Hengdeli Holdings Ltd (3389:HKG) (HENGY:OTC)

Recent Share Price: HK$0.41

Accounting: Hong Kong Accounting Standards

Fiscal Year: Dec. 31st

Market Cap: HK$1.6 billion ($238 million)


5 Year
3 Year

Hengdeli Holdings Limited operates a retail network comprising: Elegant (high-end brands, acquired 2006), Hengdeli/Watchshoppe (mid-end and mid-to-high-end brands) and single-brand boutiques. Hengdeli has 67 retail outlets, selling watches from more than 50 internationally renowned brands in Hong Kong, Macau (2010), Taiwan (2009) and Malaysia (2018). They also provide integrated after-sales warranty maintenance.

Hengdeli has a strong and intergrated relationship with SWATCH Group, LVMH Group, Richemont Group and Kering Group.

Hengdeli is heavily tied to the Hong Kong market, which has recently seen an improvement in demand.

Recent History

2018

Acquired the watch business of Watchshoppe, a well-known local watch retailer in Malaysia, thereby expanding its operations to Southeast Asia.

2017

Announced in late 2016, plans to sell majority of buisiness. The deal was fair-ish for existing shareholders with the deal valued at approximately one times net asset value.

Sold its mainland Chinese business and it low-end watch and jewelry business in Hong Kong (Harvest Max) to Yu Ping Zhang, the chairman and contolling sharehoder for RMB 3.5 billion. The valuation was based on the unaudited net asset value of the Disposal Group of approximately RMB5.1 billion and the minimum Dividend Payout of RMB1.6 billion. Profit before tax for the disposal group in 2014 and 2015 was RMB 678 million and RMB 365 million, or 5x EBT and 9x EBT respectively.

The proceeds were used to retire USD denominated debt and to pay a special dividend of HK$0.20 per share.

2013

Share price was pushed lower following a report in Next magazine which questioned the following:

  1. Some of the store outlets were either nonexistent or not branded as company stores.
  2. There was negative operating cash flow between 2006–2008 and 2010 despite being profitable.
  3. The company had lost key distribution licenses and exclusive rights for brands like Omega, Rado, Bucherer, Audemars Piguet, Fendi, and Dior.
  4. The company raised US$350 million of senior notes despite holding 3.4 billion yuan in cash as of June 30, 2012.
  5. The company has invested in a bond of 259 millon yuan with an interest rate of 13 percent.
  6. The company made three short-term loans amounting to 720 millon yuan at an interest rate of 11–18 percent.
  7. The chairman has pledged its shares to Swatch for a three-year US$100 million loan for his private business

Bloomberg wrote a follow up article in which Nick Hayek, chief executive officer of Swatch, denied the claim that relationship with Hengdeli was compromised.

Insider Ownership

Shareholders
NameEquities%
Yu Ping Zhang1,585,556,50134.00%
Jinbing Zhang452,968,0009.71%
The Swatch Group AG437,800,0009.39%
LVMH Moët Hennessy Louis Vuitton SE230,280,4004.94%
Yong Hua Huang52,172,8001.12%

Financials

Write-ups

value and opportunity [here]

Disclosure: We own shares in Hengdeli Holdings Ltd (3389:HKG) (HENGY:OTC)

8 thoughts on “Hengdeli Holdings Ltd (3389:HKG) (HENGY:OTC)

    • Mike,

      Thanks for taking the time to read my post and for the reply.

      A net-net approach immediately puts you in the nanocap and microcap buckets, so most of the benefits of the small firm effect are realized in my opinion. But I agree that it is easier to double revenue from $10 million as it is from $100 million, so the smaller the market cap the better and I work to get as small as possible. Liquidity can be a hurdle in this endeavor.

      As a godfather to quant, O’Shaughnessy’s book is a fantastic reference. He is such a fanatic on forcing the data to guide opinion that I value everything he puts out. I have the 4th edition and find it a very useful reference. He is working on “devaluing” the value of price-to-book right now, so keep me posted on that if you see anything.

      Thanks for the link to the RA paper. I have not read this before. But I’m a bit confused as they are arguing against the small firm effect saying that most of the outsized return is based on statistical biases. However, on page 3, they do show some additional alpha after backing out biases in the first two ventiles. (After that it’s a wash.) They broke the NASDAQ into ventiles. That first ventile goes up to $14 million market cap and the second goes up to $24 million (based on my rough calculations). I assume that is the point you’re making. And I agree with you. [However, note that this does not work in every market tested, which is a very important point to recognize.] “Fish where the fish are”.

      I found this excerpt from the RA paper to be interesting: “…small stocks have the potential to serve as an alpha pool for skilled active managers and rules-based strategies that primarily target factors other than size. Nonetheless, we are skeptical that investors will earn a higher return simply by preferring small stocks over large.”

      Although the smaller the better, I think discount-to-NCAV plays a larger role to performance in a net-net strategy. Tobias Carlisle found that U.S. monthly returns in a net-net strategy amount to 2.55%. Monthly returns to the NYSE-AMEX and a small-firm index amount to 0.85% and 1.24% during the same time period. By focusing on discount to NCAV, the monthly returns shoot up to 3.60%.

