Quantitative trading, or using quantitative analysis to identify trading opportunities, is slowly gaining traction in Malaysia as investors become increasingly aware of how it works and the returns it can bring.
These trading strategies essentially rely on an automated system and the rules that are encoded into the software rather than on human intuition in the decision-making process. Quant trading techniques include high-frequency trading, algorithmic trading and statistical arbitrage.
Ernest Chan, author of Quantitative Trading: How to Build Your Own Algorithmic Trading Business, says investors can either do this themselves or buy into quant trading funds.
“Technical analysts have traditionally used simple indicators based on price or volume to decide whether to buy or sell stocks. People have been trying to reduce human emotions in trading for 50 years. But they would often overrule the technical signals and not allow the system to automatically execute the trade,” he says.
“Over the years, people like me have discovered that any kind of human judgement did not add value to the trading strategy. More and more people have turned over the decision-making to computers. Nowadays, many quant traders and quant fund managers just sit around reading the newspapers and researching. These days, billions of dollars of assets are traded without any human intervention.”
Chan’s personal brokerage account, which employs his quant trading strategy, has seen a three-year return of more than 26% net of fees. This compares with the average return of 7.59% over the same period seen by equity-based unit trust funds in Malaysia. Its performance also beat the 21.86% (not net of fees) average return of small and mid-cap funds.
The quant researcher and trader with almost two decades of experience is the managing member of Canada-based QTS Capital Management LLC, which manages a commodity pool as well as individual client accounts using fully automated quantitative strategies.
The major inputs in a quant trading system are historical and live data. Quant traders need to have an investment strategy to apply the data. They can do so by coding investment patterns they trust into a strategy and testing the new programme with historical data.
“If it works to their satisfaction, they could convert the software into a live execution system. The programme will start receiving live market data as input and make live trading decisions. It will also receive trade confirmation reports and account details from the brokerage to make its next trading decision,” says Chan, adding that this is the life cycle of the quant trading system development.
A recent investment that piqued investors’ interest in quant trading was made by US billionaire and successful hedge fund manager Steven Cohen, who sank a cool US$250 million into a hedge fund launched by Boston-based investment firm Quantopian in July. He also invested in the firm through his family office’s venture capital arm.
Quantopian is a crowdsourced quantitative investment firm that provides capital, data, a research environment and development platform to authors of algorithms. The platform now has a community of more than 90,000 members who are trying to write award-winning quantitative trading strategies.
Investors who are interested in quant trading would do well to start with intra-day or daily trading, says Chan. They can test a few strategies simultaneously for three to six months and stick to those that are profitable.
There are, however, entry requirements for newcomers. Investors who want to adopt the do-it-yourself (DIY) approach should have a technical background in trading, he says. Knowledge in computer programming is also a basic requirement.
“Quant trading is not a skill you can acquire overnight or do as a hobby. You have to do it full-time for at least 12 months. If the strategies are profitable, it will still require full-time attention to do research and fine-tuning year after year. You cannot do it as a part-time job. Another avenue is to invest directly in quant trading funds,” says Chan.
Sherron Wong, managing director of VentureSkies LLP, has stricter requirements when it comes to developing quant trading strategies. Investors, he says, should have been schooled in finance, specifically quantitative finance, to be able to adopt the DIY approach. Without the relevant knowledge, it will be tough for investors to create the infrastructure or process the analysis.
VentureSkies is a Singapore-based proprietary FX trading firm with an algorithmic trading strategy. Its quant trading fund, which it launched last year, provided a high single-digit return for the three months ended August.
Wong says he does not know any investor who has done quant trading successfully on a part-time basis and that the odds are against them if they are working a day job and trying to invest using a quant trading strategy.
“You need to understand that being quantitative does not mean you have a profitable system or strategy. You still have to put in the time and effort to study the market. The quant approach just makes things easier and more effective when you need to process a large amount of data,” he says.
According to Wong, quant trading is using technology to create and confirm a trading methodology. It means dealing with a lot of data validation. Validating an approach by testing it with historical data, he says, enables investors to customise their strategies to fit the more micro market structure.
