SCM Investment Research Process - Part II

Management, Financial Analysis, Modeling & Valuation, The Pitch

Hi Everyone,

This week I provide a summary of the second half of my research process. You can read the first part of my research process, last week’s newsletter, here. Alternatively, you can read the full process in the post on my website. Note: I had to remove images that are contained in the website post due to substack email length limitations.

I look forward to hearing your thoughts as to how I can refine or improve this process. Please note that I have not included many of the granular details of this process, but I hope there are bits and pieces you think would be helpful to add to your overall framework.

Please make sure to share this newsletter, share this post, or subscribe (if you have not already) if you like the content! You can use the buttons here to do so:

DISCLAIMER:

All investment strategies and investments involve risk of loss. Nothing contained in this website should be construed as investment advice. Any reference to an investment's past or potential performance is not, and should not be construed as, a recommendation or as a guarantee of any specific outcome or profit.

---

Management

“I think you judge management by two yardsticks,” Buffett said. “One is how well they run the business, and I think you can learn a lot about that by reading about both what they’ve accomplished and what their competitors have accomplished, and seeing how they have allocated capital over time.”

CNBC, Warren Buffett

Analyzing management is one of the most important aspects of company research for me. Thinking literally about what an equity investment means, the investor is supplying equity capital to the company to be allocated towards productive uses to increase the company's (enterprise) value. Value creation for the company can be viewed through the incremental profits generated from the investment. The key metric that can be used to measure this incremental profitability "return on invested capital" or ROIC (note, one can also use "cash return on invested capital" or CROIC). The equity investor requires a certain return on this investment, the "required return". This is the "cost of equity capital" for the company.

Think about it this way. The company has to actually pay back the investor based on the investor's required return. So in order for the equity investment to add value to the company, it must generate a return on the invested capital greater than what it needs to pay back to the investor. The simple formula is then Value Creation = $ Invested * (ROIC % - Cost of Equity %).

Additionally, another quantitative heuristic for measuring management quality that I have adopted from the great Richard Lawrence is the translation of operating returns to equity returns. The two metrics to use here is 1) Return on Assets (ROA) = Net Income / Average Total Assets or [Net Income + Interest*(1-Tax Rate)] / Average Total Assets and 2) Return on Equity (ROE) = Net Income / Average Total Equity. I prefer to break ROE down further to better understand the underlying drivers of equity returns using the DuPont Method.

• Basic DuPont = Profit Margin * Asset Turnover * Equity Multiplier ("Leverage Ratio") = (Net Income / Total Revenue) * (Total Revenue / Average Total Assets) * (Average Total Assets / Average Total Shareholder's Equity)

• Extended DuPont (Preferred Method) = Operating Margin * Interest Burden * Tax Burden * Asset Turnover * Leverage Ratio = (EBIT / Total Revenue) * (EBT / EBIT) * (Net Income / EBT) * (Total Revenue / Average Total Assets) * (Average Total Assets / Average Total Shareholder's Equity)

  • The resulting simplification of each equation gets you back to the basic ROE formula of Net Income / Average Total Equity

  • The difference between the Basic and Extended Method is simply the further division of the Profit Margin into three separate component parts

The quantitative measurement of a great management team is they are able to translate high ROA into high ROE. I start with the company's Proxy Statement to answer some of these key questions:

• How are they compensated? Are their interests aligned with shareholders?

• Are they good capital allocators?

• How have they positioned the company in the marketplace?

• What is management's reputation?

• What is their background?

• Have they been successful in the past (very important)? Past history of successes and failure?

• Straightforward or cunning management style?

• What is insider ownership and selling? How much as % of their holdings and why - source: Open Insider

I specifically dive into the first three bullets on 1) Structure and Incentives, 2) Decision Making, and 3) Competitive Positioning.

• Structure and Incentives

  • Get good grasp on management team's background/history

  • Management team incentives and how well they are aligned with investors; i.e. stock ownership

  • Who makes up the Board of Directors? What do they bring to the table?

  • Who are the operators? What is their experience?

  • Does Management compensation structure align with what I want as a shareholder, as well as my timeframe for investment?

