Notes – Best Practices in Deal Flow Origination

These notes are from an article entitled “Where Are The Deals?” by David Teten. He also has resources on adding value to portfolio companies which are worth browsing. For notes on a related topic, check out the “Notes – Stanford Graduate School of Business Search Fund Primer” post.

  • the median investor in private companies had to review 80 companies in order to close one transaction
  • investments sourced through personal and professional networks have been shown to yield better results
  • in order to train your relationships, it is important that you provide them with simple, clear investing criteria, not lengthy checklists; provide them a narrowly defined niche of interest (“Retail brands with $50M in annual revenues”)
  • on average it can take 1-2 years between the first meeting with a target CEO sourced through a network and the close of the deal
  • market mapping, identifying key macro and micro drivers of an industry and creating a database of all key companies; identify those with greatest growth potential or competitive white space
  • specialization enhances deal origination through deeper knowledge base, ability to add value through enhanced network and likelihood of being top of mind to key deal sources
  •  monitor target sector for cyclical opportunities and structure shifts; M&A creates orphan divisions and downturns cause strategy refocuses; 30-46% of PE returns over last 30 years driven by EBIT arbitrage (market timing)
  • other valuable sources of deal flow:
    • regional surveys
    • “fastest growing company” lists
    • trade association membership lists
    • commercial vendors
      • Amadeus
      • Capital IQ
      • Dun & Bradstreet
      • Hoover’s
      • InfoUSA
      • Lexis-Nexis
      • Thomson-Reuters
      • OneSource
  • set up alerts in a blog reader based on key words important to your target or industry focus
  • “A large portion of my deal flow comes from people I have rejected in the past.” be kind to everyone, even those you don’t do a deal with
  • consider having a dedicated, SEO-optimized website and blog for your acquisition fund/team that explains what you’re looking for, why, what you bring to the table, etc.; many VCs and most PE investors are not using basic internet marketing techniques (competitive advantage opportunity)
  • Accel Partners and Khosla Ventures post detailed analyses of their target investment sectors; blogging and posting of internal analyses is the “VC freemium model”
  • PE investing is a relationship business and the most important relationships are with LPs, entrepreneurs, executives and intermediaries which are relatively few in number
  • blogging is the best tool for VC investing according to one experienced observer; helps investor gain information, credibility and relationships through improved visibility
  • look for access to secondary interests through directly approaching funds (particularly distressed), markets for secondary interests (SecondMarket, NYPPEX, PORTAL Alliance) and approaching ibanks specializing in secondary interests (Cogent Partners, Probitas, Triago, UBS)
  • service providers such as accountants, lawyers, etc., are typically not good sources of deal flow because they require too much education and often have a fiduciary responsibility to their client; on the other hand, connecting with service providers in a specialized domain that is being targeted can be a good source of insight
  • trawl the Q&A portion of sites such as LinkedIn to identify domain experts for further outreach
  • measure your deal origination efforts with activity measures, deal flow by source, pipeline analytics and industry benchmarking measures
  • many professional services firms do not use a global CRM system such as Salesforce.com, Act, Saleslogix, Microsoft Access or Angelsoft (angel/VC network)
  • Key data sources for CRM systems include employee networks (ContactNet Enterprise Relationship Management), business cards (Cardscan, IRIS, Neat, Presto), data from email and files (eGrabber, Gwabbit, Grab-Text, Broadlook), the “cloud” (LinkedIn, Spoke, Plaxo) and direct from target companies’ websites, media, etc.

