Industry News

Industry News

Alternative Data News : US College Tuition & Fees vs. Overall Inflation : New Forecasting Model by RavenPack Analyzes News to Predict the Winner of the 2020 US Presidential Election : Fixed Income Pioneer David Rutter launches LedgerEdge : Is R Making a Programming-World Comeback? : ESG Section

Alternative Data News. 15, July 2020

Alternative Data News. 15, July 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.

US College Tuition & Fees vs. Overall Inflation [Reddit]

Colleges and universities across the world are frantically working out how to teach thousands of students, usually crammed into lecture halls, in a safer more socially-distanced way.
This week Harvard made headlines after announcing that only 40% of its undergraduates would be invited to live on campus and that all of its courses would be online – with no discount on tuition fees. The total cost to attend Harvard for a year is $72,391 for tuition, room, board, and fees combined.
Harvard may be an extreme example, but it’s indicative of a wider trend. The cost of getting a college degree in the US has risen almost 1200% in just 40 years. During that same time, overall inflation is up ~230%. Rising college costs were pretty tough to take when courses were normal, but paying $30k, $40k or even $50k+ for tuition, feels extreme when the entirety of your teaching could be online.
Originally sent in this chartr newsletter.
Source: US Bureau of Labor Statistics
Tool: Excel
2020-07-08  Read the full story…
CloudQuant Thoughts : As a parent of a student about to enter college for a significant amount of money per year I am disgusted by the feedback being given by the colleges. It is understandable, they are businesses, they are trying to survive, they know as little as we do about the future. But had they not engaged in this ridiculous ramping up of college costs they would have had the sympathy of parents and students.

New Forecasting Model by RavenPack Analyzes News to Predict the Winner of the 2020 US Presidential Election

An alternative to polls, this new approach uses sentiment analysis and media attention to forecast election results. RavenPack, a leading big data analytics provider, has launched a free and publicly available website, offering projections and analysis on the upcoming U.S. presidential election.

RavenPack’s forecasting model combines three key inputs:

  • The level of media attention received by a presidential candidate, which has been found to be highly correlated with election success
  • The sentiment for each candidate, measured from thousands of news stories about their policies and personal life, across all 50 US states.
  • Social and economic sentiment by state, which provides a proxy for the sitting president’s approval rating and chances of reelection

Research shows the forecasting model built by RavenPack’s Data Science team correctly predicted the winning candidate in 4 out of the 5 last U.S. presidential elections, with a confidence of greater than 75%, outperforming many traditional polling methods.

2020-07-14 00:00:00 Read the full story…
Weighted Interest Score: 6.1900, Raw Interest Score: 1.6188,
Positive Sentiment: 0.3964, Negative Sentiment 0.0000

CloudQuant Thoughts : Build ’em up and knock ’em down. Let’s see how this one does!

Fixed Income Pioneer David Rutter launches LedgerEdge

David E. Rutter has gathered an expert team of financial market and technology professionals in a new company, LedgerEdge, to build an ecosystem for the exchange of data and assets in the corporate bond market.
Corporate bond market participants face serious challenges in discovering liquidity and executing trades without harmful data leakage. The corporate bond market lags behind other asset classes in adoption of digital solutions: $59bn is traded daily, but only 30% of this is traded electronically.
The last ten years have seen innovative new solutions and protocols make strides to improve market efficiency, but they have been limited by the technology available. All have required trade-offs including imprecise order tools, centralized data stores, and data leakage.
LedgerEdge is building on technology only now ready for institutional-grade solutions. The team is using blockchain technology, artificial intelligence, and secure enclave computing to empower users to locate and promote liquidity in markets.

