Alternative Data News. 30, September 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 Meteoric Rise of Among Us
Source : steamdb.info
Source : https://innersloth.itch.io/among-us/devlog
Tools : Python, Illustrator
Notes : The chart does not take into account mobile players, only concurrent players on Steam.
The chart begins from the game’s Steam release. The game has existed beforehand as a beta on various platforms, including itch.io, Android, and iOS.
2020-09-27 00:00:00 Read the full story…
CloudQuant Thoughts : If you do not know what Among Us is, it is the latest Minecraft or Fortnite (depending on how old you/your kids are). It was launched June 15th 2018 and had 18 players on its first weekend. As a Brit, I am annoyed that it is not called “Amongst Us”! But it is now the biggest thing in videogaming!. Don’t believe me? Check out Google Trends….
Here’s the shocking truth about Robinhood investors vs. Wall Street stock pros
Individual investors as a group are doing better than most U.S. equity mutual funds
Call it the revenge of the small investor. For months now, small retail investors have been ridiculed by Wall Street professionals for being hyperactive traders — addicted to risk and hopelessly irrational. But just released academic research finds that, as a group, they’ve outperformed the market.
Yes, you read that right. The research, conducted by Ivo Welch, a finance professor at UCLA’s Anderson Graduate School of Management, is entitled Retail Raw: Wisdom of the Robinhood Crowd and the Covid Crisis. The National Bureau of Economic Research recently began circulating it in academic circles.
For the study, Welch obtained data reflecting all trades over the three-year period from mid-2018 to August 2020 at Robinhood, the online retail brokerage firm. Though he did not have data on the actual composition of each individual’s portfolio at the firm, Welch was able to see the number of individual accounts that owned each individual stock. (The data was collected, with Robinhood’s tacit blessing, by the website Robintrack.com.)
Armed with this data, Welch constructed a hypothetical portfolio that weighted each stock according to the number of Robinhood accounts that owned it. That is, if stock ABC was owned in twice as many Robinhood accounts as stock XYZ, then this hypothetical portfolio invested twice as much in ABC than in XYZ. Over the three years studied, this hypothetical portfolio beat the market (as represented by benchmarks such as the S&P 500 SPX, 0.57% ), both in raw unadjusted terms as well as after risk adjustment.
2020-09-29 00:00:00 Read the full story…
Weighted Interest Score: 2.8411, Raw Interest Score: 1.3042,
Positive Sentiment: 0.2006, Negative Sentiment 0.1605
CloudQuant Thoughts : There are a significant number of stories that claim the opposite to this research. What is of no doubt is that the industry got hooked by seeing the updates of ‘holdings’ of retail investors at a much higher detail and time resolution than ever before. So interested that Robintrack.com no longer exists, as RobinHood’s investors had them turn off the spigot of that lovely retail data flow. Bloomberg News reported in October 2018 that Robinhood had received almost half of its revenue from payment for order flow from the likes of Citadel and Two Sigma. Firms that handle trades for retail traders and trade themselves (utilizing that data and trade flow to improve their models/market sensitivity and even their trading costs!) have been around for a long time. How can the rest of us gain access to such data? Head over to our data catalog and review the Intelligration Dataset!
Investing in the 2020 Election
Motivation : Sites like PredictIt give you the ability to directly bet on 2020 election outcomes, but have unfavorable fee structures and restrictive limits on how much money you can put in. I wanted to take a quantitative approach in determining which stocks to buy based on my 2020 election predictions.
Background : I define “Trump Beta” as the correlation between a stock’s daily prices changes and the daily changes in Trump’s election odds. Presidential election odds are calculated based off of trading on the PredictIt betting market, where over 100,000 users are buying and selling contracts on the outcome of this next election. I’ve been scraping the PredictIt website every day since June 2019, to get a complete picture of how each candidate’s election odds have evolved over time. Of course, both stock market prices and PredictIt election odds are noisy numbers, and correlation does not necessarily indicate causation. However, I believe that political beta is still a powerful tool for quantifying the potential stock market impact of different election outcomes.
2020-09-25 00:00:00 Read the full story…
CloudQuant Thoughts : Our first link to a WallStBets post! But this one is by u/pdpwp90 of QuiverQuant, a well know creator on dataisbeatiful and we have regularly covered his content in this blog.
