Summary:
- A
3000% gain by a thematic investing basket
-
Data is a powerful theme including 3 stocks and 1 crypto
-
How to execute the trade
September 29,
2020 -- InvestorsHub NewsWire -- Our fund positions in IPO sympathy plays as a way to play
IPOs indirectly. These kinds of plays are also known as thematic investing or
thematic investment baskets.
The last
thematic investment basket, the ‘Coronavirus basket’ resulted
in a gain of over 3000%. Companies like Novavax (NVAX) and other pharmaceutical components were largely
responsible for the gains.
The performance
of the BMY-CELG basket is an example of how well a thematic
investment basket can outperform the broader market. It’s described
in detail in the article titled, ‘Generating Alpha from
Information Arbitrage in the Financial Markets with NLP Datasets:
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That simply
means trading stocks that are moving ‘in sympathy” with another
related stock, crypto or global event of some kinds like the
‘Coronavirus basket’ above.
Let’s dig right
in and let me show you our approach for our ultimate thematic investing basket
based on current events.
Introducing the
‘Data is the new oil’ thematic investing basket
Palantir
(PLTR)
Palantir
(PLTR) is expected to fetch a
lofty market valuation of more than $20 billion at listing on a
fully diluted basis, in its transition to a public company despite
an unusually aggressive governance structure, in the latest sign of
investors’ voracious appetite for new shares. Palantir’s bankers
have told investors the shares could start trading around $10
apiece, according to people familiar with the matter. In the
private markets over the past year, Palantir’s stock has trended
higher. The volume-weighted average price in August was $7.31 and
and in September, $9.17.
The
data-mining-software specialist is eschewing the traditional IPO
route and going public through a direct listing, in which a company
floats its existing shares on a public exchange and lets the market
determine the price. The debut planned for September 30.
Palantir makes
software used by numerous government agencies — including tools to
help track terrorism suspects — as well as businesses to help sort
and analyze data.
Those average
prices are likely to help determine Palantir’s reference price, the
guidepost to where the stock could open in a direct listing. The
stock exchange publishes the price after looking at recent
private-market transactions and consulting with the company’s
financial advisers.
Palantir
(PLTR) looks like it could be
a customer of a new upcoming player in data, Tesla (TSLA). Lets
explore the relationship…
Tesla (TSLA)
Tesla (TSLA) plans to offer machine-learning training as a
web service with its new ‘Dojo’ supercomputer, according to new
comments from CEO Elon Musk.
Project “Dojo”
was first announced by Musk at Tesla’s Autonomy Day last
year:
“We
do have a major program at Tesla which we don’t have enough time to
talk about today called “Dojo.” That’s a super powerful training
computer. The goal of Dojo will be to be able to take in vast
amounts of data and train at a video level and do unsupervised
massive training of vast amounts of video with the Dojo program —
or Dojo computer.”
This would make
Dojo a truly cutting-edge supercomputer considering there’s
currently a race to break the exaFLOP barrier in supercomputing
with companies like Intel and AMD, along with governments, in the
running.
In a series of
tweets that started through a response to famed software engineer
John Carmack, Musk confirmed that Tesla (TSLA) plans to open the
supercomputer to the public as a web service to train machine
learning models:
"Yeah, we will
open Dojo for training as a web service once we work out the bugs"
— Elon Musk (@elonmusk) September 20,
2020
Determine for
yourself if that makes sense. We know for a fact that Tesla’s new
AI computing offerings will need datasets to attract customers.
They will be competing for AI customers with Amazon AWS (AMZN), Google Cloud (GOOG) and Microsoft (MSFT), trillion-dollar
companies, along with S&P Global (SPGI), Bloomberg’s alternative
data division and Snowflake (SNOW), who also use datasets to
attract new AI customers. The next company might be able to provide
Tesla’s operation with just what it needs. Lets see how they
connect…
Vectorspace AI
(VXV)
Latest research
suggests “The Next Big Breakthrough in AI
Will Be Around Language” — Harvard Business Review.
While data can
be viewed as unrefined crude oil, Vectorspace AI (VXV) produces datasets, which
are the refined gasoline which power most Artificial Intelligence
(AI) and Machine Learning (ML) systems today.
