Alternative Data News. 04, November 2020
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.
CloudQuant are in the running for a top prize at the Benzinga Global Fintech Awards November 10th 2020
CloudQuant will be attending the Benzinga Global Fintech Awards November 10th 2020. CEO Morgan Slade will be taking part in a fireside chat and our sales team will be available throughout the event to answer your questions and discuss our huge range of alternative datasets.
We are also 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.
2020-11-02 Read the Full Story…
It’s about to start…
Reddit User : JustGlowing
Data source : Google Trends
Tools : Python with the libraries matplotlib and pytrends
2020-10-29 Read the Full Story…
CloudQuant Thoughts : I bet you thought I was going to pick some Election Data Analysis, well I think we need to move on.. to Christmas.
Information Services Q&A: Lauren Dillard, Nasdaq
Lauren Dillard joined Nasdaq as Head of Global Information Services in May 2019. Markets Media recently caught up with Lauren for an update on the business.
“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
CloudQuant Thoughts : If you are not already using Alternative Data, or do not know where to start, reach out to us and we can help you get started – Fast. If you have an Alternative Data Set that you want to promote far and wide, get in touch and let us explain what we can do to turbocharge your data sales. Email Sales@CloudQuant.com, Make an appointment to speak to a CloudQuant Representative, or fill in the form on the right to be contacted back by a CloudQuant Representative. Also see our Data Catalog and our Repository of White Papers.
CloudQuant also provides Alternative Data Sets including an excellent ESG data set with proven Alpha. Head over to our data catalog for more information.
Environmentally friendly funds have been drawing cash as Biden polling lead holds
Funds focused on sustainable investing are attracting record inflows as investors increasingly prioritize ESG metrics, or a company’s environmental, social and governance factors.
Democrat Joe Biden’s ascent in the polls and his environmentally friendly proposals have driven more investors into climate-focused funds, some of which have seen their shares more than double this year.
Here’s a list of the most popular sustainable funds over the last month, according to data compiled by FactSet:…
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 4.0367, Raw Interest Score: 2.0183,
Positive Sentiment: 0.3670, Negative Sentiment 0.0000
Number Of ESG Indices Globally Rise By 40.2%
hmark survey. This year’s survey shows an industry that is growing and diversifying its products and services to meet expanding investor needs. Main growth drivers this year include indices measuring environmental, social and governance (ESG) criteria, which saw a 40.2% increase, and fixed income indices, which had a 7.1% increase.
Rick Redding, the CEO of IIA, commented: “The survey’s 2020 results demonstrate a highly competitive industry that continues to broaden its offerings to meet investor demand. Indices today are transparent and reliable representations of market segments covering a wide spectrum of asset classes and in…
2020-11-03 05:35:46+00:00 Read the full story…
Weighted Interest Score: 2.7453, Raw Interest Score: 1.5360,
Positive Sentiment: 0.1617, Negative Sentiment 0.0404
What Matters Most In ESG Investing: How To Spot Opportunities Across Market Cycles And The Capital Structure
Pensions, insurers, endowments, and foundations are asking asset managers to incorporate elements of sustainability and inclusion into their investing. Responding to investor interest is complex. There are over 600 environmental, social, and governance (ESG) frameworks and standards, and materiality—focusing on sustainability issues that drive stakeholder decision-making—varies across industries, the capital structure, and economic cycles. Sustainability Accounting Standards Board (SASB) is perhaps the most widely accepted of the sustainability standards. SASB’s Materiality Map SASB is championed by an Investor Advisory Group with an aggregate $40 trillion in assets, including BlackRock BLK +2.6%, which in January asked the 15,000 companies in its portfolio to publish disclosure in line with industry-specific SASB standards by the end of the year. SASB’s Materiality Map outlines how material ESG factors vary across 77 industries. Institutional investors and issuers would benefit from analogous materiality maps by asset class, strategy, and phase in the economic cycle.
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 2.6693, Raw Interest Score: 1.5476,
Positive Sentiment: 0.4422, Negative Sentiment 0.1769
How Data Gravity Is Forcing a Shift to a Data-Centric Enterprise Architecture
Data is the output of society and everything we do — and the enterprise is fast becoming the world’s data steward. Digital-enabled interactions are becoming the norm, increasing enterprise data exchange volumes. In fact, it’s estimated that by 2024, Global 2000 Enterprises will create data at a rate of 1.1 million gigabytes per second and will require 15,635 exabytes of additional data storage annually. While applications like artificial intelligence (AI) and machine learning (ML) are fast becoming the center of today’s digital enterprise, helping to create efficiencies and improve customer experience, they also add to the accumulation of data that must be processed, analyzed, and applied to keep businesses running smoothly and spur innovation.
