Thursday, 21 July 2011

Week Starting 18.07.2011

Achievements for this week:

Experimented to verify the hypothesis that Algorithmic Trading affects liquidity positively. That is when Algorithmic Trading Frequency rises Liquidity rises and when Algorithmic Trading Frequency  falls so does Liquidity.
  • Extracted records regarding the trades of 20 stocks for the three year duration of the data available.
  • Used MATLAB to measure the algorithmic trading frequency of these stocks on randomly chosen days for 5 minute blocks. Plotted the results on a line graph.
  • Used MATLAB to measure the bid ask spread of these stocks on the same days. Plotted the results on a line graph. The bid ask spread is an indicator of liquidity.
  •  It was observed that there is a correlation between algorithmic trading falls so does liquidity and this seems to indicate that there is a possibility that algorithmic trading may cause it. However causation does not equal correlation and so the Granger Causality Test was used on R to verify causality. Preliminary tests indicate causality does exist. However further verification is required.

Monday, 11 July 2011

Week Starting Monday, 11 July 2011

Focus for this week: To replicate the experimental analysis from the paper 'Does Algorithmic Trading Improve Liquidity?' By Hendershott et al.

Achievements for Monday:
1. Made  the plan for how to replicate the experiment from the above paper.
The most important steps are as follows:
(i) Find the number of stocks and group them into quintiles by market capitalization.
(ii)  By quintiles, graph the number of algorithmic traders involved across the time period for which data is available.
(iii) By quintiles, graph quoted half spreads, quoted depth, and effective spread.
(iv) Perform the regression analysis of algorithmic trading on liquidity.
(v) Identify whether there is an effect on frequency of AT due to liquidity.
(vi) Find Instrument Variable.
2. Towards achieving the plan above, I have done the following on Monday: Wrote up and started executing the python scripts to list and count the number of stocks, the dates for which the data exists, and the mean market capitalization for each stock. These all allow me to progress towards achieving step (i). I also sent an email to my finance supervisor Andrew Lepone regarding the question of what instrument variable we may use in the context of ASX. Since the paper focuses on the NYSE and over there they used the introduction of the autoquote as the instrument variable-and we cant do that here.

Achievements for Tuesday
Today I wrote up and executed the python scripts to extract the top market cap quintile stocks' trades. It took a very long time because of the size of the data that I was working with and the inefficiency of my scripts-which I didn't detect and correct till Thursday.
Achievements for Wednesday
Same as on Tuesday.
Achievements for Thursday
Corrected the inefficiencies in my python script so that it finished in 2 hours rather than the estimated 30 days. Also, started counting the average number of algorithmic trades as a percentage of all trades every 5 minutes, on a monthly basis for the period 31 Oct 2006 to 26 Oct 2009. This is only for the top quintile stocks.