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General Market Indicator: Volatility of Sentiment Helpful in Predicting Overall Market Performance

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The target of this research was to find an indicator that helps predict the direction of the overall US Equity market for the next week using sentiment data from the previous week. The hypothesis is when there is high volatility in sentiment over the previous week, which means investors have differing opinions, the subsequent week overall market performance will underperform. When volatility on sentiment is low or neutral, the crowd has reached a consensus and the general market will outperform over the next week. The sentiment metric used to represent volatility is Raw-Volatility in SMA’s S-Factor data feed, which captures the volatility of the sentiment from Twitter conversations. All Raw-Volatility data points were taken from the 3:40 pm ET timestamp (20 minutes before the market close). We calculated the summation of Raw-Volatility for each date as a proxy to represent the volatility of Twitter social sentiment on the entire market. The exact calculation is as follows, where “N” is the number of companies with sentiment on that date and “D” is the date:

We then created a 7-day standardized volatility using a 91-day benchmark:

This Z_Volatility score follows a roughly normal distribution.

Using the S&P 500 ETF Trust (SPY) as a proxy of general market performance, we then look at the relationship between Z_Volatility and SPY’s return series. The daily close-to-close return is calculated as:

Hypothesis: When Z_Volatility for the previous closing Date is high, the subsequent market performance will be lower. When Z_Volatility is low or neutral, the next day’s market performance will be higher.

To test this, our strategy is to open short position of SPY when Z_Volatility > 1. When Z_Volatiltiy is =< 1, the portfolio treats SPY as a long position. This hypothetical portfolio is then compared to SPY over the past 10 years:

Prior to the COVID-19 pandemic, which began in early 2020, SPY outperformed the modified portfolio. However, since then the behavior of this factor changed drastically. Here is the same graph as above starting in 2020:

Taking a closer look, the separation since the beginning of 2020 is quite significant. Adding a short position to SPY when volatility on sentiment is high, has enhanced the portfolio’s return. Even though many of the days will maintain a long position, the Z-Volatility is predictive of downturns in the market since 2020. Traders could use this metric as an indicator to stay out of the market, or at the very least trade with more caution. The COVID-19 Pandemic led to a large amount of uncertainty surrounding the stock market and the direction its heading. A high Z_Volatility score indicates the public’s opinion is more uncertain about the direction of various stocks. This research shows the value of sentiment from Social Market Analytics in predicting macro-level events and price movements.

If you are interested in learning more about how SMA’s S-Factor data can help your trading strategies, please email us at contactus@socialmarketanalytics.com or schedule a demo using this link.

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StockTwits based S-Factor Return Charts

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Social Market Analytics (SMA) aggregates the intentions of  investors as expressed on the StockTwits platform.   SMA creates proprietary S-Factor metrics that quantitatively describe the current conversation relative to historical benchmarks.  This data provides strong predictors of future price movement.  This blog will focus on the deterministic nature of the StockTwits data set when aggregated into SMA S-Factors.    StockTwits is a community for active traders to share ideas enabling you to tap into the pulse of the market:  http://stocktwits.com/

The charts and tables below illustrate the subsequent open to close return of stocks that are being spoken about abnormally positively or abnormally negatively on StockTwits twenty minutes prior to market open.  Sharpe and Sortino ratios for the theoretical portfolios are included as well.  The SMA S-Score looks at the current conversation relative to historical benchmarks and creates effectively a Z-Score.

The Green line below is an index of subsequent open to close return of stocks with abnormally positive conversations on StockTwits prior to the market open.  The Red line is an index of the subsequent open to close return of stocks with an abnormally negative conversation prior to market open.  The black line represents the market open to close return and the blue line represents a theoretical long/short portfolio.

These charts clearly illustrate the predictive information present in the StockTwits message stream. If there was no predictive power in the StockTwits data set the Green, Red, and Black lines would be nearly identical -statistically not the case.  These signals are available at 9:10 am Eastern time well before the market open.

The chart below looks at the full SMA history of StockTwits based S-Factors.  The theoretical long portfolio has a Sharpe Ratio of 1.53, theoretical short portfolio -.82 Sharpe and LS portfolio has a Sharpe of 3.68.   Sortino Ratios are above one as well.  There is strong predictive power in this data.

FullHistoryStockTwits

The last year has been particularly challenging for the Hedge Fund community.  Below is a chart with the performance of the theoretical portfolios broken out from 1/1/2015 to current.  As you can see these portfolios performed well in this volatile market period.

LastYearStockTwits

For more information on these data sets please contact Pierce Crosby:  (pierce@stocktwits.com)  or Joe Gits: (joeg@socialmarketanalytics.com)

Regards,

Joe

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Sentiment Leading Recent Market Volatility

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The chart below looks at the percentage of positive Tweets versus the percentage of negative Tweets over the last couple of weeks.  There are usually significantly more positive than negative Tweets so the fact that the negative percentage was so high is valuable data in itself.  As you can see the percentage of negative Tweets increased prior to days with significant market downtrends.

The black lines on the chart represent market activity.   The red and green bars represent negative and positive Tweet percentages.  Sentiment is captured by Social Market Analytics 24×7; you can see the growth in negative sentiment prior to the Monday (8/24 draw down).  On 8/24 the market started strong and fell significantly at session end.

The universe of Tweets is so large that when you aggregate it you get a terrific view of what people believe is going to happen.  This data is only available from Social Market Analytics.  Please contact us for more information on our market leading data sets or visit our Research Page.

PosNegTweets

Thanks,

Joe

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Twitter Sentiment and Volatility Index Directional Forecasting

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SMA has just completed a comprehensive analysis that shows the performance of classifier models, designed to predict next day directional movement for volatility indexes, improves by adding market sentiment measures derived from social media sources.  Please download the paper at:  https://socialmarketanalytics.com/research/white-papers

We present predictive models built from market data and S-Factors, a family of metrics designed to capture the signature of market sentiment as expressed in micro-blogging messages posted on Twitter. The objective of this report is to investigate the relationship between sentiment metrics generated by SMA and the volatility index of S&P 500 (VIX) and volatility indexes for individual equities (VXAPL, VXAZN, VXGS, VXGOG, and VXIBM), computed from equity option prices for AAPL, AMZN, GS, GOOG and IBM, respectively.

We used time series modelling and Logistic Regression as classifiers for predicting the direction of volatility. We tested the performance of the model with and without Sentiment Factor data. In our results, we found that the accuracy for predicting the direction of VIX using an ARIMAX-GARCH model with S-Factors was 70.86%. This was higher than the accuracy observed using a model that did not include the S-Factors (67.43%) . The same goes for most of the volatility indexes for individual equities that we picked.

Similarly, we compare the accuracy in predicting the probability of VIX going up the next day using a Logistics Regression model. The model that included S-Factors turned out to be more accurate than the model without S-Factor in all the volatility indexes for individual equities. The difference observed in accuracy was as high as almost 7.5% in the case of VXGS. The accuracy with S-factors was 62%, while without these factors it was just 54.67%.

Our analysis shows that the accuracy of a model increases by approximately 80% after adding SMA’s sentiment metrics to the model. Most of the investors are apprehensive of losses so they prefer a model that predicts the losses accurately. It is evident from our analysis that addition of S-Factors decreases the False Positive rate, thus predicting the downward movements of Volatility Indexes accurately.

Our results demonstrate enhanced predictive performance for models that include sentiment factors (S-Factors), using micro blogs like Twitter and StockTwits, as explanatory variables.

As usual, please contact us with any questions: ContactUs@SocialMarketAnalytics.com

Thanks,

Joe

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