Alternative Data News. 12, February 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.


From Reddit : Data Is Beautiful

Change in United States Presidential Election margins from 2012 to 2016

Quandl 2020 Alternative Data Conference

Alternative data conferences connect actors in the buy-side ecosystem to explore novel use cases as the demand for alternative data is increasingly intense. Only the largest and most sophisticated players with distinctly unique roles can leverage their critical edge. Increasing demands on data remains a challenge that only few can solve.

With annual purchases of alternative data by U.S.-based buy-side firms projected to reach $900 million by 2021, the competition to find, extract, refine, package, and ultimately sell alternative data is immense. Quandl, a subsidiary of NASDAQ, is the largest alternative data provider for financial professionals transforming the investment management processes not only for the buy-side but also for private equity and venture capital.
Conferences such as Quandl’s 2020 alternative data conference offer a unique opportunity to explore the future of alternative data: potentially new data sources, new approaches to extract additional signals, and novel use cases. My few observations from the conference.

2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 9.0559, Raw Interest Score: 2.3658,
Positive Sentiment: 0.3286, Negative Sentiment 0.1314

CloudQuant Thoughts : An interesting overview of the state of Alt Data at the moment. Alpha Decay, Black Boxes, refined data sets produced by AI Black Boxes, Data Scientists reluctant to use unexplained data, Glass Boxes, Graphical rendering of data,  NLP etc.

Alternative Data, It’s Not Just For Hedge Funds Any More • Integrity Research

Alternative data has become ubiquitous on Wall Street, especially among quantitative hedge funds. However, in the past few years many new client segments have started to include the use of alternative data as a key part of their business decision making processes.

In the past, most investment professionals built models and did their research, using conventional, widely available data sets, like financial statement data and historical stock prices. The advent of big data tools and enhanced computing power, have allowed larger and less structured, unconventional alternative data sets to be studied, in search of leading edge insights and advantages in the investment process.

Enterprising alternative data providers are searching for new markets to sell their data and are always on the lookout for new data sets that may be capable of improving alpha. As with any product, opening new markets to alternative data increases the potential revenue that can be generated.

2020-02-10 02:30:04+00:00 Read the full story…
Weighted Interest Score: 7.3444, Raw Interest Score: 2.3708,
Positive Sentiment: 0.3003, Negative Sentiment 0.0474

CloudQuant Thoughts : This road runs both ways, if you have data that you think may be of interest to Investment Funds or Traders in general but do not know how to reach out to them or organize the data in a way that will be of interest, we can provide that service. Get in touch with us at CloudQuant.com

Neuberger Berman bolsters ESG offering with Global High Yield Sustainable Action fund

Neuberger Berman, a private, independent, employee-owned investment manager, is launching an actively managed, differentiated global high yield fund focusing on corporate credit securities that meet sustainable investment criteria.

The strategy will target best-in-class issuers through systematic evaluation of environmental, social and governance (ESG) factors and negative exclusion criteria, emphasising active engagement with issuers on ESG factors. Engagement objectives for each issuer are established and aligned with the United Nations Sustainable Development Goals, with progress for the portfolio reported to investors annually.

The fund will invest in securities across the global high yield fixed income universe, with an emphasis on income generation. The portfolio will be diversified by industry and issuer, comprising 90-150 issuers, with a quality focus on BB and B credit.

2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 5.1985, Raw Interest Score: 2.2684,
Positive Sentiment: 0.2363, Negative Sentiment 0.0473

CloudQuant Thoughts : The most interesting quote in this article for me was “This will allow us to source best-in-class issuers and quality opportunities that are positively contributing to the future sustainability of the planet.”. I recently saw WSJ (paywall) and WealthProfessional articles quoting research done by RBC Capital Markets into the constituent components of the top ESG Funds. It turns out that “The five most commonly held S&P 500 stocks in actively managed sustainable equity funds last fall were Microsoft Corp., Alphabet Inc., Visa Inc., Apple Inc. and Cisco Systems Inc.”. At least we have the UNs Principles for Responsible Investing (PRI) to guide us.