“The volatility across the markets is lower right now, compared with the past 10 years. When you have a micro-view of the market as quant trading does, you will be able to navigate around in a much narrower space,” he adds.
Wong says quant traders use historical data to validate their strategies and confirm that their systems are profitable ones. The best validation for a strategy is to have it undergo very rigid testing and see what pops out.
“Investors should also look at the robustness of a strategy — how strong it is in terms of the hardware and software. There are different measures to test that. It should be able to weather the different market regimes that are to come,” he says.
“Since we have gone through the 2008 global financial crisis, I think we have ample market regimes as backdrops to test the system. Of course, it is by no means a confirmation of future performance. But it could be a strong indicator to validate the robustness of the quant trading strategy.”
As robust as a strategy is, can it be put to use for an indefinite period, whereas market conditions are ever changing? Chan says probably not.
“Investors can test one or several strategies simultaneously, depending on the algorithms they have. The more strategies they have, the better chance they have of sustaining decent returns. This is about investment diversification. But some players have been successful for a while by just using one strategy. So, it depends on the investor,” he says.
“Having said that, I believe no single algorithm can work forever. Most of them are extracting what we call arbitrage — the riskless profit. These opportunities are like unicorns; they become extinct when everyone is chasing after them. So, I believe we must have more than one strategy to reap sustainable profits.”
Chan says there are various reasons why people favour quant trading. In the current discretionary trading environment, human investment decisions are often subject to emotions and intuition. They can only process a limited amount of information. The fastest a human can react to a trade is half a second.
“Computer systems such as quant trading strategies are much more efficient. They analyse huge amounts of live market data and trade in milliseconds based on the scientific outcome,” he says.
Who benefits from quant trading strategies? According to Bloomberg, several hedge funds that use computers to follow trends reported gains hours after the UK’s decision to leave the European Union on June 24.
Wong says most of the quant trading funds outperformed during the Brexit period when uncertainties were high. That is because they were able to navigate through unpredictable market conditions with the analysis of live market data minus the interference of human emotions.
He notes that investment banks and hedge funds in Singapore have been using quant trading to manage a part of their assets for some time now. What is interesting is how it has trickled down to retail investors, particularly the younger generation.
“The younger crowd in Singapore has already branched into quant trading. It is almost a natural step for them. After graduating from university, instead of looking for a job at a bank, they go about their own quant trading activities,” says Wong.
What are the risks of employing a quant trading strategy? In the Hollywood movie Money Monster, which stars George Clooney and Julia Roberts, a “glitch” in a high-frequency trading algorithm causes a huge chunk of investors’ money to disappear.
In the real world, glitches do happen. In 2012, a glitch wiped out US$440 million of investors’ holdings in just 30 minutes. It happened to the US-based market making firm Knight Capital Group. According to Bloomberg, one of its trading algorithms had bought high and sold low, leading to the losses.
Chan says glitches happen, especially in high-frequency trading. “We have seen glitches before. Any glitch can be fixed, but prevention is the key. There are various ways to prevent glitches from having too much of an effect. It is essentially a software engineering problem.”
While high-frequency trading is a special kind of quant trading, not all quant strategies are traded at high frequency, he adds. Some quant trading systems only execute trades once a month or once a year.
“The software system that trades more frequently typically has a better risk-return ratio. But it has a lower trading capacity. It would impact the market if large sums of capital got in and out very frequently. That is why the pools of high-frequency trading are usually kept under US$100 million,” he says.
Quant trading strategies are not designed to beat the market. This is the big myth about the system, says Chan. Instead, the strategies are designed to generate absolute returns for the investor. A profitable quant trading strategy should be able to generate decent returns regardless of the market conditions.
Wong believes that quant trading will continue to gain traction in Southeast Asia. Some mainstream fund managers are already using quantitative analysis to forecast macro-economic elements in the market.
“However, for true blue quant traders like us, we make up a tiny part of a small market, which is the alternative investment market. There are not many quant establishments that are able to grow and fill the gap left by mainstream fund managers owing to our capacity. But we are really good at what we do,” he says confidently.