In a previous blog, we introduced the S&P 500® Twitter Sentiment Index and the S&P 500 Twitter Sentiment Select Equal Weight (EW) Index, highlighting their objective and construction.
At the core of these indices is a model-based sentiment score that captures the direction and magnitude of the sentiment associated with a given company. S&P DJI derives these sentiment scores daily by analyzing the relevant corpus of Tweets containing $cashtags for each company.
Persistent Spread between Quintiles
Using equal-weighted quintiles created from S&P 500 constituents sorted by aggregate sentiment scores each month, we observe a gradual decline in performance from the top quintile to the bottom quintile (see Exhibit 1). We also see that the Q1-Q5 spread was fairly persistent and stable over time (see Exhibit 2). The spread was also generally robust to the choice of parameters used for calculating the sentiment score, demonstrating that Tweet-based sentiment partially satisfies the criteria for being considered a viable systematic factor.
Capturing the Sentiment Premium
The indices capture the sentiment premium in different ways. The broader S&P 500 Twitter Sentiment Index takes a more diversified approach by using a higher stock count (200), while the S&P 500 Twitter Sentiment Select EW Index only selects 50 “high conviction” names. Using two-year rolling windows, we see that both have historically outperformed the S&P 500 over time to varying degrees (see Exhibit 3).
The differences in index construction influence other quantitative metrics as well (see Exhibit 4). The diversified S&P 500 Twitter Sentiment Index has about half the tracking error compared with the S&P 500 Twitter Sentiment Select EW Index, resulting in about twice the information ratio. It has historically beaten the benchmark over 50% of the time in up- and down-market periods, leading to a better overall batting average. Both indices have had relatively high turnover due to the dynamic nature of social sentiment and generally higher volatility of high-sentiment stocks. Due to lower stock count and higher turnover, the S&P 500 Twitter Sentiment Select EW Index exhibited a significantly higher active share.
Exhibit 5 shows the results of a multi-period Brinson-Fachler attribution that helps explain the outperformance of the S&P 500 Twitter Sentiment Indices. The allocation effect determines whether over/underweighting a sector relative to the benchmark contributes positively/negatively to excess return. The selection effect measures the contribution of individual securities based on their relative weights. The interaction effect measures the combined impact of allocation and selection.
For the S&P 500 Twitter Sentiment Index, IT was the largest contributor to excess return, with a total effect of 4.99%. The average weight was 3.65% more than the benchmark, and this overweight contributed 1.91% in terms of allocation effect, while superior stock selection contributed 2.64%. Financials underperformed the benchmark over the period examined, and the index benefited from a smaller allocation to it. Though the selection of stocks had a negative effect in this sector, the interaction resulted in a positive contribution, adding 1.72% in total.
The S&P 500 Twitter Sentiment Select EW Index was underweight in seven sectors relative to the benchmark, and with the exception of IT, all contributed positively to excess return. Health Care had a higher weight compared with the benchmark, and the companies in this sector had the most volatile sentiment over the past few years due to frequent COVID-19-related announcements. Though the attribution was sometimes favorable, the compound effect over time resulted in a negative performance impact. Nevertheless, positive contributions from Financials, Industrials, and Real Estate led to a healthy excess return.
Looking at total sector attribution, the outperformance of each index interestingly came from different effects. The allocation effect contributed to the bulk of the S&P 500 Twitter Sentiment Index’s outperformance, while the selection effect was the main driver for the S&P 500 Twitter Sentiment Select EW Index. The interaction effect contributed positively in both cases.
1 Back-tested information reflects the application of the index methodology and selection of index constituents in hindsight. No hypothetical record can completely account for the impact of financial risk in actual trading. For example, there are numerous factors related to the equities, fixed income, or commodities markets in general which cannot be, and have not been accounted for in the preparation of the index information set forth, all of which can affect actual performance. The back-test calculations are based on the same methodology that was in effect on the index launch date. However, when creating back-tested history for periods of market anomalies or other periods that do not reflect the general current market environment, index methodology rules may be relaxed to capture a large enough universe of securities to simulate the target market the index is designed to measure or strategy the index is designed to capture. The back-test for the S&P Twitter Sentiment Indices is calculated for the period January 2018 to October 2021. S&P Dow Jones Indices designed the sentiment scoring model using data from approximately the same time range. The sentiment scoring model is a natural language processing tool based on linguistic classification of the degree to which a Tweet is likely to be positive or negative. Complete index methodology details are available at www.spdji.com. Past performance of the Index is not an indication of future results. Prospective application of the methodology used to construct the Index may not result in performance commensurate with the back-test returns shown.
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