Alternative Data News. 17, June 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.
The Reason You’re Frustrated when Trying to Become a Data Scientist – The hidden skill that separates the best from the rest
How many times have we seen the post “5 things you need to become a Data Scientist”, “How to become a Data Scientist in 2020”, or the images of the Venn diagrams? Don’t feel bad if when you read the requirements you curled up into a ball, sucked your thumb and procrastinated even harder on your goals because it’s unlikely you’re alone in this situation. If you are frustrated, its arguably not entirely your fault as to why you are feeling this way.
Data Science is a large field with many cross sections to other disciplines, but I think we have complicated the criteria for becoming a Data Scientist with many complex prerequisites, which are required further down the line, but are not what will keep you going in the long run. Anyone can become a Data Scientist. All it takes is the will to do it and the desire to carry out whatever it takes. Two traits of which every human can realize.
2020-06-16 14:00:05.391000+00:00 Read the full story…
Weighted Interest Score: 3.4960, Raw Interest Score: 1.6280,
Positive Sentiment: 0.1732, Negative Sentiment 0.2425
CloudQuant Thoughts : “The illiterate of the 21st century will not be those that cannot read and write, but those who cannot learn, unlearn and relearn” — Alvin Toffler. Consider for a moment that throughout our development as humans we were expected to learn one set of skills, from our parents, skills needed to help us survive. Hunting, cooking, building and making clothing and tools. Even as time marched on and farming, overproduction and village markets came into existence, one still learned one skill. This is the reason some people have surnames like Cooper and (Black)Smith. Yet, in the 20th and 21st centuries, the rate at which we are expected to learn a new skill only to ‘dump it’ and learn another new skill has accelerated to a potentially unhealthy level. That the rise of the automated machine, and its ability to take over the monotonous work, may not be the destruction of our work lives but the saviour.
Alternative Data Is The New Guidance
By creating insights derived from disparate datasets and expertly synthesized by alternative data analysts, operators and investors have granular visibility into company business operations. With company executives now at a loss to forecast even their own key performance indicators, investors are left looking into the abyss with little to no guidance from management teams. This affects visibility and decision-making on every side of strategy and planning. If decision-makers aren’t making data-driven decisions in investment strategies or operations, they are at a disadvantage.
Exit traditional data, enter alternative data, and decision-makers now have a near real-time solution to a growing market need. By leveraging unique data that offers glimpses into company performance, and aggregating those individual perspectives at scale via technology, analysts are able to provide real-time snapshots and insights.
2020-06-16 00:00:00 Read the full story…
Weighted Interest Score: 3.2546, Raw Interest Score: 1.4332,
Positive Sentiment: 0.3272, Negative Sentiment 0.2025
CloudQuant Thoughts : Forbes Opinion piece or M Science advert? Yes, companies are reluctant to give guidance in a time when most of us do not know what we will be doing next week. Yes Alternative data and particularly cumulative alternative data can help. But anyone who works in data knows that cookie cutter data is only useful if you want to follow a herd. Collate the data YOU think has value and create your own data points.
Using Advanced Scouting Data to Find Liverpool a Timo Werner Alternative
Some of world football’s smartest transfer strategies are anchored and driven by data analysis.
Liverpool are a top-line example; their work with numbers and models flagged up Philippe Coutinho and Mohamed Salah (among others) as ideal additions to the team. That’s brought them goals, profit and trophies—in that order.
But most of Europe’s top order are incorporating advanced data and algorithms into their hunt for signings in some way, either using their own data-analysis staff or hiring an outside firm.
2020-06-11 Read the full story…
CloudQuant Thoughts : Its like a FIFA video game on steroids!
Space and the profusion of data – the new development frontier?
Space is not just for astronauts. It’s the next frontier for tackling humanity’s most intractable problems such as food security, climate change and social inequality, as revealed at the first World Space Forum late last year.
Developing countries are crossing over the space frontier with a growing number of maiden satellite launches and inaugural space initiatives. Yet many lack capabilities to navigate through the vast profusion of data acquired by space technologies, namely through satellite Earth observation, and satellite positioning systems, as well as to effectively utilize satellite communications.
To avoid a leap into the dark and to reap long-term benefits from emerging space programmes, developing countries need to address their capacity constraints in processing the tide of raw data that flows from satellites. The process of filtration, refinement and modelling for translating data into usable information in forecasting models requires huge computing capacities and appropriate skills in machine learning and artificial intelligence.
2020-06-10 Read the full story…
CloudQuant Thoughts : A great article about the huge volumes of data about to hit our planet from outer space!!
How COVID-19 Has Redefined What Investors Want
The coronavirus pandemic has changed the way we work, play, and invest. Working from home is the new normal. Outdoor walks, at a safe distance from family and friends, have replaced sporting events, happy hours, and backyard barbecues. And investing has evolved, too, as more investors demonstrate an increased appetite for companies with good records on environmental, social, and governance (ESG) practices.
