The roots of quant investing were established by Sam Eisenstadt in 1965, when he created the first quantitative ranking system. With quantitative investing, strategies use historical data and mathematical calculations to find patterns and trading opportunities.
This uncovers the best moment to make the most profitable investment transactions. These strategies are put in place to find an edge in the market that could be used to turn profit.
Traders build an algorithm for both buying and selling signals, which scans the market. Depending on how many are running, the algorithms can run at different intervals, and determine the best time for trading. They will automatically buy and sell without your input.
Table of Contents
What is Systematic Investing?
A systematic investing strategy example would be a quant trader writing a program that scans the market and picks out the best performer. The next time that part of the market is performing well, the program would automatically buy those stocks. This whole method requires testing over multiple occasions so the computer can fine tune what works and doesn’t work. This is an example of a trend following strategy called the ‘Momentum’ strategy.
The frequency in which you trade can also make an important difference, too. Ultra high frequency assets are held for nanoseconds, and high frequency trading is when assets are only held for a day or less. If assets are held for longer than a day, then this is called low frequency trading. Long term systematic investment strategies generally perform better. But it is possible to use systematic strategies for faster and higher frequency trading. But this would require a deeper understanding of trading technology.
Benefits of Quant Strategies
- Trustworthy and steady – because quant strategies simply use computers where numbers and data are the only factors, there is no human element to it so it’s completely dispassionate. This means that there is no worry of psychological or emotional factors that could obscure the data in any way, also cutting the risk of human error in calculations.
- Cost effective – there is no need to hire or pay for data analysts as (apart from developing the model in the first place) there is no human input needed. Computers perform all tasks, including analysing all the data and making the actual transactions.
- Much faster – quant strategies can use algorithms that utilise many different strategies all at once, each one with multiple different criteria, in an instant. No human could do this in anywhere near the same amount of time.
- Frees up your time – since the computers will do all the work, this means that you will have time to focus on other work and additional income.
Disadvantages of Quant Strategies
- Some kind of coding skill is required – you will need to learn to code and program if you want to use quant trading strategies in your work. This will feel like a burden at first but in the long run this could actually benefit you more, because you’ll be able to use your newly learned coding skills in other areas of your work.
- Computers don’t pick up on anything – if there were some kind of data breach or stock market crash then a computer software wouldn’t pick up on it as easily as a human would. So, it’s more advisable to keep an eye on what your computer is doing in case anything like this happens.
- Coding mistakes can lead to issues – if there are any errors then you could see multiple issues with your program and trading, these are called “Black Swans”. If your computer’s power goes out or has connectivity problems at any point, these are also potential black swans.
- Costly – quant trading strategies require you to pay subscriptions and costs for certain software. So, although it will be making you more money overall, you can expect it to be costly to start with.
Although there are a similar number of pros and cons, if you’re clever and willing to learn, the pros definitely outweigh the cons. There are some disadvantages to quant trading but using it correctly will increase your chances of success.