T
M
A
What’s in the News? Using
News Sentiment Momentum
for Tactical Asset Allocation
IN
A
N
Y
FO
R
MATTHIAS W. UHL, MADS PEDERSEN, AND OLIVER MALITIUS
A
M ADS P EDERSEN
TH
IS
is a managing director
at UBS AG in Zurich,
Switzerland.
[email protected]
C
E
OLIVER M ALITIUS
IT
IS
IL
LE
G
A
L
TO
R
EP
R
O
D
U
is an executive director
at UBS AG in Zurich,
Switzerland.
[email protected]
100
strongly to his remarks.2 Not only did European equity markets see a same-day rally
after Draghi’s remarks, but returns in the
next weeks and months (and even years) after
his speech were strongly positive, as depicted
in Exhibit 1. The reason the markets felt this
news event in the longer term was that it
took weeks and months for economists and
financial market participants to grasp the full
scope and sentiment of Draghi’s speech, as
this was a significant change in course for
the ECB, in order to deal with the Eurocrisis
at the time.
To stay on top of the massive and important amount of news, we have developed a
new methodology to monitor the global
news f low. Rather than looking at individual news stories, we analyze the aggregate
weekly news f low, tracking around 100,000
news pieces each week. In this study, we
analyze company-specific and macroeconomic news releases, as well as other topics
that might inf luence global financial assets,
and that are published daily in the Thomson
Reuters global news universe. Looking at
news sentiment is a way to analyze and make
sense of news content. Sentiment refers to a
specific news story’s tone and how readers
interpret that tone. Positive sentiment means
that the article portrays positive surprises and
opinions. The positive sentiment in an article
might come from either generally good
LE
inancial markets have become more
sensitive to news in the past 10 to
15 years. This phenomenon was
especially pronounced in the turbulent times during and since the recent
financial and sovereign debt crises. Traditionally, the fixed-income markets have paid
more attention to global macroeconomic
news releases.1 However, since the recent
financial crisis, the equity markets also pay
greater attention to actions by policy makers
and central bankers, because they have and
will play a key role in solving these crises.
Often, investors face the difficult task of
monitoring all news while simultaneously
assigning weights of importance for specific
news events that might affect their strategies. The interpretation and the assignment
of weights of importance for specific news
events are, naturally, subjective and tricky,
as investors can be misled in many ways in
their subjective interpretation of how their
scenario might affect financial markets and
thus their investment strategies.
For a recent example, central bank news
events have substantially affected the equity
markets. When the president of the European Central Bank (ECB), Mario Draghi,
gave a July 26, 2012, speech and said that
“[...] the ECB is ready to do whatever it
takes to preserve the euro. And believe me, it
will be enough,” the equity markets reacted
R
TI
is a director at UBS AG
in Zurich, Switzerland.
[email protected]
C
F
M ATTHIAS W. UHL
WHAT’S IN THE NEWS ? USING NEWS SENTIMENT MOMENTUM FOR TACTICAL ASSET A LLOCATION
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EXHIBIT 1
The EuroStoxx after Draghi’s Speech on July 26, 2012
Source: Bloomberg, ECB.
news, or from an item that is better than expected.
For example, if the forward-looking business sentiment
survey in the U.S. (Purchasing Manager Index - PMI)
is better than expected, even though the number might
be low, the market usually interprets this as a positive,
and stock prices tend to go up on such news. This stock
market uptick can then be attributed to positive sentiment on the PMI data release. Negative sentiment is
the opposite of positive sentiment. If data releases are
worse than expected or we hear news of a generally
bad number, the news sentiment would be generally
negative.
By analyzing news sentiment, we can assess news
reporting around the globe, in everything Thomson
Reuters publishes. We develop an approach for filtering the noisy news sentiment data and applying it
to tactical asset allocation investment decisions. To
our knowledge, we are the first to look at news sentiment data on such a global and holistic basis in order to
construct an investment strategy for the global equity
market, for implementation into a tactical asset allocation framework.
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TRADING (IN)EFFICIENT MARKETS
WITH NEWS SENTIMENT
Kahneman and Tversky [1979] as well as Shiller
[1981] and others have shown that financial markets
behave irrationally, rather than rationally. However, the
perception of rationality or irrationality might be intertwined. In fact, Kurz [2010] shows that irrationality
and rationality can be closely linked and that market
behavior under private information is different from
behavior under diverse beliefs with common information. Although a wider audience may believe that some
investors behave entirely irrationally, these investors
might think that they behave rationally.