2020-07-15 06:01:06+00:00 Read the full story…
Weighted Interest Score: 3.1687, Raw Interest Score: 1.7609,
Positive Sentiment: 0.3707, Negative Sentiment 0.3398

CloudQuant Thoughts : The biggest players have access to the most data at the highest speed. They also benefit from being able to pay to see retail flow (so they can see what you are trading and when – often they provide the trading platform on which you trade!) but they also demand the ability to, as it states in this article, “discover liquidity and execute trades without harmful leakage”. In other words, see it before you do, see what you do and not let you see what they do…. Sound fair? Drag all trades out into the harsh light of truth. With legislation due to pass to allow hedge funds with value under $3.5b to no longer have to declare their holdings, it is becoming less like a level playing field and more like trying to play soccer uphill.

Is R Making a Programming-World Comeback?

For the past few years, the narrative around the R programming language, which is used heavily in data science, has remained much the same: Although academia and specialized data-science firms used R pretty heavily, Python was rapidly eclipsing it as the language of choice for all things data-related.

However, the latest update of the TIOBE Index suggests something incredible: That news of R’s demise has been premature, and the language might be making a bit of a comeback. Specifically, R has jumped up to eighth place on the Index, up from 20th place a year ago.
2020-07-09 00:00:00 Read the full story…
Weighted Interest Score: 2.8641, Raw Interest Score: 1.9094,
Positive Sentiment: 0.1224, Negative Sentiment 0.1714

CloudQuant Thoughts : Whilst Python is our preferred language here at CloudQuant, we appreciate that a number of Data Scientists enjoy working with R and so we have just added support for R to our Data Research environment CQ AI.

ESG Section

This is our ESG section, do not forget to head over to the CloudQuant data catalog where we have various data sets available including a very interesting ESG data set!

ESG Data Coming to U.S. Via Nasdaq

North American Investors and traders interested in gaining exposure to Environmental Social and Governance (ESG) markets are about to gain a competitive advantage thanks to Nasdaq via its recent alliance with TrackInsight. TrackInsight ( is a leading global independent ETF analytics platform that operates a global platform dedicated to ETF search, analysis and selection aimed at professional investors.

TrackInsight currently has over 100,000 unique users and 2,500 qualified professional investors using its platform for their day-to-day ETF screening; it is recognized as the leading source of independent and reliable information for more than 6,000 Exchange Traded Funds listed globally.
2020-07-10 15:27:37+00:00 Read the full story…
Weighted Interest Score: 7.7970, Raw Interest Score: 2.5401,
Positive Sentiment: 0.3699, Negative Sentiment 0.0740

Refinitiv Debuts Fund ESG Scores

Building on its commitment to connect and advance the global financial community through data and analytics, Refinitiv today announced Lipper Fund ESG Scores to serve as a pivotal data-metric in the transition to sustainable investing – providing comparisons at the fund level for fund managers, advisors and investors.

Refinitiv Lipper Fund ESG Scores brings together the Lipper fund universe of 330,000 fund share classes and its deep holdings con…
2020-07-08 16:18:25+00:00 Read the full story…
Weighted Interest Score: 4.1806, Raw Interest Score: 2.2027,
Positive Sentiment: 0.1673, Negative Sentiment 0.0836

BNP Paribas taps into ESG surge with new global sustainability-focused hedge fund launch

BNP Paribas Asset Management has launched a long/short global equity hedge fund which will invest in companies grappling with looming environmental challenges, as interest in ESG (environmental, social and governance) themed hedge fund strategies continues to soar.
BNP’s new Environmental Absolute Return Thematic (EARTH) Fund will trade energy, materials, agriculture and industrials stocks in both developed and emerging markets with market caps of more than USD1 billion.
It will take long punts in innovative companies that are addressing an assortment of environmental challenges – such as carbon emissions, waste production, and food, water and energy concerns – and pair them with short bets on unsustainable firms, or those names whose business models are vulnerable to transition risk.

