Alternative Data News. 06, May 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.
Real Time R0 @ rt.live
These are up-to-date values for Rt, a key measure of how fast the virus is growing. It’s the average number of people who become infected by an infectious person. If Rt is above 1.0, the virus will spread quickly. When Rt is below 1.0, the virus will stop spreading.
Read the full story…
CloudQuant Thoughts : From Mike Krieger @mikeyk co-founder of instagram, an excellent overview of R0 with historical values and State by State views. Check it out!
Hedge funds’ use of alternative data tipped to surge, new industry study finds
More than half of hedge fund managers are now using alternative data to gain a competitive edge, according to a wide-ranging new study into alt data trends by the Alternative Investment Management Association and fund services provider SS&C. The report, ‘Casting the Net: How Hedge Funds are Using Alternative Data’, explores the ways in which hedge funds now utilise alternative datasets – defined as unconventional, non-market and non-traditional economic and financial information, such as satellite imagery, social media trends and weather patterns – in their businesses.
The study, jointly published by AIMA and SS&C today, quizzed some 100 hedge fund managers globally, collectively managing about USD720 billion in assets across strategies, including equity long/short, relative value, event driven, macro and CTAs, among others. More than a quarter of those polled (27 per cent) manage more than USD5 billion in assets, while 25 per cent of those surveyed are considered to be “market leaders” – or hedge fund managers that have been using alternative data for more than five years. The report found that well over two-thirds (69 per cent) of those market leaders now use alternative data to generate outperformance, while close to a quarter (23 per cent) of them employ it for their risk management processes.
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 8.3683, Raw Interest Score: 3.0475,
Positive Sentiment: 0.1033, Negative Sentiment 0.1291
CloudQuant Thoughts : Final paragraph “…more than half (54 per cent) of market leaders still find it tough to source quality data, with 15 per cent of the market reporting regulatory and compliance challenges, while 77 per cent of market leaders said it is difficult to back-test historical data.“. CloudQuant provides Alternative Data, it provides White Papers to confirm the efficacy of the data, it provides access to the data for testing and to the code that was used to derive the White Paper conclusions so you can reproduce the results (yes, with the same data and the same code!). We long ago recognized that the issues with alternative data were not just ETL and storage, they were proof of value and reproducibility. As a data scientist I want to focus my time on valuable work, not leg work. Head over to our Data Catalog to get more information.
Python Learning Video Courses Expand to Data Tools
There’s never a bad time to learn Python. If you’re totally new to this snaky language, never fear—there are tons of tutorials and documentation online to help you get started.
In September 2019, Microsoft launched a video series, “Python for Beginners,” with 44 short videos (most under five minutes in length; none longer than 13 minutes). It covered everything from “Hello world” to calling APIs. Now the company has added more videos in the series: “More Python for Beginners” and “Even More Python for Beginners: Data Tools.”
“More Python for Beginners” (20 videos) covers key concepts such as managing a file system and asynchronous operations; “Even More Python for Beginners: Data Tools” (31 videos) is a pretty intensive look into using the language for data science.
2020-05-06 00:00:00 Read the full story…
Weighted Interest Score: 2.6584, Raw Interest Score: 1.7116,
Positive Sentiment: 0.1092, Negative Sentiment 0.1457
CloudQuant Thoughts : Obviously we use Python for our backtesting and research systems. Quality training programmes from Microsoft are very welcomed, their expansion into Data Science is well worth checking out.
ESG data management under spotlight as investments grow
With reports of Environmental, Social and Governance (ESG) investing on the rise amid a drastic drop in oil prices, ESG data is facing scrutiny both from investors and regulators. Market participants are therefore looking to artificial intelligence (AI) to assist in data management.
“As investor demand for more clarity on ESG grows, an increasing number of companies are providing detailed information on their ESG policies, data and actions. Most ESG data, however, is self-reported and often lacks transparency and comparability,” said John Cushing, CEO, mnAI, an AI-powered M&A deal-flow search engine, in an email.
“Many businesses still use ESG factors in a box-ticking way or offer up data only on metrics where they perform well.”
The move towards data standardisation is currently industry-led, with standards such as the Sustainability Accounting Standards Board (SASB) and the Task Force on Climate-Related Financial Disclosures (TFCD) leading the way. But while these bodies set standards, they cannot provide verification of data.
