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.
CloudQuant Researchers discover indications of $TSLA price split ahead of event in SpiderRock Alternative Data set
TSLA skyrocketed ahead of stock split – How did the options market show the trading signals?
Through mid-June to the end of August, the two spikes in the acceleration of change of volatility spread implied the two rallies of the stock price one week ahead of time, while the downward spike predicted the post-split drop.
Click here for more information.
2020-09-16 Read the full story…
CloudQuant to release results of latest disruptive data set analysis at The Trading Show – Chicago – September 15th 2020
CloudQuant is all about CONTENT at this year’s Trading Show.
We are participating in 3 panels and will release our latest research paper.
Stop by our virtual booth at the show to learn more… FIRST!
Alternatively fill in the form to your right or Register for a Demo and we will contact you directly!
2020-09-14 Read the full story…
Despite the memes, the gender reveal party is only responsible for 0.4% of the area burned so far in California’s 2020 wildfire season.
More than 77% was due to unusually high numbers of dry lightning strikes. This data does not include Oregon’s fires.
I pulled the data from this Wikipedia page. Pulled it with a csv export.
It only counts fires that burned more than 1,000 acres, so the fraction caused by the gender reveal party may be even lower.
It’s a simple pie chart made in Excel then polished up in PowerPoint.
2020-09-11 Read the full story…
CloudQuant Thoughts : Latest one from DataIsBeautiful on Reddit.
More Innovation Ahead In US Displayed Markets
Ronan Ryan, co-founder and president of IEX Group, said the regulatory approval of the exchange’s new order type is a step-up in innovation for US displayed markets and there will be more to come.
Ryan told Markets Media: “There is uniform agreement that displayed trading had been suffering, both in terms of execution quality and size decreasing. All the innovation in US equities in the last 10 to 15 years has been in dark trading.”
Last month the US Securities and Exchange Commission approved IEX’s Discretionary Limit, or D-Limit, order type. IEX said the purpose of the D-Limit order type is to protect liquidity providers from potential adverse selection resulting from latency arbitrage trading strategies, and to encourage members to submit more displayed limit orders to the exchange.
2020-09-08 11:30:32+00:00 Read the full story…
Weighted Interest Score: 3.6631, Raw Interest Score: 1.7188,
Positive Sentiment: 0.2417, Negative Sentiment 0.2149
CloudQuant Thoughts : IEX are real innovators in the US Equities Exchange environment. It would behoove you to read up on their distruptive actions since Brad Katsuyama and Ronan Ryan first envisioned this method of trading. Their story made up a significant part of the book “Flash Boys: A Wall Street Revolt” by Michael Lewis.
GoldenSource launches Quant Workbench solution as financial return pressures mount
GoldenSource has launched a new Quant Workbench tool that enables financial institutions to better leverage their data by running superior analytics and quantitative research directly on best available reference and pricing data.
The new solution sits on top of GoldenSource’s existing data management system so that financial institutions can allow their quant developers and research analysts to do their work on approved validated data sources. As part of the Quant Workbench package, GoldenSource will also provide sample calculations covering both buy side and sell side use cases. For example, on the sell side, the tool allows quants to build volatility surface models – which are required for valuations and risk management of options portfolios. On the buy side, the tool can be used for portfolio risk and optimisation techniques such as Capital Asset Pricing Model (CAPM) and Factor-based returns optimisation.
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 6.2700, Raw Interest Score: 2.8833,
Positive Sentiment: 0.3204, Negative Sentiment 0.0458
Startup offers trading in ‘value of human success’
Forget companies, gold and bitcoin, a London-based startup is promising to use AI and big data to let investors trade the economic value of successful people – from sports stars to politicians to social media influences.
Aqua Digital Rising says it has used big data analytics linked to AI to construct indices based on humans. Hundreds of data points, covering things like social influence and financial performance, are collected and analysed and then benchmarked against peer groups to allow a value to be created for individuals. When trading opens early next year, investors will be able to trade the value of over 2000 individuals – including business people, entrepreneurs and movie stars – based on real-time pricing.
Yasin Sebastian Qureshi, head of strategy, Aqua, says: “For the first time in history investors will be able to invest in the source of all value creation: the individual human being.
2020-09-11 00:01:00 Read the full story…
Weighted Interest Score: 5.8442, Raw Interest Score: 2.7642,
Positive Sentiment: 0.3252, Negative Sentiment 0.0813
$26 billion Coatue is down one of its top alternative-data buyers after the firm’s quant fund that relied heavily on the unique datasets was rocked by market volatility earlier this year
Coatue — the long-running hedge fund of billionaire Philippe Laffont that manages $25.8 billion in assets — has lost one of its top people in charge of buying the data many consider to be the lifeblood of equity-focused hedge funds.
