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Ultimate Guide to Quantitative Investing – What Is It & How Does It Work?

Ultimate Guide to Quantitative Investing – What Is It & How Does It Work?

September 29, 2021 By Vash Leave a Comment

Quantitative Investing is perhaps one of the ‘hottest’ type/strategy for investing, particularly in the data-driven world we live in. But what exactly is quantitative investing? How does it work? We’ll explore all of this and a whole lot more in this article/guide.

Let’s get into it.

Table of Contents hide
1 What Is Quantitative Investing?
2 Quantitative Investing Strategies
2.1 High-Frequency Trading
2.2 Statistical Arbitrage
2.3 Algorithmic trading
3 What Skills Do You Need for Quantitative Investing?
4 Does Quantitative Investing Work?
5 Who Uses Quantitative Investing?
6 Setting up a Quantitative Investment
6.1 Strategy Identification
6.2 Strategy Backtesting
6.3 Execution System
6.4 Risk Management
7 Quantitative Investing vs Qualitative Investing
7.1 Benefits of Quantitative Investing
7.2 Risks of Quantitative Investing
8 Final Thoughts: Quantitative Investing

What Is Quantitative Investing?

Sometimes construed or interpreted as “black box investing” or something that’s straight out of science fiction, quantitative investing (or “quant investing”) is actually quite predictable and intuitive.

Fundamentally, it relies on leveraging the power of data, computing, and financial mathematics to drive investment decisions.

Put simply, quantitative investing is the practice of using mathematical and statistical / quantitative analysis to:

  • find successful trading and quant strategies,
  • execute them, and
  • manage risk

In a sense, quantitative investing is just another school of thought/practice, similar to value investing or even swing trading.

It’s more than likely the close relationship with computer programming that makes quantitative investing seem daunting.

Computers, programming languages, and complicated maths formulas are just the tools used by those who practice quantitative trading.

Investors of all kinds have tools at their disposal to increase the likelihood of success.

For instance, value investors use earnings reports, stock price data, and knowledge of the company to inform buy/sell/hold decisions.

Swing traders might use technical analysis tools and charts to find trading opportunities.

Value investing and swing trading are generally known as “qualitative” investing although this isn’t a definitive categorisation.

For example, one might apply value investing in “big data” settings, making it closer to “quantitative” vis-a-vis “qualitative” investing.

Equally, one could apply value investing strategies in a factor investing type environment, thus making it more “quant” than “qual”.

We’ll get more into comparisons in a later section.

The fundamental idea is that quantitative investing relies on quants (or math) instead of subjective/individual opinions to drive investment decisions.

Let’s now expand on quantitative investing (also called “quant investing”) by explaining the types.

Quantitative Investing Strategies

Three of the most commonly used quantitative investing strategies (ordered in terms of holding time) include:

  • High-frequency trading
  • Statistical arbitrage
  • Algorithmic trading

Let’s now consider each of the three quantitative investing strategies individually.

High-Frequency Trading

High-frequency trading is fairly well covered in the financial media.

It typically involves placing hundreds, thousands, or more trades per day (hence high frequency).

Success relies on lightning-fast network speeds, highly-skilled computer science backgrounds, and state-of-the-art equipment.

According to the Efficient Market Hypothesis, if markets are “strong-form efficient”, then prices immediately reflect all available information.

However, “immediate” can sometimes mean nano-seconds and other times a few seconds, or even minutes.

As crazy as it might sound, those nano-seconds can actually mean the difference between a real profit and an actual loss.

Statistical Arbitrage

Also known as “stat arb,” statistical arbitrage is in the middle because trades generally last anywhere from a few seconds to a few days.

This style of quantitative investing requires heavy computing power to comb through data and find out where there are opportunities.

It’s also somewhat synonyms with anomaly investing, which focuses on identifying anomalies (something other than “normal”) to earn alpha.

Statistical arbitrage usually involves buying a stock with a statistical likelihood of going up and also placing trades in support of that idea based on the analysis.

Algorithmic trading

This might be the simplest method, though “Algo Trading” still relies on some skills with programming languages.

An algorithm is simply a set of steps to do something.

A recipe for your favourite meal or dessert? That’s an algorithm.

An extensive and fancy flowchart documenting steps to get an answer from Apple’s voice assistant Siri? That’s an algorithm, too.

Similarly, one could express an entire investing strategy in a computer code or program. That would be an algorithm

It could be as simple or as complex as you want.

A simple ‘algorithm’ might be “Buy XYZ stock when XYZ reaches P price” combined with “Sell XYZ stock when XYZ reaches R price.” for example.

What Skills Do You Need for Quantitative Investing?

Apart from strong analytical skills and preferable domain knowledge/expertise, core skills for successful quantitative investing include:

  • Maths and Statistics – This is a fairly obvious one, but it’s critical nonetheless. You must have a firm grasp of mathematics equations and statistical theories to be successful.
  • Programming – Not only does quantitative investing require high fluency in one or several programming languages, but also an understanding of how they can all help each other.
  • Understanding of Investing Strategies – In addition to the previous two, knowledge of the financial markets is crucial. You need to understand how the exchanges themselves work. If you don’t know specifics of things like the difference between close and adjusted close price, which stocks are listed on which exchange and the biggest competitors in your chosen markets, your strategies are at greater risk to fail.

Does Quantitative Investing Work?

There are some fabulously successful investors who have implemented quantitative investment strategies.