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 3.7679, Raw Interest Score: 1.9058,
Positive Sentiment: 0.1713, Negative Sentiment 0.2998
CloudQuant Thoughts : As mentioned, we have Alternative Data sets available, one of those is an ESG data set from G&S Quotient. Head over to the Data Catalog for more info.
SteelEye Offers Surveillance To Support Remote Working
SteelEye, the compliance technology and data analytics firm, today announced that it is offering financial firms the opportunity to use its Communications Surveillance service for free for up to 90 days as the market adapts to a new style of working.
As firms reopen their offices, reduced density rules are likely to prevail for some time, meaning a workforce that is spread between the office and home. Monitoring communications by staff working in multiple locations will require changes in compliance processes, which may prove challenging if access to on-premise technology is needed.
To help compliance teams adapt to more flexible working conditions, SteelEye’s Communications Surveillance service is being offered for up to 90, days and 50 monitored users, at no charge and with no obligation for future use. It includes monitoring MS Exchange email and Bloomberg chat, and can be seamlessly integrated to capture communications from staff working remotely.
2020-05-05 10:27:27+00:00 Read the full story…
Weighted Interest Score: 1.6362, Raw Interest Score: 1.0587,
Positive Sentiment: 0.3850, Negative Sentiment 0.1444
CloudQuant Thoughts : If you have not read 1984, now is the time.
Machine Learning Engineer: Challenges and Changes Facing the Profession
Last year, the fastest-growing job title in the world was that of the machine learning (ML) engineer, and this looks set to continue for the foreseeable future. According to Indeed, the average base salary of an ML engineer in the US is $146,085, and the number of machine learning engineer openings grew by 344% between 2015 and 2018. Machine learning engineers dominate the job postings around artificial intelligence (A.I.), with 94% of job advertisements that contain AI or ML terminology targeting machine learning engineers specifically.
This demonstrates that organizations understand how profound an effect machine learning promises to have on businesses and society. AI and ML are predicted to drive a “Fourth Industrial Revolution” that will see vast improvements in global productivity and open up new avenues for innovation; by 2030, it’s predicted that the global economy will be $15.7 trillion richer solely because of developments from these technologies.
The scale of demand for machine learning engineers is also unsurprising given how complex the role is. The goal of machine learning engineers is to deploy and manage machine learning models that process and learn from the patterns and structures in vast quantities of data, into applications running in production, to unlock real business value while ensuring compliance with corporate governance standards.
2020-05-04 00:00:00 Read the full story…
Weighted Interest Score: 2.1734, Raw Interest Score: 1.6236,
Positive Sentiment: 0.2319, Negative Sentiment 0.3711
SEI Podcast Series: Technology transformation in investment management firms
In a recent white paper entitled Evolution in Asset Management, SEI pointed out that 70 per cent of US fund managers are currently looking to deploy advanced analytics in the front-office. The field of data science and machine learning-based data analysis is helping to transform how fund managers think about data to gain a competitive edge.
In SEI’s report, they write: “Virtually everyone is familiar with the potential value of data, but it is still not treated like a precious commodity by many firms. Through force of habit, data acquisition, integration, management, protection, analysis and disposal still often occur in an ad hoc way.”
In this latest podcast, Colleen Ruane, Director of Analytics at SEI Investment Manager Services and Mads Ingwar, Co-Founder and CEO of Kvasir Technologies, a quantitative AI-focused hedge fund, discuss how a smart approach to data consumption and management can lead to tangible benefits; not just within the front-office but across the investment firm. In short, how is advanced analytics moving from a supporting role to centre stage?
2020-05-05 00:00:00 Read the full story…
Weighted Interest Score: 4.5669, Raw Interest Score: 2.7603,
Positive Sentiment: 0.0789, Negative Sentiment 0.0789
How NLP Can Tackle The Challenge Of Multiple Languages
Natural language processing (NLP) is disrupting various industries, making it easier for humans to communicate with computers. But given there are more than 6900 languages in the world, it can be incredibly difficult to make NLP models for all of them.
In India itself, there are different dialects of Hindi, which creates a challenge for NLP professionals to build models that fit for different languages and dialects.
2020-05-06 04:16:00+00:00 Read the full story…
Weighted Interest Score: 3.9642, Raw Interest Score: 1.8056,
Positive Sentiment: 0.2452, Negative Sentiment 0.2006
GigaSpaces raises $12 million to accelerate AI workloads with in-memory computing
GigaSpaces, a startup developing in-memory computing solutions for AI and machine learning workloads, today announced it has raised $12 million. The funds will be used to scale expansion and accelerate product R&D, according to CEO Adi Paz.