• Decision Making

  • Judge management's capital allocation decisions based on realized returns and future positioning

  • Building the historical simple operating model will help with this judgement

• Competitive Positioning

  • Recall, we have already completed an industry analysis and should have a good understanding of the company's main competitors and other industry players

  • Judge viability of:

    • Company's revenue drivers in marketplace

    • Management's competitive mindset in the marketplace

To conclude the management analysis, I wanted to reiterate that one of the most important things I look for is a management team that is obsessed with the customer experience. Employees and customers are the two most important stakeholder groups of a company - make them happy.

---

Financial Review

This step is important for me to better understand the company's business model and financial position. This further informs 1) how the company is positioned to compete in the marketplace, 2) quality of past capital allocation decisions, and 3) confirmation of everything else I read in the MD&A. I will usually start with the three financial statements then move on to the Notes. The Notes to the Financial Statements often contain the most important information. Note that I am comparing the target company's metrics to its competitors.

Financial Statements - The overall process and goals

1. Go line-by-line to see if everything jives with what I think I know based on the MD&A

2. Highlight any lines or YoY changes that indicate significant strength or weakness

3. Create an ad hoc checklist of all questions / things I want to know

4. Capital allocation decisions: buying back stock, levering for acquisitions, etc.

5. Analyze Capex growth vs. sales i.e. Return on Capital Employed ("ROCE")

Income Statement (Earnings / Profitability)

1. Analyze the sales model - how visible are earnings quarter-to-quarter and year-to-year?

2. Is this a fixed or variable cost business? How much cost leverage?

3. Do earnings grow as a function of unit sales growth, prices increases, or margin improvement?

4. How sustainable is earnings growth?

5. What should stabilized margins look like and how does it compare to competitors?

Balance Sheet

• What is the company's capital structure, and how does it compare to its peers?

• What are the trends in inventory turns, days payable/receivable, and working capital?

• What are its coverage ratios on interest payments and debt service / interest coverage ratios?

• Ideally, I want companies with little or no debt (or negative Net Debt)

Cash Flow

• What are the company's capital (reinvestment) requirements and cash flow characteristics?

• How is the company choosing to invest its capital? CapEx? Buybacks? Acquisitions?

• Does the company need to access the capital markets? How soon / often?

  • What I'm trying to get an understanding of is possible dilution

Notes to the Financial Statement - Some of the key items I focus on

• Revenue Recognition

• Stock-Based Compensation (very important for high-growth and software)

• Inventory Management if applicable

• Any accounting standards that require a level of subjectivity

• How Company is accounting for pension (evaluate discount rates and other assumptions)

• Look for (little) changes in language over time - IMPORTANT to read notes in its entirety

• Stock repurchase history; how much is left in stock repurchase program and factor into models

• Segment Information

  • Should get extra scrutiny - how much each segment contributed to company as a whole

  • How much each subsegment contributed to segment and segment profitability

Valuation

While I did some valuation work (relative to peers) as part of my work on critical factors, now I develop a better understanding of the current valuation. What I do is:

• Company's forward looking valuation, including:

  • Market Value / Earnings (P/E)

  • Enterprise Value / EBITDA (EV/EBITDA) or EV/EBIT

  • Free Cash Flow Yield (After-Tax Levered FCF / Market Value) or P/FCF

  • Enterprise Value / Sales (EV/S) and EV / Gross Profit

• What is the company's growth rate in terms of Revenue, Gross Profit, Earnings, and FCF?

• What are consensus revenue and earnings estimates vs. my own expectations?

• What are the key leverage points in my own and the street's earnings models? What has to go right and where is the most chance for surprise?

• Are their accounting policies conservative and in line with their peers?

