[Latest update: 8/13/2019, please go to the bottom to see new additions]
Value Investors Club (VIC), founded by the legendary investor Joel Greenblatt, is an anonymous elite value investing club whose admission is said to be very selective. According to John Petry, the co-founder of the club, there’s “a lot of very well known money managers” and “very, very successful hedge fund managers” who all use the site. However, from traits left by these “anonymous” users, we may be able to tell these well known and successful investors. I firstly carried out some of these researches purely out of my curiosity, but later found identifying these great investors helps me focus on quality ideas and discussions. Sometime I cannot tell who exactly they are, but certainly can tell the ideas were from some greatest minds. By all means, these guys’ writings are great stuff to read regardless who’s behind.
Below is my letter for 2020 Q2, in which I commented on individual holdings of PDD, YY, AVLR & CIH. I also shared my process of evaluating opportunity cost of trading actions, and how it improves learning from own past successes, misses & biases.
Note: it’s separate post of same content from my 2020 Q1 letter. I attempted to use quantitative tools to review how different types of hypothetical “rebalancing” strategy would perform during the last market crisis.
It also relevant to revisit it as we may be thinking about whether we are at the same stage of June 2008 or March 2009.
During any crisis, investors like to talk about “rebalance”, value investors think it’s the time to sell names that dropped less and double down on the overreacted ones (let’s call it “reversion” rebalance), whereas growth investors think one should dump low quality which typically plummeted badly, for high quality ones which typically withhold better (let’s call it “momentum” rebalance).
To test which one worked better historically, I decided to backtest a 3-month momentum factor during the Great Financial Crisis (GFC). The basic idea can be explained as: I imagine different investors started to react at various points of time during the GFC and decided to rebalance based on past 3 months return. On Russell 3000 stock universe, we will slice all stocks up into 10 pieces (i.e. deciles) by ranking their past 3-months return, I ranked return ascendingly, so that decile 1 cam be defined as the losers (returned the worst), & decile 10 the winners (returned the best). Below is a snapshot of each decile’s forward 3 months return from various “reaction” time. For example, if an investor decided to buy the worst performing 10% stocks in past 3 months leading to 9/1/2008, he or she will suffer -41.97% in the next 3 months (from 9/1/2008 to 11/30/2008).
I see some noticeable observations:
In the early part of the crisis, (from 200706 to 200712), losers kept losing and winners held up;
During the first hit (from 200712 to 200803), names got hammered similarly across the row;
From 200806 to 200809, the “reversion” rebalance immensely outperformed (on a spread of +38% 3 month forward return). Also note that D1 generated +14% (recover of overacted losers) & D10 suffered -24% (delayed punish for under-reacted winners)
The following 3 month (200809 to 200812) witnessed the entire financial system melting down catalyzed by Lehman Brothers’ bankruptcy. We saw dark red (negative) returns across the row, and past losers were punished much worse than past winners (-42% D1 return vs. -19% D10 return)
Starting from March 2009 (the trough), the market started to recover strongly. Surviving past losers started to outperform the less losers (D1 returned 64% in following 3 months), but not until it flipped in next 3 months.
Furthermore, at each point of time, a “reversion” rebalance could be constructed by buying the losers (D1) & shorting the winners (D10), vice versa for a “momentum” rebalance (buy D10 & short D1). If we calculate the spread between D1 & D10 (D1-D10 for “reversion” rebalance, and D10-D1 for “momentum” rebalance), we can see on cumulative basis, how each strategy may or may not add value for the most important task in a crisis – preserve capital. Below chart tells a very interesting story.
Note that the bar is the spread for “reversion” rebalance (i.e. D1 – D10), thus “momentum” rebalance would be simply the flip signed of such value. “Reversion” style tended to underperform in higher frequency (i.e. more occurrences), but when it reversed it tends to reverse drastically. This can be seen twice during 3 months after June 2008 (the bull trap) & March 2009 (the rebound after trough), which basically wiped out accumulated value from “momentum” rebalance completely. While you might think winning for one is losing for the other, both strategies (remind here they are market neutral) ended in red ($0.8 for “value” & $0.87 for “growth”). I also added the Russell 3000 index returns during same period as a reference of “do nothing” strategy, which ended at $0.8, on par with the “reversion” rebalancing strategy.
