FAQs
1. What is your "secret sauce"?Fresh daily market information supports frequent small gains in well selected stocks. These compound more rapidly over time.
We use only objective, public facts of fundamentals, volume and price unbiased by opinions. We offer fully auditable transparency of recommendations and results. We are entirely independent and do not handle client funds. We offer the freshness, quality and breadth of recommendations that might be provided by research quants at successful hedge funds.
We are entirely data driven, use artificial intelligence techniques to exploit useful patterns, and track every result.
2. What is your value proposition for ZZAlpha?
Higher returns with well behaved risk using liquid, US exchange traded equities.
Our business is providing effective daily recommendations to subscribers, using our objective machine learning technique.
3. Do you accept money for investments? No.
4. Can you provide me with individualized investment advice? No.
5. Do you sell the securities and ETFs you recommend? No.
6. Are you affiliated with an ETF sponsor? No.
7. Are you paid commissions by a brokerage or ETF sponsor or anyone? No.
8. Do your recommendations guarantee returns? No.
9. How do you obtain your recommendations? See explanation.
11. Do you recommend equities for hedge strategies? No.
12. Do you recommend microcap stocks? No.
13. Do you recommend derivatives, options or futures? No.
14. What is "machine learning?" See explanation.
15. What data do you use? Public objective facts.
18. Do your employees trade in the equities and ETFs listed in portfolio recommendations? Yes.
19. How do your recommendations incorporate the "news" or "sentiment" ? They do not.
20. Do you modify the results after the objective ZZAlpha® machine learning engine produces recommendations? No.
21. Does a reliable third-party notarize your recommendations to prove they are created before the market opens? Yes.
22. Do the trading evaluation model results you show assume using leverage? No.
23. Do the trading evaluation model results you show reflect trading costs? No.
24. In your trading evaluation results for short portfolios, do you assume that short shares will be available for shorting? We no longer recommend short positions.
25. What additional investment risks are introduced by or aggravated by a machine learning technique? See explanation.
26. What is the difference in "annualized return" in the ZZAlpha statistics and the "average annual return" that some commentators use? See explanation.
27. Do your results have "survivor bias?" No.
28. What do subscriptions cost? See explanation.
30. Do you invest your own funds using these recommendations? Yes.
31. Do you sell or disclose the names or contact information of subscribers or persons inquiring? No.
32. Where did the name ZZAlpha come from? The notion that after you listen to all the opinions from A to ZZ, the true alpha can best be obtained from hard market facts.
1. What is your
"secret sauce"?
Fresh daily market
information supports frequent small gains in well selected
stocks. These compound more rapidly over time.
We use only objective, public facts of fundamentals, volume and price unbiased by opinions. We offer fully auditable transparency of recommendations and results. We are entirely independent and do not handle client funds. We offer the freshness, quality and breadth of recommendations that might be provided by research departments at successful hedge funds.
We are entirely data driven, use artificial intelligence techniques to exploit useful patterns, and track every result.
We use only objective, public facts of fundamentals, volume and price unbiased by opinions. We offer fully auditable transparency of recommendations and results. We are entirely independent and do not handle client funds. We offer the freshness, quality and breadth of recommendations that might be provided by research departments at successful hedge funds.
We are entirely data driven, use artificial intelligence techniques to exploit useful patterns, and track every result.
2. What is your value proposition for ZZAlpha?
Higher returns with
constrained risk using liquid, US exchange traded
equities. We sell subscriptions to daily
recommendations that our advanced machine learning
techniques produce.
3. Do you accept money for investments?
No. ZZAlpha LTD. is
not a hedge fund, mutual fund, trading company, wealth
manager or stock broker. We serve newsletter
recommendations to those entities and other professional and
knowledgeable investors.
4. Can you provide me with investment advice?
No.
ZZAlpha LTD. does not provide individualized investment
advice. We provide standard recommendation portfolios
of stocks falling in objectively defined market segments
(for example, stocks with capitalization of $5B or
more). Some institutional investors and traders have
internal or external constraints on the nature of their
investments. We can work with those institutional
investors to provide recommendations that would comply with
those constraints (such as liquidity, capitalization, etc.).
