Alternative Data News. 28, October 2020

Alternative Data Newsletter

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.


1920 Presidential Election

By Reddit User : u/SocialExplorerInc

Data source and tool: https://www.socialexplorer.com/

Interactive map: 1920 Presidential Election

Warren Harding (R) vs James Cox (D)

Read the Full Story…

CloudQuant Thoughts : As we head into the conclusion of the US Election, it is fascinating to see how the US Voting broke down 100 years ago. A very nice presentation of data.

Hands-On Guide to Datatable Library For Faster EDA (exploratory data analysis)

Working with tabular data in data science we always use the Pandas library in Python. This is widely used for data exploration, analysis, munging and manipulation. These are the primary steps for understanding the data well and making it ready for the model to fit. The only disadvantage of using pandas is its time consuming when there’s a large amount of data(big data).

Datatable overcomes the limitations of pandas and speeds up the process of EDA (exploratory data analysis). Datatable has been built by H20.ai, one of the leading AI ML companies in the world. Datatable is pretty similar to pandas and R data.table libraries. Datatable has proper documentation. Works with Python version 3.6+.

Advantages of Datatable

  • Supports null values, date-time and categorical types.
  • Efficient algorithms for sorting/grouping/joining.
  • Minimal data copying by using “rowindex” views in filtering/sorting/grouping/joining
  • operators to avoid unnecessary data copying.Faster data accessing than pandas
  • Easily convert to another data-processing framework.

In this article, I’ll be discussing the implementation of the datatable library with a large dataset.

2020-10-28 11:30:21+00:00 Read the full story…
Weighted Interest Score: 3.1234, Raw Interest Score: 1.5953,
Positive Sentiment: 0.1387, Negative Sentiment 0.1734

CloudQuant Thoughts : This is interesting, always looking for something that is faster!

Intel Geospatial is a cloud platform for AI-powered imagery analytics

Intel today quietly launched Intel Geospatial, a cloud platform that features data engineering solutions, 3D visualizations, and basic analytics tools for geovisual workloads. Intel says it is designed to provide access to 2D and 3D geospatial data and apps through an ecosystem of partners, addressing use cases like vegetation management, fire risk assessment and inspection, and more.

The geospatial analytics market is large and growing, with a recent Markets and Markets report estimating it will be worth $96.34 billion by 2025. Geospatial imagery can help companies manage assets, like network assets prone to damage during powerful storms. Moreover, satellite imagery and the AI algorithms trained to analyze it have applications in weather prediction, defense, transportation, insurance, and even health care, mainly because of their ability to capture and model environments over extended periods of time.
2020-10-27 00:00:00 Read the full story…
Weighted Interest Score: 3.6568, Raw Interest Score: 1.5120,
Positive Sentiment: 0.1055, Negative Sentiment 0.0703

CloudQuant Thoughts : It really is a pity that the only graphic they have to go with this article is an animation that looks worse than a launch Playstation 1 game from 1995. Presentation is extremely important.

CloudQuant has been nominated for a Benzinga Global Fintech Award

We are proud to announce that our industry leading technology has been nominated for a Benzinga Fintech Award 2020 in the category of Best Data Analysis Tool.

CloudQuant Data Liberator

Data Liberator API: Our single, simple data access platform resolves the ETL, timestamp, symbology, and access issues that bedevil quality research. It also serves data into our industry-leading, research applications including :

  • CQ Explorer: Visualising historical time series, alternative, and stock market data
  • CQ Mariner: Tick level market backtesting
  • CQ AI: Scaleable Jupyter Labs research tools with secure access to datasets, leading ML and AI libraries, and investment backtesting.

VOTE FOR US HERE!


ESG Section

CloudQuant has some of the best Alternative Datasets in the world, all deliverable, cleaned, tickerized, and ready to use via its Liberator API including an excellent ESG Dataset. Head over to our data catalog to find out more.

Exchanges Take Differing Approaches To ESG Derivatives

Deutsche Börse’s Eurex has launched a suite of derivatives based on ESG versions of benchmark indices while Nasdaq aims to launch bespoke products to meet the variety of environmental, social governance strategies in the market.

This month Eurex announced that it is launching ESG futures and options on DAX 50 ESG and EURO STOXX 50 ESG indices. The exchange is adding another European benchmark to its offering and covering the German market for the first time in ESG derivatives.