Key attributes of top originators in order of importance

  1. persistence (every no gets you closer to a yes)
  2. personality (people do business with those they like)
  3. business and financial judgment
  4. adequate financial sophistication
  5. seniority and appropriate title (decision-maker)
  6. internal authority to get transaction executed
  7. creativity

Important deal signals when identifying targets (utilize commercial databases, social media, data mining and targeted phone research to uncover)

  • Status of the major equity owner
    • PE funds motivated to sell due to fully invested, raising next fund or current fund has aged beyond 5-7 years
    • Large corp raising cash by selling subsidiaries
    • Time limited tax incentives
    • Family in midst of succession battle
    • Death, disease and divorce (“three Ds”)
  • Status of CEO
    • retirement
    • age
    • acknowledgement of limited competence
  • Corporate performance
    • growth too rapid for self-funding
    • underperforming/distressed
  • Industry/economic trends
    • industry consolidation
      • competitive pressure
      • seeing competitors liquidating equity for large gains
    • competitors raising capital; pressure to maintain parity
    • growth sector

Top considerations for deal intermediaries in directing deal flow

  1. Possibility of future revenue
  2. Integrity
  3. Timely responses
  4. “Fair” treatment of sellers
  5. Experience with the industry or owner type
  6. High certainty to close
  7. Friendship
  8. Feedback and referrals
  9. Maintaining a single point of contact

Most valued aspects of acquiring companies by the acquired

  1. Added operational value
  2. No extra costs
  3. Fair treatment of employees post-transaction
  4. Brand
  5. Long holding periods (no buy-to-flip)

Leading databases of institutional investors (use principles of SEO to optimize your profile here)

  • Galante’s
  • Grey House
  • VentureXpert
  • PE funds
    • Eurekahedge
    • Pitchbook
  • VC funds
    • Angelsoft
    • CrunchBase
    • PWC MoneyTree
    • TheFunded
    • VentureDeal

Market Mapping steps

  1. choose industries and geographies of initial interest
  2. define your proprietary point of view
  3. translate into investment theme (industries/geographies of interest)
  4. list major players in target industry/geography
  5. improve market map with feedback from industry contacts and investment targets
  6. determine which activities offer the highest return and outsource the rest
  7. identify areas of future growth
  8. asses fit with your overall strategy
  9. regularly update the market map with additional feedback and lessons

10 Simple Steps to Improve Your Origination

  1. Analyze your network
  2. Use market mapping to develop deep, proprietary insights about your target
  3. Monitor target ecosystem for cyclical/structural opportunities
  4. Align internal interests
  5. Divide and conquer
  6. Centralize data and become an information sponge
  7. Develop a network with limited overlap
  8. Take control of your virtual presence (marketing)
  9. Join the in-person and virtual communities of your target market
  10. Take a leadership role; find a way to stand out and attract others to you

The Free Capital Blog Digest

The following is a digest of posts from Guy Thomas’s Free Capital blog from Feb 2011 through Jan 2012.  Each post provides a link to the parent article with bullet-pointed lists of key-takeaways from each. For the complete discussion by the original author, please click the link to the parent article.

How important is analytical intelligence in investing?

  • Equity trading is not as reliant on raw mental strength (IQ, analytical ability) as fixed-income trading; instead, equity trading is more dependent upon mental characteristics such as:
    • Actively seeking information from dis-confirming sources
    • Adjusting for one’s biases
    • Accepting uncertainty for long periods
    • Deferring decisions for as long as possible
    • Calibrating your certainty to the weight of evidence
    • Responding unemotionally to new information
    • Indifference to group affiliation
  • The mental characteristics which are helpful in investing are not universal positives and may be useless or negative characteristics in other endeavors

Max, min and average payoffs

  • Most activities can be categorized as max payoff, min payoff or average payoff
  • Max payoff means the activity is “positive scoring”, your payoff is your highest or best result and failure carries no lasting consequences
  • Optimal traits for max payoff are:
    • high energy
    • irrational optimism
    • persistence
  • Examples of max payoff activities include:
    • selling
    • leadership
    • most sports
  • Min payoff means the activity is negative scoring, your payoff is your lowest result and even a single failure may have lasting consequences
  • Optimal traits for min payoff are:
    • meticulous care
    • good judgment
    • respecting your limitations
  • Examples of min payoff activities include:
    • flying a plane
    • driving a car
    • performing brain surgery
  • Average payoff activities combine elements of both max and min; investing is an average payoff activity, with particular emphasis on the min aspects
  • A lot of success in investing comes from simply avoiding mistakes (min payoff)

