Alternative Data News. 22, July 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.
Environmental Social and Governance (ESG) Section
ESG is taking over this Alternative Data Blog Post, it seems like all anyone is talking about is ESG. Don’t forget that we at CloudQuant also have Alternative Data-Sets available including a splendid ESG dataset. Head over to our Data Catalog page for more information.
BlackRock’s Fink Says U.S. Proposal to Limit ESG Investing Will Only Boost Interest
Applying environmental, social and governance (ESG) principles to investing is consistent with a manager’s fiduciary responsibility, and a U.S. Department of Labor proposal will “accelerate” interest in ESG investing, BlackRock CEO Larry Fink said in an interview after the world’s largest money manager reported second-quarter earnings.
Fink was speaking about a proposed rule by the Labor Department that would discourage retirement plans from making investments based on ESG considerations. Labor Secretary Eugene Scalia has said such investments were often made to achieve a social or political end.
BlackRock predicts there will be $1.2 trillion in global sustainable ETF assets by 2030 and is expanding its sustainable funds lineup.
2020-07-17 00:00:00 Read the full story…
BlackRock Has ‘Big Ambitions’ In Fixed Income ETFs
BlackRock had record quarterly inflows of $57bn (€50bn) in fixed income exchange-traded funds which the fund manager said validated its big ambitions for the asset class going forward.
Today BlackRock reported that in the second quarter of this year it had $100bn of total net inflows, representing 10% annualized organic base fee growth.
Larry Fink, chairman and chief executive, said in a statement: “iShares fixed income ETFs and BlackRock’s act…
2020-07-17 17:13:31+00:00 Read the full story…
Weighted Interest Score: 3.7853, Raw Interest Score: 1.8634,
Positive Sentiment: 0.1242, Negative Sentiment 0.0311
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, …
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
“An excellent tool”: New study by AIMA and Simmons & Simmons probes ESG short-selling ethics
Short selling is essential in enabling investors to hedge against ESG risks, and has bolstered market transparency by uncovering corporate wrongdoing and environmental negligence, according to a new study by the Alternative Investment Management Association and global law firm Simmons & Simmons.
The paper – ‘Short Selling and Responsible Investing’ – probed how the booming trend of ESG (environmental, social, and governance) investing interacts with short selling, the often-criticised practice that is central to most traditional hedge fund strategies.
The study found that responsible investing does not necessarily require long holding periods, and suggested shorting can be “an excellent tool” for achieving two key goals for responsible investors: mitigating undesired ESG risks, such as climate damage, and creating an economic impact by influencing the nature of capital flows through ‘active’ investing.
ESG is now seen as a key factor in many hedge funds’ portfolio-building processes, as boardrooms grapple with ongoing challenges such as climate change and improving corporate governance.
2020-07-22 00:00:00 Read the full story…
Weighted Interest Score: 3.3016, Raw Interest Score: 1.9334,
Positive Sentiment: 0.3272, Negative Sentiment 0.5354
HSBC Launches ESG Portfolio Reporting Service
HSBC launched a reporting service that provides asset owners and managers with independent measurement of how focused their listed asset investments are on environmental, social and corporate governance (ESG) issues.
The new service will allow asset owners, such as insurance companies, pension funds and sovereign wealth funds, and the asset managers that invest their money, to keep track of the ESG ratings of their large holdings and help them meet the increasing demand for greater transparency and more insight in this area.
2020-07-22 10:39:39+00:00 Read the full story…
Weighted Interest Score: 2.9851, Raw Interest Score: 1.8242,
Positive Sentiment: 0.3317, Negative Sentiment 0.0000
TSA Traveler Throughput
I know, I already put this on the AI and Machine Learning blog post earlier this week but I made this one myself and it is more appropriate for the Alternative Data Blog!
Data Source : https://www.tsa.gov/coronavirus/passenger-throughput
Language : Python Jupyter Labs (CloudQuant) with CuteChart Library (https://github.com/cutecharts/cutecharts.py)
Paint.net : CuteCharts is not great at axis control so had to add the dates manually.
