Big Data Federation, Inc. (BDF), a San Jose, CA-based financial technology company specializing in AI data analytics for institutional investors, recently announced the launch of its groundbreaking Earnings Guidance Forecasts.
The Launch of BDF’s Earnings Guidance Forecasts
BDF’s newly launched Earnings Guidance Forecasts are daily-updated estimates of public companies’ forward guidance, which will be available through the company’s flagship OdinUltra platform. These new metrics will deliver critical intelligence that often move stock prices beyond the headline earnings numbers themselves.
The Earnings Guidance Forecasts dataset leverage BDF’s proprietary machine learning algorithms, analyzing thousands of variables across traditional and alternative data sources to generate predictions that traditional financial analysis typically don’t provide. With four distinct proprietary models to forecast key metrics of each company’s upcoming guidance, the platform offers institutional investors unique insight into one of the market’s most significant but unpredictable catalysts.
Pouya Taaghol, PhD, Founder and CEO of Big Data Federation explained the rationale for their new product enhancement saying, “What moves stocks on earnings day isn’t just whether a company beats or misses analyst expectations-it’s often the forward guidance that truly determines market reaction. Our new forecasting capability represents a quantum leap in earnings intelligence by providing investors with reliable signals about what management teams are likely to communicate about their outlook.”
The Earnings Guidance Forecasts data are available immediately to Odin Ultra subscribers, with tiered access options for institutional clients.
Profile on Big Data Federation (BDF)
At Integrity Research Associates, we provide clients with a comprehensive overview of research and data resources that can help them throughout their investment decision-making process. One data source which could be useful if it is important for you to keep track on publicly available data and what this data means for stock prices is Big Data Federation. The following profile provides more background on the company.
1. Provide general background on your firm. When were you founded, who founded the firm, and what was the primary business problem you were trying to solve?
Big Data Federation, Inc. (BDF) was founded in 2015 from a simple yet powerful idea: if you could count the cars in a retailer’s parking lot, could you use it to predict its revenue? BDF’s CEO, Pouya Taaghol, PhD, a wireless technology veteran and former CTO of Intel Mobile Wireless and Cisco Smart Home, saw the potential in turning factual data into fundamental insights on stock prices and earnings.
What started as a curiosity about data’s predictive power has since evolved into a cutting-edge financial AI firm, transforming how investors forecast fundamentals.
2. What type of data, services and related technology does your firm provide? Outline the unique data elements an asset manager can receive from you that are not generally available from others?
BDF provides a powerful, all-encompassing suite of fundamental forecasts and AI tools tailored for asset managers and advisors. Beyond just delivering cutting-edge fundamental forecast data, BDF provides an advanced modeling tool that enables seamless back testing of these forecasts. Given BDF’s asset management subsidiary, the firm knows firsthand how important unbiased data evaluation is to determine whether it delivers alpha.
3. Explain the basic process you use to collect and/or create the data you produce? Is any of the data you collect considered PII? Do you have all the necessary consents to collect and store the data you sell?
BDF employs over 1400+ high-quality data sources that are publicly available, including SEC filings, company fundamentals, industry metrics, a large number of government agencies, and industry trade associations. BDF only uses factual data and avoids sample/panel data, which usually have issues like biases from limited demographic and geographic exposure that arise in sampling errors, manipulation, and extrapolation. None of the data BDF collects is considered PII. Even when deemed ‘good’ and ‘relevant’, a single data source alone is purely one dimensional, which can produce large errors.
Data acquisition is initiated either on an opportunity or need basis. BDF follows a structured data due diligence process, which includes the following steps:
a. Verifying the authority of the source and how factual the data may be:
+ BDF does not engage with panel data such as email receipts or credit card data.
+ BDF also exclude data collected using Personally Identifiable Information (PII).
b. Examining data ownership and licensing rights:
+ BDF ensures the source owns the data and has the right to share it with us.
