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Quantitative Investing (What It Is And How It Works: Overview)

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What does quantitative investing mean?

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Let me explain to you what quant investing means and how it works!

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What Is Quantitative Investing 

Quantitative investing is a type of investment strategy where you use advanced mathematical modeling, data analysis, and computer systems to determine the best time to make an investment transaction.

Some refer to quantitative investing as mathematical investing or systematic investing.

The main objective of “quantitative investing” is to make investment decisions or develop investment strategies by analyzing the historical quantitative data available.

With the advancement of technology and the availability of powerful computers able to process enormous quantities of data at lower costs, more and more investors have started using quantitative investing techniques and strategies to complement their primary investing approach.

Machine-learning technologies, artificial intelligence, and the use of sophisticated programs and algorithms allow sophisticated traders and investors to analyze and process vast amounts of quantitative data analysis to make better investment decisions.

In the past, this type of investing approach was almost exclusively in the domain of sophisticated hedge funds.

Today, more and more investors are looking into “quantitative investing” to enhance their returns.

Quantitative Investing History

Quantitative investing started in the 1900s when Louis Bachelier published his doctoral thesis on “Theory of Speculation” modeling options.

Eventually, Edward Thorp, considered the “father of quantitative investing”, was a mathematical professor at the New Mexico State University and University of California, Irvine where he predicted and simulated the blackjack card game in the casino.

In 1965, Sam Eisenstadt used a form of quantitative investing approach to try to assess stock performance.

In essence, Eisenstadt created a quantitative ranking system where he used a six-month trailing performance of different stocks to find out that the top stocks were performing better than the bottom-ranking stocks.

Eventually, over time, this type of “quantitative” investing approach evolved to become more and more sophisticated.

Over the last decades, there has been a tremendous advancement in the area of quant investing where new investment technologies have emerged to simplify the process.

Particularly, investors are taking advantage of the low-cost availability of computer systems and data to create mathematical models so they either invest in a pure quantitative manner or complement their investing approach.

Quantitative Investing Strategies 

A quantitative investing strategy refers to a mathematical model that is developed to identify a particular type of investment opportunity.

For example, you can build a model intended to predict the best time to execute a trade based on market fluctuations if you are doing high-frequency trading.

You can also choose to develop a model where your objective is to select stocks that have the potential of beating a particular index.

Depending on your investment objective and goals, you can develop a quant model to help you make investment predictions.

There are many traders and investors that use quantitative investing strategies in an attempt to generate profits and beat the market.

Essentially, the more sophisticated investors tend to develop their own systems and models to analyze the market and make predictions.

There are also quantitative investing software programs that can be purchased by anyone offering an off-the-shelf tool allowing them to adopt quant investing strategies.

In reality, the success of these applications remains debatable. 

You may have some traders that will swear by it whereas others may not see the level of success that they expected to see using such quant strategies.

Types of Quantitative Investment Strategies

Here are the most common types of quantitative investment strategies used by investors today:

  • Quant value investing (to analyze a company’s financial statement and rank the stock)
  • Event-driven arbitrage (to identify events that can affect stock price)
  • Risk parity funds (to invest in an asset class offsetting risk in another asset class)
  • Smart beta strategy (using quant funds or ETFs for passive investment)
  • Statistical arbitrage (to identify short and long stock positions to take)
  • Managed futures strategies (to predict the futures market)
  • Factor-investing strategies (to find factors that led stocks beating a benchmark index)
  • Global investment strategies (to identify countries with good investment options)
  • Big data strategies (to use machine-learning and AI to make data-driven decisions)

Quantitative Investing Advantages

The main advantage of quant investing is that you can leverage the speed and power of computers and data to profit from market inefficiencies that they’ll detect.

With the right quant model, you may be able to detect a trend early on and make profitable trades as a result.

Here are the main benefits of quant investing:

  • You have a consistent method for analyzing the market data
  • You can analyze a lot of data at a low cost 
  • If you have a good model, you get great investment advice without having to work with an analyst or portfolio manager
  • There are many types of investment strategies and models to choose from
  • You can apply the strategy to any market and any volume of securities 

Quantitative Investing Disadvantages

On the other hand, quant strategies tend to work well when they are backtested but they also tend not to perform as well in the real world.

Whether you are investing using quantitative equity investing strategies or not, the level of risk is the same for all investors no matter their investing approach.

If you do not have a proper model to work with, not only you can lose money but you can lose a lot of money very fast.

Here are the main drawbacks of quant investing:

  • Using historical data is not a reliable prediction of the future 
  • The markets can move and shift that the model did not predict 
  • You may not be able to let the “machine” make all your investment decisions
  • The models tend to work well in a test environment but their real-life success is debatable 
  • You can lose money if your model is not designed well 
  • You run the risk of losing a significant amount of money based 

Quant Investing Takeaways 

So, there you have it folks!

In summary, quantitative investing refers to an investment approach where you use mathematical models to predict the most optimal investment, time your investment entry and exit, or identify stocks that can outperform the market.

Initially, sophisticated hedge funds used to perform quant modeling and investing to make investment decisions but now, thanks to the advancement of technology, more investors are able to analyze past market data to make investment decisions.

The objective in quant investing is to analyze historical data so you can make investment decisions in the future based on identified trends, factors, or characteristics that you’ve identified in your model.

I hope that I was able to provide you with the essentials that you need to better understand quantitative investing.

Good luck!

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With that being said, let’s head right back to our main topic!

Understanding Quantitative Investing Summary

  • Quant investing refers to an investment approach where the investor uses mathematical models to make investment decisions 
  • The main objective of quant investing is to use historical data to make data-driven decisions about the future
  • Quant investing can be beneficial as you can get a cost-efficient, consistent, and reliable assessment of market data able to analyze different investment instruments or markets as a whole
  • The main drawback is that historical data may not necessarily be a good prediction of the future and if your model is not designed well, you can potentially lose a lot of money
  • Today, many investors use quantitative investing models to complement their investing approach allowing them to make better decisions using market data
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