Factor models are basically just if you tried to decompose returns into some linear regressions and then pretended it was ergodic it because of a “risk-premium”. Risk premiums don’t necessarily exist but with a lot of these there does exist increasing tail risk with…
better performance of said factors. As seen in 2008 the big brown line of momentum is eventually corrected but that is less of a risk premium reason and because of the august risk of multi-strat funds getting blown up in credit and then covering with momentum forcing…
Momentum the other way. However for other factors like quality and profitability there is little to no basis for it being a risk factor and the issue with assuming this level of ergodicity is that you may hold a position that no longer has alpha expecting to be compensated…
Of course these linear factors do have a history of laying dormant for a decade or more but these are the most famous 5 factors and hundreds have been identified in the literature with many of them decaying. It is important to note however that a linear combination…
Of factors is not a new factor. For example, many believe low volatility is a factor when it is simply a linear combination of profitability and conservative investment factors. On the long term a lot of modelling revolves around linear models and regressions, where…
Most of your features will be related to quality and a few value with the remaining being beta/momentum exposures. Quality decomposes into many areas form intangibles to pure profitability, all with different behaviours. A key point with this is that factors don’t make…
sense as a returns model because they are, like the rest of the market, non-ergodic, and will not always reward you especially for smaller factors you may be focusing on to try to gain excess returns. Because of reflexive effects of everyone treating factors like…
risk premiums etc even if there is not guaranteed outperformance of the factors they are still a good generalisation of the market returns. That’s why using the largest factors I a great way to do regime shift modelling. That alongside correlation risk premium…
volatility risk premium, and skew/correlation. VIX-VOLI is also great. There are more liquidity and option metrics I won’t go into for now.
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I get people who come up to me and say what about this paper it looks great??? If they don't show performance a lot of the time it sucks, especially if they only show wlr. Worse yet you find out they levered 10% apy 10x!!! Think about strategies as components to yours.
Stack it!
No single strategy will get you alpha. Some may get you close, but it takes a level of awareness in all steps of the process from data to features, to reduction, to ML, to sizing (use kelly), risk mgmt, regime shift, meta-label, crossing, and then super fast execution.
Crossing means you cancel to opposing orders. 500 SELL from ALGO1 against 300 BUY from ALGO2. Maybe ALGO1 is your ML strategy, but ALGO2 is Genetic Algorithm, or maybe MM, or perhaps pairs. Combine and ensemble not just predictions, but also orderflow. Reduces size and fees
I’ve seen people claim pairs is dying. That certainly isn’t true but it has become a feature or component and not a strategy. The mispricing index of copulas weighted by their copula relationship strength may serve as features to an ML model. An interesting mispricing based…
approach is where you explicitly treat the spread as it's components. This way you can only trade one leg. If you are able to use something such as LASSO and SPCA to cluster by industry and then reduce to a network of highly connected assets then you can use this...
It makes very little sense to just try and find pairs and use them as features since one asset and it's relationship won't really make up that much of the price movement, but the weighted (by relationship strength) mispricing against the network (LASSO/SPCA reduced)...
Whilst alt data can create an edge it is largely overhyped. In my experience it provides very little alpha unlike what most think. Two key reasons exist for this:
1) So much data 2) Fundamentals just aren’t that important for the short term
A word on each:
The first problem is that there is so much data out there and whilst most firms can sort through this data with reasonable effectiveness there is so much of it that the methods used provide very little alpha. The amount of articles published each day are so numerous…
That the alpha coming from a better model is slowly whittled away because a CNN-LSTM model you can make in tensorflow can usually do a good job, or worse just import an NLP library in Python or R. The second issue is as many will know from previous tweets that flow is…
Another cool flow idiosyncrasy that may interest those flow nerds out there. Cryptocurrency markets have more momentum in the short term, which is usually dominated by mean-reversion effects. This is basically all driven by over-levered degens and algorithms that...
1/idk lol
get access to these 100x leverage futures or 20x levered futures, and unlike most would think it actually gets used. Especially if you are some sort of HFT algorithm and now you can lever up as much as you want in a super volatile market. Plus with the sorts of...
2/n
profits some of these guys make, who wouldn't lever this thing up to capacity and get in while it lasts. I will probably still attribute most of the momentum still to retail guys getting liquidity cascaded out of positions. i.e. takes loss, gets out, price moves down...
3/n
For those of you who aren’t aware of the types of funds. When someone says CTA, they’re usually a momentum fund or capture some well known futures effects. A lot of these guys are a strange form of levered beta and not really alpha because a lot of their methods work best…
1/n
when lots of others use them so strangely enough not the most advanced or accurate model is the best model because really it’s just this greater fool theory flow. You can pretty easily frontrun this shit, because they typically ease into positions because they have…
2/n
Quite large books, and they compound their positions based on the simple rule of add to winners and cut losers (basically the entire premise behind momentum where losing money is a get out signal and making money is the get in signal). Anyways, if you know there is…
3/n
Large quantity of unique features
Really good dimensionality reduction
Ensemble everywhere!
A word on each...
When it comes to modeling everyone always goes to their favorite NNs like LSTMs etc or LGBMs and those are great, but everyone has them, and frankly, they aren't that hard to implement! Just look at Kaggle if you want an example of DS students using them everywhere...
For real alpha, you need to focus on the three most ignored areas (there is a fourth, speed, but that's not really modeling, and a fifth which I'm not telling you because I like my alpha unleaked). That sounded super guru-like, but I promise these work and I use them.