5. Do you sell the securities you recommend?
No. ZZAlpha LTD. does not buy or sell securities or ETFs.6. Are you affiliated with an ETF sponsor?
No. We use solely objective facts and are entirely independent.7. Are you paid commissions by a brokerage or ETF sponsor or anyone?
No. We are entirely
independent. We independently select and recommend
stocks that are well established, liquid, and reflect the
indices or segments of interest. We recommend equities
that are actively traded on US Exchanges.
8. Do your recommendations guarantee returns?
No. If a subscriber finds
the recommendations unsatisfactory for any reason, the
subscriber may cancel the subscription in accord with the
subscription agreement. Please remember: PAST
PERFORMANCE does NOT indicate the probability of similar
performance in future market conditions. Investment in
equities involves SUBSTANTIAL RISK and has the potential for
partial or complete LOSS of funds invested.
9. How do you obtain your recommendations?
The daily recommendation
emailed to subscribers comes solely from our tested,
ZZAlpha® objective machine
learning technique. It processes over half a billion pieces of
public data every night on high performance cloud
data-center computers in order to make each day's
recommendations. Not every forecast is right, but over time
they do well. By-the-way, making consistently better
recommendations across many US market segments every day is
rocket science. Borrowing from the US Air Force slogan,
"It's not science fiction. It's what we do every day."
11. Do you recommend equities for hedge strategies?
No.
12. Do you recommend microcap stocks?
No. ZZAlpha LTD.
focuses on stocks with sustained trading volume over 80,000
shares a day and price over $3.00. The machine learning
technique ignores tiny stocks. It does not recommend penny
stocks, micro-capitalization stocks, IPOs, or stocks listed
outside NYSE, Amex and NASDAQ.
13. Do you recommend derivatives, options or futures?
No. We recommend only
liquid, US exchange traded equities. Of course, a
professional trader may find ZZAlpha® recommendations useful
in considering derivatives trades.
14. What is "machine learning?"
Machine learning (also
called artificial
intelligence) is a set of computational techniques
to make faster, more accurate estimates about how best to
respond to new events, given what has happened (generally)
in the past. The earliest of these techniques in the 1950's
were rule-based expert systems ("RBES") of the sort still
used in typical stock-screen systems today. RBES have been
largely discarded in the machine learning community because
they have been found to be "brittle" - failing to handle
unexpected situations well. Today's better techniques range
widely among: automata systems, Bayesian beliefs, boosting,
control and operations theory, clustering methods,
constraint relaxation, consensus, convex optimization,
distance based associations, decision trees, deep learning,
ensembles, fuzzy logic, genetic algorithms, grammars, graph
algorithms, neural nets, nearest neighbor, optimal search,
object-pattern matching, forward-backward planning, robotic
response-intention, support vector machines, structured
meta-knowledge, vector quantization, and traditional methods
derived from principal/independent components, signal
processing filters, and statistics of multi-variate random
variables. Machine learning is foundational for Google,
Facebook, cell-phone communications, voice recognition,
commercial auto-pilots and much of the world's advanced
medical research, defense and intelligence activities.
A fine review with some practical considerations was
recently published by Professor Marcos Lopez del Prado in Advances
in Financial Machine Learning (2018).
15. What data do you use?
Public objective facts. We
use two types of data: objective end-of-day facts for US
exchange traded equities from a public free or subscription
provider (such as Google, Yahoo, Reuters, Bloomberg) and
objective fundamentals (for example capitalization or
price-earnings ratio) from a public free or subscription
provider (such as Google or Standard&Poor's). We
also use IPO information from the SEC, and announcements of
recent mergers and acquisitions. We do not use "news,
opinion or analysis."
18. Do ZZAlpha LTD. employees trade in the equities or ETFs listed in portfolio recommendations?
Yes.
