I recently finished my masters thesis and I would now like to do something that most people never do. I am going to
critique my work. I believe that being self-critical is essential not only in
research, but in life in general. In research it is necessary to further the
truth. It is not enough that others are critical of you (though it is
necessary). You must be critical of yourself – only then will you be willing to
remedy your flaws, change your convictions and pursue truth and goodness rather
than your own prejudiced agenda.
I write this post as much to myself as for any reader who
may come across it. It is another public reminder of a lesson I fear I may
forget in the future, and of which I may need to be reminded. Even if you are
not interested in my thesis, some of the points of criticism here may be useful
for your own writing. I found in writing this post that self-criticism is hard.
It’s really hard to come up with anything but weak flaws in your own work. I
reckon it will take time to cultivate a truly self-critical nature.
My thesis was about momentum. I looked at time-series and
cross-sectional momentum and their relationship with volatility and
cross-sectional dispersion, specifically considering volatility weighting as a
means of improving momentum strategies. If this sounds like Greek, do not fear,
for I will be explaining all of this in later posts. Today I just want to list
my critiques, starting with the more serious ones.
Subjective
conclusions drawn from mixed results
I often found when I ran some numbers that I got results
that were not clear-cut for any any one conclusion. Then I had to be
satisfied with making a qualified conclusion if I thought there was enough
support for it in the data. “Enough support” is subjective and others may feel
differently. It may be a wiser course of action not to draw any conclusion at
all, but it may also be far too conservative.
A lack of focus
Good academic research tends to focus on one thing and
examine it thoroughly. My thesis, I think, tried to look at somewhat too much,
and as a result ended up being huge and with not one aspect being treated quite
as it deserved.
Strict assumptions
(that I violate)
In order to prove things easily you need to make
assumptions. Often you end up assuming independence or normality where it is clearly
not the case in the data. In my case I needed to make such assumptions to prove
things about volatility weighting and in at least one case I am not even
certain there is a non-trivial (that is to say, interesting) process that
satisfies my assumptions. I have little choice but to violate my assumptions
(there are for instance no volatility estimators that satisfy the assumptions I had to
make).
Overly detailed
results that are hard to interpret
I have lots and lots and lots of tables in my thesis. They are big and make your eyes sore. Ideally
one should find a way of presenting just the right numbers, without hiding ambiguity
and evidence that doesn’t support your conclusions. It is hard to read text
referring to specific numbers in very large tables and keep track of what is
happening. Making a graph is even better as it gives an immediate impression
(but you may still want to report the numbers so that people can check them). I
had relatively few graphs as I could think of no good way to convert my tables
into something visual. This is a weakness. You may think that academics should
be able to read such dense material, that they should take the time and effort.
It is, however, a simple fact that academics are human and that they do not.
Even if they do, they are less likely to
get the right picture if the information is not presented in accessible manner.
Use of advanced
techniques without necessarily having the appropriate understanding
I used what are called “Robust regressions” in my thesis in
order to cope with the fact that financial data contains so many extreme values.
I had never used robust regressions before and only briefly looked up what the
robust regressions did, then used them. I did not take the time to get well
acquainted with their theory (as this would have been quite a task, I think)
and I simply used a standard weighting function with a standard parameter. Most likely this is still better than simply
using OLS regressions (which I think are absolutely a no-go in financial
research, except as a baseline comparison), but it is still possible that the
version of robust regressions I used were not the most appropriate (deciding what
is appropriate is of course more an art than a science) and it would have been
preferable to have had more training in using them.
Linearity
I used linear models for the theoretical and empirical
investigations. One thing that is clear from finance, though, is that nothing is
linear and so results from linear models can be misleading. We have, however, I
think, only poor substitutes and thus linearity is still common in academia. This
is thus only a weak criticism on my part, but I would like to see a move away
from linear models, if only we could find an accessible and preferably
tractable alternative.
Little thought for
practicalities
I did not consider that I was basing my results on markets
that closed at different times (essentially I assumed they closed at the same
time); I did not include transaction costs, commissions, taxes, etc. This is
not unreasonable. To include all these things meticulously would detract from
the main purpose of the study. But they are important and their inclusion could
potentially change the nature of the relationships found (though this is
unlikely).
If you read my thesis and you think there are other criticisms, then please let me know. Perhaps I'll include them in a further post.
If you read my thesis and you think there are other criticisms, then please let me know. Perhaps I'll include them in a further post.
