Alternative Data News. 05, August 2020

The AltDataNewsletter by CloudQuant

Finding sources and uses for alternative data can be difficult. At CloudQuant we regularly read and search the internet for new sources of data that can be used in our mission to find alpha signals and build quantitative trading strategies. We recognize that we are technology and data junkies so we wrote our own crawler that specifically seeks out web pages, posts, and news articles that give us a snapshot of what is going on in the world of Alt Data. The following is a collection of articles that we think you will find interesting from the past week.


How inside information moved Kodak’s stock this week

How inside information moved Kodak's stock this week

From Reddit Data Is Beautiful

On Monday, July 27, a local news affiliate in Rochester, NY (home of Kodak HQ) tweeted that a “BIG announcement” was coming concerning a deal between Kodak and the federal government. This set off a week of unprecedented trading activity in the company’s stock (KODK).

In the first chart I’ve highlighted when this information was initially leaked on Twitter, and when it was posted on Reddit later that afternoon. The lower chart shows that compared with the next couple days, Monday’s activity is pretty much invisible. While the $4 million traded on Monday was a lot for KODK, the next day we see $2 billion flood into the company ahead of the official announcement at the end of day.

These charts were done in Javascript with the Nivo library. Price and volume data is from IEX. A write-up with more details is at ChartFleau.

2020-08-02 Read the full story…

TSA checkpoint travel numbers rising again, holding over 700k for first time since March 2020

See the TSA Checkpoint website for more information

CloudQuant Thoughts : I refresh this page daily to see if people are starting to move around again, and they are. For the first time since March we had four straight days over 700k, now five straight days over 700k!

Why Aren’t More Firms Monitoring Social Media to Detect Insider Trading?

Insider dealing/trading is one of the biggest triggers of market abuse today and has been a prominent risk during the current pandemic as those with access to confidential information have transitioned to remote working. At the height of the lockdown, studies showed that the majority of traders were accessing their trading technology remotely, with almost 60% of FX traders working from home. This is concerning because of the possibility that material non-public information (MNPI) could be overheard, discovered in the trash, or inadvertently disclosed in other ways.

Of course, we are now seeing a gradual shift back to office work, but that does not necessarily reduce this risk. On the contrary, working in part from the office and from home doubles the number of locations in which authorised persons can access insider information, increasing the risk of illicit or accidental disclosure. So how do you control and mitigate for insider trading risks? The answer, in part, is the use of social media and news in monitoring market abuse.

2020-08-04 13:22:16+00:00 Read the full story…
Weighted Interest Score: 2.6855, Raw Interest Score: 1.3129,
Positive Sentiment: 0.2387, Negative Sentiment 0.4177

CloudQuant Thoughts : See our top story this week for the uproar surrounding KODK and its massive increase in trading volume the day before a deal with the Government for PPE manufacture was announced. Insider dealing is not difficult to stop, it is just difficult to prove, once that volume started to pick up every trade that followed can assert that it was just responding to market activity, so only large unusual early activity can be investigated.

Transaction data shows setback for restaurant industry recovery (Video)

Transactions at major restaurant chains nationwide are stuck in a negative holding pattern. They dropped 11 percent overall for the week ending July 26, according to the NPD Group. While the data had previously shown signs of improvement, the trend recently began to reverse as Covid-19 cases climbed. CNBC’s Kate Rogers reports.

2020-08-05 00:00:00 Read the full story…
Weighted Interest Score: 2.5381, Raw Interest Score: 1.5228,
Positive Sentiment: 0.2538, Negative Sentiment 0.7614

CloudQuant Thoughts : The food industry is already struggling, it major players are worried about the loss of the $600 unemployment stimulus payments then you can be sure that the loss of those extra payments will ripple through numerous other retail sectors.


ESG Section

CloudQuant also provides access to Alternative Data Sets including an ESG data set from G&S Quotient. See our Data Showcase for more information.

