Applications
Useful Quantitative Analysis to help you make more objective and robust decisions.
Our services are based on the development of reliable quantitative models and tools that provide objective metrics for decision-making. We don't promise miracles, but rather a decision-making process grounded in what the data can reveal and a thorough analysis of the context.
Examples of services:
Protect margins: By analyzing historical price data, market trends, and volatility patterns, quantitative methods enable companies to identify and quantify risks associated with fluctuations in commodity prices, currencies, or interest rates. These insights allow for the development of robust hedging strategies, such as the use of futures, options, or swaps, tailored to mitigate specific exposures. Additionally, quantitative research helps optimize the timing and scale of hedge implementation, ensuring cost efficiency while maintaining alignment with the organization's financial goals. This systematic approach not only safeguards profit margins but also enhances decision-making by reducing uncertainty in dynamic market environments..
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Optimization under uncertainties: In an environment where project performance depends on variables with a high level of uncertainty, particularly commodity prices, exchange rates, business cycle phase, and credit, it is crucial that decisions are made by analyzing multiple scenarios and representative probability distributions. Additionally, it is essential that the variables are properly related to mitigate risks in the most cost-effective and efficient manner possible.
M&A processes: In a merger and acquisition process, it is essential to have a robust quantitative risk analysis so that the price paid for a business is sufficiently attractive with a high probability, considering the uncertainties involved. We use our uncertainty projection tools to support M&A processes and a robust Risk Valuation.
Quantitative risks analysis: Robust quantitative modeling of the company's cash flow and its sensitivity to critical performance factors. The objective is to identify weaknesses and allocate resources in the most intelligent way possible, considering all activity risks.​​
Practical Case
Optimization under uncertainties (BIASFree)
Consider a project scope decision among 5 evaluation options (P1, P2, P3, P4, and P5). The graph on the side presents the NPV (Net Present Value) of the project options, including the median and the P10 - P90 intervals (thus, with an 80% probability of occurrence). Note that P4 is the project with the highest median, but the value of P10 (10% of the realizations have a lower NPV than it) is negative. Assuming, for example, an approval criterion that requires a positive P10 NPV, the best option is not P4 but P3, which has the highest median among the options that meet the criterion (at most 10% of the realizations can have a negative NPV).
An analysis of this kind is an example of Quantitative Analysis to support decision-making under uncertainties. Making decisions under uncertainties is a complex task, but by relying on data and robust methodologies that accurately represent the probability distributions involved, the process becomes much more objective, simple, and secure, leading to more accurate decisions in a portfolio view.
Contextual Learning (CONTEXT), generative models, and stochastic models are applied for constructing probability distribution estimates for relevant financial metrics for the project under evaluation. The three models have been combined in the tool to leverage the strengths of each.
Benefits of a reliable probabilistic analysis
We always emphasize that no one is capable of predicting the future. However, it is possible to model probability distributions considering the information available at the time and scenarios of conditioning variables.
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Enhanced risk mitigation through the incorporation of multiple potential scenarios, each assigned more accurate probability estimates.
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Better economic evaluation of projects, avoiding the implementation of low-profitability projects.
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​Better margin protection, especially for businesses heavily impacted by exchange rates and commodity prices.
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Possibility of reducing the cost of capital by lowering corporate risk and enhancing the robustness of projects, leading to improved risk perception by investors and financial institutions.
Example of Probabilistic Cash Flow