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  1. The question is if the return of Net-net can be explained by the size effect only. I am not sure about this. As a real-life example, the performance of the blog “nonamestocks.com” is impressive with yearly returns of more than 40%. However, Dan only invests in the smallest U.S. stocks and probably after a bull market of 8 years these stocks are rather expensive now. So I think it is better to buy nano stocks globally.
    see here: http://www.nonamestocks.com/p/portfolio-performance.html

    There are some new findings related to price to book ratio, see here:
    http://review.chicagobooth.edu/accounting/2017/article/why-value-investing-buy-signal-out-date

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    • Yes, according to all of the peer-reviewed papers I’ve read, size effect does not explain all the alpha. In other words, a basket of nano-cap stocks will not perform as well as a basket of net-nets. Neither will a basket of nano-caps perform as well as Dan’s portfolio.

      I’m very familiar with Dan and his outstanding returns. (I link to his site from my blog.) I have not spoken to him, but from what I’ve read on his blog and from listening to him on podcasts, his focus is typically on obscure (many times dark) thinly traded stocks that many times have some type of catalyst, e.g., management change, going from dark to reporting, etc. But I would mention that he certainly pays attention to price. He is looking for small and cheap. And I think he would say you can still find attractive prices, even today.

      Some of Dan’s stocks are net-nets, but he doesn’t limit himself to net-nets. I on the other hand only invest in net-nets. So although we have some overlap are strategies are different.

      As I mentioned, Toby did a study for U.S. net-nets from 1983 to 2008 with annual rebalancing. Mean returns were 2.55% monthly or 35% annualized. That is ignoring size (and everything else for that matter). I think a net-net strategy is comparable to what Dan is doing. (On my research page you’ll find links to a bunch of studies you can look at.)

      But, I think coming up with the strategy is the easier part; the real difficulty is executing the strategy.

      I think Dan has done a fantastic job of letting some of his winners run, and has shown a great deal of patience in holding stocks that go nowhere for a long time and in buying more as prices go down. But what he does is not easy for most people to do.

      Global nano stocks is a good place to look. I’m looking all the time.

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  2. Interesting would be a study concerning net-nets in China or Hong Kong. The problem in China is the lack of good corporate governance. The many cases of fraud on overseas stock exchanges (e.g. the US or Germany or even Hong Kong) related to Chinese stocks are also worrying. Unfortunately, I do not know such an analysis.

    See also the GMT research fraud assessment:
    “Stripping out Hong Kong and China, and the global incidence rate (“fraud rate”) was closer to 1%. In summary, no matter what technique we use to gauge the quality of financial statements, Hong Kong and China appear to come out worst.”
    https://www.gmtresearch.com/fake-cash-fraud/

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  3. Interesting is, however, the study “Ben Graham’s Net Nets: Seventy-Five Years Old and Outperforming”.
    According to this study, liquidity (or better illiquidity) is an important factor to explain the return. they write
    “Before including the liquidity factor, estimates of monthly alpha were around 1.67% to 1.9% per month, which translates to approximately 22 –25% per year of excess returns, after adjusting for risk. Including the liquidity factor, monthly alpha is around 4.5%, which translates to 70% per year.”

    Liquidity was defined as total traded USD volume. I, therefore, conclude that nano-sized net-nets (which have usually a very low USD trading volume) did on average perform (much) better than bigger net-net stocks with higher trading volume.

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    • Hi Mike,

      Thanks for your comment and for your thoughts on the liquidity factor. Although the liquidy factor is evident to me and its alpha generation is documented in the paper by Carlisle et al., it is something, I admit, I haven’t spent much time focusing on in selecting stocks for the portfolio. So thanks for bringing this up!

      I spent some time thinking about it and decided to do some digging on my portfolio. I do not have access to the data needed to run the formulas used in the paper, so I chose to use a proxy for it. Geoff Gannon has suggested a proxy that I thought useful. He suggested using the formula: (volume for the year * last closing stock price) / market cap. (This is from memory so my apologies to Geoff if this is not what he said). Essentially he is converting the stock’s liquidity into the stock’s “turnover” which makes it convenient for comparison, which I think is clever. I didn’t want to go back a full year due to the added difficulty, so I chose the 3-month average daily volume.

      So I ran this against my portfolio and found that the mean was 4.1% [(3-month average daily volume * last closing price * 64 trading days) / market cap] with a standard deviation of 5.3%. Although interesting, this didn’t mean much to me until I ran it against the net-nets from the U.S. From a basic net-net screen (ex. financials and Chinese RTOs), I got 68 stocks with a mean liquidity “turnover” of 82.9% with a standard deviation of 131.9%.

      So it turned out to be a much larger variance from my portfolio to a basic screen than I would have ever guessed. Scrolling through the data made the reasons for this immediately obvious. For example, from the net-net screen, Arca Biopharma (ABIO) scored a 743%! In other words, over the past 3 months there was enough liquidity in this stock you could have bought and re-bought Arca over 7 times! But Arca would never pass the initial screens of a Graham/Buffett cigar butt portfolio; it is raising capital just as fast as it spends it. In fact, the majority of the high liquidity stocks would not pass a general test for a sound net-net stock. As a side note, scrolling through the list made the assertion from the paper that going long the bottom 3 deciles and shorting the top 3 deciles make a lot of intuitive sense.

      My initial conclusion from this is that using a Graham approach will by definition push you towards low liquidity stocks and that liquidity and market cap do not appear highly correlated. But there’s still lots to think about.

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  4. Thank you for your comments. Probably both, the relative trading volume (trading volume / market cap) and the absolut trading volume (trading volume in USD compared to other stocks, the lower the better) are relevant.

    BTW the paper “Dissectin the Returns on Deep Value Investing”, by Jeffrey Oxman etc has the same conclusions. They write in the conclusion setction “Our results show that controlling for firm size and market-related risk factors, excess returns are higher amoung net-net stocks with low analyst coverage, low stock price per share and lower trading volume”.

    Like

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