2020-07-15 00:00:00 Read the full story…
Weighted Interest Score: 5.5337, Raw Interest Score: 2.5607,
Positive Sentiment: 0.3283, Negative Sentiment 0.3283

Best Practises In Data Cleaning That Data Analysts Should Know

Data cleaning is one of the most crucial steps to ensure data quality and database integrity. It efficiently allows managing data while determining reliability while making decisions. As the regulatory compliances are becoming more stringent and focused, ensuring high data quality is the need of the hour. Given that organisations have a lot of data internally and externally, and that most of this data is not clean, it may result in errors while running programs that may lead to revenue loss and more. Data management best practices are, therefore, crucial for better analytics.
Some of the benefits of data cleaning are:

  • It accelerates data governance while reducing time and cost of implementation to maximise ROI
  • Accurately target customers and drive faster customer acquisition
  • Consolidate applications and cost-saving
  • Improves decision-making capabilities as it supports better analytics
  • It saves valuable resources by removing duplicate and inaccurate data from databases, keeping valuable resources in terms of storage space and processing time
  • It boosts productivity as it saves time in re-analysing work due to mistakes in data and saves from making incorrect decisions

Best Practices For Data Cleaning – Chalk Out A Plan.

2020-07-14 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.3479, Raw Interest Score: 1.6825,
Positive Sentiment: 0.3761, Negative Sentiment 0.5740

Data Structures & Algorithms I Actually Used Working at Tech Companies

Do you actually use algorithms and data structures on your day to day job? I’ve noticed a growing trend of people assuming algorithms are pointless questions that are asked by tech companies purely as an arbitrary measure. I hear more people complain about how all of this is a purely academic exercise. This notion was definitely popularized after Max Howell, the author of Homebrew, posted his Google interview experience.

This article is a set of real-world examples where data structures like trees, graphs, and various algorithms were used in production. All of these are my first-hand experiences. I hope to illustrate that a generic data structures and algorithms knowledge is not “just for the interview” – but something that you’d likely find yourself reaching for when working at fast-growing, innovative tech companies.
2020-07-14 15:30:35+00:00 Read the full story…
Weighted Interest Score: 3.0319, Raw Interest Score: 1.1833,
Positive Sentiment: 0.1407, Negative Sentiment 0.2750

Hedge funds cut losses with stellar Q2 gains as equities lead in June – but industry remains down in tumultuous half-year

A number of hedge fund strategies, including discretionary macro managers and specialist technology and healthcare-focused equity long/short funds, have taken profits in the first half of 2020, as the wider industry continues to claw back losses following the bedlam that rocked markets earlier in the year.
Overall, hedge funds gained close to 2 per cent in the month of June, their third consecutive monthly advance, according to new data from Hedge Fund Research, powered mainly by equities and event driven strategies.
But the HFRI Fund Weighted Composite Index, an investable barometer of the wider industry, remains down for the year despite the recent recovery, which included a striking Q2 rise of more than 9 per cent. In what has been a memorable six-month period for markets, the index dropped 3.49 per cent – a slide stemming largely from agonising losses during March’s coronavirus-fuelled sell-off.
2020-07-09 00:00:00 Read the full story…
Weighted Interest Score: 4.4758, Raw Interest Score: 1.8947,
Positive Sentiment: 0.3008, Negative Sentiment 0.2406

Wilshire Liquid Alternative Index up 0.92 per cent in June

TheWilshire Liquid Alternative Index, which provides a representative baseline for how the broad liquid alternative investment category performs, returned 0.92 per cent in June, underperforming the 1.75 per cent monthly return of the HFRX Global Hedge Fund Index.

The Wilshire Liquid Alternative Index family aims to deliver precise market measures for the performance of diversified liquid alternative investment strategies implemented through mutu…
2020-07-14 00:00:00 Read the full story…
Weighted Interest Score: 4.4537, Raw Interest Score: 3.0006,
Positive Sentiment: 0.1783, Negative Sentiment 0.2080

A monster trading quarter saved the day at JPMorgan and Citigroup. But the second half of 2020 looks grim.