Other Metrics

Some of the other metrics I tend to look at, which culminates in reviewing the Howard Schilit red flags from Financial Shenanigans include:

• Sales and Earnings Growth Rates Y-o-Y and Sequential (Past 2-3 years)

• Gross Margins: Improving or if stabilized, in line with peers

• Earnings Yield: EBIT/EV as percentage

• ROIC: EBIT / [WC + Fixed Assets] or EBIT / [Total Debt + Total Equity]

  • CROIC: FCF / [Total Debt + Total Equity]

• Positive Free Cash Flow and improving FCF margin (30-40% stabilized for software)

• Decreasing OpEx % of Revenue - evidence of operating leverage and scale

• Pitroski F Score

• Growth of inventories to COGS - are inventories rising faster?

• Growth of AR to Sales and AP to OpEx

• Any accounting changes - smaller reserve for bad debt? Rev rec?

• Cash Flow / Interest Expense (Interest Coverage)

• Software Specific

  • Dollar-Based Net Retention (DBNER): >120% is good, > 130% is great

  • Gross Margin Adjusted CAC Payback (Sales Efficiency): (Previous Quarter S&M Expense) / (Net New ARR x Gross Margin) x 12

  • Rule of 40: LTM Revenue Growth + LTM Profitability (I prefer FCF Margin)

  • RPO Growth

---

Scuttlebutt

At this point in the process I have developed a list of questions and topics that I need to better understand or explore further. I should already have a pretty thorough understanding of the underlying technology / product / solution. I will test my knowledge of the topic using the Feynman Technique until I have a good grasp of the subject or I will fill holes in my knowledge when I reach out to industry and technical experts. At this point I will reach out to industry experts with the following goals and questions:

Expert Discussions

• Confirm my understanding of the underlying technology and its impact on the marketplace

  • What are the benefits and costs relative to existing technology of incumbents?

• Inquire about the supply and demand (also capacity) fundamentals; explore why pricing and volume trends have moved certain ways in the past

• Develop my understanding of the position of all competitors, including the target, in the marketplace

  • What is the perception of each amongst competitors and customers?

• Discuss the private market for other disruptive companies

• Try to figure out if there are any stones I haven't turned over yet

Meet with Management

I am asking a lot of similar questions of management that I asked above. This serves two purposes: I am testing the integrity of management and trying to see if the expert had missed anything. The purpose of the call is to ask management all the questions that I have developed up to this point.

A critical point that I'll add relates to preparation and time management. I make sure to have my list of questions prepared ahead of time, as well as a medium for taking effective notes. Showing up prepared when meeting with experts or management is critical for integrity and to ensure I'm not wasting anyone's time. These people are extremely busy so I want to maximize every minute spent with them. Which leads me to time management. I make sure that I have organized my questions in a manner to ensure that I don't run over the time allotted or miss asking key questions because I ran out of time. Preparation is key.

---

---

Model and Valuation

I have been trying to figure out the most value add way to discuss the role modeling and valuation plays in my process. You can reach out for additional information. I will focus on the purpose of each element throughout this summary of my process. The most important part of the modeling process for me is to understand the operating drivers (levers) of the business and how stabilized unit economics should evolve.

Modeling Process

Financial Analysis

1. Multivariate regression analysis to determine if Revenue can forecast other Income Statement items

2. Income Statement (IS) Analysis

   1. Analyze changes in historical trends of common-size IS (all items expressed as % of Revenue)

   2. Back to raw numbers, check to see that earnings and free cash flow are growing roughly in line with Revenue growth - investigate divergences

   3. Calculate Net Income / Cash Flow ratio, which should fluctuate around 1 over time

   4. Analyze changes in Average Selling Price ("ASP") to determine whether it's due to successful extraction of more value from customers or less valuable factors such as: 1) Current gains, 2) Special assessments, 3) Change in product mix, 4) Change in Revenue Recognition (policies should match peers)

3. Balance Sheet (BS) Analysis

   1. Analyze changes in historical trends of a common-size BS (all items expressed as % of Total Assets)

   2. Debt Service Analysis: Calculate times-interest-earned (cash flow / interest expense) ratio (>2.0 is healthy) among other covenant-based tests

   3. Identify assets likely to be written off through decreases in receivables turnover or inventory turnover

   4. For assets and liabilities with values based on fair value accounting, question values based on level-three inputs much more than those with level-one inputs

4. Cash Flow Statement (CFS) Analysis

   1. Look for an increase in days payable (company is delaying paying its bills) and increase in days receivable (difficulty collecting sales from customers)

   2. Negative cash from operations ("CFO") is usually a warning sign

   3. Calculate a CapEx-to-depreciation ratio over time to get an understanding of the level of growth capital being invested.

