HOW TO PITCH A STOCK
Nearly all my tweets are stream of consciousness; nearly all my written pitches, investor letters, or presentations I rewrite and edit meticulously. When you pitch a stock, you are telling a curated story. A 🧵on how to tell an investment story: BACKGROUND:

People have short attention spans and they get shorter each year.  I believe @natehindenberg is the best storyteller of stocks - his team does excellent research, but he also knows how to grab attention with creative anecdotes or pictures that convey a complicated thesis in a simple way. A biotech fraud is encompassed by one photo of the founder, as a magician, who claimed he could bend spoons with his mind. Square is used for shady purposes is expressed by a 3min video mashup of Jack Dorsey proudly referring to cash app references in songs, followed by  brief clips songs stitched together where singers discuss using it to pay for drugs, a hitman, or prostitutes. A pitch is not a book report - it is a curated narrative that uses anecdotes to make your point.

LONG POSITION SIZING: PART 2

Investors generally spend a disproportionate time and mental energy seeking to quantify position level up/down and financial projections, while often using little analytical rigor in position sizing, which also materially drives returns. This is the premise behind @alpha_theory. An (updated)🧵:

1. Summary
2. Background
3. Framework
4. Example
5. Conclusion

1. SUMMARY
At my prior fund, we historically sized positions based on a combination of our conviction and how much we thought we could lose if we were wrong. I copied this approach when I went out on my own, but layered in additional criteria adjust for factors that make a position inherently more risky or uncertain - examples include dinging sizing for companies that are burning cash, have substantial leverage, or where the margin for error in our assessment is likely to be wide.

After 1.5yrs of collecting data based on how I classified each investment at position inception and subsequent results, I believe I am able to refine my approach to sizing with a better appreciation for which factors were most correlated with good/bad outcomes, and in this way hope to improve sizing logic so we make more on our winners and less on our losers over time.  While this approach isn’t perfect, I believe it’s better than sizing based purely on emotion, instinct, and memory, all of which are faulty and subject to cognitive biases. It’s also much harder to refine your approach to sizing if the inputs to that initial decison were all done in your head.

2. POSITION SIZING - BACKGROUND
I worked at 4 funds before starting my own. At 2, sizing was a block box - I either didn’t know our position sizes or never heard how the PM thought about sizing. At the two where sizing was discussed, at one it was based on a loose feeling of conviction, irr, and max position size. At the second, there was a concept of value at risk (not wanting to lose more than a certain amount) and conviction in sizing, but position sizes tended to cluster all at the same level, about 60% below max position size, and the conceptual discipline wasn’t always enforced in practice.

At all 4, the amount of time we spent thinking about how to size positions was a fraction of the time we spent on other activities. And while there was a loose framework with all of them, sizing ultimately came down to the pm and or analyst having a feeling this was a very good idea and should be sized close to max. I’m not sure this is necessarily a bad thing - there’s something to be said about intuition, especially where the PM has a history of having very good intuition for the best ideas (my prior fund). But intuition leaves no record of thought process to be improved upon, is hard to impart to analysts without decision making authority, and changes based on mood, recent experience and cognitive biases not founded in logic.

@Alpha_Theory is great software, but many of its basic principles can be copied without using it. At its core, the premise is that you can break down the elements of “judgment” and “intuition” into variables that you are forced to record at the time of your initial investment and change over time as the position matures. This has a few benefits over an intuitive approach:
1) it enforce a discipline around why you size things the way you do and is less prone to emotion based decision making.
2) It also allows analysts to - who are often closer to the work - to contribute more to sizing because they can help inform your variables. It’s hard for them to have influence when the PM hides behind just their “judgment”
3) it allows for a fact based assessment in retrospect around where mistakes in judgment or position sizing were made, that can be incorporated into future sizing decisions. If you have robust systems, you can also take this data and put it into a tool like Lightkeeper to see which variables were most associated with the best or worst outcomes. 3. POSITION SIZING - FRAMEWORK
Let’s walk through an example of how position sizing with @alpha_theory works - we use the software but you can derive a lot of benefit without and it and just with the framework and some excel math. We are going to define a formula for our “confidence score.” . First we need to define our variables, then our weightings, and then fund level rules like max acceptable loss, preferred irr, etc. Let’s start with our variables, you can define your own, I’m going to use a simplified version of the ones I like:

