Alternative Data News. 18, March 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.

Data Science @ The New York Times

About the speaker : Chris Wiggins is an associate professor of applied mathematics at Columbia University and the Chief Data Scientist at The New York Times. At Columbia he is a founding member of the executive committee of the Data Science Institute, and of the Department of Applied Physics and Applied Mathematics as well as the Department of Systems Biology, and is affiliated faculty in Statistics.

About the talk : The Data Science group at The New York Times develops and deploys machine learning solutions to newsroom and business problems. Re-framing real-world questions as machine learning tasks require not only adapting and extending models and algorithms to new or special cases but also sufficient breadth to know the right method for the right challenge. The speaker will first outline how unsupervised, supervised, and reinforcement learning methods are increasingly used in human applications for description, prediction, and prescription, respectively. The speaker will then focus on the ‘prescriptive’ cases, showing how methods from the reinforcement learning and causal inference literatures can be of direct impact in engineering, business, and decision-making more generally.

2020-03-17 14:57:27.479000+00:00 Read the full story…
Weighted Interest Score: 3.3898, Raw Interest Score: 1.7004,
Positive Sentiment: 0.0000, Negative Sentiment 0.2429

CloudQuant Thoughts : A very interesting presentation from a very fast moving and innovative business, given at the Toronto Machine Learning Summit (TMLS) .

CloudQuant proves value in PA Signals alt date set

CloudQuant says it has proven the value in the Precision Alpha Machine Learning Signals (PA Signals) alternative data set. Its detailed data science study shows a long-short portfolio outperforms the equal-weight S&P 500 ETF by an average of 37.9 per cent per year after transaction costs.

CloudQuant found that over 91.5 per cent of the total return is pure alpha. The results of the study are significant to the 99th per cent level.

Cutting-edge machine learning is transforming quantitative analysis for portfolio managers and traders. PA Identifies structural breaks and exposes investment signals that market participants are currently unable to see. The PA Signal offers a favourable risk-adjusted return that can be used to create large-scale investment algorithms.
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 12.5769, Raw Interest Score: 3.3426,
Positive Sentiment: 0.2228, Negative Sentiment 0.2228

CloudQuant Thoughts : The news system has picked up our release and we have picked up their postings with our news scraper… All is right with the world! Head over to our data catalog to find out about this excellent data set and other quality datasets.

Killi Introduces Passive Data Dividend for Qualifying Users

Killi, a consumer-led privacy application wholly owned by Freckle Ltd. (TSXV: FRKL) (the “Company”), today announced the launch of its Data Dividend™ program to users who consent and share specific pieces of data over a predetermined time frame. Subscribers who share data will automatically receive:

  • notification of their dividends via SMS and/or email
  • the amount, in cash, of each dividend that will automatically be credited to their account,
  • a clear, transparent description of who purchased their data and,
  • clear opt-out functionality for future dividends and data sales

All of the above are market firsts.

The first Data Dividend™ payment was made to U.S. and Canadian users who shared their location data during the month of January 2020, and again to those who shared their data in February. Consumers in February saw a doubling of dividends vs those distributed in January. Starting in March Killi will pay users a weekly dividend for users who have shared location for the previous seven days, multiplied by the number of companies that purchased this data.

2020-03-17 07:10:28+00:00 Read the full story…
Weighted Interest Score: 3.8627, Raw Interest Score: 1.4715,
Positive Sentiment: 0.0613, Negative Sentiment 0.0000

CloudQuant Thoughts : Not a first, this idea has been around for a long time, even Tim Berners Lee (inventor of the internet) was touting something similar last year. But it does not seem to want to go away so it is likely to be in our future. I know I buy things online and get adverts for weeks after, waste of money. Or my daughter uses my computer and I get ads for crazy things everywhere I go. I removed the TV from my life when my daughter was born and it was interesting to ask her each birthday/Christmas what she wanted. She could not come up with anything “I don’t know, what do I want?”. No advertisers had got to her! How do we find an adult balance, I will tell you what I am interested in and what I am willing to have advertised to me (no sugar/candy or meds!). You get access to my desires. You can even see how long it takes me to make a buying decision. In return, the company holding this data for the advertisers pays my internet bill.

