Posted on : April 21st 2022
Financial institutions, bulge bracket investment banks, and investment management enterprises involved in the capital markets are adopting innovative digital technologies and techniques to manage many industry dynamics. For example, diverse regulatory regimes, evolving investor and wealth demographics, and cybersecurity threats.
These innovative digital technologies and techniques are causing an exponential increase in the volume and complexity of enterprise and alternative data. Consequently, quick, accurate, and efficient data acquisition, extraction, and transformation to directly consumable high-quality data are fundamental to data strategies for gaining actionable competitive insights.
Sourcing data has evolved from ingesting to aggregating feeds for the key participants in the capital markets. What is of essence today is getting a single view. Unified and coherent data feed enriched with accurate and updated data on tap empowers participants to perform advanced data visualization and analytics.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result-focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves. The way out is to strike the right balance between systematic and unsystematic risks and rewards by deriving real-time insights from unstructured and alternative data.
Although capital markets participants accord great importance to fundamental and technical indicators, they look further before deciding on trading, risk-taking, and financial asset holding. The information they rely on is alternative data as varied as:
The diverse sources from which they gather this information add another layer of criticality. The onus is on the capital markets participants to assess the information source's credibility. It affects their ability to incorporate information that provides the maximum opportunity.
Furthermore, as sources for information and investment research vary, the challenge of making a comprehensive analysis and informed investment decisions becomes more complex.
The practical implications of having massive amounts of unstructured data and a firehose of alternative data for the capital markets participants are twofold. One, they have to digest the enormous volume of information. Two, use it to respond to make investment decisions in real-time. In addition, valuable information within unstructured and alternative data, as shown in the bulleted list above, is under the radar and may go unrecognized.
For most financial institutions finding and processing data is a challenge. According to recent survey by insightsoftware, finance teams experience the following challenging:
Overcoming these challenges calls for an executable comprehensive data strategy and innovative solutions. Arguably, for most capital markets participants’, devising a strategy for unstructured and alternative data is new territory.
A comprehensive data strategy is a key to maximizing the value of unstructured and alternative data. The critical components of the strategy should be speed, quality, and efficiency, at an industrial scale.
For capital markets participants, a comprehensive data strategy enables the capturing and analyzing of the right unstructured and alternative datasets.
Speed – Using rough estimates, it has been determined that the ideal interval of trade for a typical U.S. stock is currently 0.2 to 0.9 seconds. ¹ Hence, the speed of acquiring data is vital because disparate sources generate a humungous volume of unstructured and alternative data. These have to be processed and delivered quickly to improve investment decisions. Even a fraction of a second delay can change market quality. ²
Quality – For financial services, investment, and securities trading, data quality ensures that data relating to clients, stock performance, transactions, and financial instruments or products, are standardized. Moreover, high-quality data can, for example, help explain how the participation of big players like pension funds in blue-chip stocks impacts capital markets.
Scale – Comprehensive datasets are available. However, what is required is the depth of that data that can be acquired from diverse sources. According to a survey by Greenwich Associates, 80% of the chief investment officers, portfolio managers, and traders at investment management firms who participated in a survey wanted greater access to alternative data sources. The scale of data is becoming broader. Increasingly institutional investors are looking at satellite images of parking lots, logistics data, and public data such as credit scores, payment history, and private company data for insights.
Automation/Efficiency – Investment and custodian banks and asset and wealth managers, for example, are moving towards automating the data acquisition, enrichment, and management process. It will minimize manual tasks, reduce effort and time, and realize efficiencies. Moreover, automation benefits spread across agility, accuracy, regulatory compliance, and customer satisfaction.
In short, if capital markets participants are thinking about transforming data into an asset, a strategy is essential. As mentioned earlier, a key element for success is creating a comprehensive data strategy focused on outcomes.
Many references and market data system components will require custom development, especially for training machine learning-based data acquisition and extraction algorithms. Straive brings proven development skills to any existing reference data project.
Data acquisition and extraction are only the first steps in providing quality data to downstream consumers. Straive's data quality and maintenance consultants can effectively manage any data pipeline's day-to-day data management activities cost-effectively.
From data investigation for possible erroneous attributes to the management of vendor data feeds when they are irregular, Straive’s consultants provide a wealth of knowledge to ensure that reference and market data are timely and accurate.
¹ Too Fast or Too Slow? Determining the Optimal Speed of Financial Markets. (n.d.). Retrieved April 21, 2022, from https://www.sec.gov/files/dera-wp-optimal-speed.pdf
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Alternative ESG data - Often associated with financial analysis, alternative data is a term that typically refers to externally sourced information about a particular company to gain additional business insights.
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