In fact, De Bondt and Thaler [1985] as well as
Cutler et al. [1989] showed that equity markets react
to news, which does not necessarily mean that there is
always irrationality among market participants. Moreover, Barberis et al. [1998] find over- and under-reaction in stock prices to be a cause of news, as this
news is incorporated only slowly into investor’s minds
and therefore into equity market prices, as was prob-
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ably the case with Draghi’s 2012 speech. Specifically,
Klibanoff et al. [1998] examine the front page of the
New York Times and report that country-specific news
affects country fund prices. Many studies followed that
considered the inf luence of Internet messages and news
on stocks.3 One study that was ground-breaking in the
domain of quantitative textual analysis was the study by
Tetlock [2007], which was one of the first to consider
bag-of-words algorithms to construct quantitative news
sentiment scores from a Wall Street Journal column. The
study results showed that high media pessimism forecasts
falling stock market prices.
However, Tetlock [2007] only considered negative
words for his analysis and did not examine the effect of
positive words on stock prices. Furthermore, it was also
not possible to consider the context or whole sentences
in his analysis. In the literature, it took a few more years
before researchers developed sophisticated algorithms
that could distinguish between both positive and negative words, while also accounting for context.
One of the few studies that consider a more sophisticated approach to news sentiment detection is by Leinweber and Sisk [2011]. Using data called NewsAnalytics
from Thomson Reuters, which is considered state of the
art in the industry, the authors nicely demonstrate that
longer news sentiment cycles exist and that it is possible
to formulate profitable trading strategies with such an
approach.
Nevertheless, Leinweber and Sisk [2011] only use
data from 2003 to 2010, with only 2010 as a out-ofsample period. Although they confirm that there has
been a “regime shift” in the news sentiment data from
Thomson Reuters, their analysis unfortunately stops in
2010. Furthermore, Leinweber and Sisk [2011] simply
filter the news sentiment data according to some criteria
defined by Thomson Reuters. They do not construct
a model that can generate signals that do not switch
as often as their approach (for lower transaction costs
and ease of implementation, in particular for tactical
asset allocators). Although their strategy delivers alpha
of more than 10% in their out-of-sample period, they
cannot generate a trading strategy that reduces the
maximum drawdown and the strategy volatility, as their
worst maximum drawdown in the examined period is
about 60%. Although we recognize the scientific and
theoretical importance of their study, we question the
practical application of their results, as the number of
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JPM-UHL.indd 102
switches and the maximum drawdown would push practitioners away.
In general, investors face two sets of challenges:
first, they need to distinguish between important and
unimportant news; second, they need to digest the news
and assess its tone, along with any potential changes.
Therefore, we want to show that it takes investors a long
time (several weeks and even months) to digest news
sentiment in news. The behavioral biases that are up for
debate around this phenomenon are herding, as well as
over- and under-reaction to new market information.4
In this study, we use a more sophisticated approach and
more recent data (up to 2013) in order to remove the
shortcomings that the findings by Leinweber and Sisk
[2011] pose, while at the same time confirming that
longer-term news sentiment cycles exist and can be used
to formulate investment strategies.
TACTICAL ASSET ALLOCATION
CHALLENGES
Tactical asset allocation has several challenges.
First and foremost, tactical asset allocators cannot f lip
positions too often, as they often manage large books
of assets on the discretionary side, as well as big client
bases.5 Concretely, this means that positions are often
opened with a three- to six-month horizon, whereas
most tactical asset allocators have a bi-weekly or monthly
investment process. Thus, it is paramount to have a
quantitative strategy for tactical asset allocation purposes
that does not switch too often during the year. Tactical
asset allocators therefore face the challenge of identifying
the larger market movements, irrespective of financial
market noise. For an equity–bond model, it is necessary to identify when equities are in general expected
to rise or fall; the model should identify when to switch
from equities into bonds, and vice versa. Traditional
macro models based on business cycle indicators, such
as purchasing manager indices (PMIs), retail sales, or
industrial production, are generally slower to anticipate
equity market trends, because equity markets usually
lead the economic cycle. Therefore, we attempt to use
an indicator that is timely and forward-looking, such
as news sentiment. The reason that news sentiment is
more timely than (for instance) industrial production is
that news reports are published constantly, with extra
attention and speed around breaking news. Industrial
WHAT’S IN THE NEWS ? USING NEWS SENTIMENT MOMENTUM FOR TACTICAL ASSET A LLOCATION
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production is published with a lag of about half a month.
New information is incorporated more quickly into
news than into traditional macro indicators.