Is Python storming ahead of Javascript in fintech?

The use of Python is catching up to Java in banking and fintech applications, but what are the reasons behind the emergence of Python? While three million developers have joined the Java community in the past year, in the banking sector, Python is fast closing in on Java’s position in top spot.

Python’s backstory in banking. Across all sectors, Python has reached seven million active developers fuelled in part by a staggering 62% of machine learning developers and data scientists who now use the programming language

2020-03-18 00:00:00 Read the full story…
Weighted Interest Score: 3.7618, Raw Interest Score: 2.0342,
Positive Sentiment: 0.3137, Negative Sentiment 0.1720

CloudQuant Thoughts : Erm, Yes!

ESG fund investment grows amidst coronavirus and oil turmoil

Environmental, social and governance (ESG) funds may see a continued spike in investment as oil prices crash, despite the Securities and Exchange Commission (SEC) cracking down on funds to clarify their intents, according to Bryan McGannon, director of policy and programs at the Forum for Sustainable and Responsible Investment (USSIF).

McGannon says the oil market crash may lead to long-term ESG investment strategies.

“I think maybe it does put a fine point on how volatile and how much risk is involved in the fossil fuel markets. That might point to a stronger direction towards ESG funds and still being broadly invested in the market, but without that component which is bringing in a lot more risk than you may not want,” says McGannon.

2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 2.9742, Raw Interest Score: 1.4590,
Positive Sentiment: 0.2245, Negative Sentiment 0.2245

CloudQuant Thoughts : Again, head over to our Data Catalog for an ESG data set that we have already tested for you and demonstrated its Alpha opportunity.

How Automation, AI, and Data Integration are Transforming the Pharmaceutical Industry

Pharma companies are more challenged than ever to bring drugs to market safely and cost-effectively. Roadblocks to success include the ever-evolving regulatory environment, growing patient safety concerns, and the burden of outdated technology solutions. Today many businesses find themselves burdened with rigid and costly compliance processes. Furthermore, there is a schism between organizations and the critical insights they need to manage their regulatory, safety, and reporting data.

Organizations must adopt more integrated, automated solutions to align safety and regulatory compliance with solving critical business challenges. There are four critical trends pharma and med-tech organizations are embracing related to digital transformation.

2020-03-16 07:35:26+00:00 Read the full story…
Weighted Interest Score: 3.4321, Raw Interest Score: 1.9044,
Positive Sentiment: 0.3304, Negative Sentiment 0.2915

Intercontinental Exchange Update on Global Operations of Exchanges, Clearing Houses, and Data Services

  • Platforms operating and functioning normally
  • Contingency plans of exchanges and clearing houses working as designed
  • Measures enacted globally to protect health and safety

ICE Data Services

ICE Data Services continues to deliver and support its customer offerings. These include evaluated bond prices for nearly three million securities, real-time exchange data, which is essential for powering global markets, and fixed income indices, which track more than $68 trillion in debt across 40 currencies. Additionally, the ICE Global Network has provided an uninterrupted backbone for financial and commodity markets, offering its ultra-secure, highly resilient network where global financial firms can access one of the broadest ranges of data sources and trading venues.

Equity Exchanges

At the New York Stock Exchange, which is both functionally and symbolically important to public confidence in the market during volatile times, the NYSE Group’s five equity and two options exchanges remain fully functional and operating as designed. The NYSE trading floor, as well as our options floors in New York and San Francisco, remain open and operating. The members of the trading floor community, exercising their human judgement over trades, play a vital role in reducing volatility of individual stocks during historic fluctuations in the market.