How AI and data enables ESG to make real world impact

ESG is a Data and AI problem. The benefits of incorporating Environmental, Social and Governance (ESG) within business targets are well understood by companies and regulators, and especially investors. Research shows a quarter of all fund investors planned to increase holdings in the sustainable sector over the next half year.

Organisations looking to cement themselves as leaders in Corporate Social Responsibility (CSR) – in any sector, financial services included – must take technology-driven approach in order to enhance operational resilience and to satisfy investors. However, at the moment, ESG initiatives at most companies rely heavily on throwing bodies at the problem, resulting in manual and time-intensive processes that limit an organisation’s ability to respond to rapid changes in the economy, geopolitics or consumer behaviour.
2020-08-04 21:31:40 Read the full story…
Weighted Interest Score: 2.8115, Raw Interest Score: 1.3507,
Positive Sentiment: 0.1381, Negative Sentiment 0.1995

Refinitiv adds Sigwatch data to Enhanced Due Diligence reports

“This alternative dataset will provide our customers with unique “on the ground” insight, with a focus on critical ESG factors”, says Charles Minutella, head of Refinitiv’s Enhanced Due Diligence business.

Provider of financial markets data and infrastructure Refinitiv is expanding the scope of its Enhanced Due Diligence (EDD) reports with the inclusion of NGO sourced data from Sigwatch, a UK-based provider of global NGO and ESG issue tracking and reputational impact data. The agreement enables Refinitiv customers to vet companies and investments against a reliable and unique source of alternative data on reputational and governance risk.

Enhanced Due Diligence reports from Refinitiv provide detailed background checks on companies and investors that require a higher level of scrutiny. The reports support compliance teams as they look to meet their regulatory obligations, optimize the due diligence process and protect company reputation.
2020-08-05 15:16:13+03:00 Read the full story…
Weighted Interest Score: 3.5075, Raw Interest Score: 1.7423,
Positive Sentiment: 0.3630, Negative Sentiment 0.2541

ESG Data – What can we extract from foreign worker visa applications?

Employers utilize H1B and Permanent visas to hire workers, often knowledge workers, for jobs they cannot easily fill from the domestic labor force. While the permanence of the suspension of several visa types including H1B (though not Permanent), which was announced in April and extended in late June, remains to be seen (the proclamation “shall expire on December 31, 2020, and may be continued as necessary”), we can still use the data – currently updated through the first quarter of 2020 – to examine trends in hiring.

The data, captured in ExtractAlpha’s ESGEvents dataset, goes back to 1999 and covers over 2000 liquid, publicly traded U.S. companies per year. 1 of the 11 event types is visa applications (H1B and permanent), including the job title and function, and total no. of workers sought (H1B only). We look at the most common words in job descriptions in US H1B and Permanent visa applications, and see which ones have increased the most versus the prior five years.

2020-07-29 Read the full story…


Getting the data right for crucial business decision making and improved productivity

Data-literacy, describing an enterprise’s ability to read, write and communicate data in context, is becoming an explicit and necessary driver of business value, demonstrated by its increasingly vital inclusion in data and analytics strategies in financial services.

People don’t make the right data choices : Proficient data-literacy is only realistically achievable if the data is high-quality. Financial Services Institutions need to have standardised, accurate and impartial datasets to succeed. Gartner Research’s Distinguished VP Analyst, Debra Logan, points out that “data-driven decisions” can seem like an unfamiliar and concerning concept.* Humans tend to want to use the data they have to support decisions they have already made. In addition, the amount of time people spend inputting data relative to their other work sometimes discourages them from providing all the necessary data at the right time. According to Copper CRM, 14 hours per week per sales person is spent on manual CRM data entry and according to SalesForce, 91% of CRM data is incomplete and 70% goes bad or becomes obsolete every year. Data gathering techniques around client engagements and client preferences that are a distraction from core tasks will always cause inconsistent data, especially in an increasingly mobile world where many client interactions are taking place outside of the office on a range of different devices.