There are also several firms that actively use quantitative trading as the foundation of their business models.

In a nutshell? Yes, quantitative investing works.

There is no guarantee of success, however, as with any financial investment.

It can take considerable time and effort to arrive at a successful strategy, but it can work.

Much of the reliance on data made it difficult in the early days, but the speed of information now is faster than ever before in history.

With all of the access to data and new technologies getting introduced each year, quantitative investing has thrived and will likely continue to do so.

Who Uses Quantitative Investing?

Several large hedge funds use quantitative investing as the core of their strategy.

Names such as Renaissance Technologies, Capula, Two Sigma, and Acadian Asset Management are all considered examples of a “quant fund.”

There are also firms such as Tower Research Capital and Citadel LLC which use high-frequency trading as a means of ensuring the flow of orders in the markets.

Apart from the generation of Alpha, quantitative investing can also be seen as a service to the market by maintaining high liquidity.

This applies to both, the institutional investor as well as the retail investor.

Modern computing, 5G, and high-speed broadband mean that it’s possible for an individual with the right skill set to set up an algorithmic system in a basement to make anything from a side income to a fortune.

Setting up a Quantitative Investment

Setting up a quantitative investment involves four parts, and they’re all are critical to ensuring success:

  • Strategy Identification
  • Strategy Backtesting
  • Execution System, and
  • Risk Management

Let’s now consider the 4 parts individually.

Strategy Identification

First, you have to figure out what strategy you’re going to use.

This includes the type of quantitative investing, such as high frequency or statistical arbitrage.

It can also include the specific angle you’re taking with a strategy.

For example, you might focus on algorithmic trading that centres/focuses on earnings announcements.

Such a strategy might include, for instance, buying stocks whose EPS was 5% greater than the analyst estimates.

Identifying the strategy will involve heavy research to arrive at the core parameters for your investing strategy.

Strategy Backtesting

When you find a strategy, you will need to run it on available pricing data from the past to see how successful your strategy would have been.

Yes, past prices do not predict nor reflect the future.

However, history can (and does) repeat itself sometimes.

Furthermore, while stock returns in general are random (given the random walk), the levels of randomness can be somewhat predictable between and across firms.

For example, consider a value investing strategy that goes long in high book-to-market stocks and shorts low book-to-market stocks.

While stock returns in general are random, the average return of stocks with high book-to-market will tend to be greater than the average return of stocks with low book-to-market ratios.

This “anomaly” is sometimes dubbed the ‘Value Premium’ per the Fama French 3 Factor Asset Pricing Model.

When it comes to backtesting, some execute their strategy on as many stocks, ETFs, and other assets as possible and just run it through all pricing data currently available until the current day.

Others work with a relatively smaller sample size.

There are quantitative investing platforms like Quantopian available that are used specifically for this purpose.

Execution System

Speed is usually crucial to success given how fast information and data become available.

You need to have a system of order execution and exit that is as detailed as possible before you start your trading.

The last thing one would want is for an arbitrage opportunity to have been found, but it couldn’t be realised because of technical glitches.

Risk Management

The vast majority of the risk management should’ve been done at the strategy and backtesting stage.

Nevertheless, once the strategies are running, one should actively check performance and tweak, iterate, and improve models to further reduce risk.

If your strategy is losing quickly, the faster you respond to errors in the code, the faster you can get back on track.

It’s important to be somewhat ruthless here. You’d rather cut your losses while you can than be a victim of loss aversion and end up with more losses than you could handle/afford.

Quantitative Investing vs Qualitative Investing

While quantitative investing relies on maths, statistics, and computers, qualitative investing is what you might call the “old-fashioned way.”

These strategies involve investing in companies and people rather than numbers.

This is, of course, still a profitable method of investing – just take a look at Warren Buffett for example!

However, the argument against qualitative investing is that, with data moving at light speed across the globe, it’s increasing the validity of the strong form of the Efficient Market Hypothesis.

This says that all information is already priced into a stock by the time it reaches your screen.

Thus there’s no way of earnings consistent abnormal returns.

That being said, the keyword here is consistent.

It’s still possible to beat the markets every now and then; just not consistently!

Benefits of Quantitative Investing

Quantitative investing has a variety of benefits. The three most useful benefits include:

  • Cost-effective – particularly for larger operations by saving on huge teams of analysts
  • Removes emotion and hesitations which could cause errors
  • High upside potential

Risks of Quantitative Investing

When considering benefits/’returns’, it’s also important to consider the costs/risks.

For quantitative investing, key risks/challenges include:

  • High skill barrier for entry
  • Strategy development can take a lot of time before profits are even seen
  • Bear markets affect quantitative strategies just as much as qualitative strategies
  • Requires constant backtesting and tweaking

Final Thoughts: Quantitative Investing

Quantitative investing is, as we said before, just another school of thought in the world of finance and investing.

It isn’t quite “black box” or “witchcraft”, but it does require a strong background in several scientific skills, any one of which might be considered very difficult.

This doesn’t mean that quantitative investing should be ignored.

Quite the contrary – it is to be at least understood in concept if not put into practice.

It will continue to grow in the future and perhaps even find its way into the retail investing space as a common investment strategy.

If you’d like to explore quantitative investing, particularly data-driven investing, then definitely check our data driven investing course.

Or better yet, get unlimited access to all our rigorous finance and investing courses via our Super Learner All Access Pass.

Filed Under: Finance, Investing Fundamentals, Investment Analysis

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