According to Morningstar, more than $10 billion, net, flowed into 314 different open-end and exchange-traded ESG funds in the first quarter of 2020. That’s an increase of more than 20% relative to the fourth quarter of 2019. The increase alone is impressive, but the timing is even more so. It was also during the first quarter of this year that the S&P 500 lost about 30% of its value. The average ESG fund, however, fell only 12.2%, according to a Bloomberg analysis.
2020-06-17 00:00:00 Read the full story…
Weighted Interest Score: 3.4926, Raw Interest Score: 1.5998,
Positive Sentiment: 0.2479, Negative Sentiment 0.2929
CloudQuant Thoughts : Don’t forget, CloudQuant has a range of datasets which we have pre-screened for you. We can supply a white paper of the performance result, the Python code used and give you access to the data so you can reproduce the results. One of our datasets is an ESG dataset which we have confirmed has positive influence. Head over to our Data Catalog to find out more.
‘Next Generation’ Of Smart Sustainable ETFs Launch
Stephane Degroote, head of ETFs & derivatives EMEA at FTSE Russell, said sustainability is involved in all the discussions the index, data and analytics provider is having regarding exchange-traded funds. Degroote told Markets Media: “Demand has become more sophisticated as issuers use factors to achieve specific environmental, social and governance exposures.”
Lida Eslami, head of business development for exchange traded products and international order book at London Stock Exchange, told Markets Media there has been more demand for ESG ETFs. “We currently list 19 sustainable ETFs and they have made up a quarter of new listings,” she added.
2020-06-15 17:31:37+00:00 Read the full story…
Weighted Interest Score: 2.7995, Raw Interest Score: 1.8675,
Positive Sentiment: 0.0865, Negative Sentiment 0.0346
ESG Fund Ratings: Not Perfect, but Still Valuable
Critics of environmental, social and governance fund ratings often cite numerous reasons as to why the ratings lack validity. While the ratings aren’t perfect, we explore some of the reasons why we believe they are worthwhile and how they may continue to improve.
Rating ESG Funds
One common argument regarding the validity of ESG ratings is that there are hundreds of ESG data, analytics and research providers, and that their scores are someti…
2020-06-09 00:00:00 Read the full story…
Weighted Interest Score: 2.7071, Raw Interest Score: 1.5094,
Positive Sentiment: 0.2012, Negative Sentiment 0.3019
XBRL: Nowcasting, restaurants in lockdown and ESG reporting in melt-up (Registration required)
Here is our pick of the 3 most important XBRL news stories this week.
- Traditionally, quant strategies have focused on forecasting prices, based on price time-series dynamics (e.g., stat arb), or based on cross-sectional data (e.g., factor investing). Forecasting made a lot of sense years…
2020-06-11 00:00:00 Read the full story…
Weighted Interest Score: 2.7687, Raw Interest Score: 1.0200,
Positive Sentiment: 0.0364, Negative Sentiment 0.1821
53.3% Data Scientists Prefer Python, According To PlaTo Survey By AIM
According to a recent report by Analytics India Magazine, the most preferred Data Science programming language used across organisations is Python, with 53.3% of the respondents utilising the language. Other languages that follow are R, Matlab, SAS, Scala, Java and more.
The report titled Analytics Platforms and Tools (PlaTo) Survey was conducted to understand the stack of platforms and tools adopted by leading Analytics, AI, & Data Science organisations. It included surveys across a wide range of platforms and tools including open source and commercial analytics platforms.
The survey was sent across the data science community to understand the adoption and usage of various Cloud Service providers, BI tools, Data Science platforms, AI frameworks, DevOp tools, distributed ML platforms, AutoML tools, Data Lake tools, and more. Respondents included a large spectrum of occupations and vocations including students, research scholars, entrepreneurs and senior professionals from various industries such as Domestic IT, BFSI, FMCG, Fintech, Fashion & Apparel and more.
2020-06-15 09:19:37+00:00 Read the full story…
Weighted Interest Score: 3.1430, Raw Interest Score: 1.8015,
Positive Sentiment: 0.1150, Negative Sentiment 0.0000
Armchair epidemiologists” and “data bros”: Inside the DIY world of Covid-19 research
Science used to be done by a select few in high-tech laboratories and dusty university offices. But this was before we had a pandemic on our hands
Science used to be done by a select few in high-tech laboratories and dusty university offices. Studies would go on for years, and when they were published, results would be locked behind journal paywalls, ready to be read by a handful of fellow specialists. But this was before we had a pandemic on our hands.
Now, everyone from billionaire Elon Musk to your high school friend on Facebook is an “armchair epidemiologist.” Data, analyses and opinions on Covid-19 are flooding social media feeds and news sites. And why not? Even public health experts are struggling to make sense of this extremely unusual situation. This crisis affects everyone, so why not offer your views on how to dampen the R rate?
But, according to the World Health Organisation, we are fighting not only a pandemic; we are also fighting an infodemic. And, it’s much harder to identify fake news than previously thought. Most of the Covid-19 misinformation contains some truth and authority, blurring the boundaries between fact and fiction.