The State of Sustainable Investing in 2020

During the past decade, governments across the world have promulgated more than 500 new measures that seek to promote the use of environmental, social, and governance (ESG) criteria in making investment decisions. A comprehensive report from global public accounting and consulting firm KPMG International explores these three broad issues related to sustainable investing and its impact on the alternative investment industry: the rate of progress in implementing sustainable investing, the barriers to more rapid progress, and responses to these barriers. A total of 135 investment managers and pension consultants from 13 countries in all key regions of the globe participated in this research project.

According to hedge fund managers surveyed for the study, institutional investors are by far the leaders in promoting ESG-driven investing. However, only 15% of these hedge fund managers “have embedded ESG factors across their strategies.”. More widespread adoption is hindered by the fact that 63% of them them find a “lack of robust templates, consistent definitions and reliable data.”

2020-02-07 16:02:43.463000+00:00 Read the full story…
Weighted Interest Score: 3.7088, Raw Interest Score: 2.0665,
Positive Sentiment: 0.2411, Negative Sentiment 0.3444

From nice-to-have to must-have: ESG becoming central to hedge fund processes, study finds

A growing number of investors require hedge funds to build environmental, social and governance (ESG) elements into their investment processes – with traditional risk-return metrics being overhauled to include ESG factors, a wide-ranging industry study has found.

The report, ‘Sustainable Investing: Fast Forwarding Its Evolution’, published jointly today by KPMG, the Alternative Investment Management Association (AIMA), the Chartered Alternative Investment Analyst Association (CAIA), and CREATE-Research, underlines the far-reaching impact that ESG is having on the investor-manager dynamic.

2020-02-06 14:14:18+00:00 Read the full story…
Weighted Interest Score: 4.8658, Raw Interest Score: 3.0201,
Positive Sentiment: 0.0000, Negative Sentiment 0.0000

CloudQuant Thoughts : If you are a regular reader of this blog you probably know what is coming. It can be hard to find quality data, there are so many sellers, so many white papers with no data to back them up. Acquiring and researching potential data feeds for Alpha is a never ending and increasingly difficult task. CloudQuant can help. We have developed a process to test Alternative Data sets, we publish white papers WITH CODE. The data can be made available in a format that is already processed and cleaned. We have examples including an ESG dataset from G&S Quotient over at our Data Catalog.

America’s Data Addiction Enabled China’s Equifax Hack – Barron’s

U.S. prosecutors announced on Monday indictments of members of the Chinese military for the great Equifax hack of 2017. The news instantly made a familiar geopolitical story of what was originally a massive failure of cybersecurity and privacy protections. But something bigger is at stake: The data-economy bubble is now bursting. And individuals everywhere are becoming the collateral damage of an industry that’s collapsing under the weight of exponential growth fueled by cheap capital, both data and venture.

I’m not the only one who sees the signs. In 2017, the highly sensitive personal and financial data of 150 million Americans spilled out from Equifax’s secure servers like oil from the Exxon Valdez tanker. On Monday, U.S. Attorney General William Barr condemned the hack as an “attack on American industry,” in effect shifting blame away from a multibillion-dollar corporation.

“For years we have witnessed China’s voracious appetite for the personal data of Americans,” Barr said. “This data has economic value, and these thefts can feed China’s development of artificial intelligence tools as well as the creation of intelligence targeting packages.”

2020-02-11 00:00:00 Read the full story…

CloudQuant Thoughts : Take care of your user’s data. Learn from every attack.

The new age of Fintech – What you need to know about data aggregators

Earlier last month, news feeds were abuzz about a major acquisition within the financial industry, when Visa purchased fintech startup – Plaid, for a substantial sum of $5.3 billion. Although major news, for the majority of the readers this was the first time they have heard about Plaid, raising the questions of how could this company that many have never heard of before suddenly get such massive valuation.