1) Conviction - your overall conviction in your thesis, or the gut feeling about how good the idea is and how likely you are right.
2) Leverage - the amount of financial leverage. This is important because positions with substantial leverage are inherently more risky, all else being equal.
3) Cash flow - in general, companies that consume cash (either via operating losses, capex, or roll up) rely on the capital markets for their existence, and if you are wrong or markets seize up you can diluted or worse.
4) Ability to research - this is an honest assessment of how feel we good about our research. A company in our geographical / industry sweet spot with only a couple segments would rank high, and a business that is newer or inherintly complex to diligence ranks low.
5) Mgmt / governance - this is a catch all for whether you think mgmt and the board is likely to add or subtract value of the course of your investment in ways you can’t model.

We then define choices for each variable, and a score for each. This is based on how much we want that criteria to drive our sizing decision. Let’s make the choice low, medium, and high for each of our 5 criteria, and a max score of 10. Scores in () for each criteria:

Conviction - low (1), medium (3), high (6)
Leverage - low (1), medium (0), high (-2)
Cash flow - low (-2), medium (0), high (1)
Ability to research: low (-2), medium (0), high (1)
Mgmt / governance (low (-2), medium (0), high (1)

It’s unlikely almost any investment will check every box perfectly, so let’s set any score above 8 to a max position, and then scale from 8 to zero proportionally. We can debate the scores and the criteria endlessly and that’s the point - everyone has their own biases and variables that they believe correlate with good and bad outcomes. By being explicit in what those are, and recording / modifying your assessment on every investment over time, you can create a process around sizing that can be refined, communicated, and enforced.

HF COMP: POINTS / CARRY

Arguably the topic of most curiosity, least transparency, and dearth of datapoints. Broken up into 4 sections: 1) basics 2) All points not created equal 3) structures 4) datapoints. A 🧵: 1) POINTS BASICS

Most HFs earn an incentive fee each year which crystallizes December 31st. This number applies to P&L net of management fee and fund
expenses (including any soft dollar). It is further subject to high water mark, potential sharing of economics with a seed, fee deals, P&l generated from non fee paying investors, and hurdles, all of which can greatly affect the end ammt. This fee is typically allocated to the GP at year end in the form of an interest in the fund.

Some fund managers will share this incentive fee with partners or employees over time, almost always via phantom equity unless there were co-founders. Phantom equity is forfeited if you leave, subject to contractual agreements like sunset provisions (rare). It is not permanent equity, but instead is a commitment to sharing upside with employees and partners via a profit-sharing interest. In general, it can be changed over time and is only as good as the ethics of your boss. While employees can sometimes receive this income as carry, which may have various tax benefits, most non-partners receive it via a cash payment at year end or over a multi-year period subject to vesting. The percentage (out of 100) that you receive (eg 3%) is otherwise known as 3 points.

JOBS AT START-UP HEDGE FUNDS:
Prior to starting my own fund, I worked at 3 start-up funds. 1 was good but didn’t scale, 1 was bad and shut down, and 1 was amazing and made my career. I'd define a start-up fund as one with either less than 1-2yrs in business or less than 100m in aum (regardless of age). While sometimes risky or destined to fail, they can also be unusually exciting and lucrative. A 🧵:

Most hedge funds fail or close eventually, regardless of whether or not they are start-ups. I think the goal of any analyst is to find a good fund to work for run by a talented, passionate, and ethical PM. If you can find one you believe in, I think you should generally join them regardless of their size. If you aren't working for one, you should try to find one ASAP. THE UPSIDE OF START-UP FUNDS

1) I think The best HF job is a partner at a good fund. At most funds the partners are usually the first analysts that joined. It's harder to move into a leadership/partner role the longer a fund has been around and the later you join.

2) Most good funds don't lose people, so you can only join them while they are growing. Your odds of landing a job at a great but still young fund are often highest if you join early.