This group is
the tip of the spear when it comes to advancing AI/ML pipelines in
any industry. Vectospace AI algorithmically generates datasets
based on formal Natural Language Processing/Understanding (NLP/NLU)
models including OpenAI’s GPT-3, Google’s BERT along with
word2vec and other models
which were built on top of vector space applications at Lawrence
Berkeley National Laboratory and the US Dept. of Energy (DOE).
Over 100 billion different datasets are available based on
customized data sources, rows, columns or language models. See also
‘Vectorspace AI & CERN Create
Natural Language Processing (NLP) Datasets in Particle Physics with
Applications in Artificial Intelligence (AI) for Every
Industry’
Vectorspace AI has announced on their corporate
communication channel, Telegram, that they have open dialogues with
Tesla (TSLA), Citigroup©, FactSet
(FDS) Snowflake (SNOW) as well as Palantir
(PLTR) on revenue sharing
dataset distribution partnerships. They’ve currently established
revenue sharing dataset distribution agreements with with S&P
Global (SPGI), Amazon (AMZN), Elastic (ESTC) and Microsoft (MSFT). Vectorspace AI is revenue
positive and collaborator with CERN and Lawrence
Berkeley National Laboratory.
Current
competitors include Palantir (PLTR), Google (GOOG) and SAS. Datasets need a place to be stored and
distributed. This is where the next component comes into
play…
Snowflake
(SNOW)
Data unicorn
Snowflake (SNOW), Snowflake’s shares soared
as high as $319 in their first day of
trading, giving the company a putative value of $88 billion. As
Almost Daily Grant’s reported,
one of the newsletter’s eagle-eyed readers noted that Snowflake’s
S-1 Securities and Exchange Commission filing said it “believes the
addressable market opportunity for our Cloud Data Platform is
approximately $81 billion as of January 31, 2020.” By Thursday’s
close, Snowflake stock had fallen back to $227.54, still a gain of
more than $100 for investors lucky enough to have received an IPO
allocation, which notably included
Warren Buffett’s Berkshire Hathaway (BRK.B).
Snowflake
provides a cloud-based data warehousing platform that is available
on the three major public clouds. While it will likely remain
unprofitable in the near term, the company is growing fast (+174%
y/y) and should benefit from the ongoing shift to cloud.
Information on
the worlds public and private companies provided by S&P Global
(SPGI) Marketplace is now
accessible via Snowflake’s cloud data platform, eliminating the
need for on-premises databases, allowing for direct integration of
S&P Global (SPGI) data into models,
visualization tools and more.
S&P Global
Market Intelligence, announced mid September that
it has collaborated with Snowflake, to seamlessly deliver S&P
Global’s industry-leading financial, textual, ESG and alternative
data through Snowflake (SNOW).
Cloud-based
delivery enables customers to simplify their data management and
work with multiple large datasets more efficiently.
The Snowflake
Data Marketplace is built on top of Snowflake’s Secure Data Sharing
technology, and provides an easy-to-use platform for organizations
to find, share and access content.
Through
Snowflake (SNOW), S&P Global (SPGI) and select-third party
data is ready to query and easily accessible via multiple cloud
platforms and enables direct integration with more than 80
third-party data vendors.
You can create
sub-themes e.g. a TSLA thematic investing basket
named ‘lithium’ where all the components have known or hidden
connections to the latest breakthroughs in battery
technology.
Executing basket
trades work best when they are swing trades. Slippage is not
much of a concern as you have plenty of time to position. One of
the most important factors would be to determine which stocks,
cryptos or other assets in the basket are oversold versus
undersold. You can use a standard 10–14 day MACD. Other factors including weighting and
deploying your capital across each trade or multiple thematic
investing baskets.
Lots can be done
with thematic investing baskets but most importantly, it’s a smart
and safe way to diversify while also maximizing gains.
In our next
article, we’ll talk about Thematic Smart Baskets. Stay
tuned!
References:
This founder
split from Elon Musk and is now launching rockets for one-twentieth
the cost of SpaceX
Generating Alpha
from Information Arbitrage in the Financial Markets with NLP
Datasets: ????
Dexamethasone Announcement Could
Have Made Hedge Funds A Fortune — Alpha Week
Vectorspace AI
& CERN Create Natural Language Processing (NLP) Datasets In
Particle Physics With Applications In Artificial Intelligence (AI)
For Every Industry
S&P and
Snowflake collaborate to deliver financial and alternative
data
Tesla (NASDAQ:TSLA)
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