Building the Financial Model

Recall, the main goal here is to understand the business levers and identify any red flags. By building the historical model I am furthering my understanding of the business. When I am building a forecast, I am putting on my operator / CEO hat and making decisions as to how I would run the business. This is always with the backdrop of having an idea of the decisions the actual CEO will make.

1. General Considerations

   1. I focus on simplicity and ease of use/updating after quarterly earnings or key events

   2. "Growth" Stock: 1) Identify the ultimate size of the market - Target Addressable Market ("TAM"), Serviceable Available Market ("SAM"), and Serviceable Obtainable Market ("SOM"), 2) Sources of funding to grow, 3) Dilution from stock options

   3. "Value" Stock: 1) Factors that have helped forecast cycle inflection points in the past, 2) Capacity additions relative to demand, 3) Off-BS liabilities

2. Build the Historical Model

   1. I will try to get the historical data in Excel from a reliable third party rather than spreading it manually

   2. My historical model focuses on elements that:

      1. Are currently critical factors, have been in the past, or are likely to become so in the future

      2. Are necessary to get the three financial statements to integrate

   3. Determine the factor that will drive the first variable in the model (volume or revenue)

   4. In addition to using statistics to determine relationships on the IS (done in the Financial Analysis section), I do the same for factors in the CFS and BS

      1. I will use a factor to help forecast others if the relationship is strong (high R-squared)

   5. Then I will build schedules of concepts / data and financials not included in SEC filings

   6. If a company has many operating divisions, sometimes I will build a spreadsheet for each division then create a rollup / consolidated worksheet

   7. While I try to keep my model as simple as possible, I will include more detail for:

      1. Current, past, and future critical factors (remember: my thesis is based on these few critical factors and their associated catalysts

      2. Where there is a competitive advantage in spotting anomalies not understood by the market

   8. Create a section of important ratios and growth rates that will help analyze important trends (e.g. ROIC, revenue growth, and margins) and potentially spot accounting yellow flags

3. Build the Projections and Forecast Scenarios

   1. Narrow down / isolate only the critical factors expected to move the stock during the investment horizon

   2. More accurately quantify the likelihood that a catalyst will occur and cause the critical factor to become part of the consensus

      1. I dig into the key drivers and keep asking "why?" - this work is highly informed by my deep research and conversations with the industry experts

      2. Incorporate the trade-offs companies make between factors such as price and market share or reinvestment rates and future growth

      3. Ensure I consider resources for future growth, such as hiring more employees or increasing R&D

   3. Setup Assumptions tab to run scenario analysis: 1) Values Currently Driving Model Output (VCDM), 2) Base-Case, 3) Upside (sometimes Management-case), and 4) Downside

      1. I'm once again focusing on the critical factors in each scenario

      2. The scenario analysis helps me understand inflection points for the company / stock

   4. Assess the outcomes of each scenario in terms of its impact on the stock, as well as the probability or likelihood that each scenario will occur

      1. Each scenario should logically make sense based on how I think the critical factors would evolve in each case

   5. Record a quantifiable figure for each scenario and each critical factor, which I need when speaking with information sources and for financial modeling

   6. Then I constantly reassess my thesis and stress test my assumptions

   7. Finally, I figure out if my base-case is similar or not to consensus - Note: The consensus is of ten more right than wrong

My preferred approach is to develop a stabilized (mature) earnings model based on my understanding of the company's cost structure and margin potential. I forecast the business to this stabilized period and then use a conservative P/E or P/FCF multiple to get an understanding of what the valuation may look like at that specific point in time. I discuss how to determine the appropriate multiple in the next section. Now what I can do is calculate the implied IRR / CAGR of my investment by comparing that value to the current market value. If the investment does not achieve a greater than 30% annual return, it's a pass for me... at this moment in time. But, it also gives me the price at which I can pull the trigger and buy the stock and then achieve the greater than 30% return (assuming nothing has changed in the underlying business).