2020-03-17 00:00:00 Read the full story…
Weighted Interest Score: 3.2892, Raw Interest Score: 1.5845,
Positive Sentiment: 0.1366, Negative Sentiment 0.1229 Announces the Release of ModelDB 2.0

According to a new press release, “, provider of Verta Enterprise, an open-core end-to-end MLOps platform, today announced the launch of ModelDB 2.0, an industry-leading, open-source model versioning system to make machine learning (ML) development and deployment reliable, safe, and reproducible. In a field that is rapidly evolving but lacks infrastructure to operationalize and govern models, ModelDB 2.0 provides the ability to track and …
2020-03-18 07:15:57+00:00 Read the full story…
Weighted Interest Score: 3.2647, Raw Interest Score: 1.9105,
Positive Sentiment: 0.2011, Negative Sentiment 0.1508

Planixs Releases Realiti® Version 10 – The Most Complete Real-Time Cash, Collateral and Intraday Liquidity Management Suite in the Market

Planixs, the leading provider of real-time, intraday cash, collateral and liquidity management solutions, today announced that it has released GA10 (Generally Available Version 10) to the market. GA10 represents the most complete, function rich and technically capable real-time treasury software in the market.

Realiti GA10 took input from customers, industry experts, regulators and the latest technology advances as part of the release development. GA10 includes a number of enhancements across the software suite including …
2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 3.2084, Raw Interest Score: 1.6986,
Positive Sentiment: 0.4583, Negative Sentiment 0.1078

Machine Learning methods to aid in Coronavirus Response

With Coronavirus on everyone’s mind and forcing almost all of us indoors, many in the ML community are wondering how they might help. While there have been other articles on fighting coronavirus with AI, few have offered a truly comprehensive view. Therefore, I decided to bring together a list of datasets and use cases of machine learning applied to coronavirus. I understand the criticism that when you have a hammer every problem seems like a nail; in other words, to a machine learning practitioner/data scientist every problem seems to have a ML solution. Nevertheless, I believe that machine learning and data analytics can help accelerate solutions and minimize the impacts of the virus in conjunction with all the other great research and planning going.

As we will see below, machine learning can help expedite the drug development process, provide insight into which current antivirals might provide benefits, forecast infection rates, and help screen patients faster. Additionally, although not currently researched, I think there are several other appropriate application areas. That said there are many barriers related to lack of limited training data, the ability to integrate complex structures into DL models, and, perhaps most importantly, access to the available data. I’m not going to detail the techniques below (not that I could as my chemistry/drug-development knowledge is severely lacking), but instead aim to summarize the different resources. Also, I will be creating a central GitHub repository to list resources for using AI to combat coronavirus. Feel free to make a pull request if you find another resource/dataset that you find helpful.
2020-03-18 02:40:25.754000+00:00 Read the full story…
Weighted Interest Score: 2.8335, Raw Interest Score: 1.4156,
Positive Sentiment: 0.2014, Negative Sentiment 0.2073

AI sector reacts to increased policy oversight and market uncertainty

Despite extreme economic uncertainty, policy makers continue attempts to regulate artificial intelligence (AI) as tech vendors adapt to the latest guidelines set by the European Commission last month, calling for a European AI strategy.

Legal counsels are advising small fintechs and companies leveraging AI to utilise regulation for a competitive edge.

“The advice that we’re giving is to feed into any consultation process as soon as you can, because it can be used almost to get a competitive advantage because if you’re the person that’s feeding in and saying ‘this is how we think you should do this’ then you put yourself in a very good position,” says Mardi MacGregor, senior associate at Fox Williams.

2020-03-12 00:00:00 Read the full story…
Weighted Interest Score: 2.7361, Raw Interest Score: 1.2358,
Positive Sentiment: 0.1696, Negative Sentiment 0.1938

AI vs. Coronavirus: How artificial intelligence is now helping in the fight against COVID-19

GeekWire’s Health Tech Podcast goes in-depth with tech innovators bringing new ideas and ingenuity to health and wellness.

Artificial intelligence often raises concerns about privacy, bias and trickery in areas such as facial recognition and deep fake videos. But amidst the outbreak of the novel coronavirus, some technology companies and scientists are looking to AI for a positive impact instead.

“AI and high tech in general have gotten something of a bad rap recently, but this crisis shows how AI can potentially do a world of good,” said Oren Etzioni, CEO of Seattle’s Allen Institute for Artificial Intelligence (AI2) and a University of Washington computer science professor.