Furthermore, we do not only look at the news per
se, but also at the interpretation of the news, that is, the
news sentiment.
DATA
The news sentiment data we use are from Thomson
Reuters’ NewsAnalytics. This is the same dataset used in
Leinweber and Sisk [2011]. The sentiment scores are
obtained from a linguistic algorithm that uses the bagof-words approach, that is, it looks at both positive and
negative words, according to a dictionary. Additionally,
the algorithm can take the article’s context into account.
This was achieved by training a learning algorithm with
articles analyzed for positive or negative sentiment by
more than hundreds of financial market experts and
economists. They were all given the same articles and
asked to assign either a positive or negative sentiment
score, according to their own opinion after they read
each article. All the sentiment scores for these articles
were then taken and fed into this learning algorithm,
forming a coding classifier to identify positive and negative sentiment within a contextual framework, not only
by a bag-of-words approach. For example, the sentiment
algorithm can distinguish between negative words and
negations of positive words. “Good” would be categorized as positive in the sentiment analysis, but “not
good” would be classified as negative.
Based on this dataset, we apply the concept of quantitatively measuring sentiment in news articles in order
to forecast equity markets. Every Reuters news article
EXHIBIT 2
Overview of Topic Codes for Company- and Macro-Specific Indicators
Source: Thomson Reuters NewsAnalytics.
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is coded with positive {1}, neutral {0}, or negative {–1}
sentiment. Thomson Reuters is one of the few providers
of sentiment-classified news in the industry, although a
few companies have emerged that offer similar services.6
The dataset that we use for this study consists of highfrequency (tick data), sentiment-rated Thomson Reuters
news pieces, classified from a wide list of topics.7 For
this study, we filter all Reuters news-sentiment items
for company- and macro-specific topics. See Exhibit 2
for an overview of the specific sentiment categories we
filter. Then we aggregate the high-frequency tick data
to daily data. After this first aggregation, we aggregate
the daily data again to weekly data, excluding Saturdays
and Sundays.8 We therefore only consider the five-day
work week. This is because weekend news is mainly
repetitions of the past week’s market events and thus is
not predictive of equity market returns in the following
week. In total, more than 100,000 Reuters news items
per week are coded for sentiment from January 2003
to December 2013. We obtained MSCI World and the
three- to five-year U.S. government bond index data as
well as the JP Morgan Cash USD Index from Thomson
Reuters Datastream.
ANALYSIS
Filtering the Noise out of News Sentiment
with the CUSUM filter
In order to better understand the characteristics
of the news-sentiment data, we aggregate the highfrequency news-sentiment tick data to daily and weekly
frequencies, as previously described. We look at company
and macro news sentiment separately. Exhibit 3 shows
EXHIBIT 3
Daily Aggregated News Sentiment
Source: Thomson Reuters NewsAnalytics, UBS.
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WHAT’S IN THE NEWS ? USING NEWS SENTIMENT MOMENTUM FOR TACTICAL ASSET A LLOCATION
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the daily aggregated news sentiment, and Exhibit 4
shows weekly aggregated news sentiment. As both
exhibits reveal, the news-sentiment data are by nature
noisy, in particular when we look at daily frequency.
We therefore need a method that can further filter the
noise and that identifies longer-term trends in news
sentiment.
Exhibit 5 shows the descriptive statistics of both
news-sentiment components. Both components have
a positive mean and median that are close to 0. Unit
root tests show that the company and macro news-sentiment data series are stationary, as the null hypothesis
that the data series have a unit root can be rejected at
the 1% level. In Exhibit 6, we show the total weekly
number of both company and macro news for which
we filtered. As Leinweber and Sisk [2011] identified,
there is a regime shift in the news sentiment data in
2007, which we can also see in Exhibit 6, as the total
number of both company and macro news increases
significantly in 2007.
It is inherently difficult to use only plain aggregated news-sentiment data, such as Leinweber and Sisk
[2011] do, as we would have too many position switches.
One possibility is to further filter the data for relevance,
novelty, intensity, and set extreme percentiles (fifth and
tenth percentiles) of the prior daily distribution of the
sentiment score, such as Leinweber and Sisk [2011] did.
In their study, Leinweber and Sisk [2011] hold the positions for 20 days, subject to a stop-loss rule set at 5% and
a profit-taking rule set at 20%. They constrain any single
position to a maximum of 15% of NAV. Although this
might be a feasible trading strategy for some investors,
EXHIBIT 4
Weekly Aggregated News Sentiment
250
1
0.8
200
0.6
0.4
150
0.2
0
100
−0.2
50
−0.4
−0.6
0
−0.8
−1
01/2003
01/2004
01/2005
01/2006
01/2007
01/2008
Company News Sentiment (lhs)
Sum of Company News Sentiment (rhs)
01/2009
01/2010
01/2011
01/2012
01/2013
−50
Macro News Sentiment (lhs)
Sum of Macro News Sentiment (rhs)
Source: Thomson Reuters NewsAnalytics, UBS.