Discussion of diversification (posts 1, 2, 3 & 4)

  • Diamonds and flower bulbs
    • Diamonds are companies with exceptional economics and long-term competitive advantages that you’d be happy to hold if the stock exchange closed tomorrow for the next five years
    • Flower bulbs are companies which are cheap at the moment but which have no exceptional business qualities (they often make a good quantitative showing but not a strong qualitative one); they can usually be counted on to bloom but should be bought in modest size because they require liquidity to get back out of the position and realize the value
    • Which should you buy? Diamonds are exceptionally rare and require outstanding foresight of long-term durability; flower bulbs are more common, simpler to spot and merely require patience and a strong stomach
    • “Investing is a field where knowing your limitations is more important than stretching to surpass them”
  • How many shares should an investor hold? Some theory…
    • The optimal number of stocks to hold, N, is a function of…
      • quality of knowledge about return dispersions (decreasing)
      • $ size of portfolio (increasing)
      • volatility of shares (increasing)
      • capital gains tax rate (decreasing)
    • Exceptional investors with exceptional quality of knowledge should hold a concentrated portfolio; Buffett from 1977-2000 appears to have held approx. 1/3 of his portfolio in his best idea and changed it annually
    • With a small portfolio, liquidity is not a concern but as your portfolio scales a large number of holdings becomes optimal to maintain your liquidity which enhances your optionality by giving you the opportunity to change your mind without being trapped in a position
    • If the companies you target have highly volatile share prices, it becomes attractive to switch frequently so that you can “buy low and sell high”, thus you want to restrict your position sizing (higher number of positions) and maintain liquidity
    • If the capital gains rate is high you are penalized for turnover so you want to keep your total number of positions low and hold them for longer
  • How many shares should an investor hold? Some practicalities
    • There is clearly a trade-off between the number of positions you have and your quality of knowledge
    • A portfolio which is higher in diversification may hold many lower quality businesses (flower bulbs) but the certainty of the analysis of each might be significantly higher than a concentrated portfolio of several high quality businesses (diamonds) whose analysis is extremely sensitive to long-term forecasting accuracy
  • Concentrated investors often “come a cropper”
    • Many investors eventually disappoint because they have concentrated their bets on companies the world turns against
    • This has happened even to great investors like Warren Buffett (ex., WaPo, which now looks like a horse-and-buggy investment)
    • The danger of concentration is that nothing grows forever, and concentration + illiquidity often make it hard to escape mistakes

Meeting management

  • Opportunity cost of time: is it better spent speaking to management or investigating other ideas?
  • Getting an edge: sometimes speaking with management helps to understand the picture in a way that gives you an edge
  • Buffett: if you need to talk to management, you shouldn’t own the stock
  • Don’t be schmoozed

Analytics versus heuristics; why I don’t use DCF models

  • Time is precious and DCF models take too long
  • A good buying opportunity shouts at you from the market; if you need a calculator, let alone a spreadsheet, it’s probably too close
  • Robustness is more important than refinement; it’s easy to find apparent discrepancies in valuation, but most are false– it’s more important to seek out independent insights which confirm or deny the discrepancy than to calculate its size; when info quality is good, focus on quantifying and ranking options, but when it is poor, focus on raising it
  • Non-financial heuristics are often quicker and sufficiently accurate to lead to correct decisions; you may make more errors than the rigorous analyst but you can work much faster and evaluate many more opportunities which is usually a good trade-off

How Did I Come Up With My 16 JNets?

A couple days ago someone who follows my Twitter feed asked me what criteria I had used to pick the 16 JNets I talked about in a recent post. He referenced that there were “300+” Japanese companies trading below their net current asset value. A recent post by Nate Tobik over at Oddball Stocks suggests that there are presently 448 such firms, definitely within the boundaries of the “300+” comment.