This study brought some very important insights for making decision during a crisis:
Both rebalancing strategies work, yet in different phases of a crisis. Timing is crucial(as doing either way at the wrong time would heavily impair one’s capital),if you decide to rebalance. So think carefully whether you have good market timing skill before you act.
Neither strategy could consistently add value for preserving capital purpose. I think it’s important that buy & sell decisions should still be made on absolute value vs. price basis, rather than on relative price movement basis.
“Do nothing” is not as bad as you might think, especially when you know you can’t consistently time the market.
Note: This is a republication of a full write-up of the machine learning research to understand and improve systematic value strategy, which was originally shared in my 2019 Q4 letter [Link]
I’m also start trying Medium. It is published on Medium too. [Link]
This article is a collection of thoughts and experiments of my personal endeavor from a quantitative perspective to understand why “Value” as an investing style didn’t perform compared to other styles for an extended long period of time. It consists of mainly two parts: 1) review of others’ relevant researches & 2) an experiment of using Clustering to explore reasons for such phenomenon and potential ways to improve systematic value strategies.
“Value” means differently for different investors. It is important to note that “Value” in context of this write-up mainly refers to the “value” factor, e.g. Fama-French HML factor or variations of it. In other words, it is a systematic long-short strategy longing a group of the cheapest stocks and shorting a group of the most expensive stocks.
My main hypothesis is that GAAP/IFRS currently does not do a as well job in representing fairly companies’ value as historically. Thus “Value” may not work on broad stock universe yet may still work in certain types of stocks. To classify stocks into such “types”, I tried to use Clustering, an unsupervised machine learning technique, to test if it works.
Jim Simons’ shop Renaissance Technology, or as insiders call it “RenTech”, is the hallmark of quant fund managers. I’ve been longing to learn about how they achieved it and can’t wait to read this Greg Zukerman’s new book on Jim Simons.
As usual, I tried to put some lessons/thoughts useful for myself, hopefully for my readers as well.
[Bonus] I also dug up some RenTech job descriptions that no one ever (at least I didn’t see) found regarding what type of talents RenTech has been hiring (thus what type of new techniques they may be using) more recently. Go straight to the last point if that is your only interest.
Below are excerpts from our 2019 Q3 letter regarding our new position Meidong Auto, for easier reference purpose.
Also, attaching the letter to shareholders from its 2018 annual report, it’s quite a good read: MeidongAuto_2018_Letter
Meidong Auto is a new position from the “Great Operation at Reasonable Price” category. It doesn’t come often that a CEO’s shareholder letter alone made me think seriously to become a shareholder. Meidong had made to this list. In short, it is a easy to understand business (luxury auto dealer), who had an exceptional “outsider” CEO who was able to communicate transparently and sincerely about its strategy and execution. It has been executing its strategy very well, generating excellent return on equity (25+%) over past 3 years.
Meteorology: China has seen its auto market slowing down after a few years of high growth. Luxury car however still enjoyed healthy growth. According to CPCA’s data, luxury brands have maintained a 10~% annual growth rate from 2016 to 2018 (while overall auto market observed +13.9%, 3% & -2.8% for the same three years respectively). Furthermore, CPCA’s most recent July 2019 data even shows an accelerating +24% yoy growth rate for luxury cars. This trend, while perplexing, could be explained by the secular shift of the consumption power in China (from tier 1 cities to lower tier ones, & a growing middle class in all tiers cities, etc.) and further inequality in wealth distribution.