Randolf Roth, member of the executive board of Eurex, told Markets Media: “The DAX 50 and Euro STOXX 50 are very succe…
2020-10-22 12:30:40+00:00 Read the full story…
Weighted Interest Score: 4.8049, Raw Interest Score: 2.0686,
Positive Sentiment: 0.1217, Negative Sentiment 0.0730

Gorman Says ESG Is ‘Not A Fad’

James Gorman, chairman and chief executive of Morgan Stanley, said the increase in interest in environmental, social and governance strategies is exploding and provides an extraordinary growth opportunity. Gorman spoke as SIFMA’s 2020 Annual Meeting, The Virtual Capital Markets Conference, today. He said: “ESG is not a fad as it is what clients want. Interest is exploding and I am not surprised as investors want to go with their heart.”

In 2014 the bank set up the Morgan Stanley Institute for Sustainable Investing, with an independent advisory board, to bring together all the sustainable initiatives across the firm and to help mobilize capital to finance the transition to a green economy.
2020-10-19 13:48:46+00:00 Read the full story…
Weighted Interest Score: 3.7726, Raw Interest Score: 2.1894,
Positive Sentiment: 0.1916, Negative Sentiment 0.0821

Talking ’bout a revolution: The disruptive impact of ESG on wealth management

The wealth management industry stands on the cusp of revolution, the implications of which extend to product, proposition and the structure of the business itself. At the frontline of this revolution is ESG investing: the use of environmental, social and governance criteria for portfolio creation and maintenance.

Millennials and more – The ascendance of a new generation of retail investors has enhanced the spotlight on this fast growing area, as has COVID-19. Indeed, the pandemic has helped extend the focus from environmental to social and governance issues among millennial and other sustainability-oriented investors. They have proven keener than ever to express their values, both via the ballot box and their investments amidst the shift to life online.
2020-10-22 10:00:00 Read the full story…
Weighted Interest Score: 3.1910, Raw Interest Score: 1.6602,
Positive Sentiment: 0.1940, Negative Sentiment 0.1940


GTCOM-US Launches Chinese Sentiment Data on Bloomberg • Integrity Research

Santa Clara, CA-based research and data analytics provider, GTCOM Technology Corporation (GTCOM-US) recently announced that it has released its sentiment data from Chinese sources on key companies, economic activity and themes on Bloomberg’s Enterprise Access Point data platform.

Leveraging its advanced Natural Language Processing (NLP) and machine learning technology, GTCOM-US has provided Bloomberg users with access to sentiment data from China along five categories, including Global Luxury Brands, the Technology Media Telecom Sector, as well as a Russell US 3000 Aggregate category. The new GTCOM-US dataset enables users to monitor sentiment trends regarding specific companies, sectors or thematic issues from Chinese sources.

2020-10-26 07:30:00+00:00 Read the full story…
Weighted Interest Score: 7.9446, Raw Interest Score: 2.7939,
Positive Sentiment: 0.0755, Negative Sentiment 0.0252

Information Services Q&A: Lauren Dillard, Nasdaq

Our market data business continues to be strong. We recently began providing real-time market data to eToro users, further expanding our footprint among individual investors. Earlier this year we launched Nasdaq Cloud Data Service, which disseminates our data on the cloud. Lowering the cost and barrier of entry for brokers and others that serve the investing public.

I think we’ve seen the most growth, however, in our analytics business. Whether it’s about supply chain, consumer spending, indications of travel, or anything else, the need for alternative data sets increased dramatically this year.

2020-10-27 07:01:39+00:00 Read the full story…
Weighted Interest Score: 3.5517, Raw Interest Score: 1.6086,
Positive Sentiment: 0.3312, Negative Sentiment 0.0473

Python For Data Science – For Absolute Beginners

What you Will learn ?

  • Python Basic Commands
  • Data types
  • Important Packages
  • Data Manipulations
  • Basic Statistics and Reporting
  • Data Visualizations in Python
  • Data Cleaning
  • Functions

Course Description – Course Covers six topics.

