Abstract
If you ever talk nance, in a blink of an eye your audience's face feels
as if they have been hit by a truck and slammed into a solid concrete
bunker. Exaggerations aside, one can hardly blame them. The desire
to line pockets with little green pieces of paper has lead to complicated
monetary philosophies. In one camp there are technical analysts, in
another value investors, then financial engineer or "quants", and finally
astrologists are thrown into the mix. Everyone has a different idea on
how to conduct this business.
In this thesis, we are going to scientically look at thoroughly used
tools from technical analysis - the moving averages. These are filters
which smooth noisy fluctuations from asset prices highlighting trends;
they show the bigger picture. We will pull these models apart and explore
them. Our aim is to discover what the relationship between smoothing
and return actually looks like, and to answer the question: does smoothing
really make us more competitive? We need to know this before we
start investing real dollars.
We will answer this question with a three step process. In the first
step I will show you how to measure smoothness and other properties of
filters. In the second step, I will show you new fllters which optimally
smooth and allow you to control the amount of smoothing. Lastly, using
our new ability to measure and control smoothing, we will look at the
aect on returns. All contributions are listed in Chapter 5 on page 139.
Ultimately, we will discover that filtering offers no "edge" to investing.
However, as smoothing increases, transactions and the money spent on them decrease.
as if they have been hit by a truck and slammed into a solid concrete
bunker. Exaggerations aside, one can hardly blame them. The desire
to line pockets with little green pieces of paper has lead to complicated
monetary philosophies. In one camp there are technical analysts, in
another value investors, then financial engineer or "quants", and finally
astrologists are thrown into the mix. Everyone has a different idea on
how to conduct this business.
In this thesis, we are going to scientically look at thoroughly used
tools from technical analysis - the moving averages. These are filters
which smooth noisy fluctuations from asset prices highlighting trends;
they show the bigger picture. We will pull these models apart and explore
them. Our aim is to discover what the relationship between smoothing
and return actually looks like, and to answer the question: does smoothing
really make us more competitive? We need to know this before we
start investing real dollars.
We will answer this question with a three step process. In the first
step I will show you how to measure smoothness and other properties of
filters. In the second step, I will show you new fllters which optimally
smooth and allow you to control the amount of smoothing. Lastly, using
our new ability to measure and control smoothing, we will look at the
aect on returns. All contributions are listed in Chapter 5 on page 139.
Ultimately, we will discover that filtering offers no "edge" to investing.
However, as smoothing increases, transactions and the money spent on them decrease.
Original language | English |
---|---|
Qualification | Doctor of Philosophy |
Awarding Institution |
|
Supervisors/Advisors |
|
Award date | 07 Oct 2014 |
Place of Publication | Australia |
Publisher | |
Publication status | Published - 2014 |