Topography: Meidong uniquely chose to position as the sole dealer of a luxury brand in tier 3-4 cities. Such position allows Meidong to earn a high gross margin (on average about 100 bps higher than multileader in a city) due to weak competition. Such position also increased the stickiness of lucrative after sale service business. it however is a narrow moat because if competitors really want to imitate this strategy, they could achieve so and drive the margin down. In terms of the sustainability of such narrow moat, I think it is still in good position at least for short term (2-3 years), based on our evaluation of incumbents (who are at least a few years behind Meidong’s business principles & executions).
Commander: The management of Meidong is the most impressive factor. In fact, the CEO Ye Tao, reminds me the CEOs from William Thorndike’s book “The Outsiders”. Ye Tao has technical backgrounds, graduated from MIT with both engineering and MBA degrees, also served as executives to various software business in United States & in Asia. The way Ye Tao approaches car dealing business can be described as quite “rational”, which can be seen by reading a single shareholder letter from him. Ye Tao think the most important principles of his business are 1) high inventory turnover, 2) grow service revenue (the high margin business) & 3) focus on new store ROI. It may worth quoting directly from its 2018 letter. On the inventory turnover, it is refreshing to read something like “We live or die by inventory turns. Fast-turns make us a cash printing machine; slow-turns turn us into a cash-sucking black hole.” In addition to the principles laid out, the company’s reporting also provides metrics to track how they performed in these three areas. On capital allocation, the management preferred to use dividends. That 4 is not the most favorable way of distribution value back to shareholders, given the price is still a bit underpriced in my opinion.
System: One thing to note is that Meidong is still very closely held by insiders. 65% of the shares are held by the family trust of Ye’s brothers.
Valuation: we have built a material position at about 12x 2019 est. earning, a reasonable price for such a high-quality business.
For what it worth, I have seen my concentrated portfolio (< 15 names) performed relatively closely to the broad market for years. However since last week, I started to see more deviation between these two. This observation coincides with the most significant value factor rebound seen in years. This WSJ article covered this interesting rebound of value factor [link], the author James Mackintosh however doesn’t think the rebound can last because he personally believes in the disrupters’ long term secular advantages.
There are two leading explanations for value’s poor performance for the past decade. The first is that unending cheap money fueled spending sprees by disruptive tech stocks, allowing them to run at a loss and so steal business from traditional companies that try to make profits. Leading examples are Tesla, Uber and WeWork, and higher bond yields offer some hope that this might reverse.
I prefer a second, linked, explanation, that there’s a wave of technological change under way and the market has divided between the disrupters, who can afford to take advantage of it, and the disrupted, who can’t.
It is also important to think through why such reversal happens now. This Barron’s article [link] sourced many street strats who think it is caused by fixed income market, specifically a recent 10 year treasury yield spike.
What was behind the sudden shift? Before last week, investors appeared to be betting that bond yields would fall. And for a while, that trade worked. Both momentum and low-volatility baskets are loaded with defensive stocks that tend to rise as yields fall—a signal that bond investors are losing faith in the economy.
No longer. The 10-year Treasury yield rose from 1.461% on Sept. 3 to 1.733% on Sept. 10, while the yield curve, which had been inverted, steepened. “The extreme factor moves we are seeing in the equity market are driven by activity in the fixed-income market,” Nomura Instinet strategist Joseph Mezrich wrote on Tuesday.
There’s a good reason for that. As investors turned more defensive this year, low-volatility stocks have attracted significant assets across both retail and institutional platforms. And that has made them expensive. Christopher Harvey at Wells Fargo now recommends that investors reduce their exposure to low-volatility stocks, a stark contrast to his position at the beginning of the year.
“About a year ago, we talked about low-vol strategies being one of the most unloved and underappreciated strategies in the marketplace,” wrote Harvey on Tuesday. “However, the style is no longer the technically oversold and underowned strategy it was in years past.”
As we’ve noted, the momentum basket, has recently shifted from being a group of fast-growing companies to include many of the market’s least-volatile stocks. Those are also the ones most dependent on yields going down, explains Bernstein analyst Sarah McCarthy.
So I decided to do a bit work to test these theories.