CloudQuant also provides access to Alternative Data Sets, going one step further than everyone else in the industry : providing analysis, white papers and python code to demonstrate the efficacy of specific data sets. Head over to our DataSet Catalog for more information.
Vanguard, BlackRock, Transamerica Launch New ESG ETFs: Portfolio Products
Vanguard, BlackRock and Transamerica added new environmental, social and governance exchange-traded funds to their offerings, reflecting the growing interest in ESG investing.
In other ESG news, Cboe Global Markets launched cash-settled options on the S&P 500 ESG Index (SPESG). The S&P 500 ESG Index was designed to align investment objectives with ESG values and the new index options are a potential tool for investors to implement hedging, risk management, income enhancement and asset allocation strategies, it said
2020-09-28 00:00:00 Read the full story…
Weighted Interest Score: 9.0886, Raw Interest Score: 3.1655,
Positive Sentiment: 0.1745, Negative Sentiment 0.0000
How women can invest in themselves and other women
Put your financial foot forward, and help others too. Investing is for anyone who wants to have control over their financial future — including you, your best friend and your mom.
But with the rise of socially responsible investing, there’s a new motivating factor for women: Your investments can also help support others. These initiatives, which focus on creating an impact with your investment dollars, have opened the door for you to not only put your best financial foot forward, but also to help others do the same in the process.
Here are four ways to invest not just for yourself, but in the success and advancement of other women.
3. If you prefer the DIY investing approach, you can find mutual funds that focus on benefiting women or other marginalized groups and add them to your portfolio.
While every fund is different, some consider whether companies offer sexual harassment training, whether a company does business with minority and women-owned firms or has fund managers who partner with organizations working to stop human trafficking. Many socially responsible investing funds are graded using ESG investing factors (ESG stands for environmental, social and corporate governance). High scores in the social and governance categories may indicate a company with a diverse leadership board or equal employment opportunities
2020-09-30 00:00:00 Read the full story…
Weighted Interest Score: 2.6670, Raw Interest Score: 1.4094,
Positive Sentiment: 0.3903, Negative Sentiment 0.1301
Questions All Impact Investors Should Ask Themselves Before Investing
Thanks to the growing popularity of impact investing strategies (i.e., those that seek to support environmental or social change in the pursuit of financial returns), a debate rages on about which methods are the ones to pursue.
Private and public approaches to impact investing each come with their own inherent benefits, drawbacks, and risks. Private investments may happen on a much smaller scale (by household, perhaps), and may only be available to certain types of high net worth investors. Investments in publicly traded companies and funds are pooled into large scale investments, making the contributions of any single retail investor less impactful.
The contrasts between the two don’t begin and end with what’s above. Determining where you want to invest depends greatly on how much risk you’re willing to incur and — ultimately — at what level and to what degree you want to see your investment make a difference.
2020-09-29 00:00:00 Read the full story…
Weighted Interest Score: 2.3779, Raw Interest Score: 1.4033,
Positive Sentiment: 0.1689, Negative Sentiment 0.0910
The future of data privacy in alternative data – An interview with Peter Greene
We had the opportunity to interview Peter Greene, Vice Chair of the Investment Management Group at Lowenstein Sandler LLP, on the topic of data privacy in alternative data. We cover the evolution of data compliance, current challenges in the regulatory scheme and how data privacy might evolve in the future. Comments have been condensed and edited for clarity.
Looking back at your presentation from the 2020 Quandl Data Conference, how important is data privacy and data compliance for a hedge fund or data-driven investor today versus 5 years ago?
2020-09-22 15:19:53+00:00 Read the full story…
Weighted Interest Score: 2.4738, Raw Interest Score: 1.2418,
Positive Sentiment: 0.0837, Negative Sentiment 0.1535
Harnessing alternative data in the fight against fraud
The recent global crisis has set off a major fraud resurgence. With the world continuing its acceleration towards becoming digital-first, and with everything from work and transactions to entertainment and shopping happening online, potential attack vectors and opportunities are exponentially growing. The UK alone has seen a 66 percent rise in scams during the pandemic so far.
This is especially true for the financial services sector, as banks and financial organisations quickly shift their operations online during the pandemic in order to reach newly remote customers. Actions such as onboarding and sensitive transactions have been forced to take place purely remotely, while ID verification methods had to be adapted to cater to remote customers – during lockdown, the FCA even announced plans to accept selfies as part of a holistic identity verification process.
