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Social media message volume as a proxy for stock volatility

Hey all,

This is something I've been working on and wanted your feedback. Social media data is generally pretty new in finance and I think we're only beginning to discover the uses for them. If you follow my other posts, I've tried creating a few fun projects using PsychSignal's trader mood signals as buy/sell signals for securities. However, after much reading and discussion with folks much smarter than I am, I've been playing around with the idea of using social media as a proxy for stock price volatility within the context of the much documented volatility effect.

As a quick summary, the volatility effect often shows that securities with low volatility often outperform (risk-adjusted returns) securities with high volatility. Much of the studies on this effect ended in the mid-2000s and the research I'm doing now can be considered "out-of-sample".

So here's what I've done so far:

  • I've begun testing two factors in Andrew Campbell's Factor Tearsheet. The two factors are stock price volatility and StockTwits message volume. From the initial tearsheet it looks like social media message volume contains more Information Coefficient and is more consistent in returns over the different factor quantiles. Your thoughts are appreciated in expanding this notebook.
  • Taking the observations from above, I plugged in the StockTwits message volume factor into James Christopher's long-short multi-factor pipeline algorithm as a quick validation of the tearsheet. I found that the factor itself alone did not provide useful as an alpha factor.

Critique, thoughts, feedback are appreciated.

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2 responses

Here's the algorithm that's discussed above

Clone Algorithm
492
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Backtest from to with initial capital
Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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Returns 1 Month 3 Month 6 Month 12 Month
Alpha 1 Month 3 Month 6 Month 12 Month
Beta 1 Month 3 Month 6 Month 12 Month
Sharpe 1 Month 3 Month 6 Month 12 Month
Sortino 1 Month 3 Month 6 Month 12 Month
Volatility 1 Month 3 Month 6 Month 12 Month
Max Drawdown 1 Month 3 Month 6 Month 12 Month
# Backtest ID: 57755bcc2d7a311207daeea1
There was a runtime error.

I'm wondering what these lines do.
def src_std_error(rho,n):
return np.sqrt((1-rho**2)/(n-2))
err=ic.apply(lamda x:src_std_error(x,obs_count))
err=err.reset_index().groupby(['sector_code']).agg(lambda x:np.sqrt((np.sum(np.power(x,2))/len(x))))