3) building something is exciting and joining early means you will help shape the fund and its success. You get some of the upside / excitement of starting a fund without the same level of financial and career risk (especially the younger you are).

4) You usually get much more exposure to the PM and LPs, as well as much more exposure to the inner-workings of the business. This is especially helpful from a learning standpoint and if you want to start your own fund one day.

5) Provided you work for an ethical PM and perform well, you will generally get points and share more in the fund's growth and success if you arrive earlier.

6) To the degree most funds have a finite lifespan of success, for a variety of reasons, the earlier you join the more likely you are to have a longer run at the fund.

7). While you may have less resources, your smaller size should give you a better shot at exploiting opportunities larger funds can't and increase probability of good returns.

What are the most valuable things a HF employer can do for younger analysts to help bolster their future career prospects / help them have a better chance of a successful career? Some thoughts curious others: 1) constant feedback in early days. Too many I think are either put in sink or swim situations.

2) freedom to make (some) mistakes - give some responsibility with oversight so analyst isn’t pigeonholed into being a model monkey

THE POWER OF NARRATIVE IN INVESTING

Before I fell in love with investing, I fell in love with stories. I am an avid film buff in college and briefly flirted with a career in it. I think investors - especially value ones - underestimate the power of narrative in stocks. A 🧵 My basic premise is that the story we tell ourselves about the stocks we are long/short is as powerful as the work we do and models we construct. Let’s explore 3 examples of how narrative can overwhelm numbers based thinking and how to exploit it to our advantage:

HEDGE FUND GP ECONOMICS

The most important factor for GP economics over time is performance. A GP should comp their team and build their firm towards maximizing investment performance, not maximizing their share. A HF is a “non-zero sum” game. Let’s play with #s. A 🧵: Performance drives all factors that influence a HF GP’s economics - incentive fee, mgmt fee, and compounding of the GP’s capital. It also maximizes firm duration, which extends the duration of which the manager gets paid.

VENDOR SPOTLIGHT: LIGHTKEEPER:

I previously wrote about @Alpha_Theory and our day 1 usage for position sizing. Today I’m going to write about Lightkeeper, our portfolio analytics tool that I’m surprised isn’t as popular as it’s better known peer, Novus. A 🧵: Most investors spend a lot of time trying to find good ideas and doing good research, and much less time thinking about how to get the most out of those ideas (position sizing) and reflecting on their mistakes and successes.

HF COMP - PART 2

Let’s talk HF comp, which is all most people want to talk about. I think analysts don’t appreciate nuances of incentives founders face based on responses. Doing this as public service and for my own clarification of thought. A 🧵: First, a few basic premises:
1) I believe the main thing that matters in comp is finding a boss who will be transparent, generous, ethical, and has or will develop a biz with good economics, and will share them. If you have that you have the ingredients to be treated fairly.

L/S HF LAUNCH COSTS -

Many an aspiring PM dreams of launching a fund one day. Through my start up process I’ve gotten some better datapoints on fund expenses for two types of institutional launches - large (150m+) and small (10-30m). Will discus small here. A 🧵 I believe a small launch can get by with $100-150k/yr of mgmt co expense and still be semi-institutional. I am more confident about large launch vs small launch numbers since that is closer to my (hoped for!) experience. Those with more knowledge please chime in.

POSITION SIZING / ALPHA THEORY:

Idea gen and research is the process of finding alpha. Position sizing and buy / sell discipline is the mechanism of maximizing it. I use Alpha Theory software to aid in position sizing decisions and buy/sell discipline. A 🧵on our framework. Most intellectual thought and firm resources are spent on the search for alpha not the maximization of the alpha you find. Alpha theory helps create optimal sizes around my key principles and a process to enforce it. Here is our process:

START UP DIARIES:

I’m nearly a month into launching an equity L/S hedge. I’ve spent the last couple months seeking advice from allocators and other launches (both successful and not) on how to launch well. A 🧵of my favorite advice received so far: 1) Leave Well - The ethical thing to do is often also the selfishly right thing to do. I left my prior fund after a good year. I have nothing but positive things to say about my former partners. This shouldn’t be rare, but it is.