Despite the obliviousness of many, chances are that if you have ever used any sort of financial app, you already are Plaid’s customer. Plaid is one of the key players among the Financial data aggregation companies – which, through partnerships with banks and other financial institutions, have direct access to consumers’ financial data. Since its foundation, Plaid has been rising to the top of the fintech industry, using data aggregation techniques, such as screen scraping and APIs, to gain direct access to consumers’ financial data, cleaning, categorizing and supplying the acquired data accordingly. What Plaid has done is it has taken a key position within the world of Fintech, acting as the connecting link between fintech startups and banks, with its services used by a number of companies, such as Venmo, TransferWise, and Level Money, utilizing Plaid’s software to power their services.

2020-02-11 11:07:21 Read the full story…
Weighted Interest Score: 4.3328, Raw Interest Score: 1.8341,
Positive Sentiment: 0.1630, Negative Sentiment 0.1630

15 Python Libraries for Data Science You Should Know – Dataquest

Python is one of the most popular languages used by data scientists and software developers alike for data science tasks. It can be used to predict outcomes, automate tasks, streamline processes, and offer business intelligence insights. It’s possible to work with data in vanilla Python, but there are quite a few open-source libraries that make Python data tasks much, much easier.

You’ve certainly heard of some of these, but is there a helpful library you might be missing? Here’s a line-up of the most important Python libraries for data science tasks, covering areas such as data processing, modeling, and visualization.

  1. Scrapy
  2. BeautifulSoup
  3. NumPy
  4. SciPy
  5. Pandas
  6. Keras
  7. SciKit-Learn
  8. PyTorch
  9. TensorFlow
  10. XGBoost
  11. Matplotlib
  12. Seaborn
  13. Bokeh
  14. Plotly
  15. pydot

2020-02-05 16:14:09+00:00 Read the full story…
Weighted Interest Score: 4.0674, Raw Interest Score: 2.1138,
Positive Sentiment: 0.3325, Negative Sentiment 0.0356

Sentieo Q&A: research workflows still using “outdated technology stacks”

Productivity provides an edge in equity research, and as equity-focused hedge funds navigate the markets this year, any opportunity to use next generation technology tools to trade more adroitly will likely be welcomed. In the following Q&A with Hedgeweek’s managing editor, James Williams, Nick Mazing, Head of Research at Sentieo Inc, discusses how its platform is using the latest AI technology to improve the research workflow and reduce the time taken to ingest structured and textual data sets, and empower portfolio managers as they look to build alpha-generating investment theses.
2020-02-10 00:00:00 Read the full story…
Weighted Interest Score: 3.3462, Raw Interest Score: 1.6096,
Positive Sentiment: 0.3604, Negative Sentiment 0.1441

Top 10 Tools For No-Code AI & ML

Enterprises primarily rely on the two domains — artificial intelligence (AI) and machine learning (ML) in order to build and deploy various kinds of models for the smooth operation of their business. However, it requires programmers or data scientists with adequate knowledge of coding, which enterprises often lack. In a bid to ease such woes of the enterprises, tech giants are now open-sourcing their platforms and providing developer tools to ensure businesses can match the ongoing pace without the need for a coding expert.

In this article, we list down ten such tools which can be used to develop models without being an expert in coding.

The list is in no particular order.

  1. Create ML By Apple
  2. Teachable Machine
  3. Accelerite ShareInsights by Amazon Web Services
  4. What-If Tool
  5. Google AI Platform
  6. Data Robot
  7. RapidMiner Studio
  8. Microsoft Azure Automated Machine Learning
  9. BigML
  10. Google ML Kit

2020-02-11 10:38:02+00:00 Read the full story…
Weighted Interest Score: 3.3456, Raw Interest Score: 1.9481,
Positive Sentiment: 0.2238, Negative Sentiment 0.1053

Top Skills To Show On Your Data Science Resume Beyond Just The Tools

With the evolving data science landscape, organisations are expecting more from data scientists. Consequently, various companies are seeking skills in data scientists that were ignored a few years ago. And, therefore, aspirants should devise a data science resume that aligns with the latest needs of the enterprise to increase their chance of getting job offers. Failing to do so can drastically decrease the likelihood of data scientists to stay relevant or differentiate themselves from others, resulting in losing opportunities.

Today, data scientists should not confine their data science resumes only with fancy tools, ML and DL techniques, and certifications. Instead, they must include skills that can demonstrate their capabilities that are now a prime focus for organisations.