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EXHIBIT 5
Descriptive Statistics of News-Sentiment Components
Source: Thomson Reuters NewsAnalytics, UBS.
EXHIBIT 6
Total Weekly Number of Macro and Company News
Source: Thomson Reuters NewsAnalytics, UBS.
it is not feasible for tactical asset allocators, and probably for other professional investors who consider their
achieved returns in relation to volatility, as they also
might look at the strategy’s maximum drawdown. The
maximum drawdown of the strategy by Leinweber and
Sisk [2011] is about 60%, which is too high for most
professional investors, and especially for market participation strategies. Furthermore, holding the position for
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JPM-UHL.indd 106
a maximum of 20 days results in at least 18 switches
per year. We want to construct a strategy with fewer
switches per year, a much better return-volatility ratio,
and less maximum drawdown, to make it feasible for
professional and tactical asset allocators, and to reduce
transaction costs. As previously outlined, more than 10
to 12 switches per year are not suitable for tactical assetallocation purposes. Thus, we need a way to handle
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the news-sentiment data so that we get at most 10 to
12 switches per year, that is, one switch per month, to
comply with the monthly investment process that tactical asset allocators usually have.
Therefore, we are confronted with f inding a
method that extracts the noise in the news-sentiment
data. There are two possible ways to do this: 1) aggregate the data from daily frequency to a lower frequency,
and 2) try to filter out the noise by using some sort of
moving average, to get the longer-term momentum of
the news-sentiment signal. We decided to combine the
options by aggregating the data from daily to weekly,
as previously described, and by turning to the literature
of frequency filtering in general and to the cumulative
sum (CUSUM) filter method in particular, in order to
construct a way of extracting the noise in the news-sentiment data. This should lead to fewer strategy switches
and to the identification of certain longer-term market
regimes based on news sentiment.
The CUSUM filter method, introduced by E.S.
Page [1954], identifies regime changes, that is, a detection of the changes in a sample’s parameter values.9 As
Page [1954] described, he worked to detect a change in
the quality parameter labeled θ, as process inspection
schemes were required to solve such issues in machine
processing. Once a certain threshold is met, the parameter θ detects that change and gives notice of a deterioration or improvement in the signal. The original
purpose of using CUSUM filter methods in the manufacturing industry was to add oversight to the systemcontrol process. Yi et al. [2006] note that the use of
the CUSUM filter technique has been extended widely
since its inception and has been used to survey changing
points in medicine and meteorology, assess the health of
a commercial or industrial company, and in the financial
markets. Some studies, including Lam and Yam [1997],
have suggested using filter techniques for trading in the
financial markets. The authors nicely illustrate that the
control chart techniques can be applied to detecting
regime shifts in financial markets and that these regime
shifts can predict future asset returns. This method is
essentially one way of calculating an asset’s momentum,
which is a longer-term trend. Yi et al. [2006] find that,
while several studies showed that applying CUSUM
filter techniques to various stock indices can result in
profitable trading strategies, the strategies are no longer
profitable after accounting for transaction costs.10 Arguably, the transaction costs that Yi et al. [2006] used are
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quite high, at 0.75% per transaction. We want to show
that, by applying the CUSUM filter method to calculating news-sentiment momentum, it is possible to
formulate profitable investment strategies, even if we
take transaction costs into account.
As Page [1954] neatly described, there are several
ways to apply the CUSUM filter method. We focus on
the two-sided schemes because we want to apply this
to the financial markets, which can either deliver positive or negative returns. More specifically, a two-sided
scheme simply combines two one-sided procedures: one
to detect an increase in the quality number, one to detect
a deterioration. We therefore have two control limits:
the upper control limit C i and the lower control limit
C i− , to detect two-sided changes in the signal.
The upper control limit C i+ is defined as follows:
C i+ = max ⎡⎣0, (
) ∗ Ci+−1 + (
)⎤⎦
(1)
The lower control limit C i− of the CUSUM filter
is defined as
C i− = min ⎡⎣0, (
) ∗ Ci−−1 + (
)⎤⎦
(2)
where xi refers to the news-sentiment time series, and
α is the exponential weighting calibration parameter.
