Algorithmic Trading Types – part II

Today, we continue our post about Types of Algorithmic Trading. Our previous article talked about 3 of them: HFT, CTA, Short Term CTA. Today, we will cover Factor Trading and ETF Rebalance.

FACTOR TRADING

Factor models gained a lot of popularity in the early 2000s. Currently, about $2 trillion is managed by funds based on factorial models.

Factor models are arranged as follows. The 3,000 US stocks are broken down into factors that have proven to be good at predicting stock performance over the medium term. Next, quants try to predict the movement of stock prices using linear combinations of factors, as well as the so-called “genetic convolutions” obtained by calculations.

Stocks are ranked by values ​​of factors. The variance is studied, for example 10% of the bottom stocks and 10% of the top stocks, ranked by a factor or by their linear combination.

Then the undervalued shares are bought and the overvalued shares are sold.

Examples of factors include: size, sector, cost ratio, volatility etc.

Factor models can struggle during periods of money printing since, this money, when it enters the market, begins to act like a vacuum cleaner, and shares of companies are bought not on the basis of in-depth analysis, but simply because investors are overly liquid and looking for risk assets.

ETF REBALANCE

Automatic rebalancing of ETF portfolios has gained popularity over the past 10 years. At the same time, the volume of issued ETFs in the world has increased to $7.5 trillion, and the share of long-term investments in the form of ETFs in the United States reached 40%, in Europe 20%.

The Robo-advisors automatically calculate new ETF portfolio allocations and give recommendations for rebalancing to the client, or perform it automatically.

Typically, such rebalancing has three main objectives:

Tax optimization (sale of unprofitable assets at the end of the tax period);

Targeting a given volatility in accordance with the client’s risk profile;

• Maintaining the weights of different asset classes.

The algorithms used are quite complex and require serious qualifications; trading systems are also quite technological. The average annual return of the top 24 Robo-advisors over the past two years ranged from 5% to 7% per annum (risky portfolios ) and 2% -4% per annum for portfolios with fixed income.

On average, Robo-advisors are more effective than human financial advisors.

The volume of funds managed by this technology is $1 trillion. It is expected to grow to $1.5 trillion by 2023 and the number of clients will increase to 150 million.

Leave a comment

Your email address will not be published. Required fields are marked *

We are eager to share with you more details in our upcoming posts next week. Meanwhile, do not forget to subscribe to our blogposts newsletter here to have all our newest posts in your inbox the time they are out!

Quant Infinity Solutions AG is committed to protecting and respecting your privacy, and we’ll only use your personal information to administer your account and to provide the products and services you requested from us. From time to time, we would like to contact you about our products and services, as well as other content that may be of interest to you. If you consent to us contacting you for this purpose, please tick below to say how you would like us to contact you:

You can unsubscribe from these communications at any time. For more information on how to unsubscribe, our privacy practices, and how we are committed to protecting and respecting your privacy, please review our Privacy Policy.

By clicking submit below, you consent to allow Quant Infinity Solutions AG to store and process the personal information submitted above to provide you the content requested.