Comments on: Can Twitter beat the stock market? http://blogs.reuters.com/mediafile/2011/06/14/can-twitter-beat-the-stock-market/ Where media and technology meet Wed, 16 Nov 2016 08:48:25 +0000 hourly 1 http://wordpress.org/?v=4.2.5 By: MyNameIsObama http://blogs.reuters.com/mediafile/2011/06/14/can-twitter-beat-the-stock-market/#comment-389072 Fri, 17 Jun 2011 04:34:28 +0000 http://blogs.reuters.com/mediafile/?p=26959#comment-389072 I suspect that this methodology about as much validity as moon cycles/gravitational pull and Elliot Wave theory – ok, maybe not as much as the ridiculously complex to the point of unusable “Elliot Wave” theory.

Open up a ThinkorSwim trading account and you can have free access to more indicators and forecasting tools than you’ll ever need. Unless you have a robust trading methodology that has an edge, they won’t work. In fact, you’ll probably blow your money.

]]>
By: jane33w http://blogs.reuters.com/mediafile/2011/06/14/can-twitter-beat-the-stock-market/#comment-389040 Wed, 15 Jun 2011 15:07:11 +0000 http://blogs.reuters.com/mediafile/?p=26959#comment-389040 Yes, but don’t forget one of the basic principles of Psychohistory (wee Hari Seldon): Predictions of behavior are valid only if the subjects (in this case, those who buy and trade in the markets) are unaware of the predictions and the analysis.

]]>
By: choral http://blogs.reuters.com/mediafile/2011/06/14/can-twitter-beat-the-stock-market/#comment-389020 Tue, 14 Jun 2011 19:40:34 +0000 http://blogs.reuters.com/mediafile/?p=26959#comment-389020 Let me first state, I really admire the initiative of the underlying study. However, I just do not see how this fund will differentiate itself from the countless other optimized quantitative strategies that ultimately fail.

The basis of this study was to use a lag of 3 days on sentiment readings as one parameter in a neural network which then learns over the historical period (in this case Feb 28 to Nov 28, 2008) to predict over the next 15 trading days.

“February 28, 2008 to November 28, 2008 is chosen as the longest possible training period while Dec 1 to Dec 19, 2008 was chosen as the test period because it was characterized by stabilization of DJIA values after considerable volatility in previous months and the absence of any unusual or significant socio-cultural events.”
They handpicked a test period where nothing happened to generate their findings and under these ideal conditions, the prediction exhibited minimal statistical significance.

“The cases in which the t -3 mood time series fails to track changes in the DJIA are nearly equally informative as where it doesn’t. In particular we point to a significant deviation between the two graphs on October 13th where the DJIA surges by more than 3 standard deviations trough-to-peak. The Calm curve however remains relatively flat at that time after which it starts to again track changes in the DJIA again. This discrepancy may be the result of the the Federal Reserve’s announcement on October 13th of a major bank bailout initiative which unexpectedly increase DJIA values that day. The deviation between Calm values and the DJIA on that day illustrates that unexpected news is not anticipated by the public mood yet remains a significant factor in modeling the stock market.”
This particular variance would represent a noticeable drawdown to the fund, exacerbated by the leverage used in implementing the strategy.

Like any other optimized quantitative strategy, recalibration is a necessary requirement. However frequent this calibration is, it does not ensure survivability of the fund. A recent Bloomberg op-ed touches on this.
“The models aren’t capable of any great level of precision. In finance, when models are calibrated, they always have to be recalibrated a week later. But those parameters are supposed to remain fixed for evermore. If they have to be changed, then the model either was wrong before, is wrong now, or more likely both.”
http://www.bloomberg.com/news/2011-05-24  /bankers-can-t-avoid-risk-by-hiding-it. html

Again, I don’t wish investors to lose capital in this investment and perhaps the final strategy is more loosely interpreted from the study with extremely favorable risk management / leverage considerations. But, how can this work be directly translated into an investment with survivability if it relies on predicting a lagged outcome with 80% accuracy using optimized historical data? Regardless of its behavioral representations (which I admire), the data appear to be victims of a suspect methodology.

]]>