Valuation

I have already determined how the market values the target company and companies in the industry. These valuation methodologies don't change often, so I am usually not forecasting or expecting a change.

Develop Appropriate Valuation Multiple

1. I start by using valuation methods of potential institutional buyers and sellers i.e. P/E, DCF, EV/EBITDA, and P/CF

   1. I look at both single period and multi-period cash flow methodologies

2. I collect stock and benchmark data for historical valuation parameters (i.e. weekly or monthly data going back at least 10 years or as long as available):

   1. Includes consensus estimates (Revenue, EPS, etc.) and prices in order to derive the forward-looking absolute and relative multiples at any historical point in time

   2. I usually avoid using backward-looking data (trailing metrics)

3. For stocks with minimal trading history or achieving growth rates not experienced in the past (i.e. current SaaS), I collect the data above for companies with similar characteristics, preferably within the same sector.

4. I use the historical valuation data to compute peak, trough, and average multiples on an absolute and relative basis

   1. Something that is really helpful for me is to look at the valuation ratio and its two components in graphic form to help identify anomalies or periods that should be removed

5. Then I review the historical valuation data in order to accomplish:

   1. Isolate when significant absolute multiple expansion and contraction does not coincide with the benchmark's in order to isolate the causes of relative multiple changes

   2. Isolate periods when the company's stock price or sector index outperforms and underperforms materially from the benchmark (when relative multiple changes)

      1. Try to isolate how much is due to the market anticipating consensus revisions vs. the actual revisions that take place

   3. Identify lead or lag time between outperformance or underperformance and the catalyst, in order to better understand causes of historical changes in valuations and when investors tend to rotate into or out of a sector

6. If there's a strong historical relationship between a company's / sector's valuation multiple and measurable factors (i.e. consensus EPS, growth rate, or current ROIC), I ensure my target multiple is consistent with historical relationship

   1. Don't just apply a historical average multiple to a company's current financials

   2. If there are no periods in company's history comparable to present, it's helpful to look at historical and current multiples of other companies with similar growth and risk prospects, preferably in the same sector

Build a Comparables Page to Identify Stocks with the Most Upside and Valuation Outliers

1. I automate as much of the data collection as possibly by setting up formulas that pull in information from third-party vendors (i.e. Finbox)

   1. I show valuation multiples using my expectations and consensus expectations (if available)

   2. It's often helpful to create valuation multiple columns based on this year's forecast as well as next year's forecast

2. At a minimum, I include the following sections in the comps table for each company:

   1. Financial forecast data used for valuation such as: 1) EPS, 2) FCF, 3) Revenue - clearly identifying my forecast and consensus' forecast.

   2. Valuation multiples as computed using consensus' expectations and my own

   3. Historical forward-looking average multiples, adjusted for anomalies

   4. Other financial metrics that influence valuation (should auto-update)

3. I also include my conviction level and entry/exit thresholds in hidden columns

4. My list of comparable companies are grouped such that:

   1. Companies are in the same or similar sector or sub-sector

   2. Have growth and risk characteristics similar to the company being valued

5. For each group of companies, I calculate at least a mean and median for each metric (equally weighted basis)

6. Finally, I use Conditional Formatting to highlight certain things like:

   1. Stocks with the most upside and downside

   2. When my financial forecast differs from consensus by a pre-defined threshold (i.e. 5%)

   3. When the current valuation multiple for a company differs from its historical levels by a pre-defined threshold (i.e. 5%)

   4. When the median average valuation multiple for a group of companies differs from the mean by a pre-defined threshold

Model Wrap-Up / Other Model Considerations

Upon finishing the financial model, I have a clear view of the potential range of business outcomes resulting from how the company's critical factors may shake out under different scenarios. For the financial model, I make sure to: 1) Derive projections from my thesis, 2) Stress-test projections, and 3) Derive a range of values for the business. At the end of the day, I need to have a firm grasp of the business's value drivers: 1) Growth, 2) Returns, 3) Cost of Capital (Discount Rate)

Recall how each critical factor has to have an associated catalyst? Well those catalysts need to be seen / modeled at least implicitly. After all, it is the critical factors and catalysts upon which the investment recommendation rests. The two questions I am asking myself and reflecting in the model are: 1) What is going to cause the market to realize (my view of) the intrinsic value of the asset? and 2) When will the catalyst occur / market realize the intrinsic value?

