forked from ALFarch/mlfinlab
-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge branch 'develop' into risk_estimators
- Loading branch information
Showing
10 changed files
with
82 additions
and
16 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,72 @@ | ||
|
||
============== | ||
Research Tools | ||
============== | ||
|
||
As researchers, we often neglect finding the right tools to streamline | ||
the progress. Financial Machine Learning is no different in that a lot of the papers are scattered | ||
across different journals and different fields. Ranging from journals on econometrics to machine | ||
learning, researchers often struggle to find the best academic papers to begin their studies. | ||
|
||
At Hudson & Thames, we primarily use two resources: `Connected Papers`_ and `EThOS`_. These two | ||
free sites have been invaluable and offer an advantage to search through the most cutting edge | ||
resources available for our MlFinLab library. | ||
|
||
.. _Connected Papers: https://www.connectedpapers.com/ | ||
.. _EThOS: https://ethos.bl.uk/Home.do | ||
|
||
|
||
Connected Papers | ||
################ | ||
|
||
Connected papers is unique in that it is not a citation tree. A citation from a paper does not | ||
necessarily lead the reader to another paper. The two topics might be completely different and | ||
an unimportant topic for the researcher. | ||
|
||
It uniquely identifies the related papers by looking at the cocitation and bibliographic coupling. | ||
More about the website is available at the connected papers founder’s `medium`_ post. | ||
|
||
To give a brief demonstration, we will examine a `paper`_ by Li and Hoi that started our Online Portfolio Selection module. | ||
|
||
If you type in the name of the paper, you will see a graph like the one below. | ||
|
||
.. image:: getting_started_images/graph.png | ||
:width: 50% | ||
:align: center | ||
|
||
It immediately shows which are the most associated papers. The darker circles indicate that they are | ||
more recent, so we can easily follow from the older papers to the newer ones. Connected papers also | ||
has an amazing feature for prior works and derivative works. | ||
|
||
Prior works is available for researchers to see what are the most famous and cited papers in this field | ||
to recognize the importance and start with the baseline material. If we click the button for prior works, | ||
for our current search, we see an image like this: | ||
|
||
.. image:: getting_started_images/prior.png | ||
:width: 50% | ||
:align: center | ||
|
||
We can easily see which were the most cited papers. It is not surprising that the number one paper | ||
associated with Online Portfolio Selection is Thomas Cover's Universal Portfolio, the original paper | ||
that began the studies in Portfolio Selection based on information theory. | ||
|
||
Once the researcher gets more familiar with the topic by going through literature review with prior | ||
works, they can move on to the derivative works, which cover the most recent papers associated with | ||
the paper of interest. | ||
|
||
.. image:: getting_started_images/derivative.png | ||
:width: 50% | ||
:align: center | ||
|
||
.. _medium: https://medium.com/connectedpapers/announcing-connected-papers-a-visual-tool-for-researchers-to-find-and-explore-academic-papers-89146a54c7d4 | ||
.. _paper: https://arxiv.org/abs/1212.2129 | ||
|
||
EThOS | ||
##### | ||
|
||
`EThOS`_ is a online library sponsored by the United Kingdom to make publicly-funded research available | ||
to all researchers. | ||
|
||
The best feature for EThOS is the availability of all doctoral theses in the UK. If your topic of | ||
interest does not have too many sources from journals, there is a high chance that you can find | ||
good works in EThOS as it is not limited to published journals but rather all doctoral theses as well. |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,6 +1,6 @@ | ||
[metadata] | ||
name = mlfinlab | ||
version = 0.11.2 | ||
version = 0.11.3 | ||
author = Hudson and Thames Quantitative Research | ||
author_email = [email protected] | ||
licence = All Rights Reserved | ||
|