Fighting fraud with traditional techniques is no longer enough. As fraud becomes digital-first, so should anti-fraud techniques – businesses need to combine technology and data to create intelligent, real-time responses to problems, without a customer, or potential fraudster, ever even knowing. To do this, alternative data and machine learning are quickly becoming go-to solutions.
2020-09-28 00:00:00 Read the full story…
Weighted Interest Score: 3.4262, Raw Interest Score: 1.4047,
Positive Sentiment: 0.1155, Negative Sentiment 0.7697
Google’s Cloud TPUs now better support PyTorch
In 2018, Google introduced accelerated linear algebra (XLA), an optimizing compiler that speeds up machine learning models’ operations by combining what used to be multiple kernels into one. (In this context, “kernels” refer to classes of algorithms for pattern analysis.) While XLA supports processor and graphics card hardware, it also runs on Google’s proprietary tensor processing units (TPUs) and was instrumental in bringing TPU support to Facebook’s PyTorch AI and machine learning framework. As of today, PyTorch/XLA support for Cloud TPUs — Google’s managed TPU service — is now generally available, enabling PyTorch users to take advantage of TPUs using first-party integrations.
Google’s TPUs are application-specific integrated circuits (ASICs) developed specifically to accelerate AI. They’re liquid-cooled and designed to slot into server racks; deliver up to 100 petaflops of compute; and power Google products like Google Search, Google Photos, Google Translate, Google Assistant, Gmail, and Google Cloud AI APIs. Google announced the third generation at its annual I/O developer conference in 2018 and in July took the wraps off its successor, which is in the research stage.
2020-09-29 00:00:00 Read the full story…
Weighted Interest Score: 3.3147, Raw Interest Score: 1.9243,
Positive Sentiment: 0.1241, Negative Sentiment 0.0310
Drive Your Digital Business With Data — The Data Strategy Track At Forrester’s Data Strategy & Insights Forum
The great thing about digital businesses is that there’s a data trail of breadcrumbs for everything you, your customers, and your partners do. The tough thing about digital businesses is that actually using that data to optimize your business takes a degree of data management maturity very few organizations have. Many firms are working hard to up their game, but there is no quick fix or one-size-fits-all solution. Data strategy is hard!
Fortunately, help is on the way: Coming up on October 13–15 is Forrester’s Data Strategy & Insights North America Forum. It’s our third year for this Forum and is looking to be bigger and better than ever — and it’s our first time doing this one in an all-virtual format. Last year, you told us that you wanted more sessions on data: data strategy, best practices, technology architectures, all things data. So this year, we have curated a track completely dedicated to driving your digital business with data.
2020-09-28 13:33:47-04:00 Read the full story…
Weighted Interest Score: 2.7283, Raw Interest Score: 1.5915,
Positive Sentiment: 0.2274, Negative Sentiment 0.0758
IIT Madras & ESPNcricinfo’s AI-Powered Tool Is Enhancing The Indian Cricket (IPL) Experience This Season
Artificial intelligence-powered tool, ‘Superstats’ by Indian Institute of Technology Madras and ESPNcricinfo is enhancing the experience of Indian Premier League (IPL) matches for its fan by providing a context to every game event in a game and also provides insights into factors such as ‘luck.’ It uses data science to enable the same. Superstats takes into account the context of every performance, batting and bowling. Context includes pitch conditions, quality of opposition, and match situation – in terms of the pressure on the player.
The AI Engine leverages the rich data collected from over a decade-old ESPNcricinfo’s ball-by-ball updates. The work was led by Prof. Raghunathan Rengaswamy and Prof. Mahesh Panchagnula of IIT Madras along with the ESPNcricinfo team. The AI tool was developed in 2019 through a collaboration between ESPNcricinfo, IIT Madras and Gyan Data Pvt. Ltd., an IIT Madras-incubated company. It is a suite of metrics that helps fans judge performances in limited-overs cricket – T20s and ODIs – in a far more nuanced manner than conventional metrics do.
It has a feature called ‘Forecaster’ that can predict the final score of an ongoing inning and the win probabilities of teams using statistical and machine learning models. The predictions take into account several factors including the current run rate, number of overs and wickets left, quality and form of the players.
2020-09-28 09:57:47+00:00 Read the full story…
Weighted Interest Score: 2.5545, Raw Interest Score: 1.1860,
Positive Sentiment: 0.1873, Negative Sentiment 0.1561
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