In the first wave of bank earnings, Wall Street traders proved the difference makers. Banks took massive hits as consumer banking faltered and they built up cushions against bad loans. But JPMorgan Chase and Citigroup still beat expectations and turned profits, thanks in large part to stellar trading performances. At JPMorgan, trading revenues reached $9.7 billion — an all-time record.
2020-07-15 00:00:00 Read the full story…
Weighted Interest Score: 4.2620, Raw Interest Score: 1.9236,
Positive Sentiment: 0.2061, Negative Sentiment 0.3160

The nominees for the VentureBeat AI Innovation Awards at Transform 2020

At our AI-focused Transform 2020 event, taking place July 15-17 entirely online, VentureBeat will recognize and award emergent, compelling, and influential work through our second annual VB AI Innovation Awards. Drawn from our daily editorial coverage and the expertise of our nominating committee members, these awards give us a chance to shine a light on the people and companies making an impact in AI.
Here are the nominees in each of the five categories — NLP/NLU Innovation, Business Application Innovation, Computer Vision Innovation, AI for Good, and Startup Spotlight.

2020-07-14 00:00:00 Read the full story…
Weighted Interest Score: 3.8131, Raw Interest Score: 1.8553,
Positive Sentiment: 0.3092, Negative Sentiment 0.0957

Xignite Offers Financial Data APIs to Early Stage Fintechs

Xignite Introduces New Development Program Offering Financial Data APIs to Early Stage Fintechs

Market and financial data solutions provider offering support to next wave of entrepreneurs during these especially challenging times

Xignite, Inc., a provider of market data distribution and management solutions for financial services and technology companies, announced today it has created a new program to assist early stage and start-up financial technology companies during the COVID-19 pandemic. To apply for this program visit
The costs associated with developing and launching fintech products and apps can be daunting, even in the best of times. A particularly challenging aspect can be the procurement of quality financial and market data. Hidden fees, restrictions on the use of data, poorly written APIs and exchange requirements are just some of the factors, in addition to costs, that can make it difficult for new or small fintechs to survive.

2020-07-14 14:11:04+00:00 Read the full story…
Weighted Interest Score: 3.7789, Raw Interest Score: 2.0332,
Positive Sentiment: 0.3441, Negative Sentiment 0.2502

Databases vs. Hadoop vs. Cloud Storage

How can an organization thrive in the 2020s, a changing and confusing time with significant Data Management demands and platform options such as data warehouses, Hadoop, and the cloud? Trying to save money by bandaging and using the same old Data Architecture ends up pushing data uphill, making it harder to use. Rethinking data usage, storage, and computation is a necessary step to get data back under control and in the best technical environments to move business and data strategies forward.
William McKnight, President of the Data Strategy firm the McKnight Consulting Group, offered his advice about the best data platforms and architectures in his presentation, Databases vs. Hadoop vs. Cloud Storage at the DATAVERSITY® Enterprise Analytics Online Conference. McKnight explained that today’s Data Management needs call for leveling up to technology better suited to obtaining all data fast and effectively. He said:
“Getting all data under control is the thing that I say frequently. It means making data manageable, well-performing, available to our user base, believable, advantageous for the company to become data-driven.”

2020-07-15 07:35:56+00:00 Read the full story…
Weighted Interest Score: 3.3355, Raw Interest Score: 1.9197,
Positive Sentiment: 0.3200, Negative Sentiment 0.0640

Productizing IT Services for Data Management Projects

Many organizations overcome a lack of data engineers by outsourcing their Data Management needs to IT service providers. Startups, small and mid-sized businesses, and even larger organizations are rich in domain expertise but inexperienced in preparing data for insights, which is why they hire outsiders to perform these vital tasks.

Many organizations overcome a lack of data engineers by outsourcing their Data Management needs to IT service providers. Startups, small and mid-sized businesses, and even larger organizations are rich in domain expertise but inexperienced in preparing data for insights, which is why they hire outsiders to perform these vital tasks.
Although this approach offers some short-term value, the following three challenges reveal why they are inadequate in the long term:

  • Loss of Control: Businesses should own the knowledge for extracting insights from data without being mired in the technical details required to do so. Dependence on service teams for Data Management prevents organizations from controlling access to their own data and data insights. Relying on these “middlemen” for this basic necessity delays time to value, impairs flexibility, and hampers productivity.
  • Difficulty Scaling: Organizations must scale their data teams alongside their business, which quickly proves expensive and impractical. The more business units rely on data, the more data team members are required to match that demand. With this approach, it’s impossible to scale the business without increasing costs for data teams — which aren’t cheap, to begin with.
  • Slow Iterations: The increased time to value of employing service teams makes iterations extremely slow, depriving organizations of agility and delaying time to market in today’s fast-paced, customer-centric world.

