Quantitative analysis is increasingly used in the field of finance. Find out everything you need to know about the profession of “quant”: definition, history, advantages, training …
In finance, quantitative analysis uses mathematical and statistical analysis to determine the value of a financial asset such as a stock market share.
The quantitative trading analysts, also called “quants” , use a wide variety of data to develop trading algorithms and computer models. The data can for example come from historical investments or the stock market.
With the information generated by these computer models, investors can analyze investment opportunities and develop a promising trading strategy. This strategy typically includes specific information on entry or exit points, estimated risks, and expected returns.
The ultimate goal of quantitative financial analysis is to use quantifiable metrics and statistics to assist investors and help them make profitable decisions .
What is quantitative analysis?
Quantitative analysis is the process of collecting and evaluating measurable and verifiable data such as revenue, market share, or wages with the aim of understanding the behavior and performance of a business.
Rather than relying on their intuition and experience as in the past, business leaders and other decision makers can rely on data .
The main task of a quantitative analyst is to present a given hypothetical situation in the form of numerical values. Quantitative analysis helps to assess performance and make predictions .
Quantitative analysis techniques
There are three main techniques of quantitative analysis . First of all, regression analysis is a technique used by business leaders as well as statisticians and economists.
It consists of using statistical equations to predict or estimate the impact of one variable on another. For example, it can be used to determine how interest rates impact consumer behavior on an investment asset.
It is also widely used to measure the effects of education and work experience on annual employee earnings. In businesses, regression analysis can determine the impact of marketing spend on profits.
The second popular technique of quantitative analysis is linear programming . It allows resources to be allocated efficiently, determining how to achieve the optimal allocation. This method is also used to determine how the company can optimize its profits and reduce its costs according to the constraints.
Finally, Data Mining combines computer programming and statistical methods. Faced with the explosion in the volume of data available, this technique is increasingly popular. It is mainly used to evaluate very large datasets in order to find patterns or correlations.
History of quantitative analysis
The origin of quantitative analysis is generally attributed to the article “Portfolio Selection” published by Harry Markowitz in March 1952 in the Journal of Finance. In this article, Markowitz introduced the Modern Portfolio Theory (MPT) explaining to investors how to build a diversified portfolio of assets to maximize returns at different levels of risk.
To quantify diversification, Markowitz used mathematics and is often considered one of the first to apply mathematical models to investing.
Another important element in the history of quantitative analysis concerns the work of Robert Merton on mathematical methods for derived tariffs . These two precursors laid the foundations for quantitative analysis.
Quantitative analysis vs qualitative analysis
The job of a “quant” is different from that of a traditional qualitative investment analyst . He doesn’t visit companies, meet with managers, and study their products to identify opportunities.
He is generally not interested in the qualitative aspects of a company or its products and services in making investment decisions. In reality, the quant relies solely on mathematics.
The quants typically have a strong scientific background and a degree in statistics or mathematics. They use their computer and programming knowledge to develop customized trading systems capable of automating trading processes.
These programs are based on relatively simple things like critical financial ratios, or more complex calculations like the valuation of discounted cash flows.
The progress in the field of IT have enabled the development of quantitative analysis. More complex algorithms can be calculated very quickly, allowing the automation of trading strategies. A real boom took place during the famous dotcom bubble.
Despite a dismal failure during the Great Depression , quantitative strategies are widely used today, especially for high-frequency trading relying entirely on math for decision-making.
A data-driven method
Thanks to advances in IT, it is now possible to process huge volumes of data very quickly . This has led to increasingly complex quantitative trading strategies, as traders seek to identify patterns, model them, and use them to predict price changes.
To implement these strategies, the quants are based on publicly available data . Identifying these patterns allows them to set up automatic levers for the sale or purchase of collateral.
Take the example of a strategy based on trading volume patterns , with a correlation between trading volume and prices. The quant can decide to automatically sell a stock when it reaches the price at which the trading volume is about to drop.
Similar strategies can be based on a company’s financial results , forecasts, or a wide variety of factors. The hard-core quants therefore base their decisions solely on the numbers and patterns they have identified.
Quantitative analysis can also help reduce risk, by identifying investments with the highest level of return relative to their level of risk. For two investments with a similar level of return, the quant will choose the least risky. The aim is to avoid taking unnecessary risks in relation to the target level of return.
Benefits and risks of quantitative analysis
Quantitative analysis has advantages, but also drawbacks . First of all, it allows a cold and objective approach to investment decisions. Only patterns and numbers are taken into account for buying and selling, eliminating the emotional part often involved in trading decisions.
In addition, this strategy helps to reduce costs. The computers do all the work , and it is not necessary to hire a large team of analysts and portfolio managers. Also, unlike qualitative analysts, quants do not need to travel to inspect companies as they just analyze data.
That being said, the data can lie . Quantitative analysis involves exploring large volumes of data, and a problem with the quality of that data can have a heavy impact on the results.
Even a trading pattern that seems to work can suddenly turn out to be unsuccessful. There is no miracle recipe for risk-free investing , even based on mathematics and data science.
The external factors such as the stock market crash in 2008 , can also ruin quantitative strategies very suddenly changing patterns. In addition, the data does not always reveal all the details such as an internal scandal or a corporate restructuring.
Note also that a strategy loses in effectiveness as it is used by investors. The same is true if a large number of investors try to profit from a pattern…
How to become a quantitative analyst?
The job of quantitative analyst is very profitable , and guarantees many job opportunities in the long term. To practice this profession, however, you must acquire solid technical skills.
In order to achieve this, you can opt for DataScientest training . Our training in Data Science professions allows you to learn how to use all the tools and techniques of data science.
Through the Data Analyst training, you will become an expert in Python programming , Machine Learning, DataViz, databases, Business Intelligence and of course data analysis.
All our courses adopt a Blended Learning approach , and can be carried out in Continuing Education or in BootCamp. At the end of the program, learners receive a diploma certified by the University of the Sorbonne and 93% of our alumni have found employment immediately.
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