Our results show that investors can enhance the performance of existing earnings
momentum strategies by combining this with a news flow signal. We find that the
information ratio more than doubles as compared with traditional earnings momentum
strategies. We also consider portfolios based on the combination of earnings momentum
and news flow. For example, a strategy based on buying upgraded stocks with positive
accounting news flow and selling downgraded companies with poor strategic news has
generated an information ratio of 1.47 vs. a standard earnings momentum strategy of
0.66. The most interesting of these backtests, however, is the final row which highlights a
strategy based on anticipating which companies are likely to see analyst revisions post
announcements. Here we identify companies that are currently out of favour by sell-side
analysts but have announced positive news flow. We buy these companies in anticipa-
tion of analyst upgrades over the following few days, taking advantage of the fact that it
takes them longer to process and interpret the information content of news. Similarly,
we sell companies that are currently in favour by analysts but have announced bad news.
Such a strategy has the highest information ratio of 1.87, with an annualized return of
15.6%.
Our results show that news-flow-based strategies can add value to investors. Those
that can react quickly can benefit from the short-term momentum following news (in
particular, earnings news) and gain an information advantage by incorporating news
flow ahead of analyst revisions. Alternatively, investors can enhance the performance of
existing earnings momentum strategies by either combining this with a news flow signal
(buying upgraded stocks with positive news flow, selling downgraded companies with
poor strategic news) or by trying to forecast which companies are likely to see analyst
revisions post news announcements.
8.7 Summary and discussions
We began this article by highlighting that the earnings revisions strategies that the
majority of investors employ typically do not identify the piece of information that
has triggered the change in forecasts. Our aim has been to understand what type of
information causes analysts to revise their earnings expectations, how the informational
content of the signal varies according to the news catalyst, and whether investors can use
news flow signals as input to their models. We have shown that there is a hierarchy to the
informational content of news and that investors can gain an advantage by using news
flow to trade ahead of analyst revisions.
Our initial analysis on news flow has since stemmed into a variety of research projects.
With over 80% of corporate data estimated to be unstructured (the most common
unstructured data is text), this is a growing area of research. With data vendors
dedicating greater resources to the collection and analysis of news flow, there is no
reason for research to be limited to traditional corporate events such as earnings and
trading updates. Data vendors and researchers are able to scour through internet blogs,
social network sites, and Google Trends to find novel sources of information.
One of the perhaps surprising results from our earlier analysis in Section 8.4 is that
there is no serial correlation in corporate news. Companies are equally likely to report
good and bad news going forward. Since undertaking this initial research we have
focused on more granular news flow and have found that there is serial correlation
The impact of news flow on asset returns: An empirical study 227