2020-07-15 07:25:34+00:00 Read the full story…
Weighted Interest Score: 3.3205, Raw Interest Score: 1.8683,
Positive Sentiment: 0.1779, Negative Sentiment 0.3855

Introduction to Python for Data Science – Simple and interactive tutorial for beginners

In this post, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. I will start by introducing you to our friend Python and a field called Data Science in couple words then we will start with the interactive exercises. In the hands-on tutorial section, we will go through the following topics Lists, Functions, and Methods. I can’t wait, let get started!

Python is a general-purpose programming language that is becoming ever more popular for data science. Python also lets you work quickly and integrate systems more effectively. Companies from all around the world are utilizing Python to gather bits of knowledge from their data. The official Python page if you want to learn more.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Today, successful data professionals understand that they must advance past the traditional skills of analyzing large amounts of data, data mining, and programming skills.
Almost every interaction we make with tech devices these days includes data — such as our Amazon purchases, Facebook feeds, Netflix recommendations, and even the facial recognition that we use to sign in to our phones.
2020-07-15 02:49:52.501000+00:00 Read the full story…
Weighted Interest Score: 3.2075, Raw Interest Score: 1.7925,
Positive Sentiment: 0.1887, Negative Sentiment 0.0000

“A healthy dose of scepticism”: How Cheyne Capital’s Richard Woolf is building success with thematic equity hedge fund

As manager of Cheyne Capital’s Thematic Long/Short Fund, Richard Woolf (pictured) brings what he describes as a “healthy dose of scepticism” to his portfolio management style and trading approach.

His contrarian perspective on markets is underpinned by a broader mix of ideas influenced by technological disruption, societal changes, and economic dislocations arising from the ongoing coronavirus pandemic.

2020-07-10 00:00:00 Read the full story…
Weighted Interest Score: 3.0669, Raw Interest Score: 1.4785,
Positive Sentiment: 0.2576, Negative Sentiment 0.3360

Modelling Credit Card Frauds. Is artificially balanced data always…

Credit card frauds are a “still growing” problem in the world. Losses in frauds were estimated in more than US$27 billion in 2018 and are still projected to grow significantly for the next years as this article shows.
With more and more people using credit cards in their daily routine, also increased the interest of criminals in opportunities to make money from that. The development of new technologies puts both criminals and credit card companies in a constant race to improve their systems and techniques.
With that amount of money at stake, Machine Learning is surely not a new word for credit card companies, which have been investing on that long before it was a trend, to create and optimize models of risk and fraud management. This quick video from Visa shows in a friendly way the tip of the iceberg which is a deep and complex system that is worth millions.

In this notebook I will develop a machine learning model using anonymized credit card transaction data, to show what a somewhat simple model can achieve in terms of fraud detection. I will also discuss some relevant points in model selection from a practical perspective.
2020-07-15 00:39:47.247000+00:00 Read the full story…
Weighted Interest Score: 2.9436, Raw Interest Score: 1.2231,
Positive Sentiment: 0.2709, Negative Sentiment 0.5172

Aligning Data Architecture and Data Strategy

Peter Aiken disagrees with the popular idea that it’s impossible to put a dollar value on Data Architecture.
“It won’t be the right number, but it will be at least a dollar value on it, and if there’s money involved, people should be paying attention to it.”Aiken is an author, an associate professor of Information Systems, a researcher, and the Founding Director of Data Blueprint. He spoke about Data Architecture and Data Strategy with attendees at the DATAVERSITY® Data Architecture Online Conference.
Data Architecture: Here Whether You Like it or Not
When clients ask Aiken’s company to develop a Data Architecture for their organization, he says they are asking the wrong question. “All organizations have architectures. The question is: do you understand your architecture?” If the existing architecture isn’t documented, he said, then it can’t be understood, and if it’s not understood, it cannot be useful to the organization. “Consequently, people say it is hard to put a dollar value on it.”