Font : xkcd http://www.xkcd.com/fonts/xkcd-Regular.otf
How easy was this, grab the data from TSA and run this little python script…
pip install cutecharts
import cutecharts.charts as ctc
import pandas as pd
from cutecharts.globals import use_jupyter_lab; use_jupyter_lab()
df = pd.read_csv('TSA.csv', parse_dates=['date'],infer_datetime_format=True) # csv of data from www.tsa.gov/coronavirus/passenger-throughput
df = df.iloc[::-1] # Flip!
df['Date'] = df.apply(lambda row: str(row.date)[0:-5], axis = 1)
chart = ctc.Line('TSA Checkpoint Travel Numbers',width='1000px',height='800px')
chart.set_options(y_tick_count = 10, labels=list(df['Date']), x_label='This Year', y_label='Last Year')
chart.add_series('This Year', list(df['thisyr']))
chart.add_series('Last Year', list(df['lastyr']))
chart.render_notebook() # change this to chart.render() if not using a jupyter notebook.
Farmer’s share of a chocolate bar – Vizzu.COM
CloudQuant Thoughts : Finding, processing data and finding interesting outcomes is only one part of our jobs. If you cannot present your data in a beautiful and clear manner then all the work that went before is for nought. Vizzu.com is a particularly nice service for animating data.
Hands-On Guide To Using YFinance API In Python
YFinance came as a support to those who became helpless after the closure of Yahoo Finance historical data API, as many programs that relied on it stopped working. YFinance was created to help the programs and users who were relying on the Yahoo Finance API. It solves the problem by allowing users to download data using python and it has some great features also which makes it favourable to use for stock data analysis.
Finance not only downloads the Stock Price data it also allows us to download all the financial data of a Company since its listing in the stock market. It’s easy to use and is blazingly fast. This library is pretty famous for Financial Data Analysis.
In this article, we will explore YFinance and learn what we can do. The stock we will be working on here is Pfizer, a Pharmaceutical Company listed in NASDAQ. YFinance is highly recommended for Financial Reporting as it provides you with each and every detail you require about the company and its stock. Through this article, we will cover the following points:-
- Download and Analyze Stock Data using YFinance
- Finding various Information on Downloaded Data
- Visualize the Stock Data
2020-07-22 10:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6887, Raw Interest Score: 1.4654,
Positive Sentiment: 0.0505, Negative Sentiment 0.0842
CloudQuant Thoughts : yfinance is a really nice library, you should try it out.
Academic Project Used Marketing Data to Monitor Russian Military Sites
Commercially available location data is increasingly used for sensitive surveillance by researchers, government agencies. In 2019, a group of Americans was observing the cellphone signals coming from military sites across Eastern Europe.
At one of the locations, the Nyonoksa Missile Test Site in northern Russia, the group identified 48 mobile devices present on Aug. 9, one day after a mysterious radiation spike there generated international headlines and widespread speculation that a Russian missile test had gone wrong.
2020-07-18 08:00:00 Read the full story…
CloudQuant Thoughts : This is only interesting because it is the military. We are all leaking huge amounts of data all day every day. And before we get too cocky at this second Russian military phone gaffe (first was “we are not in Ukraine” yet their soldiers were tweeting GPS tagged photos saying they were!), our US military have previously leaked the entire shape of their training facilities from soldiers fitbits and soldiers in Afghanistan have leaked their locations by carrying around their mobile phones and facebooking/tweeting.
Democratizing Data: Do Your People Have the Access They Need?
Organizations have invested heavily in engineering resources to centralize data across the enterprise, often creating sophisticated environments with robust data pipelines. But even as they have successfully gathered and corralled data this way, many still struggle with effectively sharing and orchestrating the data across the enterprise.
That’s a pressing concern because, to successfully experiment, explore and activate data for the entire organization, IT, analytics and marketing teams must all have the data access they need to succeed. This notion isn’t new, but for many businesses, despite their commitment to democratizing data, that access—leveraging each group’s strengths—is insufficient or absent.
2020-07-13 Read the full story…
How Does Data Management Drive Efficiency for Organizations?
Data-driven analytics continue to deliver sophisticated solutions for manufacturing efficiency, early disease detection, and smart capabilities building in workplaces. Thus, industry operators and leaders continue raise their expectations and demands from data technologies with every passing year. Looking Behind the Curtain: What Really Drives Value from Data reveals some insights that global managers can learn from.
