The Causal Intelligence Model (CIM) is a cutting-edge technology developed to enable anyone to easily simulate complex economic causal inferences in real-time by analyzing economic data over time.

CIM, which offers server-based simulation of economic causal inferences in real-time, was developed through trial and error using machine learning technology. A prototype version with stock prices as the output was released in November 2022 leading to a public beta of the StockCode product on the web in March 2023.

The approach for economic causal inferences used within CIM leveraged many insights from "Natural Experiments Help Answer Important Questions for Society" developed by David Card, Joshua Angrist, and Guido Imbens, who won the Nobel Prize in Economics for their research in 2021.

CIM achieves real-time performance while balancing accuracy and explainability through the time-series analysis of economic data using multiple linear and nonlinear models. For example, as of December 22, 2023 when this article was written, our stock price forecasts achieved a high average accuracy of 88.7% for the top 100 U.S. companies by market capitalization for the prior one-year period. (Note: Please refer to the FAQ for the formula used to calculate forecast accuracy.)

Using our CIM technology, StockCode offers real-time simulation of the causal relationships between economic indicators and stock prices through a simple user interface screen.

The output of CIM is not limited to stock prices. In the future, by adding more input parameters, we believe that the time will come when simulations of economic causal inferences performed in real-time can provide a diverse range of outputs.

We expect that this technology will enable us to quickly identify economic causal relationships and correlations through time-series data analysis, mainly in the field of economic.