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Systematic Active Equity
Seeking an information edge
Big Data in asset management.
A cost-effective and risk-managed way to target above-market returns
Capital at risk. The value of investments and the income from them can fall as well as rise and are not guaranteed. Investors may not get back the amount originally invested.
How does a systematic strategy differ from other investment strategies?
BlackRock’s Systematic Active Equity (SAE) is about creating algorithms from Big Data to select stocks.
What is Big Data?
Each second, oceans of new data are generated by the internet, smart phones, satellites and other innovations. This data is often referred to as “Big Data” or “alternative data”. Big Data is larger and more complex than traditional data sets and cannot be managed by traditional data processing.
What is the difference between Big Data and Traditional Data?
| Traditional Data | Big Data | |
|---|---|---|
|
|
Types of Big Data
| Sources | Data |
| Internet | Web traffic |
| Smartphones | Electronic text |
| Satellites | Geolocation |
What are the advantages of Big Data in financial markets?
In an age of information overload, the ability to process large amounts of data can create a significant advantage. So can the ability to anticipate and interpret flows in fast-moving markets that have been transformed by higher volumes and rapid trading.
Many asset managers are seeking to harness the power of Big Data by using technologies like natural language processing (computer programs that analyse human speech), image recognition (the automated recognition of objects, people, places and writing) and machine learning (computer algorithms that improve through experience) to analyse it and uncover new investment insights.
Big Data in action – Smartphones
There are currently more than six billion smart phones in use, each one generating vast quantities of data. (Ericsson, June 2020) Some of this data will relate to text and numerical data, while other data will relate to location. But how does a fund manager use this vast amount of data?
Data of various types gathered by smartphones can give us vital information about companies. What footfall are premises receiving? How many positive mentions are a company’s products acquiring online? What sales data can we gather from smartphones? And this information will all be up-to-date and in real time. These are all data points that we would never find on a balance sheet or in a quarterly earnings report.
How is Big Data used to build portfolios?
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Developing the ideas we think will drive performance
BlackRock’s SAE team of researchers and portfolio managers identify certain characteristics that other equity investors have overlooked or are misunderstood by the market. In order to increase the chances of discovering the stocks that hold these characteristics, SAE team develops “signals” from a wide range of data sets, as opposed to relying on the company fundamentals (such as earnings and cash flow) favoured by traditional fund managers.
Risk. There is no guarantee that research capabilities will contribute to a positive investment outcome
Signals are constructed from data to assess which companies, industries or countries have or improving against the characteristic.
For example-
Company - Characteristic – sales growth
- Signal – increased foot traffic identified from smart phones
Industry - Characteristic – increasing costs
- Signal – increased online hiring for in-demand and therefore more expensive job titles or skills
Country - Characteristic – increasing exports
- Signal – increases bookings of cargo shipments from the country on online platform
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Find the data sets that can spot the signals we are looking for
Utilising the numerous data sources that Big Data offers provides more opportunities to invest in stocks whose potential other investors may have missed. But investment teams need to be sure that we are using the right data. Each new data set is cleansed and tested to ensure it is identifying those companies that have the have the characteristics that we believe can lead to outperformance.
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Automating the data analysis and stock-selection process
Once data sets are proven to pick up the desired characteristics – this process is automated. This is the best way to take advantage of the amount of data and the speed with which it is generated. It also makes the investment process disciplined and repeatable. But this step is not the end of the investment team’s involvement.
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Monitoring and constant reassessment
Teams monitor the data and the stock picks to ensure that our signals still stand up to scrutiny and that the data sets we are using remain robust. The team is also constantly discussing and researching new signals that they believe could drive share-price performance in the months and years to come.
Risk: There can be no guarantee that the investment strategy can be successful and the value of investments may go down as well as up.
Risk. There is no guarantee that a positive investment outcome will be achieved.
Risk. While the investment approach described herein seeks to control risk, risk cannot be eliminated.
Risk. Diversification and asset allocation may not fully protect you from market risk
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We believe that better and more differentiated investment insights from new data and new techniques are becoming necessary conditions for investment success.
— Jeff Shen
Co-Head of Systematic Active Equity (Blackrock, May 2020)
SAE is a 34-year old investment platform* managing money in every investable market in the world. Discovering new market inefficiencies is key to our success, as they can disappear as quickly as they are found. We believe that an investment process underpinned by continual innovation is vital to our goal of delivering sustainable performance.
*Including time with Barclays Global Investments and Wells Fargo Nikko Investment Advisors
With more time, more data
Growth in sources of investment-relevant information
Source: BlackRock, as of May 2020.
Beating the market has always required an information edge ― something harder to achieve, and maintain, in a hyper-informed world. We believe staying ahead requires three imperatives:
Systematic investing is more than computerised stock picking; human intellect is central to investment decision-making. Just as our process has evolved over time, so too has our people power. Members of BlackRock’s earliest SAE team had backgrounds in academic finance and accounting, as well as physics and math. Today, they are joined by team members with experience in computer science, statistics and engineering. This multi-disciplinary foundation is seen as a key source of strength and differentiation.
BlackRock SAE at a glance
Source: BlackRock, as of May 2020. Subject to change.
* Director and above. † Degree subject of highest education level.
This material is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or financial product or to adopt any investment strategy.
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