2020-07-07 07:35:30+00:00 Read the full story…
Weighted Interest Score: 2.8538, Raw Interest Score: 1.3627,
Positive Sentiment: 0.3061, Negative Sentiment 0.2567

BT switches on NB-IoT in UK to underpin country’s “largest” smart water pilot

UK water utility Yorkshire Water is in the final stages of an NB-IoT and AI pilot with BT to connect almost 4,000 acoustic, flow, pressure, and water quality monitors to manage leaks and interruptions in the water network in the north of England.
BT, which owns UK mobile operator EE, has switched on NB-IoT for the first time on the back of the project, which is billed as the “UK’s largest smart water network pilot”. Yorkshire Water said final NB-IoT installations are underway. It said NB-IoT will deliver “significant improvements in data quality and battery life”, enabling it to identify and prevent leaks and network incidents more accurately.
The pilot will integrate data from new and existing sources, and present it in a single management dashboard, which will include a digital twin of the water network in the region. The platform will use artificial intelligence (AI) to cluster data sets, and remove false positives, to accurately inform asset and operational decision making, it said.
2020-07-15 00:00:00 Read the full story…
Weighted Interest Score: 2.8405, Raw Interest Score: 1.7486,
Positive Sentiment: 0.4736, Negative Sentiment 0.1093

How Data Science Is Revolutionising Our Social Visibility

Artificial Intelligence has the potential to revolutionize the social visibility of brands, paving the way for more incisive approaches towards marketing.
The huge potential of AI in social media has led to Markets and Markets forecasting that the industry of deep learning, machine learning and NLP within sales marketing, customer experience management and predictive risk assessment within social platforms will grow to more than $2.1 billion in value by 2023.
The rise of AI has been well documented, but how exactly can it enhance your social media marketing strategies? Let’s take a deeper look into the role that AI is set to play in boosting our exposure on social platforms:
The definition of AI varies depending on who you ask. But the chart above illustrates its value to various organisations. With as much as 84% of businesses believing that AI will aid them in obtaining a competitive advantage over rivals, the potential of the technology is clear.

2020-07-06 23:24:57+00:00 Read the full story…
Weighted Interest Score: 2.8377, Raw Interest Score: 1.3043,
Positive Sentiment: 0.2531, Negative Sentiment 0.0487

Google Announces BigQuery Omni To Unify Analytics Experience On Multi-Cloud

Google Cloud kicked off Next OnAir today and announced new solutions across its smart data analytics and security portfolios to help accelerate customers’ ability to digitally transform with cloud computing. Data is one of the most important assets for driving digital transformation but is often siloed across on-premises machines, proprietary systems, or multiple clouds.
The tech giant introduces BigQuery Omni, a multi-cloud analytics solution, that enables customers to bring the power of BigQuery to data stored in Google Cloud, Amazon Web Services (AWS) and Azure (coming soon). Today, BigQuery Omni is available in Private Alpha for AWS S3, with Azure support coming soon. BigQuery Omni supports Avro, CSV, JSON, ORC, and Parquet.
Powered by Google Cloud’s Anthos, BigQuery Omni will allow customers to connect directly to their data across Google Cloud, AWS and Azure for analysis without having to move or copy datasets. Through a single user interface, customers will be able to analyze data in the region where it is stored, providing a unified analytics experience.

2020-07-14 12:55:11+00:00 Read the full story…
Weighted Interest Score: 2.7311, Raw Interest Score: 1.4282,
Positive Sentiment: 0.2756, Negative Sentiment 0.1002

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