There is no doubt the US securities markets have shifted dramatically in the past twenty years. Not only has there been an increase in volume of traders, but there has been a change in the types of traders. In today’s blog post, we will explore how and why investing has changed from active to passive, as well as some other general trends. We will also define and analyze quantitative trading and “alpha,” and how the two relate.

As technology advances and technical analysis becomes more ubiquitous, it is important to understand quantitative trading, especially as larger hedge funds and financial institutions dominate the market. While it is important to understand a company’s financial statements and underlying business principles, the technical and quantitative aspects cannot be neglected. To hedge risk, maximize opportunities, and maximize returns, the numbers must drive the data.

## What is Quantitative Trading?

Quantitative (“Quant”) trading is an investment method that relies on mathematical computations and number crunching to isolate and execute trading opportunities. It relies heavily on price and volume as data points and uses these variables to guide the numeric models. While more and more individuals are beginning to have advanced models to execute quant trading, it still is dominated by hedge funds and large banks (and other financial institutions). In quant trading, the data is “backtested,” meaning that simulations are run using historical data to analyze risk and profitability. This simulated data are then applied to the models.

One benefit of quant trading is that it easily eliminates the emotional aspect of investing, because trades are only executed in large volume if the historical numbers predict a favorable outcome. A mathematical simulation is great in certain individual situations. If the market is stable and price and volume aren’t drastically changing, this approach will yield immediate results. However, a downside of quant trading is that other investors learn of your approach, it is easily inimitable. Moreover, if the market shifts significantly, there will be major losses. The mathematical simulations often are great at predicting certain market conditions, but cannot account for changes.

## What is “alpha,” and why is it so important?

In trading, and similar to “beta,” alpha is a financial measurement of a stock’s performance. Specifically, it is the excess return relative to a benchmark’s performance. While beta measures a stock’s volatility, and thus its risk, alpha measures a stock’s “performance edge.”  In essence, this measurement reflects how well an investor is doing overall. In theory, markets are efficient, so there is no way to systematically earn returns that beat the broad market as a whole. This is why alpha is also known as the “abnormal rate of return.”

It is important to note that alpha can be a positive or a negative number in active investing. Alpha, along with the other four popular technical investment risk ratios, is a statistical model found in modern portfolio theory. Because it represents a gain or loss against an investment and is usually used in hedge funds, it is used to see how much a hedge fund manager adds or subtracts from the funds returns.

## What are some general market trends over the past twenty years?

One of the most significant shifts in the market over the past two decades is the volume and type of traders. While trading used to be dominated by large hedge funds and portfolio managers, we are now seeing a rise in successful individual investors who use advanced softwares and technical analysis to simulate and beat the market. This has caused a shift from active to passive investing; passive investing is when investors are more “hands off” and make fewer trades less often. This change has paralleled and mirrored the shift from discretionary trading to algorithmic trading. Rather than a broker making trades without the client’s knowledge, clients are now using algorithms and statistical modeling to make their own trades.

## Conclusion

Alpha and quantitative trading are two invaluable tools needed by all types of traders to understand how their strategies are working (or not). Quant trading is a heavily mathematical model that uses historical data to predict the future. On the other hand, alpha is a measurement to assess how well or how poorly returns are in a portfolio relative to the broad market.

As there is a technological shift and technical analysis becomes the more dominant method of investing, quant trading with numerical models will begin to dominate the market. Investors will rely on the data, price, volume, and volatility and input them into their statistical models to best beat the market.