  • Basic Commands of python and important packages
  • Data Manipulations with python
  • Basic Statistics
  • Data Visualizations with python packages
  • Data Validation and Cleaning in Python
  • Python Objects and Functions

We assume that the participants have no background in python and start with very basic topics. After this course, you can learn Machine Learning, Deep Learning, and Other Data Science sources. You must download the resources to learn this course

Course Requirements

  • Google Colab need to be pre-installed.
  • Basic Knowledge on Data science

2020-10-28 09:31:42+00:00 Read the full story…
Weighted Interest Score: 6.0266, Raw Interest Score: 2.9683,
Positive Sentiment: 0.0000, Negative Sentiment 0.2047

Predictions 2021: The Time Is Now For AI To Shine

AI is transformational. AI is exciting. AI is mysterious. AI is scary. AI is omnipresent. We’ve heard this oscillating narrative over the last few years (and will continue to in the future), but in this unprecedented year, one thing became clear — enterprises need to find a way to safely, creatively, and boldly apply AI to emerge stronger both in the short-term and in the long-term. 2020 gave leaders the impetus, born out of necessity, and confidence to embrace AI with all its blemishes.

In 2021, 35% of adaptive- and growth-mode firms will invest in workplace AI solutions to help workers deal with disruption.

The kinks in AI still remain: lack of trust, poor data quality, data paucity for some, and a dearth of the right type of tools and talent. 2021 will see companies and C-level leaders tackle some of these challenges head on, not because they want to but because they have to. The time is now for AI to shine.

2020-10-22 14:00:22-04:00 Read the full story…
Weighted Interest Score: 5.5483, Raw Interest Score: 2.1069,
Positive Sentiment: 0.1844, Negative Sentiment 0.2107

Top 10 Datasets For Cybersecurity Projects One Must Know

The techniques of machine learning have been found to be an attractive tool in cybersecurity methods, such as primary fraud detection, finding malicious acts, among others. Besides these use cases, machine learning can be used in various other cybersecurity use-cases, including malicious pdf detection, detecting malware domains, intrusion detection, detecting mimicry attacks and more.

Below here, we listed the top 10 datasets, in no particular order, that you can use in your next cybersecurity project.
2020-10-28 09:30:07+00:00 Read the full story…
Weighted Interest Score: 5.3755, Raw Interest Score: 1.8335,
Positive Sentiment: 0.0573, Negative Sentiment 0.4011

Finastra launches Fusion Data Cloud next generation data platform for rapid financial services innovation

A data ecosystem: Supported by secure Microsoft Azure technology, Fusion Data Cloud enables banks to share their data with leading fintechs, as well as ingest data from external data sources, to create innovative new data solutions in weeks, instead of months. These solutions are pre-integrated with Finastra core products to drive scale, enable fast delivery, and provide flexibility to help institutions grow and increase customer value.

Underpinned by the FusionFabric.cloud open developer platform, Fusion Data Cloud provides:

  • A data ecosystem: Supported by secure Microsoft Azure technology, Fusion Data Cloud enables banks to share their data with leading fintechs, as well as ingest data from external data sources, to create innovative new data solutions in weeks, instead of months. These solutions are pre-integrated with Finastra core products to drive scale, enable fast delivery, and provide flexibility to help institutions grow and increase customer value.
  • Actionable insights: Artificial intelligence (AI) and machine learning (ML) algorithms create predictive and prescriptive analytics and delivery of real-time decision-making and insights as a service. For example, institutions can detect potential churn and better understand customer behavior to recommend the Next Product To Buy (NPTB) based on retail banking data. This equips financial institutions with intelligent insights to mitigate risk and optimize operational efficiencies.
  • Connected experiences: Business Intelligence (BI) tools provide analytics visualization and omnichannel interaction. With six AI- and ML-driven BI solutions available today, financial institutions can, for example, gain an operational and 360-degree view based on payments data, and optimize loan processing and application conversion based on mortgage data.

2020-10-26 00:00:00 Read the full story…
Weighted Interest Score: 4.4190, Raw Interest Score: 2.2941,
Positive Sentiment: 0.6350, Negative Sentiment 0.0205

Forrester: Top Emerging Technology Trends To Watch In 2021 And Beyond

CAMBRIDGE, Mass. , Oct. 22, 2020 /PRNewswire/ — According to Forrester (FORR: NASDAQ), the next decade will require CIOs to both respond to digital acceleration and proactively manage uncertainty. Rapidly changing consumer trends, complex security concerns, the ethical use of artificial intelligence, and the increasing impacts of climate change will drive businesses to incorporate systemic risk into their long-term planning.

