Here's what I like about it:
- deep discount to tangible book value (over $10 million TBV - after deduction of non-controlling interest vs. $8.14 million market cap),
- recently profitable (if only marginally),
- cash exceeds 50% of market cap ($4.3 million cash),
- stock price roughly doubling earlier this week and now almost back to where it started, despite no news in interim that I've been able to find (i.e. someone liked it at $3.50 just 3 days ago), and
- activist investors (Twinleaf) circling.
Purely on the basis of financial statement and trading metrics, THST was rated 15th most attractive buy per the DDIM.com algorithm as of last Friday's close. Note that our algorithm does NOT consider qualitative factors (such as involvement of activist investors).
My major concern re: having a big THST position is its lack of liquidity. Before I get too heavily into something, I like to look at how I'm going to get out in a company-specific worst case scenario.
Paradoxically, lack of liquidity also correlates with superior returns and mitigates losses in overall market downturns. So owning a little bit of many different illiquid stocks (like THST) makes sense.
Very few big names have THST's kind of valuation. Consider our DDIM ratings, which are based primarily on value metrics, though 12 month relative strength is also factored in. If you go to the screener at DDIM.com, you will find only one stock in the largest market cap decile (Best Buy - BBY) that is also in the most highly rated 100 of the 3000+ stocks followed by DDIM, coming in at #80 (based on 1/6/17 closing prices).
Needless to say, a lot of stocks that deserve to be cheap get high DDIM ratings (e.g. many Chinese stocks). That is why DDIM links its screening results to relevant Seeking Alpha articles, which provide a qualitative perspective.
If you want to see how ratings and Seeking Alpha input translate into an actual portfolio, the DDIM screener can also tell you which rated stocks I was long or short as of the 1/6 close (i.e. positions worth more than $100).
THST, incidentally, was ranked #15 at that time. This made it the 6th most highly rated stock among the 61 rated stocks I owned at the 1/6 close with positions > $100.
Many of the stocks I'm currently long were bought in order to exploit heavy December selling which may have been tax-related. I don't expect to be owning many (if any) of them come February.
These stocks include (in descending order of DDIM rating):
SORL SORL Auto Parts
GURE Gulf Resources
MEMP Memorial Prod Partners
CMCI China Modern Agriculture Info
VSI Vitamin Shoppe
MICT Micronet Enertec Tech
EFOI Energy Focus
CPST Capstone Turbine
STKS The ONE Group Hospitality
BBRG Bravo Brio Restaurant Grp
HRT Arrhythmia Res Tech
GROW U S Global Invs
SNCR Synchronoss Techn.
NHTC Natural Health Trends
ESES Eco-Stim Energy Solutions
CLBS Caladrius Biosciences
RUBI The Rubicon Project
GNK Genco Shipping & Trading
SSNI Silver Spring Networks
ACUR Acura Pharmaceuticals
CBIO Catalyst Biosciences
CDTI Clean Diesel Technologies
RKDA Arcadia Biosciences
OGES Oakridge Global Energy
IMDZ Immune Design
NEOS Neos Therapeutics
VYGR Voyager Therapeutics
Though I wouldn't feel comfortable taking a position of any significant size in Truett-Hurst (NASDAQ: THST), I'd be fine holding a portfolio of 50 stocks with similar attributes, such as:
- deep discount to tangible book value,
- marginally profitable,
- cash approximately 50% of market cap,
- stock price roughly doubling earlier this week and now almost back to where it started, and
- activist investors circling.
Small investors can't go too far wrong with portfolio allocation of 2% to THST while looking for 49 more situations just like it.
* Synergies from integrating DDIM strategy and crowdsourced Seeking Alpha research continued. Returns since I became an active SA user in July 2015 are over 60%.
* Overall trading portfolio returns since July 2000 grew to 3783.6% through November 2016 (documented with brokerage statements at DDIM.com).
* Favorable returns achieved largely through prompt post-election buying of stocks with high DDIM ratings likely to benefit from election results. Significant leverage used, but SPY puts hedged margin purchases.
* Consistent with DDIM strategy used since 2000, most stocks bought were small or mid-cap value. This strategy also calls for buying stocks experiencing good news and selling on bad news.
* DDIM strategy (explained in Data Driven Investing) is informed by over 50 years of backtesting and academic studies regarding psychological biases (e.g. confirmation, cognitive dissonance, conservatism, and anchoring biases).
Calculation of Portfolio Returns
As I write this on Saturday, 12/10/16, my trading account at TD Ameritrade is showing an equity balance of $447,600 (up from the 11/30/16 figure of $424,243). Since I've already withdrawn $2250 from it this month, my December gains so far (excluding accrued margin interest, accrued dividends, etc.) are $25,607, a bit more than 6% of November's closing balance. And since my accrued margin interest is well under $2000 at this point, I am on solid ground saying that December returns to date comfortably exceed 5%.
As has been documented at DDIM.com, returns since July 2000 and July 2015 were 3783.6% and 55.4%, respectively, at the end of November. Thus, December's returns to date (which exceed 5%) have increased these figures to at least 3978% and 60%.
Whereas 3978% gains over a period of 197.3 months works out to an annual compounded return of 25.3%, my annual compounded returns since July 2015 from integrating the DDIM strategy with crowdsourced Seeking Alpha research have exceeded 38.5%.
Please note that past performance does not guarantee future performance.