Using AI to get the right data at the right time…

2020-08-05 09:32:17 Read the full story…
Weighted Interest Score: 4.7281, Raw Interest Score: 1.9885,
Positive Sentiment: 0.2696, Negative Sentiment 0.4044

The Data Science Interview Blueprint

After my Data Science Manager offer with Deliveroo was rescinded a few months after I was preparing to leave my cosy consultancy job, and I didn’t have much of a safety net to fall back on and be unemployed for too long. I’ll share everything that helped me land two Data Scientist offers with FaceBook, with the hope that it might help one of you who also finds themselves in the unfortunate place I was in a few months ago.

  1. Organisation is Key
  2. Software Engineering
  3. Applied Statistics
  4. Machine Learning
  5. Data Manipulation and Visualisation

2020-08-03 18:13:01.456000+00:00 Read the full story…
Weighted Interest Score: 4.4203, Raw Interest Score: 1.6521,
Positive Sentiment: 0.2313, Negative Sentiment 0.3139

Machine learning: foe and friend for market surveillance

The rise of machine learning in trading is posing new challenges for market surveillance, but the technology could also be a useful tool for identifying abuse risks, according to a report from the FICC Markets Standards Board (FMSB). The increased use and sophistication of algorithmic trading and machine learning technologies is posing a significant challenge for the surveillance capabilities of firms, says the industry-led FMSB.

Firms now have huge amounts of structured and unstructured data but this creates the problem of noise, making it difficult to extract the data signals necessary to isolate and identify suspicious activity. The increasing complexity of trading strategies and the nascent deployment of machine learning techniques also creates new challenges related to evidence of “intent, complexity, and the risk of self-learning machines actively choosing to manipulate markets,” says the report.

But machine learning could also help market surveillance because it can process large complex data sets efficiently.

2020-08-04 14:03:00 Read the full story…
Weighted Interest Score: 3.7339, Raw Interest Score: 2.1247,
Positive Sentiment: 0.2742, Negative Sentiment 0.6854

Why You Should Learn R — Learn Data Science with Dataquest

So you want to learn data skills. That’s great! But we offer tons of data science courses. Why should you learn R programming specifically? Would it be better to learn Python?

If you really want to dig into that question, we’ve demonstrated Python vs. R to show how each language handles common data science tasks. And while the the bottom line is that each language has its own strengths, and both are great choices for data science, R does have unique strengths that are worth considering!

  1. R is built for statistics.
  2. R is a popular language for data science at top tech firms
  3. Learning the data science basics is arguably easier in R.
  4. Amazing packages that make your life easier.
  5. Inclusive, growing community of data scientists and statisticians.
  6. Put another tool in your toolkit.

2020-07-30 20:48:59+00:00 Read the full story…
Weighted Interest Score: 3.6066, Raw Interest Score: 2.2656,
Positive Sentiment: 0.5664, Negative Sentiment 0.1049

Financial Market Data Spend To Decline

Global spending on financial market data is expected to decline marginally in 2021, with 33.7% of respondents in Burton-Taylor’s Financial Market Data/Analysis 2020 Global Demand Survey expecting spending to decline by more than 2%, with 8.5% of respondents expecting total spending to decline by 6% or more according to a new study published today by Burton-Taylor International Consulting, part of TP ICAP’s Data & Analytics division. User segments expected to see the largest declines included salespeople and corporate C-Suite users, with 15% and 13% in each category expecting spending declines in excess of 6%, respectively.

Despite the broad economic impact of COVID-19 shutdowns, the financial industry remains uncertain about the influence on market data spending, with just over half of respondents (54%) expecting COVID-19 to have a significant influence on market data spending in 2021 while 46% expect little to no influence from the virus on spending activity.

