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Quantitative analysis: what is it and how do I become a quant?

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Quantitative analysis: what is it and how do I become a quant?

Quantitative analysis is increasingly used in finance. Find out all you need to know about the "quant" profession: definition, history, benefits, training...

In finance, quantitative analysis exploits mathematical and statistical analysis to determine the value of a financial asset such as a stock on the stock market.

Quantitative trading analysts, also known as “quants”, use a wide variety of data to develop trading algorithms and computer models. The data may, for example, come from historical investments or the stock market.

Using the information generated by these computer models, investors can analyze investment opportunities and develop a promising trading strategy. This strategy usually includes specific information on entry or exit points, estimated risks and expected returns.

The ultimate aim of quantitative financial analysis is to use quantifiable metrics and statistics to assist investors in making profitable decisions.

What is quantitative analysis?

Quantitative analysis is the process of collecting and evaluating measurable, verifiable data such as revenues, market share or salaries in order to understand a company’s behavior and performance.

Rather than relying on 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 quantitative analysis techniques. Firstly, regression analysis is a technique used by business managers, statisticians and economists alike.

It involves 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 employees’ annual earnings. In business, regression analysis is used to determine the impact of marketing expenditure on profits.

The second popular quantitative analysis technique is linear programming. It enables resources to be allocated efficiently, by determining how to achieve the optimum distribution. This method is also used to determine how the company can optimize its profits and reduce its costs in line with constraints.

Finally, data mining combines computer programming and statistical methods. Given the explosion in the volume of data available, this technique is becoming increasingly popular. It is mainly used to evaluate very large data sets 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 Modern Portfolio Theory (MPT), explaining to investors how to construct 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 Robert Merton’s work on mathematical methods for derivative pricing. These two pioneers laid the foundations of quantitative analysis.

Quantitative vs. qualitative analysis

The work of a quant is different from that of a traditional qualitative investment analyst. They don’t visit companies, meet managers or study products to identify opportunities.

He or she is not generally interested in the qualitative aspects of a company or its products and services when making investment decisions. In fact, the quant relies solely on mathematics.

Quants generally have a solid scientific background and a degree in statistics or mathematics. They use their knowledge of computers and programming to develop customized trading systems capable of automating trading processes.

These programs are based on relatively simple elements such as crucial financial ratios, or more complex calculations such as discounted cash flow valuation.

Advances in computer technology have enabled the evolution of quantitative analysis. More complex algorithms can be calculated very quickly, enabling trading strategies to be automated. A real boom took place during the famous dotcom bubble.

Despite a bitter failure during the Great Depression, quantitative strategies are still widely used today, particularly in high-frequency trading, which relies entirely on mathematics for decision-making.

A data-driven method

Thanks to advances in computing, it is now possible to process immense 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, quants rely on publicly available data. Identifying these patterns enables them to set up automatic levers for selling or buying collateral.

Let’s 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 fall.

Similar strategies can be based on a company’s financial results, forecasts or a wide variety of other factors. 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 similar levels of return, the quant will choose the less risky one. The aim is to avoid taking unnecessary risks in relation to the targeted level of return.

Advantages and risks of quantitative analysis

Quantitative analysis has both advantages and disadvantages. First and foremost, it provides a cold, objective approach to investment decisions. Only patterns and numbers are taken into account when buying and selling, eliminating the emotional element often involved in trading decisions.

What’s more, this strategy cuts costs. Computers do all the work, so there’s no need to recruit a large team of analysts and portfolio managers. What’s more, unlike qualitative analysts, quants don’t need to travel to inspect companies, as they simply analyze the data.

That said, data can lie. Quantitative analysis involves the exploration of vast volumes of data, and a problem with the quality of that data can have a heavy impact on results.

Even a trading pattern that seems to work can suddenly prove unsuccessful. There is no magic formula for risk-free investing, even when based on mathematics and data science.

External factors, such as the 2008 stock market crash, can also ruin quantitative strategies by suddenly altering patterns. What’s more, data doesn’t always reveal all the details, such as an internal scandal or corporate restructuring.

It’s also worth noting that a strategy loses effectiveness the more investors use it. The same applies if a large number of investors try to profit from a pattern…

How do I get to be a quantitative analyst?

The job of quantitative analyst is a highly remunerative one, guaranteeing many long-term employment opportunities. However, to become an analyst, you need to acquire solid technical skills.

To achieve this, you can opt for DataScientest training courses. Our Data Science training courses enable you to learn how to handle all the tools and techniques of data science.

With our Data Analyst training, you’ll 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 taken as Continuing Education or as a BootCamp. At the end of the program, learners receive a diploma certified by Sorbonne University, and 93% of our alumni find immediate employment.

Our training courses can be financed by different funding options. Don’t wait any longer, and discover our Data Scientest training courses today!

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