I have studied the article "Algorithmic Trading Strategy Optimization Based on Mutual Information Entropy Based Clustering" in depth and presented it at the research group meetings that Sanjay Chawla holds weekly for all of his research students.
I had to teach myself Information Theory in order to understand this article.
I found many mathematical errors as well as a lack of explanation for why the authors did certain things. As a consequence, my supervisors and I dismissed it as a Tier C article with too many flaws to be taken seriously.
However, I still consider it to have been a good learning experience as it taught me how to understand an academic article.
I read these two resources to help me out in how to read an academic article:
"Evaluating Research Articles From Start to Finish" by Ellen R. Girden
"The Research Student's Guide to Success" by Pat Cryer.
At this stage I have still not decided what my question is going to be in my thesis and am still reading articles to understand what the area of algorithmic trading is all about.
Here are some other articles that I have studied, though not in as much depth as the one mentioned above:
"Data Stream Mining For Market Neutral Algorithmic Trading"
"Efficient Trade Execution Using A Genetic Algorithm in an Order Book Based Artificial Market"
"Efficient Event Processing through Reconfigurable Hardware for Algorithmic Trading"
I also organized an interview with an undisclosed financial institution's algorithmic trading team member to discuss how they use algorithmic trading and how they will be able to help me in my research thesis-Contact me for more information if interested.