Etzioni was speaking on a call Monday organized by the White House Office of Science and Technology Policy, as part of an announcement of a project called the COVID-19 Open Research Dataset, aka CORD-19.

2020-03-17 15:31:57+00:00 Read the full story…
Weighted Interest Score: 2.7296, Raw Interest Score: 1.5484,
Positive Sentiment: 0.3011, Negative Sentiment 0.2151

Some of the Top Free Online Data Science Courses For 2020

Organisations across the world are turning to data science professionals to help businesses extract insights from the vast reserves of data. This means that there is a resilient push by recruitment agencies for people skilled in data mining, programming, and statistical modelling, among others.

Although the demand for talent is high, it is a travesty that this has not been met by appropriate skill sets among people. How can candidates plug this gap without taking on the burden of a massive education loan?

With the proliferation of online courses and tutorials, students can enhance their knowledge for a lucrative career in data science. What is more, a lot of these courses are available for free.

2020-03-18 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5911, Raw Interest Score: 1.4520,
Positive Sentiment: 0.1613, Negative Sentiment 0.0993

6 Spectacular Reasons You Must Master the Data Sciences in 2020

Everyone has heard about Data Science in 2020. But not many people understand what it really is and how it’s going to change the world. It’s a skill that you would want to learn this year considering how its demand is growing. The field might already be too saturated before you can enter the profession. However, this doesn’t mean you should jump right in without any research. First, you should learn how Data Science is relevant to you, whether you will like, and if there are opportunities for you. Let’s start by first understanding what this field is, and then we will discuss why you need to learn it.

Data Science is a field that extracts useful information from loads of structured and unstructured data using algorithms, statistics, and programming. Its primary focus is to use user-generated data to good use. The insights extracted from data are presented to a human in a friendly form, or a computer program uses it to make decisions without any hardcoded instructions.

2020-03-17 20:26:08+00:00 Read the full story…
Weighted Interest Score: 2.5872, Raw Interest Score: 1.5685,
Positive Sentiment: 0.2811, Negative Sentiment 0.0888

The Shift Towards Sustainable Pensions: How Plan Beneficiaries are Shaping the Future of Pension Systems

Many institutional investors are addressing global issues by making allocations into ‘sustainable investments’. Sustainable investing is an umbrella term referring to a spectrum of investment approaches such as ethical screening, ESG (Environmental, Social, and Governance) investing, impact investing in alignment with the Sustainable Development Goals. Pension funds are one of the asset owner groups taking a plunge into the domains of sustainable development. USSIF Foundation reported that, as of 2018, public pension funds accounted for more than half of the $8.6 trillion worth sustainable, responsible and impact investing assets that were managed on behalf of US-based institutional investors¹.

Pension fund asset managers are bound by their fiduciary responsibility to act in the best interests of their plan participants. Over time, these beneficiaries have grown outspoken about aligning their finances with personal values. Most recently in Australia, Mark McVeigh, a 24-year old council worker, took his pension provider REST (Retail Employees Superannuation Trust, $57 billion) to court over limited disclosure on climate-change-related risks.
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.4901, Raw Interest Score: 1.5308,
Positive Sentiment: 0.1148, Negative Sentiment 0.0510

The Corona Correction Isn’t Blind Panic. It’s Markets Drilling the Data

Panic. That’s how the world’s media characterized last week’s sharp drops on financial markets as the threat from coronavirus (COVID-19) became clear. Over $5 trillion was wiped off stock indices ‪in five days and the S&P500 had its worst week since the Global Financial Crisis. No wonder the Fed moved to cut rates on Tuesday (3 March 2020). A turbulent week, no doubt about it. But is this characterization of panic (and the implication that markets acted on emotion) a fair one? If not, what does it say about where markets go next as we enter uncharted territory on COVID-19?