Brent Gleeson of Forbes, who regularly contributes about organizational excellence, warns that in spite of having the best infrastructure, technological support, and military intelligence, the United States could not stop many attacks against them. This important observation signals the need for speedy, data-enabled, decision-making at a time of crisis.
2020-07-21 07:35:02+00:00 Read the full story…
Weighted Interest Score: 4.3911, Raw Interest Score: 2.5047,
Positive Sentiment: 0.3354, Negative Sentiment 0.2620
8 Best Open-Source Tools for Data Mining One Must Know
One of the popular terms in machine learning techniques is data mining. It is the process of extracting hidden or previously unknown and potentially useful information from the large sets of data. The outcome can be for analysing and achieving meaningful insights for the development of an organisation.
In this article, we list down the eight best open-source data mining tools one must know. (The list is in alphabetical order)
- Apache Mahout
2020-07-21 12:30:00+00:00 Read the full story…
Weighted Interest Score: 3.9135, Raw Interest Score: 2.2195,
Positive Sentiment: 0.2065, Negative Sentiment 0.0516
Kerala Govt (India) Launches AI Course For Graduates
The Additional Skill Acquisition Programme (ASAP) of the Higher Education Department in Kerala has come up with a new artificial intelligence and machine learning course for graduates. It has been introduced with the aim of improving the employability of graduates in the state and equipping them with skills to meet industry requirements.
The course aims to help students in the areas of gaming, speech recognition, language detection and robotics. The course designed is 776-hours long and is aimed at creating skilled professionals who can fill the demand in areas of data science, AI and ML.
The program called the AI Machine Learning Developer Programme for Graduates will help gain practical knowledge and prepare for new-age jobs such as AI/ML scientist, data scientists, ML engineer, robotics scientists, business intelligence developers, AI research scientists and more.
2020-07-21 05:53:51+00:00 Read the full story…
Weighted Interest Score: 3.6415, Raw Interest Score: 2.2088,
Positive Sentiment: 0.1606, Negative Sentiment 0.1205
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
Will Value Investing Continue To Work?
Why Value Investing Works? We cannot be sure that value investing will beat the market. What has worked in the past is not predictive of what will work in the future. However, if we dig deeper into why value investing has worked for a long time, we are likely to get insights into what may or may not continue to work in the future.
First, what do we mean when we say “value investing?” Historically, most people using this term have referred to the practice of purchasing stocks at a price that is low relative to some fundamental measure such as earnings or book value. The term was used in contrast to “growth investing” which typically involved purchasing stocks at relatively high ratios of earnings or book value based on the expectation that the company’s future growth potential would compensate for that starting disadvantage.
2020-07-17 20:36:37+00:00 Read the full story…
Weighted Interest Score: 2.9705, Raw Interest Score: 1.7638,
Positive Sentiment: 0.2682, Negative Sentiment 0.3198
We Need A Lot More Than Data: How Startups Can Harness AI
You open your phone, take an eye exam, and find out your risk of Alzheimer’s 3-5 years ahead even though you have no symptoms.
A doctor takes data (CT scans, genomic tests, blood labs, demographics etc) as inputs, the software is trained specifically on the patient’s biomarkers, and gives a recommendation what drugs would work best.
Veterinarians find out what drugs approved for humans can be repurposed to crush a dog’s specific cancer. Eventually the data can be used for humans since cancer is a shared malady.
These are not pipe dreams but examples of real companies — Neurotrack, Onc.ai, and FidoCure (full disclosure: invested through Tau Ventures) respectively. Personalized medicine, which has been science fiction for generations, is less fiction and more science these days. In many ways AI is the army and startups the generals of this revolution. This post is not about the promises and pitfalls of AI but really where the world is today, highlighting invariably the opportunities.
2020-07-19 00:00:00 Read the full story…
Weighted Interest Score: 2.8973, Raw Interest Score: 1.2926,
Positive Sentiment: 0.2452, Negative Sentiment 0.0669
Systemic Racism is Strengthened by Data Science.
