Product Overview:

Background SEC analytics empowers companies to make informed investment decisions by efficiently analyzing financial data and conducting fundamental analysis. This is made possible by the EDGAR website, which automates the collection, validation, indexing, acceptance, and forwarding of documents that companies and others are required by law to file with the SEC. Through this system, firms can access comprehensive and reliable information about companies' financial statements, enabling them to make well-informed decisions with greater confidence and accuracy.

SEC’s EDGAR presents vast amounts of data. Intelligent reporting, and real-time updates ultimately drive the quality of investment decisions. Investors' primary interest lies in:

• 10-K report - which covers the annual performance of the publicly traded company helps investors make important decisions about their investments

• 10-Q -shows quarterly changes in the company financials provides investors with information to compare and evaluate the outlook of a stock’s performance

• Form 13F - quarterly report by institutional investment managers with at least $100mn in AUM that enables small investors to use the data to guide their own investment strategies.

• Form 3 – filed by a company insider to disclose ownership helps investors gain information as to insider’s views of the prospect and performance of the company

• Form ADV – submitted by professional investment advisor provides transparent evident of the asset mix of the firm to potential and current clients

The analysis for generating insights can be carried out on any form from SEC apart from the ones named above.

Digital Alpha’s analytics platform is:

• Built to extract relevant information from SEC forms or reports using smart extraction techniques, Natural Language Processing and Machine Learning

• Parse reports or forms that are in multiple formats like text, html, XBRL etc into structured format for analysis

• Organize the data as per requirement and

• Deliver it via API or directly ingest into the business applications

The platform is trained on financial market datasets and can be customized to comprehend and extract the right data points.

Features

• Democratize data for all stakeholders

• Harness intelligent insights from alternative data sources

• Establish a common data foundation for a unified and accurate version of truth

• Utilize a single platform across multiple functions including risk, treasury, finance, and compliance

• Enable support for external analytics engines, applications, and models from diverse sources

Benefits

• Automate extraction of relevant data, save up to 70% in costs:

• Automate identification and extraction of relevant data to reduce manual labor and increase efficiency.

• Improve decision-making with real-time access to relevant information.

• Empower analysts with NLP to generate insights from structured and unstructured data.

• Use NLP to analyze unstructured data and extract insights from social media, news articles and other sources.

• Provide tools to extract insights from both structured and unstructured data.

• Leverage NLP for risk analysis and better investment decisions.

• Use NLP to identify market trends, competitive forces and potential risks to improve investment decisions.

• Evaluate potential impact on investment portfolios and identify potential risks and threats.

Common Use Cases

• Manage credit risk with ratings and public financial data

• Track early warning signals of credit risk

• Build Systematic Trading Strategies

• Customer and Competitor Intelligence

Deliverables -

• One Click Solution where the Tech Stack is fully automated with AWS CDK

• A framework that allows the Data Engineers to write ETL code using any language and deploy them as Lambda Functions within the App

• Providing CI/CD pipelines for Infrastructure and ETL code using AWS Code Pipeline

Add-ons:

1. Distribution

2. Data visualizations with AWS Quicksights, PowerBI, Streamlit

3. APIs with AWS API Gateway-Integrate with other platforms like Snowflake, DataBricks

4. Integration with Analytics/ML platforms

5. Managed Service