The Forrester report “Top Trends And Emerging Technologies, Q3 2020” highlights important trends and organizes emerging technologies into seven key domains that will play a big role in accelerating this shift: artificial intelligence; business automation and robotics; enterprise risk management; human experience and productivity; new compute architectures; next-generation communications; and Zero Trust security.

2021-10-28 00:00:00 Read the full story…
Weighted Interest Score: 3.0519, Raw Interest Score: 1.4881,
Positive Sentiment: 0.2790, Negative Sentiment 0.0775

Bringing Real Options Trading to the Commercial Real Estate Market

Consider the value of a 15-story Class B office building with four elevators in the central part of any American city. While its value may have always fluctuated, based on some tangible, measurable factors you could estimate closely its value in February 2020. Then came the pandemic.

All the tenants are now working successfully from home. You don’t know how many will renew their leases or what office space in general will be worth post-pandemic. Now multiply that scenario tens of thousands of times across every class of commercial real estate and you begin to see the scope of the commercial real estate valuation problem.
2020-10-27 07:01:52+00:00 Read the full story…
Weighted Interest Score: 3.0286, Raw Interest Score: 1.6430,
Positive Sentiment: 0.2215, Negative Sentiment 0.1846

Why Data Scientists Are Increasingly Using Z by HP Workstations

“Pressure to convert massive volumes of data into real-time actionable insights has triggered a data race—and if you’re not in it, you’re already losing.”

Datasets are getting larger by the day and unpacking them for insightful business leverage has become tedious. Data science leaders across many organisations have started looking out for solutions that can take this burden off their shoulders. At the cusp of this rising challenge sits HP whose cutting edge workstations are empowering data scientists around the world to explore multi-billion record datasets in real time.

When it comes to workstations, HP has been leading the roost for over a couple of years now. The Z series workstations especially, by HP, pack a punch with an on board NVIDIA graphic unit among other state of the art components.

2020-10-21 12:30:57+00:00 Read the full story…
Weighted Interest Score: 3.0158, Raw Interest Score: 1.6184,
Positive Sentiment: 0.2597, Negative Sentiment 0.1598

Is Snowflake Stock Overpriced?

Recent-IPO darling Snowflake (NYSE:SNOW) deserves its high valuation. The data-warehouse-as-a-service specialist has been growing revenue at a fast pace over the last several years, and it remains exposed to the long-term growth opportunities that cloud and data analytics represent. But the company’s lofty valuation suggests the market expects phenomenal long-term performance. Does that mean that Snowflake’s stock overpriced, despite the company’s attractive potential?

Phenomenal growth in data storage and analysis – As enterprises have been digitizing their operations, they have been accumulating vast amounts of data. That trend isn’t likely to wane: Research outfit IDC estimates the amount of worldwide data will grow at a compound annual rate (CAGR) of 61% by 2025. The good thing is companies can exploit that knowledge to make business decisions. However, until recently, analyzing such a growing amount of data required cumbersome processes, hardware, and software.

2020-10-28 00:00:00 Read the full story…
Weighted Interest Score: 2.8509, Raw Interest Score: 1.5735,
Positive Sentiment: 0.3631, Negative Sentiment 0.1210

Microsoft Partners With Netflix To Create New Data Science Learning Modules

With the increasing requirement for more data scientists, ML experts, and AI engineers in every industry, Microsoft, in partnership with Netflix, has launched three new learning modules to guide learners through beginning concepts in data science, machine learning and artificial intelligence.

Inspired by the new Netflix original film — ‘Over the Moon’ these learning modules include three paths — planning a Moon mission using the Python Pandas Library; predicting meteor showers using Python and VC Code; and using AI to recognise objects in images using Azure Custom Vision.

The growing requirement of data scientists comes with criteria of having a broad set of skills from data analysis with no-code and low-code solutions which will help them with designing and writing intricate ML models and solve some of the planet’s most difficult problems. Keeping this in mind, Microsoft, partnering with Netflix, has launched these modules for providing high quality, free content to help learners develop their skills depending based on their professional goals and personal interests.
2020-10-26 06:02:27+00:00 Read the full story…
Weighted Interest Score: 2.7502, Raw Interest Score: 1.4764,
Positive Sentiment: 0.1988, Negative Sentiment 0.0852


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