QPI (defined in the original article) is a volatility-adjusted measure of how rare a stock’s 3-day move is relative to its own 5-year history. You take all 3-day returns from the past 5 years, locate today’s move in that distribution, and rescale it so that 0 means an extremely rare tail event and 100 means a very common one.
I don’t use fixed %-drop thresholds because they ignore regime and volatility: a -5% move in Stock A during a calm period is completely different from a -5% move in Stock B during a high-vol regime. QPI solves this by anchoring everything to each stock’s own distribution.
And yes, computing QPI on residual returns (relative to the S&P 500 or sector) is a natural extension, since it isolates true idiosyncratic dislocations rather than broad market moves: thanks for the suggestion! Will definitely try it!
In the next few articles, I will extend/improve on this idea... not necessarily shorter timeframes, but more/different universes, more data (that helps us differentiate fundamental price drops from transient ones), and overlaying a risk model :)
About the "residual returns" idea, even if you find no improvement (and very likely, far fewer trades), you shouldn't throw it away immediately; it would be worth comparing the two schemes in non-US markets.
Totally! In the past 2 months, I implemented 3 different papers... the results were so off, I couldn't even find a way to transform the work into posts to share here!
I looked at your first article. With apology for further pestering you on details, but these are important for requisite pre-judging (not to mention the "thousand" cuts you mentioned):
1. Are the 3 day intervals independent or overlapping?
2. (likely I know the answer, but just to be explicitly clear:) To create the distribution, did you strictly use history at the time, compared to (for example) using the distribution-of-returns that looks ahead?
3. I'm not sure how this might apply exactly, since it depends on your symbol selection criteria, but:
When going back ~20 years, one should be very wary of "survivorship bias". In other words, there were many symbols in the S&P500 that have been dropped, since. One must be sure to include the dropped names for the time of walk-forward window. Also, vice versa for the added names: not to use them at time-windows prior to their inclusion.
Obvious part: If a symbol was dropped it may well be one that went bankrupt and you would not included the losses in your test
Less obvious part: merely being a member of S&P500 should be considered a major cause of price-action, especially in the age of passive index "investing" (aka dumb-money dumping)
Impressive results.
This'd only work in the nutty world of the S&P where there are _very_ few meaningful downtrends.
1.what is QPI?
2.can you give a rough idea of the value of the drop threshold?
(at 3-5k-trades/yr it is likely <5%, either way perhaps a window of acceptable drops may weed out some large drops that will not recover.)
3. Should also try distribution of returns to be relative to the centroid (S&P index)
4.Since you hinted at it, how would you modify this for shorter time frames?
Thanks, Tim!
QPI (defined in the original article) is a volatility-adjusted measure of how rare a stock’s 3-day move is relative to its own 5-year history. You take all 3-day returns from the past 5 years, locate today’s move in that distribution, and rescale it so that 0 means an extremely rare tail event and 100 means a very common one.
I don’t use fixed %-drop thresholds because they ignore regime and volatility: a -5% move in Stock A during a calm period is completely different from a -5% move in Stock B during a high-vol regime. QPI solves this by anchoring everything to each stock’s own distribution.
And yes, computing QPI on residual returns (relative to the S&P 500 or sector) is a natural extension, since it isolates true idiosyncratic dislocations rather than broad market moves: thanks for the suggestion! Will definitely try it!
In the next few articles, I will extend/improve on this idea... not necessarily shorter timeframes, but more/different universes, more data (that helps us differentiate fundamental price drops from transient ones), and overlaying a risk model :)
Thanks! I will find the original article.
About the "residual returns" idea, even if you find no improvement (and very likely, far fewer trades), you shouldn't throw it away immediately; it would be worth comparing the two schemes in non-US markets.
I strongly concur with your comment about "..far from published results.." it's a major source of wasted time!
Totally! In the past 2 months, I implemented 3 different papers... the results were so off, I couldn't even find a way to transform the work into posts to share here!
Impressed by your approach, definitely gonna apply myself!
I looked at your first article. With apology for further pestering you on details, but these are important for requisite pre-judging (not to mention the "thousand" cuts you mentioned):
1. Are the 3 day intervals independent or overlapping?
2. (likely I know the answer, but just to be explicitly clear:) To create the distribution, did you strictly use history at the time, compared to (for example) using the distribution-of-returns that looks ahead?
3. I'm not sure how this might apply exactly, since it depends on your symbol selection criteria, but:
When going back ~20 years, one should be very wary of "survivorship bias". In other words, there were many symbols in the S&P500 that have been dropped, since. One must be sure to include the dropped names for the time of walk-forward window. Also, vice versa for the added names: not to use them at time-windows prior to their inclusion.
Obvious part: If a symbol was dropped it may well be one that went bankrupt and you would not included the losses in your test
Less obvious part: merely being a member of S&P500 should be considered a major cause of price-action, especially in the age of passive index "investing" (aka dumb-money dumping)
Hi Tim!
1) Using both produce the same results
2) obviously only looks backward, that would be a huge & rookie mistake
3) we also obviously took care of survivorship bias. All delisted index members are included.. this would also be a huge & rookie mistake :)