Some of you may be asking why I haven't really discussed the Discount Rate or Cost of Capital yet. It's a great place to close out this discussion on modeling. As a note, I usually reverse-engineer what discount rate the market is implying. However, the discount rate should not drive investment decisions. If your valuation proves inaccurate, it is most likely a result of inaccurate cash flow or normalized earnings projections rather than your discount rate. I personally use a flat 10% discount rate. Sure, in today's market environment that may be too conservative. In others, maybe too aggressive. But at the end of the day, I am looking to be correct directionally; meaning, I am placing my bet on the company based on my estimation of their future business prospects and execution, as reflected in my cash flow assumptions.

As a final comment on the discount rate, I should warn against the dangers of false precision. I have mentioned the +17,000 line models I had to build in my prior life as a private equity analyst. These models were as detailed as you could imagine, with every nuance of the business modeled out. If there was a correlation of model detail / complexity to model accuracy, we should have been pretty accurate in our monthly cash flow forecasts. We were not. Why? If we think about it simplistically, if we were modeling 17,000 lines of information (no, not every line was an input and there were a lot of lines that were purely automated calculations), then we had to get 17,000 things right in order to have an accurate forecast! That's a tall ask. Additionally, it always required deep forensic detective work just to figure out where we were wrong. Then additional time was spent building bridges between the incorrect forecast and actual results. That's a lot of time on marginally valuable work.

So then what was the point? It could have been a very common cognitive bias - these detailed models gave the partners and our team a false sense of control. This bias is known as illusion of control. The detailed models may have been comforting ex-ante, but was rarely valuable ex-post. This informs a great lesson and has a profound impact on my approach to modeling today. I focus primarily on the 1-4 critical factors and their associated catalysts. Taking this approach I 1) Don't suffer from the illusion of control (or false precision), 2) Save a lot of time in the overall modeling exercise; time which can be spent on value-add research, and 3) I can easily track why my model output differs from actual results, all centered around the critical factors.

“It is better to be approximately right, than precisely wrong.”

Warren Buffett

---

Other Investment Considerations

What is the Major Misperception in the Market?

True alpha is generated by: 1) Having a differentiated (unique) perspective that is different from the consensus and 2) Being right. This is a very tough combination to achieve since more often than not, the market has it right. But, there are certainly instances in which the market doesn't truly understand the story due to a variety of reasons. This manifests itself in alpha-generating opportunities. But how can we know when one exists? We must have an advantage over the market. In Michael Mauboussin's brilliant piece, Who's On the Other Side, he discusses the four sources of alpha (advantage) that result from inefficiencies in the market through the BAIT Framework.

The BAIT acronym stands for Behavioral, Analytical, Informational, Technical. Without digging into the details, I must have an advantage in one of these areas in order to develop a unique and potentially correct view of a stock. This provides an answer to the primary question I ask myself: why does this this opportunity exist? From there, I try to understand what may have caused the misperception. This is important because in order for my investment to be successful, I need to have an idea as to how the next guy (or, the market) will figure it out (the catalyst!).

Another framework that I sometimes use is the FaVeS framework, which is very similar to BAIT but framed differently. The FaVeS framework dictates that I must have an edge over the market in one of the following categories: Forecast, Valuation, or Sentiment.

What are the Risks?

My analysis of risks centers around an exercise created by Gary Klein; I perform a pre-mortem. A pre-mortem is similar to its post-mortem cousin, however it is performed ex-ante. The exercise is more effective in a group setting, but what I do is imagine that my investment in the stock has failed, and then work backward to determine what potentially could lead to the failure. This is not an exercise of determining what might go wrong. The premortem operates on the assumption that the investment has failed, and then asks what did go wrong? This is a creative exercise that makes me think out-of-the-box as to the exact and tangible reasons why the investment failed. This is a form of a mental model popularized by Charlie Munger: inversion. By identfiying all the reasons why the investment failed before actually making the investment, I am well positioned to avoid these pitfalls.

