Quantitative analysis has improved financial decision-making by applying mathematical and statistical models.
One of the constants in the progress of finance is to provide them with different analysis tools that improve decision-making. In this dimension, quantitative finance operates, applying mathematical and statistical models in its analyses. Although its initial focus was on making better investment decisions, its current applications go much further.
How did quantitative finance come about?
The roots of quantitative finance are in the Russian Andrei Markov mathematical models. His work on stochastic (random) models, known as Markov chains, is based on the probability of an event occurring related to the previous event.
The next step was taken by the American economist Harry Max Markowitz, who focused on investment. The Markowitz model seeks to optimize the composition of a bond or stock portfolio. In other words, it aims to form the most efficient portfolio, with the lowest risk, for different profitability objectives.
The three great promoters of quantitative finance are the sociologist Robert King Merton and the economists Fisher Black and Myron Scholes. Their Black-Scholes-Merton model, which earned them the Nobel Prize in 1997, was a real revolution in finance when it was applied to the value of derivative or hedging products, such as options.
Big Data and quantitative finance
The current boom in quantitative finance is related to the increase in the data we handle and the greater capacity for analysis. To achieve the best results from quantitative analysis, it is necessary to have the greatest (quantity) and best (quality) information. This entails a collection process and the screening and discarding of information that does not provide value.
In this process, the company may encounter two types of data with differentiated treatment :
- Quantitative information: This is structured data insofar as it can be tabulated directly in databases, relatable or not, and spreadsheets. With this, models can be made more quickly.
- Qualitative information: It is unstructured data but of great value that requires to be transformed to create a structure that facilitates its analysis.
All these data sources are the ones that allow finding patterns using mathematical models, algorithms, and other quantitative methods.
How is quantitative finance applied in companies?
- Credit risk analysis: estimates the probabilities of delay, non-payment, or insolvency of the different clients.
- Liquidity risk analysis: predicts the future problems of the company itself when making its payments to its suppliers.
- Operational risk analysis: very useful when auditing or reviewing processes and seeking to determine dangers and contingency plans for operations, technical equipment, and even personnel failures.
- Market risk analysis: studies how certain businesses or investments may be affected by relevant economic events such as the evolution of interest rates, inflation, and changes in commercial policies.
How does quantitative finance apply to credit insurance?
Quantitative finance largely explains the current operation of credit insurance. The leader in credit insurance has a modular system that takes into account more than 400 variables to analyze the credit risks of any company. This tool allows them to immediately respond to nearly 80% of their policyholders’ requests for coverage of commercial lines of credit. In this sense, the market share is vitally important: it means more and better information exchanged between the insurer and its thousands of policyholders on late payments or provisional insolvencies of customers, which feed the mathematical and statistical models.
In addition to determining risks, quantitative finance is also being applied intensively to analyze trends in financial markets using time series analysis. These continue to be a succession of quantitative observations of a phenomenon that are analyzed, looking for patterns or trends, essentially seasonal behaviors having multiple applications in the company.
The applications of quantitative finance are becoming broader and will continue to be so as data processing and the growth of Big Data grow. With all this, companies will have better tools to control risk and predict changes.