It’s often been argued that in-memory computing is a critical piece of the big data analytics puzzle. It promises to mitigate slow data accesses by relying exclusively on data stored in RAM, minimizing the need to move data between storage and processors and theoretically speeding up the training time of machine learning algorithms. The result could be substantial cost savings in the case of algorithms that take days (or even weeks) to train.
2020-05-05 00:00:00 Read the full story…
Weighted Interest Score: 3.0129, Raw Interest Score: 1.5932,
Positive Sentiment: 0.0678, Negative Sentiment 0.1356
Expanding Data Governance into the Future
Shortened time frames to leverage business insights and navigate data privacy and ethics call for the next generation of Data Governance (DG). This DG describes a collaborative, thoughtful, long-term framework consisting of processes managing trusted data assets across the organization. Kelle O’Neal, Founder, and CEO of First San Francisco Partners, sees a need to make firms aware of Next-Gen Data Governance, while at the same time helping companies adapt to successful Data Governance practices with other business areas.
Recognition that good Data Governance has become a must has come none too soon. Donna Burbank, Managing Director at Global Data Strategy, notes that many companies are beginning or planning to begin a Data Governance program, including a broader range of industries than before.
2020-05-05 07:35:27+00:00 Read the full story…
Weighted Interest Score: 2.9881, Raw Interest Score: 1.8451,
Positive Sentiment: 0.2590, Negative Sentiment 0.1403
Chief Analytics Officer Vs Chief Data Officer: What’s The Difference?
Data executives are essential for a clear business strategy research as data-driven innovation has been critical for many years now. Of all the C-Level executives, there are only a few positions that deal with data. Two of the most popular ones include chief analytics officer, aka. Head of analytics, and chief data officer. But what is the fundamental difference between the two job roles?
In this article, we will compare the two and bring out similarities and differences.
2020-05-06 09:30:00+00:00 Read the full story…
Weighted Interest Score: 2.9797, Raw Interest Score: 1.7381,
Positive Sentiment: 0.1580, Negative Sentiment 0.0226
Financial Institutions Should Leverage for Ethical Data Management –
As financial institutions weigh both the business benefits and potential consequences of having access to vast amounts of consumer data, FIs should leverage five pillars at the top of the organization and across geographies and lines of business to ethically manage data. As such, these pillars for ethical data management are agnostic to the domain or business units to facilitate organization-wide adoption.
Being transparent with data means ensuring that how data is being collected, stored, and used is thoroughly documented. This documentation (in a digestible and accurate format) must be accessible to both regulatory bodies and consumers whose data the FI uses to derive insights, make predictions, or decisions.
Full transparency requires going beyond the raw data itself to include the processing and feature engineering of data, as well as the intent behind any analytics or AI model in which the data is leveraged.
2020-05-05 20:14:17+00:00 Read the full story…
Weighted Interest Score: 2.9224, Raw Interest Score: 1.4286,
Positive Sentiment: 0.1176, Negative Sentiment 0.2353
Deutsche Bank Creates ESG Group
White-glove brokerage Deutsche Bank has announced a new group and several new hires.
Given the increased focus on Environmental, Social & Governance (ESG) matters from the firm’s investment bank clients, a dedicated Sustainable Finance team has been formed within Capital Markets as part of Deutsche Bank’s broader strategy to offer ESG products and solutions to all client groups.
Operating within the existing Capital Solutions & Sustainable Financing (CS&SF) group led by Boris Kopp, the new team will partner closely with a network of regional and sectoral ‘ESG champions’ to be announced in due course, while aligning with other IB and bank-wide initiatives to ensure a consistent messaging and approach.
2020-05-04 15:41:03+00:00 Read the full story…
Weighted Interest Score: 2.4330, Raw Interest Score: 1.4711,
Positive Sentiment: 0.2263, Negative Sentiment 0.0189
Deep Learning Research and How to Get Immersed
So you’re interested in learning more about deep learning research, but you haven’t had a chance to work in a research lab. Maybe you just finished an online course or a bootcamp, or perhaps you’re just curious about the latest developments in the field. Where do you start?