  • Work On Specific Projects
  • Converting Business Problems Into Data Science Problems
  • Explainability
  • Business Acumen
  • Communication Skills


2020-02-11 06:30:00+00:00 Read the full story…
Weighted Interest Score: 3.1868, Raw Interest Score: 1.6038,
Positive Sentiment: 0.2708, Negative Sentiment 0.4999

Top 6 Must-Attend AI & ML Conferences In India For 2020

The emergence of artificial intelligence (AI) and machine learning (ML) has led to a proliferation of tech-focused conferences and summits in India. Not only do they offer a platform for researchers, tech enthusiasts, policymakers and business people to deepen their understanding of emerging technologies, but also present a fitting setting to discover potential collaborations and make critical connections.

Without implying any ranking in the order, here is a list of AI & ML conferences for 2020 that you should mark on your calendars:

2020-02-10 12:03:52+00:00 Read the full story…
Weighted Interest Score: 3.1354, Raw Interest Score: 1.4668,
Positive Sentiment: 0.3603, Negative Sentiment 0.1801

Four factors businesses need to consider when it comes to automation and decisioning

analytics such as Machine Learning and Artificial Intelligence to help transform their processes.

With this transformation, many are currently investing in major projects to bring together disparate data sources together into one coherent framework. And although a central, accessible data source is essential, organisations need to consider several factors if they want to take full advantage of the myriad of ways data can improve decisioning, efficiency, and customer journeys.

Here are four aspects businesses need to consider:

  1. Relevancy of data
  2. New data sources
  3. System issues
  4. Exploring new models

2020-02-10 10:34:19 Read the full story…
Weighted Interest Score: 2.8053, Raw Interest Score: 1.7808,
Positive Sentiment: 0.4452, Negative Sentiment 0.1370

Facts & Figures of Amazon lending and the Goldman Sachs X-factor

As I was listening to Cathy Wood`s interview, CEO of ArkInvest, from the Exponential Africa Show; she triggered an insight around investing and disruptive innovation.

One can`t be a banking analyst or an automobile industry analyst anymore, with the same silo-ed focus required over the past decades. Industry-specific analysts bring a lot of experience from their respective sectors but lack the insights of innovative business models enabled by the `future technologies` (that are already here by the way).

I will elaborate on this topic over the next couple of weeks with several examples and insights on where I see the market heading to.

2020-02-11 00:00:00 Read the full story…
Weighted Interest Score: 2.7652, Raw Interest Score: 1.3111,
Positive Sentiment: 0.1669, Negative Sentiment 0.0715

Top 9 ETL Tools For Data Integration In 2020

One of the essential aspects of data warehousing is the ETL (Extract Transform Load) tool. An ETL tool is a combination of three different functions in a single tool. One most crucial property of ETL is to transform the heterogeneous data into homogeneous one, which later helps data scientists to gain meaningful insights from the data.

In this article, we list down the top nine ETL tools one must use for data integration in 2020.

The list is in alphabetical order.

2020-02-11 12:36:18+00:00 Read the full story…
Weighted Interest Score: 2.5847, Raw Interest Score: 1.6221,
Positive Sentiment: 0.2317, Negative Sentiment 0.0535

Sebi Shortlists IBM India, Infy & Wipro For Data Analytics Project

Sebi has shortlisted around eight companies, including Infosys, Wipro and IBM India, in order to implement a data analytics project through which the regulator wants to track possible market manipulations such as insider trading and front running.

This move is part of Sebi’s efforts to address the challenges arising out of technological advancements in the markets. In November, the regulator had invited expression of interest (EoI) from “reputed and reliable solution providers for implementation of data analytics project and building of data models at Sebi”.

The analytics/model development would include developing new models, implementing analytics project, establishing linkages between various entities in the market, automated extraction of details from documents filed with Sebi, and prediction of market manipulations such as insider trading and front running.

2020-02-11 11:30:00+00:00 Read the full story…
Weighted Interest Score: 2.5424, Raw Interest Score: 1.7295,
Positive Sentiment: 0.1774, Negative Sentiment 0.2217


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