To be honest, I have no idea how many there are currently, nor when I made my investments. The reason is that I am not a professional investor with access to institution-grade screening tools like Bloomberg or CapitalIQ. Because of this, my investment process in general, but specifically with regards to foreign equities like JNets, relies especially on two principles:

  • Making do with “making do”; doing the best I can with the limited resources I have within the confines of the time and personal expertise I have available
  • “Cheap enough”; making a commitment to buy something when it is deemed to be cheap enough to be worthy of consideration, not holding out until I’ve examined every potential opportunity in the entire universe or local miniverse of investing

That’s kind of the 32,000-ft view of how I arrived at my 16 JNets. But it’s a good question and it deserves a specific answer, as well, for the questioner’s sake and for my own sake in keeping myself honest, come what may. So, here’s a little bit more about how I made the decision to add these 16 companies to my portfolio.

The first pass

The 16 companies I invested in came from a spreadsheet of 49 companies I gathered data on. Those 49 companies came from two places.

The first place, representing a majority of the companies that ultimately made it to my spreadsheet of 49, was a list of 100 JNets that came from a Bloomberg screen that someone else shared with Nate Tobik. To this list Nate added five columns, to which each company was assigned a “1” for yes or a “0” for no, with category headings covering whether the company showed a net profit in each of the last ten years, whether the company showed positive EBIT in each of the last ten years, whether the company had debt, whether the company paid a dividend and whether the company had bought back shares over the last ten years. Those columns were summed and anything which received a “4” or “5” cumulative score made it onto my master spreadsheet for further investigation.

The second place I gathered ideas from were the blogs of other value investors such as Geoff Gannon and Gurpreet Narang (Neat Value). I just grabbed everything I found and threw it on my list. I figured, if it was good enough for these investors it was worth closer examination for me, too.

The second pass

Once I had my companies, I started building my spreadsheet. First, I listed each company along with its stock symbol in Japan (where securities are quoted by 4-digit numerical codes). Then, I added basic data about the shares, such as shares outstanding, share price, average volume (important for position-sizing later on), market capitalization, current dividend yield.

After this, I listed important balance sheet data: cash (calculated as cash + ST investments), receivables  inventory, other current assets, total current assets, LT debt and total liabilities and then the NCAV and net cash position for each company. Following this were three balance sheet price ratios, Market Cap/NCAV, Market Cap/Net Cash and Market Cap/Cash… the lower the ratio, the better. While Market Cap/Net Cash is a more conservative valuation than Market Cap/NCAV, Market Cap/Cash is less conservative but was useful for evaluating companies which were debt free and had profitable operations– some companies with uneven operating outlooks are best valued on a liquidation basis (NCAV, Net Cash) but a company that represents an average operating performance is more properly considered cheap against a metric like the percent of the market cap composing it’s balance sheet cash, assuming it is debt free.

I also constructed some income metric columns, but before I could do this, I created two new tabs, “Net Inc” and “EBIT”, and copied the symbols and names from the previous tab over and then recorded the annual net income and EBIT for each company for the previous ten years. This data all came from MSN Money, like the rest of the data I had collected up to that point.

Then I carried this info back to my original “Summary” tab via formulas to calculate the columns for 10yr average annual EBIT, previous year EBIT, Enterprise Value (EV), EV/EBIT (10yr annual average) and EV/EBIT (previous year), as well as the earnings yield (10yr annual average net income divided by market cap) and the previous 5 years annual average as well to try to capture whether the business had dramatically changed since the global recession.

The final step was to go through my list thusly assembled and color code each company according to the legend of green for a cash bargain, blue for a net cash bargain and orange for an NCAV bargain (strictly defined as a company trading for 66% of NCAV or less; anything 67% or higher would not get color-coded).

I was trying to create a quick, visually obvious pattern for recognizing the cheapest of the cheap, understanding that my time is valuable and I could always go dig into each non-color coded name individually looking for other bargains as necessary.

The result, and psychological bias rears it’s ugly head

Looking over my spreadsheet, about 2/3rds of the list were color-coded in this way with the remaining third left white. The white entries are not necessarily not cheap or not companies trading below their NCAV– they were just not the cheapest of the cheap according to three strict criteria I used.