If you’re not sure whether you want to focus on reading research papers, one of the easiest ways to be consistent about staying up to date on new research is by subscribing t…
2020-05-05 13:26:19.408000+00:00 Read the full story…
Weighted Interest Score: 2.2532, Raw Interest Score: 1.8282,
Positive Sentiment: 0.0914, Negative Sentiment 0.1280
Immuta Unlocks The Cloud for Sensitive Data Analytics
Press Release : Immuta, the automated data governance company, today announced native support for Snowflake, along with new privacy and security automation capabilities, that help organizations fully leverage cloud-based data analytics and data sharing — even on their most sensitive data sets.
Enhancements to the Immuta platform include k-anonymization, the latest addition to Immuta’s suite of Privacy-Enhancing Technologies (PETs), automated decryption of cloud-based data, and a new, native integration with Snowflake that lets joint customers easily analyze and share sensitive data. Organizations are increasingly migrating analytics workloads to cloud environments for greater scalability, flexibility, cost savings and performance. Yet, 53% of U.S. and 60% of EU IT professionals are not confident that their organization currently meets privacy and data protection requirements in the cloud.
These concerns are forcing data governance teams to more tightly control who has access to what sensitive information, and for what purpose. The operational burden in manually enforcing rules and controls for compliance is inhibiting the success of cloud-based data analytics.
2020-05-05 07:05:07+00:00 Read the full story…
Weighted Interest Score: 2.1184, Raw Interest Score: 1.3481,
Positive Sentiment: 0.4333, Negative Sentiment 0.1444
Trusting Data Delivery: What to Look for in a Data Validation Solution for Replication
Many of us have experienced moving to a new home or city. And in any moving process, it’s common to end up with missing valuables or broken items that leave you wondering if you should have packed better, picked a different moving company, or just thought through the integrity of your valuables before, during, and after the move. In my experience working in the data integration and replication space, many customers share similar concerns when moving their data. How can you trust the integrity of the data being moved and delivered from its “home” to your business users?
When your cloud data movement projects involve hundreds of gigabytes of data per day, latency and data integrity can appear to be at odds. However, most enterprises fueled by data-driven decisions just can’t accept this perceived trade-off. For example, in a use case of moving financial data into a cloud-based data lake like Amazon S3, low latency and high fidelity are equally critical and can’t compete for priority.
Considering that the success of data movement projects depends on the integrity of the data being delivered, let’s examine the reasons Data Quality could be compromised within a data pipeline, data validation solutions, and things to look out for when evaluating data validation options.
2020-05-04 07:35:52+00:00 Read the full story…
Weighted Interest Score: 1.8214, Raw Interest Score: 1.1621,
Positive Sentiment: 0.2347, Negative Sentiment 0.2794
Limitations Of Online Learning For Data Scientists Looking to upskill
Data science courses have been keeping many industry professionals and enthusiasts busy amid the lockdown. While data scientists are looking to shield themselves against an oncoming recession by actively upskilling, others are embracing it to make a career shift for the opportunities the field provides. Irrespective of the reasons for this mass shift to online learning, edtech firms are responding to this demand by opening up access to some of the premium course materials, launching additional data science courses, and even making some of them available for free.
Although this trend aligns well with the need to continuously learn to enhance career prospects and stay relevant in these uncertain times, it may also indicate an overdependence on e-learning. Distance education, for all its benefits, has its limitations, especially in a field like data science, where practical implementation is paramount. Upskilling should be a part of any data scientist’s career path, but are they relying too much on online courses? Online courses can be a contributing factor, but they cannot build a robust data science portfolio by itself. That is not to say that they are unhelpful, but depending solely on e-learning platforms may not be prudent. Let us attempt to understand why:
2020-05-05 10:30:00+00:00 Read the full story…
Weighted Interest Score: 1.7888, Raw Interest Score: 1.0446,
Positive Sentiment: 0.2576, Negative Sentiment 0.3864
The Favorite Part Of My Job As A Data Scientist
The one part of my job as a data scientist that I love above all other parts
TLDR: This article wasn’t designed to provide technical knowledge or insight. Below I tell a story about selecting a stratified sample, calculating sample weights, and sharing that information at a workshop for colleagues and co-workers. This is a story about how data scientists can use their non-technical know-how to help their company build strong data-savvy cultures.
2020-05-06 02:36:16.134000+00:00 Read the full story…
Weighted Interest Score: 1.6998, Raw Interest Score: 0.9390,
Positive Sentiment: 0.4160, Negative Sentiment 0.1070
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