For a number of years now, we’ve increasingly been hearing about quantitative finance, also known as quantum finance, which has steadily been gaining prominence in the financial investment sector.
While quantum finance first made an appearance during the second half of the 20th century, some models and key theories date from the early 1800s.
That being said, the first real cornerstone of quantum finance was laid by the French mathematician Louis Bachelier in 1900 with the publication of his doctoral thesis “The Theory of Speculation” which outlines an evaluation model for share options.
Midway through the 20th century we saw the publication of important works by two economists regarded as the fathers of quantitative finance: Harry Markowitz and Robert Merton.
The first presented his doctoral thesis in 1952, covering the modern theory of diversification-based portfolios, while the second one gained acclaim for his pioneering formula to value derivatives.
Technological development over the past thirty years has played an important role in the evolution of quantitative finance and has led to a rise in popularity of quantitative analysis.
The aim of quantitative analysis is to understand what causes market fluctuations, to predict them and to anticipate them in one way or another.
Quantitative analysis starts from a large number of variables, which each are assigned a numerical value on the basis of which quantitative analysts create a mathematical replica of reality.
Let’s concentrate on a number of the main benefits of a quantitative analysis for a minute.
Quantitative analyses make it possible to harmonise the evaluation criteria, to devise a scientific framework and to streamline internal procedures.
This system of automated buying and selling can be executed in a coherent manner, without human interference, feelings, preconceptions or subjective speculation.
On the basis of historical data and scientific hypotheses, the algorithms can continuously record the minimum turnover of securities, ever before it comes to serious financial losses.
More than two decades on, quantitative investing, which does differ significantly from the “traditional” investment methods, has become more commonplace.
One approach is based on shares being selected by investment specialists who assess companies by analysing their balance sheet, financial accounts, management reports, etc.
But quantitative investing differentiates itself in particular by its systematic and non-emotional approach: every choice is effectively based on objective data, never on human emotion.
Quantitative investing doesn’t rely on human judgment to take investment decisions: everything revolves around systematic and repeatable processes.
Quantitative investment strategies allow us to avoid behavioural traps, and, in sum, prevent that emotions start clouding our judgment and, hence, interfere in our investment decisions.
The use of a quantitative algorithm makes the quantitative analysis of data far more efficient, with the result that shares, for one, can be assessed and processed much faster, meaning that any investment decisions can be taken far quicker.
Quantitative investment managers are certainly not blind to these advantages, for technology now gives them access to innovative strategies that offer greater diversification compared to the traditional strategies, not to mention helps to keep costs down.
The term quantitative portfolio management denotes selecting securities on the basis of numerical and statistical data which makes it possible to make the very best investment choices.
Those very principles also underpin quantitative trading, a form of investing that focuses on the use of quantitative data to compute the probability of a given outcome producing itself and, hence, to decide which securities to buy or sell.
These days, more and more quantitative investment solutions are coming on stream, which is reflected in the broad range of opportunities across all the main investment categories, all with the aim of maximising returns at limited risk.
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