"So I'm going to look for stocks with low Hurst exponent"
Reasonable assumption or wishful thinking?
9/n
Same procedure...
First "Hurst Exponent Go Brrrr" over daily log returns for each stock each year.
(Probably some sample size bias in that simple function - but all my samples are the same size everywhere, so it'll be similar everwhere)
10/n
Now scatterplot.
Each point an observation for a stock for a year. (No overlaps)
Hurst exponent on the y-axis. Hurst exponent last year on the x-axis.
11/n
There's no real evidence of any kind of persistence here.
A stock with a low Hurst exponent last year isn't more likely to have a low Hurst exponent next year.
The assumption of persistence - at least generally and at this timescale - would be unreasonable.
12/n
Screening for mean-reversion candidates on the basis of past estimations of Hurst exponent alone probably isn't a good idea.
You knew that already, of course...
Market prices are highly efficient and returns are dominated by noise.
13/n
Does this mean that "data-mining/screening for past time series characteristics" is worthless?
No - we do some of it (not often tho)
It means you need to *carefully identify the assumptions you are making* and do your best to ensure they are reasonable.
14/n
Remember our most useful tools as a trader are:
- Economic Intuition
- Simple Data Analysis
Never forget the first...
15/n
An understanding of the structural constraints / pressures that would *cause* the price behavior you are interested in is more valuable than trying to observe it directly in the past time-series.
I think of the returns from "fundamental investing" coming from two sources...
1. Risk Premium - The tendency of risky assets to be relatively cheap vs their expected cashflows. This leads them to "carry" more than they would if their real cashflows were riskless
2/10
2. Mispricing - For behavioural/structural reasons, some assets are under/over-priced vs a reasonable estimate of their ex-ante "fair value".
On average, we expect them to converge towards fair value over some long time horizon.
First, put aside any expectation that you can isolate and quantify effects with great precision.
The market is a highly efficient beast - why means that any non-random effects we observe tend to be extremely noisy.
But just cos something is hard, doesn't mean we shouldn't try.
In fact, it's essential that we try to understand and isolate effects as best we can.
The best tools for the job (at least to start) are:
- economic intuition
- very simple data analysis (the kind of thing you could do in an excel pivot table)
Shall we do some analysis on a *really dumb* factor which might predict relative returns in stocks?
"Are cheap stocks expensive?"
A research thread 👇👇👇
Options on stocks with a low share price tend to be overpriced.
Equity options (at 100 shares a pop) are quite big for a small retail trader. So we might say there is excess retail demand for options on cheap stocks - which would result in them being overpriced.
But are low priced stocks also expensive?
The AMZN share price is $3k+. There are Robinhooders who can't afford a single stock.
Do we see the same effect in Stocks as we do in the options?