IN 60 SECONDS
BYTES OF DATA A DAY
5=MOST EDGE 4 3 2 1=NO REAL EDGE
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FUNDAMENTAL PUBLIC COMPANY DATA
OTHER ALTERNATIVE DATA
ALTERNATIVE PUBLIC COMPANY DATA
PRIVATE COMPANY DATA
EVALUATED ASSET PRICE
HISTORICAL MARKET DATA
REAL-TIME MARKET DATA
DATA TYPES PROVIDING THE MOST EDGE
Source: Greenwich Associates 2016 Alternative Data for Alpha Study
Note: May not total 100% due to rounding. Based on responses from 46 asset managers.
Source: McKinsey Global Institute (MGI), The age of analytics: Competing in a data-driven world, 2016.
Note: Results compared to a 2011 research by MGI
Note: Results compared to a 2011 research by MGI
Companies creating the perfect ‘omni-channel experience’ by using data analytics increased their shareholder's value to 8.5 times
UNITED STATES RETAIL
(Source: Vanson Bourne on behalf of Google, 2018)
54 percent of transport and logistics organisations say mapping technology has led them to reconsider their organisation and/or product strategy
( Source: the Society of Actuaries, 2018)
60% of healthcare executives forecast that using predictive analytics will save their organization 15% or more over the next five years
Companies creating the perfect "omni-channel experience" by using data analytics increased their shareholder's value by 8.5 times
(Source: the Society of Actuaries, 2018)
60% of healthcare executives forecast that using predictive analytics will save their organisation 15% or more in annual costs over the next five years
THIS WAS UP
IT WILL REACH
$1BN BY 2020
60% ON 2016
Amount spent by asset managers in 2017 on data
sets and new hires to analyse it
Source: BlackRock’s Scientific Active Equity Group, February 2015. These are cumulative specific returns for owning names in the top and bottom quintiles of employee sentiment in the MSCIACWI IMI Index, as determined by SAE. The names in these quintiles are not static and change through time. This is a frictionless simulation (i.e. no transaction costs are applied).
Source: CRISIL Global Research & Analytics. The above are not mutually exclusive.
SENTIMENT AND CUSTOMER
For illustration purposes only. Source: BlackRock, as at March 2018. The chart shows the SAE model scores and final combineed alpha score.
Alpha Fundamental Sentiment Macro
Sentiment score significantly improves
Aggregate alpha score from neutral to positive
Macro score also supports improvment
of asset managers plan to build cross-functional teams with a mix of internal and external people among quants, data scientists and fundamental analysts
Percentage of firms that plan to work with external vendors to execute and proof data methodologies and pilot use case projects.
SAE analyses job-focused social media to find employees’ attitudes towards their company and managers
Data includes comments, CEO approval, likelihood of recommendations and a total score
Trends in the employee’s views can drive positive and negative views across the investment universe
Overall, analysis shows that companies with satisfied employees tend to outperform
Tracking business-to-business (B2B) electronic invoices from a global sample of 150,000 major companies worldwide, covering 100bn+ USD transactions per year
Aggregated data on country- and industry-level business spending provide early read on trends in economic activity
Timeliness of invoicing information allow getting ahead of both macro data and company sales accounting data
Data from both listed and unlisted firms provides a more complete picture of the economic strength of individual countries and sectors
GPS, WiFi and Bluetooth in mobile phones can map user’s location – if location access is allowed through apps, third parties can collect the data
*Pew Research Center. Survey conducted Nov 2016. **BlackRock, Feb 2017.
Stocks with positive exposure to macro themes
Stocks underpinned by attractive fundamentals
Stocks supported by positive sentiment and market activity
50 million active users provide on average 100+ daily data points each, giving 60 billion total data points per month**
Data points can be mapped to individual locations and used to find companies that are seeing increasing or decreasing consumer footfall – a key driver of sales
Tracking consumer footfall through geolocation
Analysing social media to see employee opinions on company and management
Capturing B2B spending activity using e-invoices
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For illustrative purposes only. Source: BlackRock, as at January 2018. This information demonstrates, in part, the firm’s Rick/Return analysis. This material is provided for informational purposes only and is not intended to be investment advice or a recommendation to take any particular investment action. Tracking error is defined as the divergence between the returns of a benchmark, and is a measure of the risk in an investment portfolio that is due to active management decisions.
POTENTIAL EXCESS RETURN OVER BENCHMARK
ACTIVE RISK (MEASURED A TRACKING ERROR)
3: SYSTEMATIC ALPHA
2: SMART BETA / FACTORS
1: MARKET CAP
5: HIGH ALPHA
4: TRADITIONAL FUNDAMENTAL
For illustration purposes only. Source: BlackRock, as at December 2017.
RESPONSIBLE INVESTMENT OVERLAY
Source: BlackRock Research & Markit. As at September 2016. Both B2B spending indicator and PMI YoY growth are demeaned.
UK B2B SPENDING INDICATOR UK PMI YoY GROWTH
- Website visits
- Card transactions
- Employee sentiment
- Broker sentiment
- Corporate calls
- Foot traffic via GPS
- Job postings
- Central bank transcripts
- Labour costs
2. ALPHA SIGNALS
3. ALPHA MODEL STOCK RAnK
4. ALPHA SCORE
Find innovative signals in data sets to predict returns.
Portfolio managers build and maintain investment models using 20 to 80 signals.
Models score and rank stocks on a daily basis and monitor any changes.
Signals are aggregates to give a final alpha score which represents the return forecast for each stock.
The optimisation process seeks to find the best trade off between return, risk and cost.
The team monitors market conditions and signal efficacy to be ready for the next opportunities.
If a team is to be truly innovative, BlackRock believes there must be a culture of collaboration and constructive debate.
Members from junior to senior are encouraged to work together regularly to improve current data techniques and to test ideas in a beta environment without fear of failure.
SAE teams regularly engage in “hackathons” where team members work together and compete to create technologies and new uses for current technologies that can aid the investment process.
A SHARED ARCHIVE
supports efficient idea generation through documenting both successful and unsuccessful