Dave Schwartz, a vice president focused on data acquisition and strategy, is no longer at the firm, sources tell Business Insider. It is not clear if Schwartz was dismissed by Laffont or if he left on his own accord. Coatue declined to comment, while Schwartz did not immediately return requests for comment. Schwartz’s role, which nearly all funds Coatue’s size now have, is to vet and bring in alternative data streams that will help portfolio managers and analysts project market moves before more traditional numbers, like earnings and jobs reports, are released. The multi-billion alternative data space has been even more important during the ongoing pandemic, as investors are scouring data feeds for a sign of life returning to normal.
Coatue’s data science team, led by Alex Izydorcyzk, is well-regarded in the industry, with more than two dozen people on it. But it ran into some speed bumps this year when the team’s young quant fund was unable to keep up with the market volatility caused by the coronavirus in the spring.
2020-09-11 00:00:00 Read the full story…
Weighted Interest Score: 5.0079, Raw Interest Score: 2.0485,
Positive Sentiment: 0.0000, Negative Sentiment 0.3152
Is More Data Always Better For Building Analytics Models?
Data is foundational to business intelligence, and training data size is one of the main determinants of your model’s predictive power. It is like a lever you always have when you are driving a car. So more data leads to more predictive power. For sophisticated models such as gradient boosted trees and random forests, quality data and feature engineering reduce the errors drastically.
But simply having more data is not useful. The saying that businesses need a lot of data is a myth. Large amounts of data afford simple models much more power; if you have 1 trillion data points, outliers are easier to classify and the underlying distribution of that data is clearer. If you have 10 data points, this is probably not the case. You’ll have to perform more sophisticated normalization and transformation routines on the data before it is useful.
The big data paradigm is the assumption that big data is a substitute for conventional data collection and analysis. In other words, it’s the belief (and overconfidence) that huge amounts of data is the answer to everything and that we can just train machines to solve problems automatically. Data by itself is not a panacea and we cannot ignore traditional analysis.
2020-09-16 05:53:01+00:00 Read the full story…
Weighted Interest Score: 4.2629, Raw Interest Score: 2.1513,
Positive Sentiment: 0.2776, Negative Sentiment 0.2545
Neural Parametric Methods: Models Off the Bias
Thijs van den Berg, a consultant and author on machine learning in quantitative finance will present a talk on neural parametric models, novel modeling methods in finance for the CQF Institute on 22nd September. Thijs will present a novel, generic machine learning modeling method to learn and extract parametric models and calibration algorithms directly from data.
What Thijs does is split the model into having two types of parameters; a set of fixed parameters might define the shape family, like functions that are oscillating, for example, and then have some additional parameters that you can very quickly calibrate that, for example, specify the frequency or amplitude.
The Implications : “If you have a lot of data that that shows all kinds of frequencies, then you train the model through exposure to all the types of data that you can see and in finance an application I’m going to talk about is fitting implied volatility curves and interest rate curves.” Says Thijs.
2020-09-09 07:22:25+00:00 Read the full story…
Weighted Interest Score: 4.1270, Raw Interest Score: 1.8230,
Positive Sentiment: 0.1004, Negative Sentiment 0.1338
Data Strategy & Insights: Come For The Insight, Stay For The Impact
We have only about five weeks until our Data Strategy & Insights live virtual event on October 14-15, and I’m excited to share a glimpse of what’s on our program across our six keynotes and three main tracks. Our theme this year is “Insight To Impact,” and as a data and analytics leader, it’s your time to shine.
Over the course of two days, our keynotes will let you peer into a crystal ball of what the future of data and AI might look like, which, in turn, will help you reimagine and plan for the future of work and AI-led augmentation. We will also show you how to prioritize your insights efforts — especially at a time when what you thought you knew about your business and customers was put to the ultimate test this year — all while continuing to shore up on data literacy across your organization. A panel discussion with industry data and analytics leaders will demonstrate how organizations are pivoting or staying on course with their data and analytics efforts and will give you pointers on your own planning efforts.
We also have 18 deep dive sessions across three main tracks:
2020-09-10 17:10:49-04:00 Read the full story…
Weighted Interest Score: 3.7024, Raw Interest Score: 1.8705,
Positive Sentiment: 0.1079, Negative Sentiment 0.0719
Behind Tata Elxsi’s Artificial Intelligence Centre of Excellence
Bengaluru-based Tata Elxsi has been enabling technology-based innovations over the past 25 years. From self-driving cars to video analytics solutions, it has a wide range of innovations enabled by AI and analytics. The Artificial Intelligence Centre of Excellence (AI CoE) by Tata Elxsi deals with the growing needs for intelligent systems. Its cloud-based integrated data analytics frameworks, with patent-pending technologies, enable customers to quickly implement and configure the landscape to obtain actionable insights and better results.
One of the important offerings by the company is the Cognitive Video Services Framework which is essentially an AI-Based Video Analytics solution that helps in tasks such as personalising content for users, transforming video into value using AI, suggesting new revenue generation, automating the content analysis, and more.