In addition to the BAIT Framework, a critical question as part of my risk analysis is asking the title of Mauboussin's paper: "who is on the other side (of the trade)?" From the Executive Summary of his paper (emphasis mine):

If you buy or sell a security and expect an excess return, you should have a good answer to the question “Who is on the other side?” In effect, you are specifying the source of your advantage, or edge. We categorize inefficiencies in four areas: behavioral, analytical, informational, and technical (BAIT)

Who is on the Other Side?, Blue Mountain Research

From this initial question, I explore the reasons as to why someone is selling the stock to me. What may they know that I don't? How do I know that I am right and they are wrong? You would be surprised as to the potential risks you can uncover in this exercise.

Sentiment

I start by looking at analyst estimates to get a sense of the market sentiment for the stock. Seeing how many are bullish or bearish and the % upside or downside of the current stock price to the average analyst price target is helpful. Some of the ways that I try to monitor sentiment are:

1. Short interest for the stock and its competitors

2. Company insider buying and selling

3. Changes in the types of investors who own the stock

4. Changes in sentiment toward the following to understand the herd mentality:

   1. The names and types of stocks receiving:

      1. The most attention (Where is everyone spending their time?)

      2. The least attention (Which stocks are forgotten or written off as dead?)

   2. Biggest investor concerns by company or sector

   3. Expectations that are above or below the published consensus

   4. General view toward the market and risk (bullish or bearish)

5. I analyze the market's relative appetite for risk by monitoring:

   1. Treasury yields

   2. VIX

   3. The size of the deal calendar (M&A, equity offerings, etc.)

   4. Recent stock performance of:

      1. Weak companies vs. stable companies

      2. Emerging markets vs. domestic markets

      3. Small cap vs. large cap

6. I pay close attention to when bad news no longer makes stocks go down, or when good news no longer makes them go up; it's a sign that market psychology is shifting

7. Similarly, I note when a stock continually overreacts in one direction to news flow during a relatively short period of time because it could be a sign of irrational buying or selling

8. Finally, I monitor technical indicators

Timing

Some of the things I analyze to determine timing are:

• What is the expected trigger (catalyst) on the misperception for the company's proper valuation to be realized (do a timeline)?

• What good news, and bad news, will affect the company in the coming year?

• Who owns the stock - long-term or short-term, momentum investors?

• How difficult is it to build a significant position (float, volume)?

• Draw a timeline of expected events and dates. What might go wrong and when?

• Can the rising stock price be self-fulfilling for a while (financing opportunities, etc.)?

• Where does the company stand in terms of the fantasy, transition, reality paradigm?

---

---

Technical Analysis / Entry Point

I use technical analysis to reasonably determine an entry price based on specific chart patterns that empirically have a high probability of continuing a move higher. I juxtapose the entry price with value ranges (from my model - Bull vs. Bear. vs. Base) and timeline. I am not going to elaborate much further on technicals in this newsletter, as it requires a newsletter on its own.

---

The Pitch

A stock pitch is something I do for myself and, depending on the shop, how analysts recommend a stock to the portfolio manager or sector head. Similarly, I create a pitch for myself prior to investing, even though I don't have a PM to answer to, for two reasons: 1) It's critical to always practice best practices and 2) Synthesizing a stock pitch tests my true understanding and conviction of the thesis.

I want to add that every investor, PM, etc. is different as to how they want to be pitched. While I include some of the key points, sometimes the order of information may change. So, how do I structure my pitch? Well, there are three different forms of the pitch and the structure varies slightly depending on which form I am using.

