Canalyst Newsletter: Quantamentally Bullish
In this edition, we get to the core of quantamental investing. Also, we dive into Weyco Group, Inc., discuss our role at the Sidoti & Company LLC Spring Investor’s Conference, and share our love for the IPO unicorns of late.
Interested in learning more about how you can uncover more investment opportunities with Canalyst financial models? Request a demo
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canalyst newsletter: quantamentally bullish
In this edition, we get to the core of quantamental investing. Also, we dive into Weyco Group, Inc., discuss our role at the
Sidoti & Company LLC Spring Investor’s Conference, and share our love for the IPO unicorns of late.
For starters, it’s a term that makes people cringe the first few times they say it. However, it’s a lot better than alternatives like
fundatative
or
tradomatic
so we think it’s here for the long run. If it’s going to stick around, it would be nice to narrow in on
exactly what lies at the core of quantamental investing.
Quantamental investing is commonly described as being a hybrid of fundamental and quantitative investment approaches.
Although that’s not untrue, such a broad definition leaves the door open to almost anything. Let’s narrow in on what might be
described as the “core” of quantamental investing, and start with a high-level view by taking a look at the three stages of the
investment process: security selection, risk analysis & portfolio construction, and trading.
There are funds that use a purely fundamental approach for security selection but lean heavily on quantitative techniques in
one or both of the latter stages. Quantitative techniques are particularly useful for analyzing massive amounts of data and
making very fast decisions which are critical for the tasks in the latter two stages of the investment process.
security selection: fundamental vs. quantitative
Fundamental (aka traditional or discretionary), is an investment process in which highly skilled professionals analyze a mosaic
of information sources to form a view on a given security. These information sources include a lot of numeric data (such as the
financial accounts of the company being analyzed) but they also include softer sources of information such as the impression
that the investor forms of the management team. The work product that leads to an investment decision generally consists
of a valuation model and an accompanying investment thesis that is presented to an internal investment committee where
human judgement is used to decide if the investment will move forward. Fundamental investing is generally characterized by a
relatively small number investments and longer holding periods.
Quantitative may also be referred to as systematic or programmatic investing. In a quantitative strategy, a large amount of
data is analyzed to find patterns that are predictive of changes in price. This analysis generally requires advanced knowledge
of mathematics and sophisticated tools for analyzing large quantities of data. The quantitative strategy is then encoded into
an algorithm that is deployed to analyze incoming data and to automatically make a stream of buy and sell decisions. The
types of data that are used in quantitative strategies are wide ranging and include tick data, fundamental data, and alternative
data. Similar to how fundamental investing involves the analysis of numeric data, quantitative investing involves a reasonable
amount of human input in order to succeed. One of the biggest risks in quantitative analysis is that if you throw enough data
at a powerful computer, it will find correlations that have been predictive in the past but are completely coincidental and
therefore likely to fail in the future; particularly in the event of economic regime change. Discretionary (human) input is needed
to assess whether the patterns have any grounding in reality. The primary work product that leads to an investment decision
for a quantitative strategy is a rigorous backtest that evaluates how a given algorithm would have performed in the past.
Quantitative investing is generally characterized by a collection of algorithms that make a large number of small investments,
often with shorter holding periods than those of fundamental investors.
hybrid security selection strategies
With that background, consider two types of hybrid security selection strategies which get us closer to the core of quantamental
investing.
In the first hybrid strategy, quantitative techniques are used as a screen to narrow down the list of equities for a fundamental
investor to analyze. Let’s imagine that a given portfolio manager has a potential investable universe of 5000 possible equities.
No portfolio manager can apply fundamental techniques to such a large universe and so a screen is applied. In the past, a
screen may have been as simple as narrowing down the universe based on sector, market cap, and/or P/E ratio. Going forward,
increasingly advanced screening tools that leverage quantitative techniques will be used by fundamental investors. As an
example, the investor may decide to look across the retail sector and screen for companies where customer foot traffic is
increasing based on an analysis of alt data that tracks customer location from smartphone apps. In many cases, the screens will
even include backtests so that the fundamental investor can check to see if the screening factors could have been effective on
their own at selecting a list of equities that would have delivered alpha. The key thing to note about this hybrid strategy is that
it is sequential. Once the fundamental investor receives the output of the screen, they still proceed with traditional methods for
analyzing the equities and the final investment decision is made in the traditional way.
In the second hybrid strategy, the outputs of fundamental analysis are used as an input to a quantitative strategy. One example
of this can be seen today in quantitative strategies that use consensus estimates and changes in consensus estimates as a
key input. Estimates are an output of detailed fundamental analysis and are a core part of the fundamental investing process.