Analytics India Magazine got in touch with Biswajit Biswas, Chief Data Scientist at Tata Elxsi to further understand some of the projects they are working on, how AI CoE addresses the growing needs of intelligent systems, AI in video analytics and more.
2020-09-15 09:30:00+00:00 Read the full story…
Weighted Interest Score: 3.6154, Raw Interest Score: 1.4951,
Positive Sentiment: 0.1940, Negative Sentiment 0.1826
Data.World raises $26 million to address data processing pain points
Cloud-based data catalog startup Data.World today closed a $26 million venture capital funding round led by Tech Pioneers Fund. According to cofounder and CEO Brett Hurt, the proceeds will support Data.World’s efforts to accelerate its data governance initiatives and scale to meet demand.
Data scientists spend the bulk of their time cleaning and organizing data, according to a 2016 survey by CrowdFlower. That’s perhaps why firms like Markets and Markets anticipate that the data prep industry, which includes companies that offer data cataloging and curation tools, will be worth upwards of $3.9 billion by 2021.
Data.World aims to eliminate a few of the pain points with a catalog that maps data to business concepts, creating a unified body of knowledge. The platform’s suite provides cloud and on-premises management tools that can be used to inventory and organize data within enterprise systems.
2020-09-15 00:00:00 Read the full story…
Weighted Interest Score: 3.5401, Raw Interest Score: 1.9628,
Positive Sentiment: 0.1753, Negative Sentiment 0.2454
Enterprise Data Literacy: Understanding Data Management
To truly understand data-as-an-asset requires Enterprise Data Literacy, an organizational capability to take, analyze, and use data to remain secure and competitive. But achieving a high Enterprise Data Literacy can remain daunting when business and IT interact together.
All too often in the middle of a project sprint, IT gets stuck on a minor problem, such as new customers only being able to see their monthly invoice in landscape view. IT implements a fix, and the bill is sent. However, new customers get billed twice. Communication between IT and business missed the need for an extra check before sending an invoice. Throughout the ordeal, both IT and business tear out their hair, trying to work with each other, as the company’s Data Literacy remains low.
2020-09-08 07:35:18+00:00 Read the full story…
Weighted Interest Score: 3.1059, Raw Interest Score: 1.6815,
Positive Sentiment: 0.1071, Negative Sentiment 0.2035
Don’t Make These Six Big Data Mistakes
Why do big data projects fail? They do; that’s for sure.
Gartner estimated that 60 percent of big data projects fail to achieve their desired objectives. A year later, they revised this figure to 85 percent, admitting they were “too conservative” with the original estimate.
So, going back to the original question — what’s the reason so many big data projects are unsuccessful? Well, there is a combination of reasons. Most of the time, technology is not even the main culprit. Let me explain.
2020-09-16 07:25:13+00:00 Read the full story…
Weighted Interest Score: 2.9721, Raw Interest Score: 1.4511,
Positive Sentiment: 0.2134, Negative Sentiment 0.4126
PIMCO wants to create its own version of BlackRock’s Aladdin. Read the memo the bond giant just sent laying out its approach.
PIMCO, the $1.9 trillion asset manager known for its fixed-income prowess, is creating a new unit focused on getting the firm’s research, tools, and analytics into the hands of its clients like pension funds and wealth managers, according to a memo distributed internally last week and reviewed by Business Insider.
The memo from PIMCO’s marketing chief Cathy Stahl and technology chief Dirk Manelski said the new team will build on the firm’s current practices of providing clients with market and investment research with analytics and tools like portfolio stress tests and risk management analysis. The impetus for the new group included global clients’ demand for high-quality digital experiences, a spokesperson said.
2020-09-16 00:00:00 Read the full story…
Weighted Interest Score: 2.9223, Raw Interest Score: 1.7450,
Positive Sentiment: 0.1472, Negative Sentiment 0.0526
Spoonshot Raises A Seed Investment Of $1M Led By SRI Capital
Spoonshot, a food science company using AI to predict consumer taste and food trends announced a seed investment of of $1M led by SRI Capital. As the company stated, they will use the funding amount to fuel growth plan and further grow its proprietary technology and team.
The startup founded by Kishan Vasani and Sai Sreenivas Kodur has raised $1.8M to date, including this round. Spoonshot was backed by Techstars (Farm To Fork) in its pre-seed round.
“With the backing and expertise of our new investor, we’re truly excited about this next phase for Spoonshot,” said Kishan Vasani, Spoonshot Co-Founder and CEO. “Despite challenging global economic conditions, we’ve proved that our frontier technology and rich insights are invaluable to CPG companies who still need to innovate but with increased agility and focus.
2020-09-09 04:05:18+00:00 Read the full story…
Weighted Interest Score: 2.8085, Raw Interest Score: 1.5829,
Positive Sentiment: 0.3562, Negative Sentiment 0.1187
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