19. How do ZZAlpha® recommendations incorporate the "news" or "sentiment" ?
They do not. The
ZZAlpha® machine learning technique uses price, volume and
fundamentals data from standard data sources. We do
not use news reports or opinions, whether on-line, on paper
or on TV. We do not use tips, inside information,
rumor, opinions, interviews, blogs, tweets, the "buzz" on
the street or rants of TV entertainers (no offense intended
to Jim Cramer of Mad Money). We are not located in
NYC. We do not interview company executives, attend
company meetings, or tour company sites to acquire
information. We do not obtain annual or quarterly reports,
press releases or most SEC filings or anything labeled
"forward looking." Objective facts drive our approach to
behavioral finance. We do not talk to economists,
politicians, forecasters or astrologers about the future.
20. Do you modify the results after the objective ZZAlpha® machine learning engine produces recommendations?
No. The results of the learning from objective facts are not filtered by human opinions or biases.21. Does a reliable third-party notarize your recommendations to prove they are created before the market opens?
Yes. Beginning in 2011 we
send a complete digest of every
recommendation for the day to an independent, certified
electronic notary service at the same time recommendations
are delivered to clients (typically before 8:30 am Eastern
Time). Once timestamped and notarized, the
recommendations cannot be modified or repudiated without
breaking the notary seal. The electronic notary
service to which we subscribe, DigiStamp Inc, is an
unrelated third-party, Trusted Time-stamp Authority meeting
the standards set by the United States National Institute of
Standards and Technology (NIST) and audited by two certified
independent auditors. The timestamp and contents of
the recommendations are encrypted with advanced Public Key
Encryption Infrastructure (PKI) supplied by DigiStamp. It is
impossible for us or anyone to "back-date" or "revise" or
"correct" the recommendations. We archive the notary
timestamp with the recommendations for each day to support
transaction level auditing of each recommendation in each
portfolio. For more on the security of the electronic notary
service, see http://www.digistamp.com/faqDGS.htm#notamper
22. Do the trading evaluation model results you show assume using leverage?
No. Obviously, some
institutional and professional investors may choose to use
leverage, options, or derivatives to attempt to increase
profits from ZZAlpha® portfolio recommendations.
23. Do the trading evaluation model results you show reflect trading costs?
No. Internet discount
retail brokerages no longer charge commissions.
Institutional investors may have very low costs. Use
of a limit order controls slippage. In the past, we
have modeled the effect of commissions and found that as
assets under management grow beyond $50,000, even the old
discount commissions decline to relative insignificance.
24. In your trading evaluation results for short portfolios, do you assume that short shares will be available for shorting?
We no longer recommend
short positions.
25. What additional investment risks are introduced by or aggravated by a machine learning technique?
Such risks include, but are not limited to:a) Bad data
- Occasionally, public data suppliers supply incorrect
information about equities. Although we use techniques to
validate arriving data every day, sometimes current
recommendations will be based on some bad data. Our
historic results usually have the benefit of any subsequent
corrections by the data suppliers.
b) Turn risk - The world can change overnight. The ZZAlpha® machine learning technique typically "believes" that tomorrow will be a lot like today and a lot like the past. It can take several days before the ZZAlpha® engine learns about and reacts to a new economic environment. In the interim, until the model "turns course," the ZZAlpha® recommendations may give poor results. The statistics community sometimes calls this a "non-stationarity" risk.
c) Sunset risk - The ZZAlpha® machine learning technique provides recommendations that assume a future specific close-out date. Environment, politics, news and company events will affect the stock price before that sunset is reached. Once ZZAlpha® recommendations are made, they are not modified in light of information that may become available during the hold period. We do not make "sell early" recommendations.
d) Diversification risks - The ZZAlpha® machine learning engine works to find improved returns (alpha) within a specified domain of stocks. There is currently no attempt to diversify recommendations within each domain. A user who needs the ability to diversify should acquire a larger portfolio of recommendations from ZZAlpha, and then winnow according to its own diversification standards and investment manager’s advice.
