Review and Update of Reddit-Based Sentiment Indicator
We have built a proprietary database of comments from the WallStreetBets sub-reddit extending back over two years. In this research note, we update our short-term market indicator based on aggregate sentiment toward the S&P 500.
Our indicator is based on the overall sentiment of Reddit users toward the S&P 500, and proxies for retail sentiment in the market. It has shown to be an effective contra-signal over short-term holding periods. We construct the indicator by assigning a sentiment score to each comment about the S&P 500, then aggregating those sentiment scores at a daily frequency. Fig. 1 shows how the sentiment proxy has evolved over time.
To evaluate the effectiveness of the sentiment indicator, we examine the subsequent market performance conditioned on the level of the sentiment indicator. When the sentiment indicator gives a low reading (below -1 in Fig. 1) there is more pessimism among Reddit users. When readings are high (above +1 in Fig. 1), Redditors are generally more sanguine about the market. The indicator is currently in the middle of its range, but has been trending downward in recent days.
Fig. 2 shows the subsequent 5-day return of the SPDR S&P 500 ETF Trust (ticker SPY) as a function of the sentiment indicator since the start of our data history (dark blue bars) and for 2022 only (light blue bars). Historically, the market has performed best following periods of negative sentiment. Specifically, in the 5 days following a negative sentiment reading, the market returns 0.8% on average. This phenomenon continued in 2022, as last year the market returned 0.68% in the 5 days following a negative sentiment reading.
Additionally, the market does worst when sentiment is positive. Historically, the 5-day return of the market following a positive sentiment observation is -0.2%. Last year, this figure worsened to -0.36%, meaning the trend of market losses following a positive sentiment reading also continued last year. Performance last year was also quite poor when the sentiment measure was in the middle range (middle set of bars) – this is not surprising, given the market’s overall poor performance in 2022.
We also examine periods when the sentiment indicator reaches extreme levels. In the previous analysis, we used cutoff values of +/-1 to indicate high/low readings for the sentiment. For this study, we increase the threshold to +/-1.5 to focus on instances of extreme positive and negative sentiment. Obviously, when we focus on extreme values, the sample size falls (in this case, the total number of negative and positive sentiment observations falls by more than half) but the trends based on these extreme values are notable.
We then examine the subsequent performance of the market, conditioned on the sentiment indicator reaching these extreme positive and negative levels. Fig. 3 shows the results of this analysis, with the dark blue bars showing performance for the full history, and the light blue bars showing performance for 2022.
From Fig. 3, we see that the sentiment indicator shows higher efficacy when limited to extreme observations. The market returns following an extreme observation are larger in magnitude than for the standard breakpoints used in the initial analysis above; in particular, over the entire history, the S&P 500 returned 1.36% on average in the 5 days following an extreme negative sentiment observation (vs. 0.8% for a “standard” negative sentiment reading). We also see that the trends observed for the history continued in 2022. While the number of actionable observations falls, limiting ourselves to periods when the sentiment indicator reaches extreme values increases its efficacy as a contra-indicator.
In this research note, we update our Reddit-based sentiment proxy for the market. The sentiment indicator has continued to be an effective contra-indicator for the market, particularly when it reaches extreme values. In recent days, the indicator has been trending downward, which could indicate a market rally in the short term.
 Alternative Data: Sentiment from Social Media