Forecasting variables such as interest rates, exchange rates, and commodity prices is an inherently complex task that requires the use of probabilistic workflows. The scenarios generated must be internally consistent, ensuring that both short and long-term correlations are carefully preserved. Additionally, stress testing plays a critical role in evaluating potential correlation breakdowns under extreme market conditions. Our algorithms are designed to meet these objectives, providing robust, reliable, and actionable insights to support your decision-making process. To ensure the highest quality of information, all forecasts are rigorously backtested against available data, empowering our clients to make well-informed business decisions.

Forecast

Consistent Short and Long term forecasts

Contextual Learning: Integrating AI with Fundamentals

Artificial Intelligence has the capability to process vast amounts of data, making it significantly more effective than humans in inferring how the current context influences the probability distributions of future events. With this in mind, we developed CONTEXT, an AI system designed to analyze extensive historical data and project probability distributions for key variables that impact the financial performance of your project—an essential tool for effective decision-making. To minimize the risk of AI model hallucination and enhance explainability, we integrate the AI model with fundamental models that ensure consistency with the underlying concepts governing the dynamics of the corresponding variables.

Backtesting example

Forecast models combine advanced statistical techniques to jointly forecast multiple variables critical for understanding market dynamics. This approach enables more robust analyses compared to solely examining discrete scenarios, providing a comprehensive and integrated view of potential market outcomes.

Context learning has been the key to the success of top language models (ChatGPT, LLaMa, BERT, etc). This concept works well for explainable hybrid models in applications where parameter modification is context-dependent. Inspired by this, we created CONTEXT to be a generalist contextual learning model that can be refined for various applications.

Trustworthy Artificial Intelligence

Trust in AI is essential to ensure that decision-making leads to ethical, fair, and secure actions. "Black-box" models may be useful for less critical tasks (such as suggesting an illustration, a text, or performing a translation), but for investment and financial decisions, it is necessary to have confidence that the predictions make sense. Additionally, good documentation is desirable.

Trustworthy AI is a set of principles and practices aimed at ensuring the reliability, transparency, accountability, and ethics of AI.

How to ensure Trustworthy AI?

Ethical development
Quality data
Continuous evaluation
Governance and regulation

Explainability allows the user to make decisions using AI and other methods, incorporating their expertise and particularities. Our goal is to assist, not replace, the human analyst.