Systematic investing gives investors an opportunity to make an informed decision about investing, based on math and historical performance data.
Also known as quant investing, traders will hypothesize a stock’s performance and use historical statistical analysis to test and then build an automated trading process. This way a trader can develop mathematical models to determine whether a trade is risky or if the return is good.
And there are plenty of examples of successful systematic investing strategies, but what the publishers of such strategies don’t show is the exact detail of what they do.
Quant is short for quantitative (as opposed to qualitative strategies). These strategies use a variety of data including historical data to develop firm mathematically-based trading algorithms, to bring about the best returns.
This way an investor can create a portfolio that delivers more predictable outcomes.
A qualitative strategy will look at a company’s assets. It’ll try to understand a businesses performance potential based on what its assets and liabilities are and look at any other factors that could affect how it performs.
Once a strategy is designed it is put through a backtesting process, where the theory is put to the test over a period of time using historical data. THis gives an investor the information to make a calculated risk and see if the math will stand up and bring about a good return.
In a nutshell, a quant trading system is about developing customised trading systems that can be tested to give the best information about whether an investment is likely to be good or not. They help reduce the risks by giving the investor information based on the mathematically historical data.
What Does Systematic Investing Look Like?
There are various types of strategies but as a basic example a quant trader could write a program that picks out the best performers when the market is doing well. The next time the market is doing well, the program would buy those well-performing stocks.
This is a simplified version and the success lies in which parameters the trader chooses as ‘performing well’ and then backtesting the theory to be able to make an informed guess on its chances of success.
Above is an example of a trend-following strategy or ‘Momentum’ strategy.
The frequency of trading is important too. When assets are held for longer than a day it’s known as low frequency trading. High frequency trading is when assets are only held within the time period of a day and ultra high frequency assets are held for nanoseconds.
Generally speaking, systematic investment strategies perform better over a longer term period. It’s possible to use systematic strategies for the high frequency and faster trading, but the trader will need to deeply understand the trading technologies involved when an asset is held for nanoseconds.
Are There Risks?
Data doesn’t always give the whole picture. A stock market crash or some other downturn might not be so easily picked up. As with any data, it’s only as good as the person who defines which data to use and how to use it.
Classically data can, and is, misinterpreted or manipulated and in trading terms if many people start using the same strategy model it will affect the results. Like the small print always says… ‘capital is at risk’.
And generally speaking systematic strategies also require long time periods to be successful.
So, while there are no sure-fire guarantees, in a systematic strategy the math helps an investor mitigate the risks and make an informed decision.
But what makes quant strategy appealing, and successful is that strategies can be pushed out with consistency. The numbers are just that – numbers. There’s no need for emotion in the decision making, just data.
It’s cost efficient too as there is no need for huge numbers of data analysts to pore over the information. Computers do it all.
Trading is complex and systematic investing strategies determine the potential value of stocks and shares by using combinations of data. Once a theory has been proven a computer model can be used to invest, including exit points, potential risks and the potential return.
In reality many investors will make a decision based on both quantitative and qualitative data – that is the potential of the numbers based on analysis and statistics and by looking at the company itself.
This article is for information and educational purposes only and does not form a recommendation to invest or otherwise. The investments referred to in this article may not be suitable for all investors, and if in doubt, an investor should seek advice from a qualified investment adviser.