
2 month break, timelapse stock trading simulator
Just started a summer internship, will be taking a break from posting on IEMH until late Aug/early Sept. In the meantime, check out the timelapse stock trading simulator I had been working on in the last month: predictd.com ...
Continue reading
Continue reading

Socially generated financial data the next big thing?
A ton of articles/papers released recently on this topic:
- Twitter mood predicts the stock market with 87% accuracy (source of the above picture, research paper http://www.technologyreview.com/blog/arxiv/25900/)
- Hedge fund that trades on Twitter sentiment raises almost $100m (http://www.mediabistro.com/alltwitter/higher-than-expected-demand-for-twitter-hedge-fund-causes-delays_b6818)
- Twitter sentiment and the effect on individual stock returns (research paper: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1702854)
- ex-Goldman banker starts hedge fund trading AI algorithm that analyzes Japanese blog ...
Continue reading

The first of month effect exists?
Many have noticed a predictable “first of month” effect in equities; the hypothesis is that the beginning of the month is when funds buy the shares of last month’s top companies, thus pushing up prices. Black is SPY, red is DIA, and green is QQQQ (now apparently just QQQ?). I use those three ETFs to represent the stocks in ...
Continue reading
Continue reading
My final research report for STAT 520, on the post earnings announcement drift effect
For my STAT 520 Applied Econometrics class. Troy Shu STAT 520 PEAD Project Writeup Final ...
Continue reading
Continue reading

larger earnings surprises associated with higher returns
Surprisingly I find myself working on my PEAD research more during finals week… Below is the component residual plot for the current model: stock's return in the next 30 days ~ last 30 days price change + last 30 days historical volatility + earnings surprise > There’s an apparent association b/w larger earnings surprises and higher returns. Looks like ...
Continue reading
Continue reading
400 lines of code and 1.6 million data points later
And the data is finally in “response variable, explanatory variable 1, explanatory variable 2,… explanatory variable n” format. My code (Java) is very unreadable, but it gets the job done. There were only about 18000 earnings surprise data points. But my other explanatory variables right now, momentum and volatility, require historical price data and so I had to process ...
Continue reading
Continue reading