Why The Data Tells You To Be Racist.
With the current political climate, I took it upon myself to dive into data science and discover ways in which the field is compromised by racism and discrimination. What I found is nothing short of shocking.
Through this article, I hope to highlight ways in which machine learning and data science are being used to explicitly penalize underprivileged societies across the world. We’ll go through a plethora of examples as well as ways in which we — as people — can counteract these recurring biases.
Without the right precautions in place, machine learning — the backbone technology that drives decision making in a wide array of sectors — explicitly castigates underprivileged communities. Left alone, algorithms will count a black defendant’s race as a strike against them; yet, several data scientists in the community are supporting calls to turn off the safeguards and unleash the hells of computerized prejudice.
2020-07-21 23:19:52.574000+00:00 Read the full story…
Weighted Interest Score: 2.8141, Raw Interest Score: 1.3816,
Positive Sentiment: 0.0987, Negative Sentiment 0.6908
Statistical Measures of Central Tendency
In statistics, measures of central tendency are a set of “middle” values representative of the data points. Central tendency describes the distribution of data focusing on the central location around which all other data are clustered. It is the opposite of dispersion that measures how far the observations are scattered with respect to the central value.
As we will see below, central tendency is an elementary statistical concept, yet a widely used one. Among the measures of central tendency mean, median and mode are most frequently cited and used. Below we will see why they are important in the field of data science and analytics.
2020-07-21 23:20:36.362000+00:00 Read the full story…
Weighted Interest Score: 2.7969, Raw Interest Score: 1.4403,
Positive Sentiment: 0.1172, Negative Sentiment 0.1507
Is Tesla’s green investment bubble about to burst?
Tesla’s nosebleed-inducing rise in share price shows no sign of slowing down. From lows of $185 last May, the company’s shares reached new highs of $1,643 this week ahead of its crucial second-quarter earnings on Wednesday.
Long doubted and dismissed, it has now posted three consecutive quarters of profit, including one that took it through a global pandemic. It is worth $250bn, the most valuable car company in the world, and it attracts devoted…
2020-07-22 00:00:00 Read the full story…
Weighted Interest Score: 2.7022, Raw Interest Score: 1.3436,
Positive Sentiment: 0.2495, Negative Sentiment 0.2687
Mosaic Smart Data Launches Stand-Alone Data Normalisation
Mosaic Smart Data (Mosaic), the real-time capital markets data analytics company, is launching its data normalisation process as a new stand-alone service. Mosaic will employ its best-in-class enrichment technology and flexible data model to process firms’ transaction data, allowing institutions to analyse their activity in a given asset class at both the micro and macro levels, and in real-time, for the first time.
Mosaic Smart Data has combined its deep domain expertise in financial products, data science and software engineering to develop a service that cleanses, normalises and enriches streaming data in real-time for all major FICC asset classes including cash and derivatives. The service can be provided in the cloud or deployed on premises behind the client’s firewall. The resulting data is stored and made available via an API allowing data to be accessed remotely, making digital and distributed working feasible.
2020-07-20 09:15:19+00:00 Read the full story…
Weighted Interest Score: 2.6087, Raw Interest Score: 1.6614,
Positive Sentiment: 0.2110, Negative Sentiment 0.3428
Machine Learning challenges in legacy organisations
Fans of machine learning suggest it as a possible solution for everything. From customer service to finding tumours, any industry in which big data can be easily accessed, analysed and organised is ripe for bringing about new and compelling use cases. This is especially attractive for legacy organisations, such as financial services firms, looking to gain an advantage.
These businesses are usually well embedded in their markets, fighting with competitors over small margins and looking for new ways to innovate and drive efficiency. They also have an abundance of historical and contemporary data to exploit. One asset any start-up lacks is owned historical data, which gives legacy firms an edge in the competitive landscape. The promise of machine learning is therefore particularly seductive – feed in your extensive customer and business insights along with your desired outcome and let algorithms work out the best path forward.
2020-07-14 15:15:03 Read the full story…
Weighted Interest Score: 2.5202, Raw Interest Score: 1.6064,
Positive Sentiment: 0.4431, Negative Sentiment 0.2216
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