The accumulation of this data describes an effect similar to what occurs with the gravity between objects like the earth and the moon — data gravity. The data becomes harder to move, which can cause complexity and prevent digital transformation from occurring. For instance, if enterprises aren’t monitoring their data gravity challenges, it can cause slow response times, create information silos, and ultimately stall profitability and growth.
2020-11-02 Read the Full Story…
How consumer data provider Yodlee can help bolster the buy-side portfolio-building process
Envestnet | Yodlee, a data aggregation and analytics platform specialising in consumer spending data analytics, says asset managers are increasingly seeking out such alternative data insights in their hunt for alpha. Nikhil Nadkarni, Vice President, Data Products, explains how the consumer spending data analytics can help provide asset managers a view into consumer interactions with brands and incorporating insights into the investment research processes.
“Equity Researchers and Investment Managers can use consumer spending data analytics in their fundamental research to understand and forecast revenues, customer retention, customers’ lifetime values, customer churn and competitive analysis. Learning consumer spending patterns around online versus offline provides visibility into how consumer discretionary spending is shaping consumer behaviour especially during Covid-19.”
2020-11-03 00:00:00 Read the full story…
Weighted Interest Score: 6.1352, Raw Interest Score: 2.3926,
Positive Sentiment: 0.0443, Negative Sentiment 0.0443
An Important Guide To Unsupervised Machine Learning
It’s become very clear that unsupervised machine learning and artificial intelligence can be very helpful for business growth, but how do they work? There are some key methods you’ll want to know so your market research, trend predictions, and other machine learning uses are effective.
We’re living in an era of digital switch-over with only one constant – evolve. And that digital transformation is being introduced by high-tech solutions. Hence, it comes as no surprise that mundane business tasks are being completely taken over by tech advancements. Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries.
2020-11-01 18:08:28+00:00 Read the full story…
Weighted Interest Score: 5.1657, Raw Interest Score: 1.9476,
Positive Sentiment: 0.1885, Negative Sentiment 0.0628
Is Your Data Ready for AI?
We’ve already figured out that AI has an immense potential to enhance business processes of many kinds in almost any industry imaginable. AI is poised to redefine conventional business models, enhance productivity, and drive value overall. When deploying it, companies tend to stick to a traditional scenario: first, outline an elaborate strategy, then attract excellent talent, secure the budget, develop a PoC, and so on. However, artificial intelligence experts argue that this traditional roadmap is missing one integral component of successful AI adoption. Namely, data readiness. In fact, even when data gets enough attention, it still remains a solid roadblock on an already thorny path.
The common trap that organizations tend to fall into is to assume that large amounts of data imply it’s usable. In reality, most data that have been collected without solid governance principles can’t be fed into AI algorithms. Data becomes useful only when it’s properly cleansed, labeled, and structured. Contrary to popular opinion, it’s usually a bad idea to purchase datasets from other vendors as in most cases each company requires its unique data to extract maximum value.
These are a few steps that companies can make to prepare their data for AI implementation.
2020-10-29 06:48:28 Read the full story…
Weighted Interest Score: 4.4589, Raw Interest Score: 1.7616,
Positive Sentiment: 0.2381, Negative Sentiment 0.3333
Top Open Source Recommender Systems In Python For Your ML Project
Recommender systems have found enterprise application by assisting all the top players in the online marketplace, including Amazon, Netflix, Google and many others. These systems are the decision support systems that make the personalisation process better as well as smoother. It predicts and estimates the content of user preferences by extracting from various data sources such as previous database, data history, among others.
Here, we have listed the top eight open-source recommender systems in Python, in no particular order, that you must try for your next project.
- Case Recommender
2020-11-04 11:30:01+00:00 Read the full story…
Weighted Interest Score: 4.2817, Raw Interest Score: 1.4205,
Positive Sentiment: 0.2029, Negative Sentiment 0.0609
AIM Partners With NASSCOM To Invite Organisations For AI Case Studies
Analytics India Magazine (AIM), in association with NASSCOM, has launched an initiative to unearth some of the best India-based AI use cases that have transformed organisations’ value-chain. To drive this initiative, AIM is conducting an online survey for businesses to jump in and share relevant details of the AI implementations.
Various enterprises and organisations in India, in recent years, have implemented artificial intelligence across their value chain to boost operational and business efficiency. And to implement these AI solutions, these enterprises, in most instances, have partnered with various organisations and service providers who specialise in AI and other technology services. In an attempt to identify these best use cases, Analytics India Magazine is inviting organisations to share their AI/ML case studies with the larger ecosystem.
