Data beats opinion >>> your weekly update on the alternative data space

1. Most popular datasets over the last 6 months.

2. Case study of the week: Pricing pressure for China Autos.

3. Research question for a Bespoke Project: What alternative data sources can be used to track consumer interest in smarthome technologies?

4. Events: Legal considerations of alternative data to be discussed in Hong Kong on May 12th, Eagle Alpha presenting at conferences organized by JPMorgan and Citi.

5. Articles of the week: 1) Four in five asset managers plan big data boost in 2017; 2) CFA exam to add big data and AI as topics; 3) Trends tracking interest in technologies and programming languages; 4) Eagle Alpha to start a Series A process.

6. Lesson Learnt Example: Isolating organic consumer comments in Twitter.


1. Most popular datasets over the last 6 months.

This week we want to highlight the 3 datasets (out of 486) that our clients have clicked on the most over the last 6 months.

1) Email purchase receipts data.

Vendor overview: This vendor provides anonymized purchase data from more than 1.5 million active U.S. consumers, scanned from email purchase receipts. Covers over 550 merchants from more than 25 industries.

Dataset Overview:

  • History: Since 2013.
  • Geography: United States.
  • Delivery: API or CSV.
  • Frequency: Weekly.

2) CPG, retail and healthcare consumer transaction data.

Vendor overview: As a provider of consumer transaction related sales data for leading companies in FMCG, Retail and Healthcare industries, this vendor delivers a wealth of information on a weekly basis about consumer purchasing activity.

Dataset Overview:

  • History: Since 2012.
  • Geography: Worldwide.
  • Delivery: API or CSV.

3) Chinese transaction data.

Vendor overview: This provider produces indicators on credit card data. There are 10+ indices to choose from e.g. an economic automobile index and a luxury automobile index. Indices are delivered in xlsx format and can be updated in 3 ways:

  • National Monthly.
  • National Weekly.
  • Regional Monthly.

Dataset Overview:

  • History: Since 2011.
  • Geography: China.
  • Delivery: Xlsx.

Our database now has 486 providers. Contact us for more information:enquiries@eaglealpha.com.


2. Case study of the week: Pricing pressure for China Autos.

On May 9th 2017, we published a research note analyzing China Autos sector using Chinese bank card data.

Chinese car sales have slowed down this year according to official figures from the China Automotive Information Net (CAIN) that showed 4.8m retail unit sales in Jan – Mar 2017 vs. 5.4m for the same period last year.

Our analysis of Chinese bank card data demonstrated that the downward trend continued into April with the market experiencing pricing pressure for both economic and luxury models. We concluded: “Sustained pricing pressure is evident in the luxury market, and this is likely to negatively impact margins for both dealers and manufacturers.”

We showed a strong correlation between the CAIN data and indices based on bank card data from our partners. A correlation of 78% was recorded in the case of unit sales of economic vehicles and 71% in the case of luxury autos.

Contact us to learn about this dataset: enquiries@eaglealpha.com.


3. Research question for a Bespoke Project: What alternative data sources can be used to track consumer interest in smarthome technologies?

Contact us to receive a free bespoke project proposal.


4. Events 

Following a successful event in NYC to discuss the legal considerations of alternative data, we are hosting a similar event in Hong Kong tomorrow. Once a similar event in London is complete, clients of our Thought Leadership offering will receive a detailed synopsis of the three events.

Next week Eagle Alpha is presenting at the JPMorgan annual quant conference in New York and the Citi technology conference in London.

5. Articles of the week

Four in five asset managers plan big data boost in 2017.

CFA Exam to Add Big Data, Artificial Intelligence as Topics.

Introducing Stack Overflow Trends.

Eagle Alpha to start a Series A process.

6. Lesson Learnt Example: Isolating organic consumer comments in Twitter.

Clients have asked us to add a “Lessons Learnt” section to our Thought Leadership offering in order to benefit from insights we have gained from working with alternative data since 2012. One example is presented below.

Isolating Organic Consumer Comments in Twitter Data

Eagle Alpha Web Queries is a query based tool that enables our research analysts and clients to search multiple web sources e.g. review sites, blogs, forums, videos and Facebook.

While the Query Wizard on Eagle Alpha’s Web Queries tool has several built-in filters for eliminating noise such as competitions and giveaways, second-hand sales, job adverts, profanity and real estate adverts, we have found other methods of cleaning Twitter data.

Eliminating retweets by including raw:RT as an additional NOT term in your query reduces noise from competitions or retweets of celebrity comments.

A useful method to isolate consumer comments, and eliminate corporate accounts or bots, is to exclude tweets that include links. Although this technique does risk eliminating tweets from genuine consumers, we have found it to be very effective way of isolating genuine consumer comments with a high degree of confidence. Below is an example of the query language used to isolate genuine consumer tweets.

(NIKE NOT links:(.com OR .ru OR .net OR .org OR .de OR .jp OR .uk OR .br OR .pl OR .it OR .in OR .fr OR .nl OR .au OR .info OR .ie OR .gov OR t.co OR bit.ly OR .co.uk))

The result is a clean set of tweets from consumers on a topic.

Contact us if you would like to learn more.

Data beats opinion,

The Eagle Alpha Team

www.eaglealpha.com