Ultimate Position Sizing Guide Part 3 - Fundamental Optimization & Position Sizing Best Practices

Hi, this is Cameron Hight from Alpha Theory.

In the last two parts of this series, I was talking about The Ultimate Position Sizing Guide, a new white paper that we've recently released.

In the first part, I talked about how empirically our managers outperformed the average fund by about 6 times when it comes to position sizing.

In the second part, I spent some time talking about the mechanics of why that happens.

And in this part, I'm going to talk about some of the softer parts of why Alpha Theory managers and process-oriented position sizers in general outperform.

To start, let's recognize that the way that a process-oriented position sizer works is they make sure that they have a suggested position size for every name in the fund based on their rules and their research.

That gets frequently updated, and it gets compared to their actual position size, pointing out discrepancies.

When those discrepancies happen, it's a forced conversation between the portfolio manager and the analyst about the details of why that discrepancy is there.

And it's either they need to change the assumptions, the research that led to this position size, or they need to change the position size.

But either way, something needs to be reconciled.

That forced conversation is incredibly valuable.

On top of that, most of Alpha Theory clients and people that come up with a process-oriented position size, come up with not only an upside price target, they come up with a downside price target.

That downside price target is critically important because two things happen once.

One, they are thinking about the counterfactual, what could go wrong and how much could I lose.

But then on top of that, with that downside, they can also adjust their position size and name with bigger downside to get a smaller position size.

On top of that process-oriented position sizers come up with explicit probabilities.

They're going to say there's a 70% chance of this happening.

That screams of false precision.

But let's compare that to what typically happens inside of a firm where an analyst walks in and says, I'm pretty sure that's going to happen.

It's very difficult for a portfolio manager to have a debate with an analyst about pretty sure, but they certainly can when they say 70.

On top of that, process-oriented position sizers come up with checklists.

They come up with the things that matter to them when they size a position.

It could be like how good is the balance sheet?

How good is the management team?

What's my conviction level?

Are their upcoming catalysts? How much work have we done?

And these are things that they want to score out.

The higher the score, the higher the confidence.

The lower the score, the lower the confidence, and so by forcing yourself to have a checklist, you end up making better decisions.

On top of that, when you use a system like Alpha Theory, you accrue lots of data.

You can then analyze that data to figure out if you're process oriented.

Position sizing actually creates value.

Does it outperform what you actually do?

Should you follow it more closely?

How good are your price targets?

How good are your probabilities?

How good are your checklist items?

When you score something as a strong management team, does it outperform something that has a weak management team?

And when you have this data, this analysis, you can hone in on the things and amplify the things you do well, eliminate the things you do poorly, or figure out ways to improve those things you do poorly.

At the end of the day, most of this comes down to common sense.

What a process based position sizing framework does is make sure that your 6th best idea is your 6th largest position.

Your 16th best idea is your 16th largest position.

Eliminating those inefficiencies leads to better returns.

Our data shows that, and we hope you get a chance to read The Ultimate Position Sizing guide and check it out for yourself.

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