How News Events Impact Market Sentiment

RavenPack | May 03, 2010

News sentiment is shown to outperform one-month price momentum when predicting future returns of the S&P500.

Executive Summary:

  • Market and industry-level sentiment indexes are constructed based on a bottom-up approach considering the impact of company-specific news events and their corresponding sentiment.

  • As part of constructing the indexes, I show that company relevance and event novelty are important elements of a news-based strategy, since including only the most relevant and novel news stories result in improved Information Ratios.

  • From May 2005 through December 2009, the strategies tested deliver double digit positive returns in out-of-sample testing.

  • In addition, I show how industry sentiment can add value when constructing market-neutral strategies taking long and short positions in top-ranked and bottom-ranked industries, respectively.

  • Finally, I show that targeted directional exposures to top-ranked and bottom-ranked industries can improve a trading strategy beyond simple S&P500 index exposures.

Market Sentiment

1. Introduction

It is broadly accepted that financial news moves stock prices either through a direct impact on a company’s expected future cash flow, the discount factor that one uses, or through more behavioral or sentiment-based mechanisms. Even though news-based trading has a long history of being part of investment decision-making, only in recent years has it been possible to test ”quantitatively” the impact of news events on individual stock prices or markets. Extensive academic and industry research has shown that news, particularly stories conveying sentiment, can add value in both high and low frequency trading and investment strategies - improving the prediction of price direction, volatility and trading volume.

In a previous study, I made the case for applying a news sentiment index in predicting future returns of the Dow Jones Industrial Average, and showed that taking this approach significantly outperformed a price momentum strategy [Hafez, 2009a]. Tetlock looks at a regression model and finds that ten-day reversals are reduced following company specific news, which indicates that public news is a proxy for information that has not yet been incorporated into the stock price [Tetlock, 2009]. Engelberg et.al. find that short sellers trades are more than twice as profitable in the presence of recent news, which provides strong evidence in favor of the idea that news presents profitable trading opportunities for skilled information processors [Engelberg et al., 2010]. Mitra et.al. included news sentiment as part of the construction of forward-looking covariance matrices [Mitra et al., 2008]. Interestingly, they found that sentiment could add value to the volatility prediction process beyond what could be captured by option-implied volatility. Also, Zhang and Skiena show that news is significantly correlated with both trading volume and stock returns [Zhang & Skiena, 2010].

While studies show the impact of scheduled news events can be measured in milliseconds, signals from unscheduled news events, can be measured in minutes, days, weeks, and months. For example, the intra-day abnormal return impact of positive and negative sentiment events can be measured in minutes and hours when looking at intra-day abnormal returns (see Hafez, 2009c]). Focusing on longer horizons, Cahan et.al. found that the effect could be measured in days and weeks [Cahan et al., 2009a, Cahan et al., 2009b]. In addition, using a one year investment horizon, Kittrell found value in using net sentiment as a measure for long-term stock selection [Kittrell, 2010].

Applying structured news data or News Analytics in a trading model allows for the possibility to not only react in real-time to scheduled and unscheduled news events in a fully or semi-automated fashion, but also to consider the prevailing sentiment trend on a given market. Such trends can be captured by looking at aggregated news sentiment on single companies, sectors, industries or even on broader equity portfolios. As part of previous research, a methodology was presented on how to construct market and sector sentiment indexes that were used as part of a directional sector-rotation type strategy [Hafez, 2009d]. To address news flow seasonality, the indexes were based on counts of positive vs. negative sentiment news stories that were considered to be highly relevant to one of the index constituents.

As part of the study, I find that considering a Company Relevance metric is an important element in constructing sentiment-based strategies as the out-of-sample return correlation improves by a factor of 3 after filtering for relevance. In this study, I take relevance filtering a step further and include only news that is contextually relevant to the companies in the S&P500. That is, where a company has been detected to be playing a prominent role in the news story and has been involved in some type of categorized event (e.g. earnings announcement, analysts rating, product recall, etc.), and therefore has received a relevance score of 100. For more information on relevance, see Appendix B. Furthermore, I consider how it may be desirable to treat stories differently in terms of sentiment impact depending on the detected event category. That is, a bankruptcy story should count more towards a sentiment score than a story about a product or marketing campaign. Finally, I consider how event novelty may influence the construction of sentiment indexes, where novelty in this case represents how ”new” or novel a news story is over a 24 hour time window.

Generally, I find that considering the impact of different company events adds value when constructing market-level sentiment indexes. For industry-level indexes, I noticed that the total number of company-specific events varied depending on the industry. In order to improve the confidence around the sentiment estimates, I apply a slightly less restrictive relevance score moving from 100% to 90% relevant. This permitted the use of other sentiment analytics available from RavenPack which provide more information by examining various aspects of each story (i.e. events, language tone, story type). Here I consult 5 different sentiment scores that classify each news story as being either positive, negative or neutral. The same approach was considered in a previous study [Hafez, 2009d]. Rather than normalizing only for news flow, I consider a normalization for changes in the event category characteristics, which seems to bring further value in the sentiment ranking of industries.

The study proceeds as follows. In Section 2, I provide an overview of the methodology on how to construct market-level sentiment indexes considering an Event Sentiment Score. Furthermore, I consider a simple trading strategy based on a US Market-level Sentiment Index. In Section 3, I describe how to construct industry sentiment indexes based on aggregated news sentiment. Using an industry rank, I first consider a simple market-neutral strategy, followed by a targeted directional strategy based on industry rather than broad market exposures. Finally, in Section 4, I present the conclusion of the study.

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