TAP INTO INCOME

BlackRock Systematic Global Equity High Income Fund

Combines AI-driven insights with human experience to seek reliable income and growth.

The big question we have been hearing from clients is, ‘How can AI help us invest?’

Hi, I’m Rui Zhao, a portfolio manager within BlackRock’s Systematic Active Equity team. 

With so many stocks in the global market, it’s extremely difficult for human investors to analyze all the information available from a multitude of sources on their own, and then extract useful insights of all the companies such as the company’s products, profitability, and growth prospects. That is where the benefit of AI come in, and why we have been using it across our systematic platform. 

We began using AI back in 2008 - building infrastructure that accumulate all the big data, and train machines to analyze the data. 

We assess a universe of five thousand securities every day, constantly advancing our machine learning technique and evolving the signals that we use to generate alpha more effectively. 

These signals can help us identify trending products among consumers, such as products recommended by online influencers, and assess investor sentiment from social media posts across different platforms and languages. We can also spot early red flags in company documents exchanged with regulators, which may indicate a potential negative impact on shareholder value. 

So by using AI in our investment process, we can strive to better forecast returns and generate consistent income for our clients.

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We constantly advance our machine learning techniques to identify potential for income and outperformance.

Rui Zhao
Portfolio Manager, Systematic Active Equity, BlackRock

Why invest in the BlackRock Systematic Global Equity High Income Fund?

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Achieve consistent income
• 10+ year record of delivering income
• Analyses huge quantities of alternative and traditional data to forecast stock returns, income returns and dividends
Technology
Use AI to select stocks more effectively
• Evaluates 800+ signals to identify income and outperformance opportunities
• Combines real-time data, AI and human insight to invest in higher income, lower risk stocks
Direction
Protect returns during change
• Team of 200+ professionals constantly evolve investment approach
• Uses innovative strategies to take advantage of increased market volatility

An award-winning solution

BlackRock has been recognised as Best Fund Provider - Equity Income for the BlackRock Systematic Global Equity High Income Fund


Awards for Excellence

Fund performance

Using systematic investing to tap into income

Systematic investing, or quantitative investing, combines insights from huge quantities of data, human expertise, and advanced computer modelling. By using a systematic approach, we can evaluate companies and their profitability more effectively and forecast stock and income returns more accurately.

Powered by AI, the BlackRock Systematic Global Equity High Income Fund aims to deliver low volatility and consistent income for investors.

Transforming how we see the world

Just think about how much data is out there and how it escalates rapidly. In 2022, Amazon.com saw 3.16 billion visits on average per month, while Alibaba generated over US$94 billion in e-commerce sales. And every minute of every day, we have about 97 million WhatsApp messages, and 500 hours of new videos on YouTube. And it goes on.

The funny thing is, we don’t just want data or information. Instead, we want to know what it all means, why it matters to us, and to be able to use the insights we have to predict outcomes

At BlackRock, our systematic investing platform has been doing this for over 35 years with a track record of innovation, creativity and reinvention. Our diverse team of 200+ financial professionals, academics and data scientists, blends the best of humans and machines to unlock new ways to generate differentiated outcomes

They harness cutting-edge technology such as AI and machine learning to review 800-plus data signals, assessing the fundamentals, sentiment, macro, and ESG characteristics of thousands of securities, at scale, and on a daily basis. They can then analyze and combine traditional inputs along with unconventional data sources in faster and more effective ways than any human could ever do.

So, how does this work in the real world?

Since 2007, we have been using natural language processing techniques to capture local text in the form of regulatory filings, news articles, broker reports, social media updates, and other sources to gather timely, nuanced views about a company’s outlook at scale

For example, our model can identify early investment signals by using sentiment analysis, tracking the number of positive and negative words included in a document, and assigning an overall sentiment score based on word counts.

Our approach also constantly evolves. We now apply transformer-based large language models like ChatGPT. These can process long sequences of elements – such as words in a sentence – accounting for the relationship between each individual word with other words, and focusing on the most important points.

And language is not a barrier as we can read text with 7 major languages, which allows us to get local context.

Another source of AI-led alpha might come from the physical world. Here, we can use AI as a tool to compile and analyze location-specific data – such as GPS signals from cell phones, and more to track consumer traffic. Or we can track trucking routes across a country, based on a view that heightened traffic may be a proxy for increased company fundamentals like future sales.

AI and data availability also allow us to better understand corporate intentions and macro dynamics. For example, in the US we tracked millions of online job postings to gauge the health of a company or to target future growth areas based on the skill-sets in demand. The rate of hiring can also be a proxy for activity at the company, industry, or country level.

Yet despite this outlook, machines will not take over. Human input and analysis is a critical component, and fundamental and classic information still have a major role to play. We call this a human-machine-team approach to investing.

Investing evolved

Transforming how we see the world

Just think about how much data is out there and how it escalates rapidly. In 2022, Amazon.com saw 3.16 billion visits on average per month, while Alibaba generated over US$94 billion in e-commerce sales. And every minute of every day, we have about 97 million WhatsApp messages, and 500 hours of new videos on YouTube. And it goes on.

The funny thing is, we don’t just want data or information. Instead, we want to know what it all means, why it matters to us, and to be able to use the insights we have to predict outcomes

At BlackRock, our systematic investing platform has been doing this for over 35 years with a track record of innovation, creativity and reinvention. Our diverse team of 200+ financial professionals, academics and data scientists, blends the best of humans and machines to unlock new ways to generate differentiated outcomes

They harness cutting-edge technology such as AI and machine learning to review 800-plus data signals, assessing the fundamentals, sentiment, macro, and ESG characteristics of thousands of securities, at scale, and on a daily basis. They can then analyze and combine traditional inputs along with unconventional data sources in faster and more effective ways than any human could ever do.

So, how does this work in the real world?

Since 2007, we have been using natural language processing techniques to capture local text in the form of regulatory filings, news articles, broker reports, social media updates, and other sources to gather timely, nuanced views about a company’s outlook at scale

For example, our model can identify early investment signals by using sentiment analysis, tracking the number of positive and negative words included in a document, and assigning an overall sentiment score based on word counts.

Our approach also constantly evolves. We now apply transformer-based large language models like ChatGPT. These can process long sequences of elements – such as words in a sentence – accounting for the relationship between each individual word with other words, and focusing on the most important points.

And language is not a barrier as we can read text with 7 major languages, which allows us to get local context.

Another source of AI-led alpha might come from the physical world. Here, we can use AI as a tool to compile and analyze location-specific data – such as GPS signals from cell phones, and more to track consumer traffic. Or we can track trucking routes across a country, based on a view that heightened traffic may be a proxy for increased company fundamentals like future sales.

AI and data availability also allow us to better understand corporate intentions and macro dynamics. For example, in the US we tracked millions of online job postings to gauge the health of a company or to target future growth areas based on the skill-sets in demand. The rate of hiring can also be a proxy for activity at the company, industry, or country level.

Yet despite this outlook, machines will not take over. Human input and analysis is a critical component, and fundamental and classic information still have a major role to play. We call this a human-machine-team approach to investing.

Investing evolved