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Cool New Paper from MIT on Sentiment Data from using Quantopian for Analysis

Hey everybody,

I wanted to point out a cool new paper that was just published. It uses sentiment data and studies how the market responds to extreme sentiment. I think this is an amazing example of some top-tier research into anomalies and strategies, so I encourage users to check it out as a reference for their own work.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3197874

I pasted the paper's abstract here:

We analyze the relation between stock market liquidity and real-time measures of sentiment obtained from the social-media platforms StockTwits and Twitter. Linear regression analysis shows that extreme sentiment corresponds to higher demand and lower supply of liquidity, with negative sentiment having a much larger effect on demand and supply than positive sentiment. An intraday event study shows that booms and panics end when bullish and bearish sentiment reach extreme levels, respectively. After extreme sentiment, prices become more mean-reverting and spreads narrow. To quantify the magnitudes of these effects, we conduct a historical simulation of a market-neutral mean-reversion strategy that uses social-media information to determine its portfolio allocations. Our results suggest that the demand and supply of liquidity are influenced by investor sentiment, and that market makers who can keep their transaction costs to a minimum are able to profit by using extreme bullish and bearish emotions in social media as a real-time barometer for the end of momentum and a return to mean reversion.

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2 responses

Let's see, backtest period ends at 2014 while it's currently 2018, stationary test uses fixed lag of 4 for some reason, backtest uses limit orders, profits evaporate when applying fees, relative magnitude of regression coefficients are interpreted as being indicative of effect-strength. Cool.

@delaney is there a way to access the intraday data? The strategy is executing every 30 minutes - is that still possible on Quantopian? thx!