The key thing to note about this hybrid strategy is that the quantitative investor proceeds to develop an algorithm using
quantitative techniques and the final decision to deploy the investment strategy is based on the results of a robust backtest, as
it would be for any other quantitative strategy.
If we were to accept these sequential strategies as quantamental, instead of categorizing them on the basis by which the
final investment decision is made, then we would fall into the trap of having to eventually label almost all strategies as
quantamental. Over the next decade, fundamental analysts’ screens will increasingly use advanced quantitative techniques and
quantitative investment strategies will increasingly find ways to leverage the output of fundamental analysis.That brings us to
what we are proposing as the core of quantamental investing:
Quantamental investing is a process in which the final investment decision in the security selection process is simultaneously dependent
on both fundamental analysis and quantitative analysis.
When phrased this way, it’s not hard to see why quantamental investing is so difficult to deploy in the real world. Accountability
at the point in time when the investment decision is made is at the heart of nearly all investment processes; quantamental
investing requires a dramatically new way to think about how to assign and share accountability.
what does quantamental look like in the real world?
Three examples of what would qualify as quantamental investing using this criteria:
1)
Leveraging alt data that doesn’t pass the test for quantitative strategies but can provide useful insight. The cutting room
floor of quant shops is full of interesting data sources that don’t meet the extremely high bar (breadth, history, ability to be
mapped to securities, ability to be queried point-in-time, etc.) required by a purely quantitative strategy. The number that
we have heard quantitative analysts (“quants”) use is that they reject 95% of the data sources that they evaluate. Although
this data is full of useful information, it’s not in a format that can be used by fundamental investors and the analysis requires
data scientists and quantitative techniques. This is one of the easiest and most obvious use-cases for a truly quantamental
strategy: quantitative analysis can provide insights that become directly intertwined into the valuation models used by
fundamental investors.
2)
Pairing quantitative models with fundamental models. In a crude approximation, quants tend to build probabilistic models
that use hard-facts as inputs and fundamental analysts build deterministic models that use estimates as inputs. There is
great potential to unlock alpha by leveraging the best of both of these methods. As an example, a quant model may be
very accurate at predicting revenue for a specific geographic segment on the basis of a range of alt data sources. However,
quant models may struggle to then determine the impact on EPS, cashflow, and other fundamental factors because public
companies are highly non-stationary and undergo meaningful changes from quarter to quarter (acquisitions, new business
segments, debt financing, etc.). The models built by fundamental analysts capture all of these deterministic relationships
between lines in the financial statements and can be used as a lens through which to assess the impact of changes in the
input drivers that are provided by probabilistic models.
3)
Dealing with regime change and edge cases. Is the input data outside of the historical range that was used to backtest
the algorithm? Has the company made a change to their fiscal year-end? Is this a new listing? These are all scenarios that
result in quants blacklisting a data source or equity but which fundamental investors are able to manage quite effectively,
and often exploit a major source of alpha as a result. As an analogy, there is a massive difference between building a 100%
self-driving car and implementing a car with smart cruise-control that still requires human supervision. The combination of
fundamental and quantitative techniques can shine the light of quantitative analysis into many of the dark corners where
inefficiencies still linger.
These three examples all have something in common: the final investment decision rests on the trust that the fundamental
investment thesis is correct AND that the quantitative analysis is correct. It is impossible to unwind or unravel the two types
of analysis. The problem is that taken alone, either of the standard investment decision processes will reject many great
quantamental opportunities. A traditional investment decision process will reject the ideas on the basis that the people
involved do not have a deep enough understanding of the quantitative techniques to trust them but the investment case falls
apart without their contribution. A quantitative investment decision process will reject the ideas on the basis that the strategies
do not pass the numerical criteria regarding diversity or backtests and they don’t given any extra credit for the contribution
from the traditional techniques.
Although there may be some portfolio managers out there today who have enough depth of knowledge in finance and data
science to be comfortable putting their name on the line for these truly quantamental strategies, those people are few and
far between. A more realistic approach that organizations should consider is to pair fundamental and quantitative portfolio
managers into quantamental teams and to give them the freedom to implement new investment decision processes. With
these partnerships in place, no investment would move forward until both portfolio managers sign off on it. Accountability and
credit would be shared equally by both co-portfolio managers.
Industry trends have continued to push attention away from small-cap equity research. The continuous rise of index and passive
investing causes a large-cap concentration on the buyside, while mounting cost pressures force sellside firms to reduce their
coverage of smaller names.
