Generally, market sentiment indices try to capture the prevailing sentiment trend for a particular index or sector. In order to capture such trend, it seems reasonable to consider an aggregation of news sentiment over a well-defined time window to capture the general "mood" of the market. When performing such aggregations it may be necessary to consider normalizing the data since equity news flow, as indicated in a previous posting, is characterized by strong seasonal patterns. One way of performing a sentiment aggregation is by considering net sentiment, which simply measures the count of positive minus the count of negative news items. Without normalizing for seasonality, it becomes difficult to evaluate what values reflect high and low net sentiment as for instance high values may simply be the result of certain bursts of information.
As an alternative to net sentiment, one may consider the sentiment ratio as the baseline for constructing market sentiment indices. As the ratio is measured as the count of positive to negative news items, it indirectly takes into consideration changes in news volume. This approach is expected to work well under the assumption that seasonality impacts the count of positive and negative news symmetrically.
In order to calculate the sentiment ratio for a particular index, one has to filter the data to obtain only news items that at least mentions the index constituents. In addition, further filtering based on the relevance or aboutness of the identified stories as they relate to the constituents of the index may be required to reduce noise in the signal. In a previous blog-posting, it was indicated that only about 20% of news stories hold high relevance, thus indicating that as much as 80% could be adding noise.
To arrive at the market sentiment index, it is necessary to perform a final mapping of the sentiment ratio into index values using a mapping schedule that reflects information about what level or cut-off point is considered neutral sentiment. Generally, large counts of positive and negative stories can be observed during both bull and bear markets, still with larger sentiment ratios in the former market environment, see Figure 1. Therefore, it may be somewhat challenging, independent of market data, to decide on the neutral sentiment position. Especially, it seems important to evaluate the sentiment ratio in a historical perspective in order to deal with this "sentiment bias".
Figure 1: Sentiment ratio for the Dow Jones Industrial Average based on a six months backward-looking news aggregation window (without relevance filtering).
Assuming that the period January 2005 through April 2009 is a good representation of both bull and a bear markets, even though somewhat extreme, the neutral position could simply be measured as the mean ratio over this period. Similarly, the mapping into the positive and negative sentiment domain can be done based on the sentiment ratio volatility, which ensures that more extreme sentiment ratios maps into values closer to zero on the negative side and closer to 100 on the positive side, this in a symmetric fashion.
In Figure 2, I have included the price series of the Dow 30 index as well as the market sentiment index with and without relevance filtering. Relevance is calculated using a company score (0-100) where higher values indicate the identified firm plays a more prominent role in the news story. For example, Bank of America (BAC) would get a score of 100 in a story mainly about the firm reaching a legal settlement with another company, but a score of 5 or 10 if it was simply mentioned as a source in a story towards the end among many other companies.
In one of the coming postings, I will present some results on how well these sentiment indices capture the return of the relevant equity index over different investment horizons. Preliminary results indicate that using the relevance filtered index as compared to the non-filtered index improves the correlation between market return and the sentiment index by a factor of three.
Data Source: RavenPack
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