Quant is a shorthand for a quantitative analyst who applies mathematical and statistical models to assist in the making of management and investment decisions.

While almost every big company applies such models to aid their decision making, quants are most often associated with the financial markets.

For example, quantitative trading systems use computer algorithms that crunch market numbers with historical data and mathematical equations to derive predictions of:

  • where a market is moving
  • what is the project rise or fall in share price
  • arbitrage opportunities
  • overvalued and undervalued stocks
  • etc

Quant trading attempts to automate trading decisions as much as possible so as to remove the human element of emotions.

The people behind creating, monitoring and improving these quantitative trading models that software would follow are quantitative analysts.

The application of trading software systems become more widespread due to their necessity in keeping track of different and diverse markets, the expertise of quants are in high demand.

For example, a computer might send alerts to an investor to remind him or her that a stick has hit it’s historical high and that breaking past that ceiling threshold is highly improbably. Therefore, it would be wise to sell before it drops in value.

When quantitative prediction systems are merged with trading systems, traders can set standards and protocols for the computer to follow. After which computers would trade on their behalf based on the criteria that traders set.

Such practices however, are highly risky as computers make decisions based on numbers are are unable to practice judgment.

And as most people who have spend some time trading equities would know, the market is highly driven by emotions. Not just by performance numbers announced by listed companies.

The specialist numerical and analytical skills of quants are also highly valued in hedge funds, especially in areas of risk management.

More industries

However, even though quants are most frequently associated with the financial markets, their roles can also be highly desired in corporations.

This is especially so with businesses that sells products and services with fluctuating prices.

Flight tickets sold by airlines for example, require the constant prediction of pricing models to determine the best prices that induce customers to buy without pricing themselves out of the market.

The goal is always to “sell out” at the highest price.

While low prices might create high demand and high sell-through, it might not be the most profitable for a business. The ideal scenario is to set prices at the highest level and still achieve a sell out.

The use of data-driven pricing models that account for seasonal demand, supply and competition is essential for an airline to maximize profits to maximize shareholder value.

The same can be said of the hotel industry.

This is made even more complex with the rise of data aggregation portals where users are able to easily compare the prices of one hotel against another.

Price is a key condition for a traveller to book a room. And pricing strategy based on quantitative analysis can be critical.

Quants are set to play a very influential role in the global economy in the years ahead. They are also set to make a significant contribution to the development of artificial intelligence (AI).



Straight Time

Site Footer

Sliding Sidebar


Copyright 2022 | Terms | Privacy