+ Licensing terms and costs are thoroughly reviewed.
c. Receiving and evaluating sample data:
+ BDF analyzes the sample data and its applicability.
d. Applying ML models:
+ Machine learning models are used to test if the data improves forecasts for targeted industries or companies.
e. Decision-making:
+ If BDF proceeds with a data source, the licensing agreement is finalized, and the engineering and data science teams integrate the data into BDF’s process.
+ If BDF decides not to proceed, they inform the vendor in writing and delete all sample data from all systems.
4. How much history can a client obtain from you? How frequently is this data updated?
BDF’s point-in-time fundamental forecasts date back to 01-01-2016. Their fundamental forecasts are updated daily, as new data becomes available.
5. Describe at least one “case study” of where your firm’s data, services or technology has proven to be predictive.
BDF’s forecasts are industry agnostic, covering over 1400 US publicly traded tickers. As a case study, BDF’s revenue beat precision — when both their forecasts and actual revenue surpass consensus — typically hits around 90%. In other words, when BDF predicts a revenue beat, it is highly likely the company will deliver. However, whether the stock moves up or down on earnings day is another story.
6. Who is your firm’s target market in the financial services vertical? What other types of consumers purchase your data?
BDF’s primary target for the fundamental forecasts data in the financial services vertical are hedge funds. Quant funds typically access the data feed for seamless integration into their models. While discretionary and fundamental funds tend to prefer BDF’s OdinUltra portal, which provides deeper insights into the drivers behind the forecasts, based on the tickers’ ecosystem.
On the services side, BDF’s modeling tool is perfect for smaller hedge funds that need quantitative support for strategy back testing. For wealth managers, it can help personalize client portfolios and plan for various macro scenarios. Lastly, professional retail investors can use it for idea generation, portfolio construction, and seamless execution and monitoring.
7. Who are some of your firm’s chief data competitors in the financial services market?
BDF believes that panel data should be avoided in quantitative fundamental forecasting. As a result, the firm’s input data spans a wide range, avoiding the sampling biases often found in demographic or geographic-focused data. As an asset manager, BDF also understands the importance of point-in-time information, and as a result they do not revise their forecasts. Most importantly, BDF’s machines model thousands of data points to deliver forecasts for key fundamentals, rather than simply acting as a proxy.
8. What makes your firm different from other firms providing similar types of data, services, & technology?
What sets BDF apart is their deep understanding of client pain points, thanks to their background managing their asset management subsidiary. BDF’s offerings are comprehensive, featuring both forecast data and a powerful modeling tool. BDF’s fundamental forecasts are driven by robust data and scientific modeling, ensuring an independent and unbiased perspective. The modeling tool covers a wide range of assets, including stocks, ETFs, options, cryptos, and hundreds of thousands of indicators. Additionally, BDF mitigates bias by incorporating point-in-time constituents and factors.
9. Who are a few cornerstone clients in the financial services market that use you? How do they generally use your platform?
BDF does not disclose the identities of their clients. BDF only recently began selling their enhanced forecast data and just launched the Earnings Guidance Forecast dataset. As a result, the firm is now actively launching go-to-market (GTM) campaigns both digitally and through their sales team.
10. What is your firm’s commercial model? What is the price range for your service?
BDF offers subscription models to access their fundamental forecasts data, tailored to meet the needs of different clients.
Portal Access: $50K for up to 5 seats, $10K for each additional seat. Clients get access to OdinUltra.
Data Feed Access: Starting at $100K+
11. What are a few of your firm’s next major targets/milestones?
A few of BDF’s next major targets and milestones include:
a. Forecasts Offering
+ Expand geographic coverage into Asia and Europe
b. Modeling Offering
+ Gen AI Chat interface will be available in the Summer of 2025
+ Partner with additional distributors
12. What are a few interesting facts about your firm?
BDF is as close to institutional quality as you can get, with their background as an asset manager themselves. The BDF team is based in Silicon Valley, outside of the Wall Street bubble, bringing a fresh perspective. They have a diverse team of mathematicians, data scientists, industry experts, economists, and engineers, who are outsiders to finance, offering unique, innovative solutions.
13. Contact information
info@bigdatafed.com