PART 21: The No-Catalyst Catalyst

Many investors are uncomfortable investing without an obvious catalyst - usually something good that will happen that will make the stock go up soon. I think these are overvalued. Let’s discuss the benefits of “no-catalyst” investing. A 🧵: I don’t like obvious catalysts - exploration of strategic alternatives, announced spinoff, etc. The catalyst is usually quickly priced in, and all investors are usually playing for the same event.

PART 20: FACTOR AWARE INVESTING 2/2

In my prior thread I walked through the basics of factor mismatches. Let’s have fun with numbers. I put this together quickly - I welcome criticism. In short, factor mismatches are so significant that they often dwarf stock selection alpha. Let’s do some simple math - let’s recreate factor mismatch by creating two 100 long / short 50 portfolios. Value Fund is long 100% VTV (value ETF) and short 50% ARKK. Growth Fund is long 100% ARKK and short 50% VTV.

PART 20: FACTOR AWARE INVESTING

When people think about risk, they tend to think about leverage and concentration. I’d argue controlling factor exposure is also important. This is because many positions can (and do) often act as one. A thought experiment and 🧵: I’d define a factor as a set of traits that tends to make seemingly different stocks trade in tight correlation. Easy examples of factors are high interest shorts, unprofitable growth, value, high leverage, etc. ETFs and primes can give you good examples of constituents.

PART 19: THE PM JOB

Where does the analyst job end and the PM job begin? Defining Analyst vs PM responsibility helps create a more consistent investment process and better outcomes. Many conflicts and mistakes stem from a lack of clear delineation. A 🧵: I view the analyst’s job as executing and collaborating on investment research with the PM. An analyst makes mistakes of execution and omission - a PM is responsible for mistakes of judgment and oversight.

PART 18: SHORTING

We short to generate alpha and hedge unwanted risks in longs, in a high return on time way that minimizes risk of a blow-up. This means we generally only short stocks owned by investors we can understand who we think are wrong on terms they care about. A 🧵: Part of investing is knowing yourself. There is a good argument to be made that only shorting zeros, frauds, fads, and failures owned by irrational people produces the most alpha over time. But this is only true if you can handle the vol, which most can’t (myself included).

PART 17: TIME HORIZON
Most investors are either short term or long term - ST will only invest when a biz is improving or inflecting (<6mo). LT doesn’t care and looks out years (2+). We like living in the middle - a 🧵 on time horizon in idea and alpha generation. I’d argue there is the most competition in trying to predict what a biz will do in the next 2 quarters. Most datapoints - calls, alt data, etc tell you what is happening right now, usually with decent accuracy. Many investors won’t own a company where “numbers are too high”.

WHY FUNDS FAIL PART 2 - ENVY

@pjme73 proposed 4 deadly sins as reasons for why funds fail, two of which were greed and envy. I’ve struggled with both of these at times, but envy more than greed. Some observations on how envy is destructive, and how to avoid it. A personal 🧵: Envy seems almost uniquely suited for finance professionals - you have driven, competitive people with a natural inclination to quantify things. A simple definition of self worth is net worth. Thus begins the competition to get a higher number than someone else.

PART 16: MAINTENANCE WORK

Maintenance work - both the amount and how it’s done - is the single biggest waste of time at most funds. Most people usually focus on the wrong things, reach the wrong conclusions, and detract value here. A 🧵on proper maintenance work. Maintenance work refers to updating expert calls, sell-side calls, frequent management check ins, conference attendance, prepping earnings previews, and other activities designed to find slight changes or compare your estimates vs consensus.

WHY I TWEET

Most good investors don’t tweet. Having a twitter account is generally frowned upon by allocators. It’s a potentially (big) distraction. It doesn’t generate P&L. So why would anyone who isn’t an insecure egomaniac without better things to do, do it? A 🧵: I have a hypothesis I have been testing for 4 months that I’ve funded mostly from my leisure time - twitter is a powerful platform with massive optionality, where the talent that lurks is much greater than the talent that posts. Before posting, I was a lurker.