Prior to the election, I had grown cautious and reduced my use of margin considerably. My usual practice of borrowing to fund small cap value buys and hedging this exposure with puts on SPY had backfired in October. Though SPY was down, small cap value was down even more, leading to a minus 8.3% return for the month.
On the morning of November 9th, though, it was clear to me that the DDIM strategy of buying aggressively on good news would apply in spades to the election's most likely corporate beneficiaries. Shortly after the market opened, I began selling my stocks that were flat to down and loading up on those with high DDIM ratings whose price gains were strongest. As my transaction log for the day indicates (found in the Research subsection of the FAQ's at DDIM.com), I kept it up until late into the afternoon. (Note that TD Ameritrade appears to be using Paris time to report trades.)
Overall, the stocks I bought that day have done well, though I've sold many of them since then - often as a result of negative factors brought to my attention by fellow Seeking Alpha contributors. As the table below my transaction log indicates, their prices have increased an average of 21.9% between when I bought them and 12/9.
Stocks with DDIM ratings of 10 are in the "most attractive to buy" decile of the 3000+ stocks that DDIM covers, based purely on algorithmic analysis of financial statement and trading data. Interestingly, the 27 stocks I bought on 11/8 having ratings of 10 that day showed average price increases of 23.6% during the following 31 days (on top of whatever they were already up on 11/8 before I bought them). The five "non-10's" I bought that same day, however, had average price increases of only 12.4%.
Even among the 10's there is a hierarchy. The 12 stocks I bought that had rankings in the top 100 of DDIM's universe had an average price increase of 26.5% between the moment I bought them and 12/9.
Note that DDIM ratings and rankings are available at no charge through the Screener function at DDIM.com.
More Empirical Evidence That the DDIM Strategy Improves Investors' Odds
The basic assumptions upon which DDIM ratings are based (other than in late December and early January, when tax loss selling muddies the waters) are that:
- value beats growth, and
- high 12 month relative strength beats low.
So where is the evidence backing up these assumptions?
In writing Data Driven Investing, Mitch Hardy and I thoroughly analyzed the Compustat database, considering millions of financial statement and trading-related data points for thousands of stocks from between 1951 and 2002. The book, which is available at no charge in its entirety through Google Books, backtests a variety of strategies by constructing and annually balancing portfolios of 100 stocks.
Some examples of our findings are that:
- stocks with the lowest ratios of price to book value beat those having the highest, with compounded annual returns of 23.08% vs. 2.52% (Exhibit 20, pg. 53),
- stocks with the lowest ratios of price to sales beat those having the highest, with compounded annual returns of 21.12% vs. .36% (Exhibit 20, pg. 53), and
- stocks with the highest relative strength beat those having the lowest, with compounded annual returns of 13.84% vs. 2.70% (pg. 59).
Psychological Biases Typically Delay Market Reaction to News
The key assumption guiding my trading activity is that, in the words of Robert Haugen: "Markets overreact slowly."
The specific psychological biases underlying this assumption are:
- confirmation bias, which leads us to look harder for facts that support pre-existing beliefs. If we know and love the stocks in our portfolios, we may tend to actively look for facts that support the decision to continue owning them - even when objective analysis of breaking news would call for an immediate need to replace them. Snyder and Cantor conducted a landmark study of confirmation bias that clearly indicated a tendency for confirming data to be given greater weight than disconfirming data.
- cognitive dissonance, which may cause investors who considered buying a stock at $30 - but decided to hold off - to be even less likely to buy if favorable news causes its price to rise to $35. To pay $35 would create uncomfortable dissonance with their earlier opinion that the stock was worth less than $30. The phenomenon of cognitive dissonance was notably explored in a Journal of Personality and Social Psychology article entitled"Effect Of Initial Selling Price On Subsequent Sales."
- conservatism bias, which leads us to require unnecessarily large amounts of new data to revise our opinions. This was famously illustrated by Ward Edwards through an experiment in which subjects were asked to envision two very large containers filled with poker chips. Red chips outnumbered blue 7:3 in one container, and blue outnumbered red by the same margin in the other. The subjects were then asked to imagine one of these containers being picked at random and 12 chips being randomly pulled from it. They were then instructed to estimate the chances (in percentage terms) that the container with 70% red chips had been selected, assuming that 8 of the chips drawn were red and 4 were blue. Most subjects guessed the answer to be in the area of 75%, when, in fact, the correct answer is about 97%.
- anchoring, which causes us to give inordinately great weight to any value that has been established or proposed, however outrageously incorrect that value might be - even if we later obtain contradictory information. In one study, several dozen real estate agents were shown a home and asked to estimate its value. Although given identical information about the home in all other respects, their estimates were an average of 15% higher when told that the listing price was $149,900 rather than $119,900.
- endowment effect, which causes us to value things we already own more than those we don't. It can delay market reaction to bad corporate news by making us unwilling to sell at a falling price. And it can also delay market reaction to good news by causing us to overvalue our gains, resulting in premature selling of stocks with rising prices. A recent study examined this phenomenon in the trading of Indian IPO stocks.
Disclosure: I am currently long AIR, ARII, CMC, ERS, GBX, GTLS, HBP, HII, IIIN, KELYA, LYTS, MOD, OSK, PJC, PLPC, RYI, SLGD, TG, TTMI, WIRE, and WNC.