Akanomics provides data-driven revenue estimates through its IntraCast product, integrating big data with company fundamentals for improved investment timing. This service helps fundamental portfolio managers and analysts reduce risk by identifying companies likely to beat or miss consensus estimates. Systematic managers and risk managers utilize these real-time adjustments to enhance performance and assess portfolio downside risk from economic fluctuations.
Funding
Funding not disclosed
Founders
Product
Problem
Financial analysts often rely on lagging indicators and company guidance, which fail to capture rapidly changing global economic conditions, leading to inaccurate revenue forecasts and potential investment missteps. Traditional methods struggle to incorporate real-time data effectively, resulting in consensus estimates that can deviate significantly from actual earnings.
Solution
AKAnomics' IntraCast Nowcasting product leverages big data and AI to provide real-time revenue estimates for U.S. industrial companies, enabling analysts to identify firms likely to exceed or fall short of earnings expectations. By incorporating a continuous flow of global macro, industry, and company-specific data, IntraCast offers a data-driven approach to improve investment timing decisions. The system models over 250 company segments and utilizes more than 200 industry indexes to generate accurate and timely revenue predictions.
Target Audience
The primary users are fundamental analysts, systematic portfolio managers, and risk managers seeking to improve earnings predictions and assess portfolio risk in the U.S. industrial sector.
Features
- Weekly updated revenue estimates for over 130 U.S. industrial companies.
- Granular modeling of company segments to arrive at overall revenue estimates.
- Incorporation of over 7,500 macro, industry, and company data series from across the globe.
- Real-time industry indexes representing growth for key regional industries.
- Historical hit rate of over 65% for predicting whether companies will beat or miss consensus estimates.