive Advantage: Just as the investment team aimed to gain a competitive edge through NLP, digital marketers use textual data analytics to stay ahead of competitors. Understanding customer preferences and staying responsive to trends allows marketers to position their brands more effectively.

• Customer Feedback and Reviews: Both in investment and digital marketing, customer feedback and reviews play a pivotal role. NLP can help in analyzing and summarizing customer reviews, which can be invaluable for product development and service improvement. In digital marketing, this feedback can guide content creation and marketing strategy.

• Support and Data Access: The availability of support and ease of data access, as highlighted in the case, is equally important in digital marketing. Marketers need user-friendly tools and platforms that provide them with the necessary data insights and support when needed.

In summary, the use of textual data analytics and NLP techniques is not limited to investment; it can be an asset in digital marketing as well. Marketers can extract actionable insights from textual data sources to enhance customer engagement, optimize campaigns, and gain a competitive edge in the digital landscape. Just as the investment firm outsourced their NLP needs to a reputable provider, digital marketers can benefit from leveraging third-party services and tools to extract meaningful insights from textual data.

Source: An Alternative Investment Team Harnesses Textual Data Analytics to Find New Sources of Alpha (no date). Available at: investment-team-harnesses-textual-data-analytics-to-find-new-sources-of-alpha (Accessed:
16 October 2023).


1.1 Using relevant research elaborate on what is natural language processing. (15)

1.2 Examine how NLP can assist the organisation with digital marketing. (25)


Utilizing relevant marketing theory, choose a company and conduct a comprehensive analysis of its product line and product depth. Specifically, investigate how the breadth and depth of its product offerings empower the company to effectively compete in the marketplace.

Answers to Above Questions on Textual Data Analytics Case Study

Answer 1: Artificial intelligence is getting significant attention from people worldwide and natural language processing is one of the branches of artificial intelligence. The main focus of Natural Language Processing is on the interaction between computers and human languages. Natural Language Processing allows computers to understand, interpret and generate human language in a way that is highly relevant and meaningful to them.


Get completed answers to above questions on Textual Data Analytics as offered by the South African assignment writers of Student Life Saviour.

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