2020-06-14 Read the full story…
Top 8 Algorithms For Object Detection One Must Know
Object detection has been witnessing a rapid revolutionary change in the field of computer vision. Its involvement in the combination of object classification as well as object localisation makes it one of the most challenging topics in the domain of computer vision. In simple words, the goal of this detection technique is to determine where objects are located in a given image called as object localisation and which category each object belongs …
2020-06-16 07:30:00+00:00 Read the full story…
Weighted Interest Score: 2.8661, Raw Interest Score: 1.6450,
Positive Sentiment: 0.2663, Negative Sentiment 0.0783
How Analytics Professionals Can Improve Their Business Acumen Amid This Crisis
Well, it has already been established that learning technical skills, including tools and languages like Python, Hadoop, SQL, and data visualisation, will indeed get you a data science job. However, it is also vital for a data scientist to have business knowledge in order to survive in this competitive landscape. Having a business understanding will not only help data scientists and analytics professionals to know the elements of a business model but will also be valuable for businesses to maximise their returns. With business knowledge, analytics professionals can effectively use the understanding for collecting and interpreting data.
In fact, in a recent event, the global service delivery head of analytics at Wipro, Sohini Mehta has stated that, with so many automated machine learning platforms in the market, most of the jobs have become easy and quite simple to perform. “It no longer needs just the technical expertise, but there is a lot of business knowledge too that comes into play.”
2020-06-16 05:16:52+00:00 Read the full story…
Weighted Interest Score: 2.8154, Raw Interest Score: 1.6777,
Positive Sentiment: 0.3386, Negative Sentiment 0.1231
Evolution of data science: How it will change over the next decade
Although data science, as an academic discipline, has been around for more than 50 years, it wasn’t until around 2010 that it entered the mainstream consciousness. It happened as a new wave of businesses recognized that data was the key to mastery of modern markets and started making it their strategic focus. In the years since, the field of data science has seen explosive growth as well as some fast-paced developments as higher demand has spurred innovation.
As far as the field of data science has come since 2010, there’s every reason to believe that the next decade will bring even more change. With simultaneous advances in related technology fields and new approaches by the best and brightest minds in the industry, data science in 2030 will bear little resemblance to the state of the art today. Here’s a look at how data science is set to evolve over the next decade.
2020-06-16 10:33:57+00:00 Read the full story…
Weighted Interest Score: 2.8079, Raw Interest Score: 1.4761,
Positive Sentiment: 0.2312, Negative Sentiment 0.1601
6 Important Big Data Future Trends, According To Experts
Many people agree that big data is here to stay and not a mere fad. Something that is not so clear-cut to everyday individuals concerns the future trends of big data analytics. These technologies are quickly evolving. What does that mean for the businesses that use them now or will soon?
What is big data in simple terms? It encompasses both the structured and unstructured information kept by an entity that is collectively too large for traditional systems and techniques to process. It also relates to the speed of the processing capability. Some businesses need insights in virtually real-time, and big data software can provide them, whereas traditional methods could not.
Understanding what’s ahead for big data technologies and use cases is more straightforward if people tune in to what experts have to say. Here are some glimpses into what’s possible, based on their perceptions.
2020-06-09 09:05:00+00:00 Read the full story…
Weighted Interest Score: 2.5419, Raw Interest Score: 1.5291,
Positive Sentiment: 0.1133, Negative Sentiment 0.0566
Why Is That Entrepreneur Raising So Much More Than Me?
How much to raise is both an art and a science, a topic discussed at length in many other posts. What this article will focus on are the reasons specifically an entrepreneur similar to you might be raising more more money. It’s really a function of five factors: stage, geography, investors, credibility, and strategy.
2020-06-14 00:00:00 Read the full story…
Weighted Interest Score: 2.3742, Raw Interest Score: 1.4238,
Positive Sentiment: 0.0949, Negative Sentiment 0.0475
COVID 19 Impact On Machine Learning Models
A lot of business processes are functioning with the help of data science implementations, i.e. machine learning models, time series models, AI solutions etc. These models take into consideration the historical data as well as past trends. In the Pre-COVID arena, all models were working well with the changing environment. All predictions were serving the purpose of the task as desired. With the advent of 2020, COVID 19 emerged on this Earth and caused a major disruption in our usual modelling behaviour.
COVID has affected every industry in a different way. For example, consumers have engaged in a lot of panic buying situations and supply hoarding due to the lockdown scenario in major parts of the world. Hence the consumer goods industry saw a heavy increase in supplies in the month of March and April. In western countries like the UK, US the ‘eating at home scenarios’ increased substantially. There was a major shift from eating outside to eating at home due to restricted movement for people.
The sales for most of the food products went up due to the situation. This sudden spike in sales has been very beneficial from the performance standpoint. The traditional models can no longer be used for sales predictions as they are unable to capture these unusual spikes in sales over the past two to three months. This is just one of the use cases. A similar kind of situation will be observed with respect to every industry.
2020-06-16 10:30:00+00:00 Read the full story…
Weighted Interest Score: 2.2859, Raw Interest Score: 1.1628,
Positive Sentiment: 0.2907, Negative Sentiment 0.1744
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