Short Form Pitch

1. Quick Company Overview and answer 1) What is the consensus view and 2) How is my view different?

   1. Why is the market / consensus wrong?

2. What are the critical factors? How do I know?

3. What is the catalyst(s) for each?

   1. Discuss field research and discussions with experts

4. Why does this opportunity exist - what is the % upside?

5. What are the risks - what is the % downside?

One Page Summary

1. Company Summary: Name, Price, Market Cap, Key Multiples

2. Forecast / Target: Earnings target and multiple, IRR, time horizon, upside / downside

3. Differentiated Perspective: What is my view, what is the consensus, why is the market wrong?

4. Thesis: Each critical factor, how I know I am right vs. consensus?

   1. Each must be quantitatively falsifiable: Critical factor value and catalyst

5. Risks / Pre-Mortem

6. Conclusion if Necessary

Longer Form Pitch Deck

Slide 1: Company Overview: One line pitch, Country/region, Company name, Ticker, Market Cap, Average Daily Volume, Summary of Business

Slide 2: Summary Data

• Stock chart and basic stats (current price, 52 week range, shares O/S)

• Valuation Metrics: EV, Mkt Cap, P/E, EV/EBITDA, etc.

• Income and CF Metrics: 5 Years of annual revenues, gross profit margins, operating profit margins, EPS, EBITDA, and FCF

• BS Metrics: Total Assets, Debt, SH Equity

• Profitability Metrics: 5 Years of ROIC, etc.

Slide 3: Variant Perspective

• Question 1: How much can I make? I.e. Intrinsic vs. Mkt Value

• Question 2: How will the next guy figure it out? I.e. Catalyst

• Question 3: Is this too good to be true? I.e. Why Mispricing

• Question 4: How much can I lose? I.e. Downside

Slides 4-7: Dive deeper into each of Questions 1-4

Slide 8: Contact Information

---

Success as a Stock Picker - How to Generate Alpha

To conclude this article, I want to review the framework through which you can increase the probabilities of your success as a stock picker.

1. For a stock call to generate alpha, I must have differentiated insights superior to the market in at least one of the FaVeS or BAIT frameworks. If none exist (or it doesn't differ much), there is no stock call to be made.

   1. Forecast or financial results, such as EPS or cash flow (Analytical)

   2. Valuation multiple or methodology (Analytical)

   3. Sentiment of the market toward the stock (Behavioral)

   4. Informational or Technical

2. When my forecast is out of consensus, I do additional work to determine if the consensus is more accurate by:

   1. Ensuring consensus includes many sell-side estimates and is not isolated to just a few who happen to have forecasts for the time period being used

   2. Comparing the informed consensus of the most accurate sell-side analysts with the overall consensus (TipRanks is excellent for this)

   3. Ensuring that the sell-side submissions aren't stale and that there is no disagreement in terms of special items that may be in the numbers

3. If after this work my estimate is still out of consensus, it's critical for me to speak with market participants (i.e. PMs, buy-side analysts, sell-side analysts, and company management) to understand why

4. Before making a stock recommendation, I ensure that there is a catalyst that:

   1. Pertains to a critical factor that's material enough to move the stock

   2. Not currently appreciated by the market

   3. Can be forecasted with some level of certainty

   4. Likely to occur during the investment time horizon

5. Finally, before making a stock call, I answer these questions:

   1. What unique insight isn't consensus?

   2. Why doesn't consensus have this insight, or why is consensus ignoring it?

   3. What is the catalyst that will get the market to accept this out-of-consensus view, and when will it occur?

   4. What could go wrong? What are the most likely things that will derail the investment thesis?

---

Conclusion

I hope this summary of investment process / checklist was helpful for you. I would urge you to not replicate it exactly; rather, take bits and pieces that best fit your stile to incorporate in your own process. Every investor is different, so do what works best for you.

An important source to learn more and dive deeper into the process I use is the foundation of my process: Best Practices for Equity Research Analysts by James Valentine. James was formerly the Associate Director of North American Research for Morgan Stanley, as well as the Director of Training, for the firm's global Research department. Additionally, Pitch the Perfect Investment by Paul Sonkin and Paul Johnson (Columbia Business School professors) was instrumental in my understanding of how to pitch a stock.

As always, please comment with anything you would change or think I am missing. Good luck out there!