Historically, heart has ruled head when it comes to the way in which markets have reacted to black swan events. But is this still the case in our data-driven age? Just because our 17th Century forebears manically piled into tulip contracts, or in 1929 went with their gut in dumping everything, we shouldn’t assume today’s ‘corona correction’ is the triumph of emotion (i.e. fear) over hard-headed analysis. In fact, I would argue that what happened last week was not panic at all, but a clear, news and data-driven response to a fast-shifting set of circumstances.
2020-03-11 01:48:25+00:00 Read the full story…
Weighted Interest Score: 2.2939, Raw Interest Score: 1.2119,
Positive Sentiment: 0.0433, Negative Sentiment 0.3895

Chinese recovery offers “springboard”, with a revalued yuan boosting post-virus global economy

firepower helping to swerve a deep recession in the next year, according to Toscafund Asset Management’s Savvas Savouri.

Savouri, chief economist and partner at Toscafund, the renowned GBP4 billion multi-strategy London-based hedge fund founded by Martin Hughes, said China is “an engine which will fire up again and far sooner and more powerfully” than the current consensus indicates.

As global stock markets went into freefall this week on the back of the Covid-19 pandemic, Savouri believes Beijing’s response is “the only response that matters”.

Savouri told Hedgeweek: “I have every confidence it will be…
2020-03-13 00:00:00 Read the full story…
Weighted Interest Score: 2.1727, Raw Interest Score: 1.1871,
Positive Sentiment: 0.1696, Negative Sentiment 0.3674

How To Use Effective Data Storytelling For Business Impact

An extraordinary amount of data passes through businesses on an ordinary day.

Data-driven insights are driving a new wave of business intelligence, helping move the needle with quick business impact.

However, with the increasing dependency and usage of analytics that is embedded into day to day decision making at enterprises, the demand for easily consumable and interactive data dashboards is on the rise.

Analytics dashboards have become critical in helping managers and executives make fast decisions. For these dashboards to be truly effective and impactful, the data insights need to be communicated using the best practices of design. But it should not stop there. Analysts need to communicate the data insights to the stakeholders with infectious passion and enthusiasm.

2020-03-17 15:30:00+00:00 Read the full story…
Weighted Interest Score: 2.1660, Raw Interest Score: 1.0506,
Positive Sentiment: 0.3113, Negative Sentiment 0.1686

Path Solutions achieves Microsoft Gold Partner Competency for Data Analytics

Path Solutions demonstrates best-in-class capability and market leadership through demonstrated technology success and customer commitment

March 11, 2020 – Path Solutions, a global Islamic software provider, today announced it has achieved the Microsoft Gold Partner Competency for Data Analytics, demonstrating a best-in-class ability and commitment to meet Microsoft customers’ evolving needs in today’s mobile-first, cloud-first world, and distinguishing itself within the Microsoft partner ecosystem.

To earn a Microsoft Gold Data Analytics Competency, partners must submit client references that demonstrate successful projects in data analytics and must also complete training and assessments to prove their level of technology expertise, thereby ensuring the required level of competency.

2020-03-11 00:00:00 Read the full story…
Weighted Interest Score: 2.0495, Raw Interest Score: 1.2753,
Positive Sentiment: 0.5751, Negative Sentiment 0.0500

Machine learning has uncertainty. Design for it.

We can productize and ship more data science insights — even imperfect, probabilistic ones — with the right designs.

We live in the age of machine learning. That means fewer and fewer of the products we build deal in facts as we know them: instead, they rely more and more on probabilistic things like inferences, predictions, and recommendations. By definition, these things have uncertainty. Inevitably, they will be wrong.

But that doesn’t mean they have no product value. After all, you’d probably rather know there is a 50% chance of rain than have no forecast at all. How can we unlock user value from algorithms that are bound to be wrong? We can do what forecasts do: design our products to be upfront about uncertainty.

In the age of machine learning, designing products that communicate their degree of certainty can be a huge competitive advantage…

2020-03-16 22:51:32.393000+00:00 Read the full story…
Weighted Interest Score: 2.0202, Raw Interest Score: 1.2380,
Positive Sentiment: 0.2251, Negative Sentiment 0.2532

This news clip post is produced algorithmically based upon CloudQuant’s list of sites and focus items we find interesting. We used natural language processing (NLP) to determine an interest score, and to calculate the sentiment of the linked article using the Loughran and McDonald Sentiment Word Lists.

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