A smaller percentage of our respondents expect market data spending to increase in 2021, with 23.0% expecting spending to increase by 2% or more, and 5.4% expecting spending to increase by 6% or more. The importance of data to support risk and compliance processes remains top of mind in the industry, with 49% of respondents expecting spending to rise by more than 2% and 15% expecting the growth to exceed 15%. The growth expectations represent a continuation of recent trends, with the segment seeing a 10% in spending in 2019.
2020-08-05 09:10:44+00:00 Read the full story…
Weighted Interest Score: 3.3279, Raw Interest Score: 1.9721,
Positive Sentiment: 0.0411, Negative Sentiment 0.3287

How Do Data Scientists Create High-Quality Training DataSets For Computer Vision

For any large-scale computer vision application, one of the critical criteria to success is the quality and quantity of the training dataset required to train the relevant machine learning model.

Open-source datasets such as ImageNet are sufficient to train machine learning models for computer vision applications that do not require high accuracy or are not too complicated, But for more complex use cases, obtaining a large amount of high-quality training data can be quite challenging, such as autonomous driving, safety monitoring systems, medical image diagnosis and more.

In this article, we take a look at how to quickly create (including collection, labelling, and quality inspection) high-quality training data sets for various computer vision scenarios.

2020-08-05 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.2146, Raw Interest Score: 1.8328,
Positive Sentiment: 0.2676, Negative Sentiment 0.1204

Big banks back Canadian launch of Financial Data Exchange

Canada’s big five banks are among 31 organisations onboard for the launch of Financial Data Exchange (FDX) in the country, vowing to work together to promote Open Banking through the development of a secure, common, interoperable, flexible and royalty-free industry standard for financial data sharing.

Royal Bank of Canada, TD Bank, Tangerine Bank (Scotiabank), Bank of Montreal and CIBC are joined by a swath of smaller fintechs, aggregators, and credit card networks for the Canadian launch of FDX, which already operates in the US.

Canada is in the process of a lengthy Open Banking review. The Department of Finance Canada set up an Advisory Committee on the issue in 2018 to investigate whether the country should follow the UK in making it easier for people to let third party financial services providers access their banking data.

Recently, the latest stage – consulting stakeholders on standards to enhance data protection, examining issues such as governance, consumer control of personal data, privacy, and security – was put on ice because of the Covid-19 pandemic.

2020-07-30 00:01:00 Read the full story…
Weighted Interest Score: 3.1190, Raw Interest Score: 1.7274,
Positive Sentiment: 0.2399, Negative Sentiment 0.0480

Open banking: the lucrative benefits for smaller FIs and fintechs (VB Live)

Open banking is a global trend changing the way financial data is accessed and shared in the U.S. Learn about the benefits this new and growing open banking ecosystem offers for FIs and fintechs positioned to leverage it when you join this VB Live event.

“One of the biggest changes coming with open banking is in how transaction data from user end accounts gets aggregated,” says David Nohe, CEO of the fintech company FinGoal. “We’re going to see a significant transition from the old paradigm of what was essentially screen scraping, or a host of one-off connections, to standardized APIs and data flow. And that’s a great stride forward.”

Right now, the smaller financial institutions — and there are about 10,000 small credit unions and community banks spread across the U.S. — are struggling with user data. For many of them, a user might link their accounts, and two weeks later that link is broken because of the lack of open banking APIs

2020-08-05 00:00:00 Read the full story…
Weighted Interest Score: 3.0776, Raw Interest Score: 1.5183,
Positive Sentiment: 0.2667, Negative Sentiment 0.1436

Human Vs. Machine – Who’s The Better Stock Picker?

FinTech has seen an explosion in tech-driven financial service offering with over $50 billion invested as of 2018. But does it work? Do seasoned investment analysts or algorithms make superior stock picks? Researchers at Indiana University have recently examined this question.

The research was conducted by Braiden Coleman, Kenneth Merkley and Joseph Pacelli examining 76,568 robo-analyst reports over the 2003-2018 period. This is what they found.

Key Differences…

  • More Balanced Recommendations
  • More Revisions
  • Different Windows

2020-08-04 00:00:00 Read the full story…
Weighted Interest Score: 2.9237, Raw Interest Score: 0.8933,
Positive Sentiment: 0.2707, Negative Sentiment 0.1624


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