The initiative has been aimed to create an AI Case Study Compendium for the industry by discovering some of the critical use cases, spanning across all sectors, implemented by key solution providers – both public and private. These use cases will be covering the implementation journey of artificial intelligence at any particular level or all levels of the organisation.
2020-11-02 10:19:13+00:00 Read the full story…
Weighted Interest Score: 3.5520, Raw Interest Score: 1.4220,
Positive Sentiment: 0.2091, Negative Sentiment 0.0836
The Ultimate Guide to Data Engineer Interviews
What to expect and how to prepare for data engineering interviews.
Although data engineer (DE) was the fastest-growing tech job role in 2019, there aren’t many online resources on what to expect in a data engineering interview and how to prepare for it.
In the past year, I have interviewed for data engineer roles with several tech companies in the Bay Area and helped many connections succeed in their interviews. In this blog post, I will explain the most important technical topics in data engineering interviews: your resume, programming, SQL, and system design. I will also teach you how to prepare for the non-technical part of the interview, which I believe is key to a successful job interview but is often ignored by candidates.
2020-11-02 22:01:27.975000+00:00 Read the full story…
Weighted Interest Score: 3.0175, Raw Interest Score: 1.7927,
Positive Sentiment: 0.3006, Negative Sentiment 0.2895
AWS releases models and datasets to help predict COVID-19’s spread
Amazon Web Services (AWS) today open-sourced a new simulator and machine learning toolkit for anticipating and mitigating the spread of COVID-19. AWS says that the suite, which comprises a disease progression simulator and models to test the impact of various intervention strategies, can help to accurately capture many of the complexities of the virus in the world.
While there have been a number of breakthroughs in understanding COVID-19, such as how soon an exposed person will develop symptoms, building an all-encompassing epidemiological model remains an uphill battle. Challenges in model building include identifying variables that influence disease spread across cities, countries, and populations. A performant model must also combine intervention strategies such as closures and stay-at-home orders and explore hypotheticals by incorporating trends from COVID-19-like diseases.
2020-10-30 00:00:00 Read the full story…
Weighted Interest Score: 2.5451, Raw Interest Score: 1.2741,
Positive Sentiment: 0.0593, Negative Sentiment 0.1778
Embracing the new reality. A Bank’s perspective
Highlighted in Deloitte’s report on the “outlook” for the industry, a new, forceful, wave of disruption is coming, even before the pandemic, so imagine the need for digitalisation post-Covid. The combined effects of this technological disruption will greatly affect the banking industry. Banks need to re-evaluate their platforms across multiple dimensions in order to exploit the opportunity that comes with every disruption, modernising their systems to support:
- Tailored products – customers are the asset, the offering needs to be close to their needs, while safeguarding Bank’s revenue
Real-time transactions – this becomes the norm for the banking of younger generations
- Top quality of data – data analytics is paramount now, and no Bank can rely on poor data to make decisions, nor have large cost overheads on data normalisation and cleansing
- Deployment to take place in stages as the bank grows – it is imperative that system modernisation develops in line with the Bank’s current operations with technology efficiently supporting this approach
- Human capital through automation of processes – Bank’s personnel are valuable assets that may shift their focus from system operational tasks to more productive activities, provided that the majority of operations are automated by the systems.
2020-10-29 00:00:00 Read the full story…
Weighted Interest Score: 2.5104, Raw Interest Score: 1.6352,
Positive Sentiment: 0.4146, Negative Sentiment 0.1842
How NVIDIA Powered America’s Fastest Supercomputer In Fight Against COVID-19
“Using Dask, RAPIDS, BlazingSQL, and NVIDIA GPUs, researchers are leveraging Summit supercomputers from their laptops.”
Working on data-intensive projects like protein folding research, drug discovery, or deep space leads to several TBs of data. And, using queries on CPUs to sort information can take days. Time is a key constraint while fighting global pandemics. Research labs and governments around the world have accommodated money and manpower to speed up drug discovery. But this isn’t sufficient. There is a need for a smart, diligent solution that combines the existing technologies without trying to reinvent the wheel.
At Oak Ridge National Laboratory, which has been at the forefront of the fight against COVID-19, the researchers have been leveraging the powerful SUMMIT supercomputer to skim large datasets in search of solutions. SUMMIT, the world’s second-fastest supercomputer is powered by NVIDIA’s Tesla V100s and the team at OLCF (Oakridge Leadership Computing Facility) has been looking for solutions that would fit well into their technology stack.
2020-11-04 05:30:43+00:00 Read the full story…
Weighted Interest Score: 2.4012, Raw Interest Score: 1.2632,
Positive Sentiment: 0.2071, Negative Sentiment 0.0828
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