Week 8 & Mid Sem Break Update

For these two weeks I was just finding and reading more articles, some of which are useful  some of which are not-although they provide general background knowledge for my thesis.

This is in preparation for the literature review due in week 13.

It was hard these last few weeks because my external supervisors went on work related international travels so there was no one to guide me or explain things to me where i was lost-was pretty much making my own way.

Will talk to Sanjay when he gets back in week 9 though.

Week 7 Update

Met with an investment bank employee who suggested that the Flash Crash and the effect of Algorithmic Trading on Volatility was not as interesting a topic as the effect of Algorithmic Trading on Liquidity. She stated that she had come across evidence that Algorithmic Trading reduced liquidity. Which I contrasted to the 'Does Algorithmic Trading improve Liquidity?' paper by Hendershott.

Week 6 Update

Met with an investment bank employee and discussed the possibility of doing my research thesis on the effect of  algorithmic trading on market volatility. He stressed the Flash Market Crash as a good example of how algorithmic trading is bad for market volatility. I thought the paper regarding Algorithmic Trading and Information written by Hendershott provided a good contrast with this and presented an opportunity to explore further.

Wrote up my research thesis proposal based on this.

Week 5 Update

This week I attended the meeting with an investment bank employee regarding Algorithmic Trading. It turned out the content of the meeting was insufficient for my purposes and further meetings were organized.

Sanjay Chawla gave me the following book to read "Empirical Market Microstructure: The institutions, economics, econometrics of securities trading" by Joel Hasbrouck.

I started to write up my project proposal.

Week 4 Update

This week I studied the paper "Algorithmic Trading and Information" by Hendershott et al. It addressed the question that Andrew asked me to consider very well. That is how can we study and exploit the relationship between the market and algorithmic trading.

The research was done based on the data collected from the top stocks on the Deutsche Market. It conjectured and proved that Algorithmic Trading improves liquidity, has no impact on volatility, can be detected through fee reductions, has a more significant impact on human trading than vice versa and also that it is more close to the efficient price than human trading more often.

The paper used vector auto regressions, impulse functions and covariance matrices in its methodology to understand the relationship between the stock market and algorithmic trading.