b) Turn risk - The world can change overnight. The ZZAlpha® machine learning technique typically "believes" that tomorrow will be a lot like today and a lot like the past. It can take several days before the ZZAlpha® engine learns about and reacts to a new economic environment. In the interim, until the model "turns course," the ZZAlpha® recommendations may give poor results. The statistics community sometimes calls this a "non-stationarity" risk.
c) Sunset risk - The ZZAlpha® machine learning technique provides recommendations that assume a future specific close-out date. Environment, politics, news and company events will affect the stock price before that sunset is reached. Once ZZAlpha® recommendations are made, they are not modified in light of information that may become available during the hold period. We do not make "sell early" recommendations.
d) Diversification risks - The ZZAlpha® machine learning engine works to find improved returns (alpha) within a specified domain of stocks. There is currently no attempt to diversify recommendations within each domain. A user who needs the ability to diversify should acquire a larger portfolio of recommendations from ZZAlpha, and then winnow according to its own diversification standards and investment manager’s advice.
26. What is the difference in "annualized return" in the ZZAlpha statistics and the "average annual return" that some commentators use?
This is important.
Suppose an investment that has these returns for two years:
-50%, +50%. Clearly, the "average
annual return" is zero: It makes naive investors
think they came out even. But, they LOST MONEY!
because what you have is .50 x 1.50 = .75 i.e. you
lost 25% cumulatively over the two years. "Annualized return" gives
a more accurate picture: you LOST 13% a year. "Average annual return"
misleads and should be banned by the SEC for use in
financial reporting of time series results.
27. Do your results have "survivor bias?"
No, with a nuance.
Throughout each historical week of forward testing we use a
list of stocks that were actually trading on NYSE, AMEX or
NASDAQ at the start of that week, regardless of whether they
still exist today and are traded on the exchanges
today. Companies from years ago may have gone out of
business, merged, changed their name, or trade now as penny
stocks and we now know are not "survivors." When we
step-forward test, we use only information that was actually
available before trading on that historical day of the test.
The nuance is this: As dividends and splits affect stock prices over time, the relative price on an earlier day grows gradually less. This results in rounding errors where prices are reported to the nearest cent. These rounding errors can become significant errors in the historic prices of the survivors, and can lead to mis-statement of the historic returns of survivors, when using current reports of historical prices, rather than historical records of then current prices. Our calculations of returns prior to 2012 are affected by this nuance and are less accurate. Returns calculated for recommendations since Nov 2011 are based on daily contemporary data.
The nuance is this: As dividends and splits affect stock prices over time, the relative price on an earlier day grows gradually less. This results in rounding errors where prices are reported to the nearest cent. These rounding errors can become significant errors in the historic prices of the survivors, and can lead to mis-statement of the historic returns of survivors, when using current reports of historical prices, rather than historical records of then current prices. Our calculations of returns prior to 2012 are affected by this nuance and are less accurate. Returns calculated for recommendations since Nov 2011 are based on daily contemporary data.
28. What do subscriptions cost?
If you employed a mid-level quant to make hopeful recommendations for a single portfolio, you might typically pay salary alone of $230k (including bonuses)(outside of NYC, London or Singapore where compensation would be higher).A subscription to one of our proven, daily updated machine learning portfolio recommendations is much less. Subscriptions range up from $109,000 USD annually. Contact us concerning exclusive and custom-constraint subscriptions.
30. Do you invest your own funds using these recommendations?
Yes. For validation,
for understanding quirks of real-time market trading, for
testing, and for profit, the founder invests in various
recommendation portfolios. His return (after commissions and
trading costs) in 2012 to date were significantly more than
the S&P 500.
32. Where did the name
ZZAlpha come from?
"Behavioral finance" has generated thousands of analyst's
and entertainer's opinions, some of which are sold for
hundreds of thousands of dollars. But, after you
listen to all the opinions from A to ZZ, at the end of the
day it is the market that speaks. We show that
alpha (excess return) is obtainable from hard market facts
using advanced artificial intelligence pattern recognition
techniques.