Next week, I will be presenting in London at the CARISMA workshop on "News Analytics Applied to Trading, Fund Management and Risk Control." The workshop will be held on February 1st, 2010 at 09h15 (local time) at the MWB, 25 Canada Square, Canary Wharf, London, E14 5LQ.
My presentation is entitled “The Role of News in Financial Markets" where I will provide an overview of how news analytics may be implemented into ones trading or investment environment considering aspects such as sentiment and the impact news may have in formulating and constructing trading strategies.
I also plan to show some of my latest research and review a few interesting studies recently published on news sentiment analysis.
The News Analytics workshop is a precursor to the 5th Annual CARISMA Conference on “The Interface of Behavioural Finance and Quantitative Finance” being held on February 2nd through February 3rd, 2010 also at the MWB.
You can find more information about the CARISMA News Analytics Workshop and Conference at: http://www.optirisk-systems.com/events/carisma2010.asp.
Tuesday, January 26, 2010
Friday, January 8, 2010
Capturing the Seasonal and Trend Components of News Sentiment Indices
Equity news flow is characterized by strong seasonal patterns that includes quarterly bursts of information due to earnings releases. To address this issue, I indirectly normalize by news flow and focus on the Sentiment Ratio (count of positive divided by the count of negative news items) when constructing Sentiment Indices. This seems to be a good approach under the assumption that the seasonal bursts of information impact both the positive and negative news counts symmetrically. Unfortunately, this does not seem to be the case.
Looking at Sentiment Indices constructed based on a one month trailing aggregation window reveals what seems to be a seasonal pattern in the Sentiment Ratio itself. This is not particularly surprising in that earning seasons are likely to contain more company-initiated news stories than on average. Generally, such stories tend to have more of a positive spin than the ones initiated by more objective third-parties.
In Figure 1, I have included two graphs that present both the "raw" and the seasonal-adjusted Sentiment Indices based on something called the Berlin Procedure. This approach helps decompose a time series into five primary components including trend, cyclical, seasonal, calendar, and irregular components. After the decomposition, you generally have three tools to assess the sentiment environment. Firstly, the seasonally-adjusted series that more clearly reveals the trend-cycle movements. Secondly, the estimates of the trend-cycle components of the time series; and thirdly, the original "raw" Sentiment Index. As expected, the seasonal-adjusted index becomes smoother since it removes the strong seasonal components.
Figure 1: A raw and seasonal-adjusted Sentiment Index (Jan 2005 through Nov 2009) based on a one month trailing news aggregation window. The seasonal-adjusted Sentiment Index (using the Berlin Procedure) considers an adjustment of the Sentiment Ratio due to an asymmetric impact of the count of positive and negative news items caused by company initiated news stories.
Before we perform a seasonal adjustment, we have to ask ourselves whether this is really necessary given a trading strategy. After all, these bursts of mainly positive news are "real" and could in fact affect the markets; hence we may simply want to use the "raw" index. That being said, it can probably be expected that market participants are well-aware of this Sentiment Bias as captured by the seasonal component, and thus to some extend discount such company initiated information. Taking this into consideration, it may be desirable to consider both indices simultaneously before making ones trading decisions. Doing so, it is possible to arrive at an estimate of the Sentiment Bias and thereby form a set of hypotheses on how such information should be considered in a trading model.
Comments and ideas are as always more than welcome.
Looking at Sentiment Indices constructed based on a one month trailing aggregation window reveals what seems to be a seasonal pattern in the Sentiment Ratio itself. This is not particularly surprising in that earning seasons are likely to contain more company-initiated news stories than on average. Generally, such stories tend to have more of a positive spin than the ones initiated by more objective third-parties.
In Figure 1, I have included two graphs that present both the "raw" and the seasonal-adjusted Sentiment Indices based on something called the Berlin Procedure. This approach helps decompose a time series into five primary components including trend, cyclical, seasonal, calendar, and irregular components. After the decomposition, you generally have three tools to assess the sentiment environment. Firstly, the seasonally-adjusted series that more clearly reveals the trend-cycle movements. Secondly, the estimates of the trend-cycle components of the time series; and thirdly, the original "raw" Sentiment Index. As expected, the seasonal-adjusted index becomes smoother since it removes the strong seasonal components.
Figure 1: A raw and seasonal-adjusted Sentiment Index (Jan 2005 through Nov 2009) based on a one month trailing news aggregation window. The seasonal-adjusted Sentiment Index (using the Berlin Procedure) considers an adjustment of the Sentiment Ratio due to an asymmetric impact of the count of positive and negative news items caused by company initiated news stories. Before we perform a seasonal adjustment, we have to ask ourselves whether this is really necessary given a trading strategy. After all, these bursts of mainly positive news are "real" and could in fact affect the markets; hence we may simply want to use the "raw" index. That being said, it can probably be expected that market participants are well-aware of this Sentiment Bias as captured by the seasonal component, and thus to some extend discount such company initiated information. Taking this into consideration, it may be desirable to consider both indices simultaneously before making ones trading decisions. Doing so, it is possible to arrive at an estimate of the Sentiment Bias and thereby form a set of hypotheses on how such information should be considered in a trading model.
Comments and ideas are as always more than welcome.
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