Going into earnings season with a full suite of models on small and mid-cap names can help you identify opportunities in
under-covered names. One name with low sellside analyst coverage is Weyco Group, Inc.[NASDAQ: WEYS]. Incorporated in 1906,
Weyco Group is a designer and wholesaler of quality footwear under a portfolio of well-recognized brands. These include Stacy
Adams, Florsheim, and Nunn Bush. The company operates through three segments: North America Wholesale, North America
Retail, and Other. Wholesale includes wholesale sales and licensing revenue. The retail segment consists of nine retail locations
and internet-based sales in the United States. The Other group sales consists of wholesale and retail sales in Australia, South
Africa, Asia Pacific, and Europe.
The summary page tab of a Canalyst model is a great place to start on any name. Highlighting the main points for each
company, the page includes segment figures, income statement, a cash flow summary, balance sheet ratios, and profitability
ratios to name just a few items. The cash flow analysis section can help you get up to speed on how a company is using cash.
Here we can see Weyco Group has been consistently spending on capex, working capital, and to buy back stock. They’ve also
historically paid out around 50% of their net income per share in dividends.
Opening up the model page, we can quickly get a grasp on sales performance and margin performance. The top of the model
highlights sales for the different products in the company’s wholesale channel as well as sales performance in North American
wholesale and retail. The model also includes same store sales growth metrics which helps you gauge performance in their
North America retail division. We can see significant year over year growth in the Florsheim line continuing in Q4 resulting
in a total increase of 20% for the year. Sales growth for BOGS/Rafters was strong, increasing 35% year over year, the brand’s
strongest quarter since 2014. Retail same store sales growth, while driving a smaller part of overall sales, was strong, increasing
21% year over year for Q4.
Scrolling down we can see margin evolution for the three segments. In Q4, strong sales drove higher gross margins in North
America wholesale. This led to higher profitability for the segment, a full percentage point higher than the same quarter in
the previous year. The fiscal year was the best since 2012. Similar story for North America retail, which saw strong sales growth
through their website more than making up for lost sales from the closure of one of their brick and mortar locations. The
increase in online sales helped push quarterly EBIT margins to their highest point since before 2013. Overall, Q4 operating
profitability is at the highest level since Q4-2014.
Tying this into stock performance, the fact that Canalyst models have all routinely-reported information by the company
allows you to quickly figure out what matters most to the market. When we look at WEYS, it was a very sleepy name from 2014
to the start of 2018. However, a dramatic re-acceleration of North America retail same store sales growth made the stock a
great performer through Q3-2018. After a Q4 where all small and mid-caps were punished, will WEYS continue to outperform?
While Canalyst does not have a view, our clients have quarterly updates for their watchlisted names delivered within hours of
reporting, allowing them to quickly judge whether important KPIs mean an investment thesis is intact.
Interested in learning more about how you can uncover more investment opportunities with Canalyst financial models?
Request a demo
.
Copyright © 2019 Canalyst, All rights reserved.
what exactly is quantamental?
under-covered name dive: weys
On March 28, 2019 Canalyst was proud to sponsor the
Sidoti & Company Spring 2019 Investor’s Conference.
Bringing together leading institutional investors
and management of nearly 200 small and micro-cap
companies, it was a great opportunity to share our
models on the under-covered names presenting at
the conference. That, and sink some threes in between
meetings at our Canalyst Madness shooting contest.
Read the full press release here
.
It was a busy quarter filled with multiple hot companies
going public, from Levi Strauss & Co. [NYSE: LEVI] to
Tradeweb Markets [NASDAQ: TW]. Canalyst guarantees
to build pre-IPO models within 48 hours of the S-1
filing, but last quarter our clients had an especially
sizeable head start on the Lyft [NASDAQ: LYFT] and
Pinterest [NYSE: PINS] models, which were complete
within 12 hours of the S-1 filing. Follow us on Twitter
(
@
CanalystModels
) and
LinkedIn
to be kept in the loop
on all our pre-IPO model offers.
sidoti madness
ipo
s
galore
James Rife
Canalyst, Head of Equities
Prior to founding Canalyst, James had 10 years’ experience in equity research and portfolio management. He started his career in
equity research with Fidelity Canada’s investment team, covering sectors including Utilities, Forestry, Technology, and Energy from
2006 to 2010. After Fidelity, he took a role as Portfolio Manager at a Boston-based $1B long/short fund, rounding out his experience
across most other sectors in the process.
James holds a Bachelor of Commerce from the University of British Columbia and is a recipient of a Leslie Wong Fellowship from UBC’s
Portfolio Management Foundation, and is a CFA Charterholder.
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