After reviewing the results, my desire was to purchase all of the net cash stocks (there were only a handful), all of the NCAVs and then as many of the cash bargains as possible. You see, this was where one of the first hurdles came in– how much of my portfolio I wanted to devote to this strategy of buying JNets. I ultimately settled upon 20-25% of my portfolio, however, that wasn’t the end of it.

Currently, I have accounts at several brokerages but I use Fidelity for a majority of my trading. Fidelity has good access to Japanese equity markets and will even let you trade electronically. For electronic trades, the commission is Y3,000, whereas a broker-assisted trade is Y8,000. I wanted to try to control the size of my trading costs relative to my positions by placing a strict limit of no more than 2% of the total position value as the ceiling for commissions. Ideally, I wanted to pay closer to 1%, if possible. The other consideration was lot-sizes. The Japanese equity markets have different rules than the US in terms of lot-sizes– at each price range category there is a minimum lot size and these lots are usually in increments of 100, 1000, etc.

After doing the math I decided I’d want to have 15-20 different positions in my portfolio. Ideally, I would’ve liked to own a lot more, maybe even all of them similar to the thinking behind Nate Tobik’s recent post on Japanese equities over at Oddball Stocks. But I didn’t have the capital for that so I had to come up with some criteria, once I had decided on position-sizing and total number of positions, for choosing the lucky few.

This is where my own psychological bias started playing a role. You see, I wanted to just “buy cheap”– get all the net cash bargains, then all the NCAVs, then some of the cash bargains. But I let my earnings yield numbers (calculated for the benefit of making decisions about some of the cash bargain stocks) influence my thinking on the net cash and NCAV stocks. And then I peeked at the EBIT and net income tables and got frightened by the fact that some of these companies had a loss year or two, or had declining earnings pictures.

I started second-guessing some of the choices of the color-coded bargain system. I began doing a mish-mash of seeking “cheap” plus “perceived quality.” In other words, I may have made a mistake by letting heuristics get in the way of passion-less rules. According to some research spelled out in an outstanding whitepaper by Toby Carlisle, the author of Greenbackd.com, trying to “second guess the model” like this could be a mistake.

Cheap enough?

Ultimately, this “Jekyll and Hyde” selection process led to my current portfolio of 16 JNets. Earlier in this post I suggested that one of my principles for inclusion was that the thing be “cheap enough”. Whether I strictly followed the output of my bargain model, or tried to eyeball quality for any individual pick, every one of these companies I think meets the general test of “cheap enough” to buy for a diversified basket of similar-class companies because all are trading at substantial discounts to their “fair” value or value to a private buyer of the entire company. What’s more, while some of these companies may be facing declining earnings prospects, at least as of right now every one of these companies are currently profitable on an operational and net basis, and almost all are debt free (with the few that have debt finding themselves in a position where the debt is a de minimis value and/or covered by cash on the balance sheet). I believe that significantly limits my risk of suffering a catastrophic loss in any one of these names, but especially in the portfolio as a whole, at least on a Yen-denominated basis.

Of course, my currency risk remains and currently I have not landed on a strategy for hedging it in a cost-effective and easy-to-use way.

I suppose the only concern I have at this point is whether my portfolio is “cheap enough” to earn me outsized returns over time. I wonder about my queasiness when looking at the uneven or declining earnings prospects of some of these companies and the way I let it influence my decision-making process and second-guess what should otherwise be a reliable model for picking a basket of companies that are likely to produce above-average returns over time. I question whether I might have eliminated one useful advantage (buying stuff that is just out and out cheap) by trying to add personal genius to it in thinking I could take in the “whole picture” better than my simple screen and thereby come up with an improved handicapping for some of my companies.

Considering that I don’t know Japanese and don’t know much about these companies outside of the statistical data I collected and an inquiry into the industry they operate in (which may be somewhat meaningless anyway in the mega-conglomerated, mega-diversified world of the Japanese corporate economy), it required great hubris, at a minimum, to think I even had cognizance of a “whole picture” on which to base an attempt at informed judgment.

But then, that’s the art of the leap of faith!