The total signal that we obtain from the CUSUM filter
method then results in
C i = C i+ + C i−
(3)
In a study applied to the German stock market
with German corporate disclosures and Reuters articles on the CDAX index, Hagenau et al. [2013] conf irm that longer-term news-sentiment cycles exist,
using news tone (i.e., sentiment) momentum up to
12 weeks. Hagenau et al. [2013] show that news-sentiment momentum has the highest and most stable
accuracies and returns for investment horizons of four
to six weeks, making a strong case for medium-term
news-sentiment momentum investment strategies.
However, one study shortcoming is that they only
had limited news sources available and were not able
to consider company-specific news.11 In our study, we
have a much larger and more extensive news-sentiment dataset, with both macro- and company-specific
news sources ranging from 2003 to 2013, totaling
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EXHIBIT 7
Tactical Asset Allocation Strategy Based on News-Sentiment Momentum
over 20 million news pieces for that period. Furthermore, we use crossing moving averages to calculate
the news-sentiment momentum between two and 50
weeks, with a rolling window of one year to generate
out-of-sample results. The signals we obtain with
the CUSUM filter method are normalized between
+100% and −100%.12
Tactical Asset Allocation Based
on News-Sentiment Momentum
Based on the previously described method, we
apply the CUSUM filter to see raw news-sentiment
data momentum and construct an investment strategy.
The signals resulting from the CUSUM filter method
range between +100% and −100%. A signal above 0%
indicates a positive news-sentiment trend filtered with
the CUSUM method, whereas a signal below 0% indicates a negative news-sentiment trend filtered with
the CUSUM method. Positive signals correspond to
suggestions to invest in the asset class, that is, in equi108
JPM-UHL.indd 108
ties, and negative signals correspond to suggestions
to invest in bonds. These signals can be practically
applied in various ways. As mentioned earlier, we
want to construct an investment strategy based on
news-sentiment momentum for tactical asset allocation. Naturally, there are numerous ways to implement
a tactical asset allocation strategy, depending on the
underlying strategic asset allocation and the trackingerror target.
One option is to construct a portfolio with equities and bonds and let the TAA equities moves be either
+10% or −10%, depending on whether the signal suggests an overweight or underweight in equities versus
bonds. Another option is a more pronounced version,
where an underweight signal for equities would translate
to a 100% position in bonds (and thus 0% in equities),
and an overweight signal for equities would translate to
a 100% position in equities (and thus 0% in bonds). A
neutral allocation (and therefore also the benchmark)
would be an investment of 50% in MSCI World equities
and 50% in U.S. high-grade government bonds with an
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EXHIBIT 8
Tactical Asset Allocation Strategy Based on News Sentiment Momentum
average duration of three to five years. In this article, we
opt for the simple and more pronounced latter option: a
50% equities and 50% bonds neutral allocation, a 100%
equities allocation in the case of an overweight equities
signal, and a 100% bonds allocation in the case of an
underweight equities signal.
We tested the news-sentiment strategy in an outof-sample environment from August 2004 to 2012.
Since 2013, the investment strategy has run live,
giving us a true out-of-sample period. Exhibit 7 gives
a graphical representation of the performance of the
strategy versus the benchmark, and Exhibit 8 shows the
results of the strategy versus the benchmark in a table.
Over the tested time horizon since 2004, the strategy
has outperformed the benchmark (50% MSCI World
and 50% three- to five-year U.S. government bonds)
in most of the back-tested years. For the whole time
period between August 2004 and December 2013, the
total average return was 12.4% per year; the alpha (i.e.,
the additional return over the benchmark) generated
from the sentiment strategy was 7.3% per year. Portfolio metrics, such as Sharpe or information ratios,
give investors a better understanding about a strategy’s
achieved returns in relation to risk or volatility.
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The news-sentiment strategy has a Sharpe ratio of
1.5, compared with the benchmark’s 0.7 and an information ratio of 0.8.13 With the news-sentiment strategy,
the maximum drawdown is heavily reduced to −11%,
as opposed to the benchmark, which has a maximum
drawdown of −27%. In the same time period, the MSCI
World Equity Index had a maximum drawdown of more
than −50%. The news-sentiment strategy therefore not
only generates significant alpha over the benchmark but
also greatly reduces the risk of an equity–bond portfolio.
Furthermore, the strategy only switched eight times
(on average) per year. This makes the strategy practicable for tactical asset allocators who mostly operate on
monthly investment cycles and can therefore handle a
maximum number of 12 switches per year.
Although we have seen that a trading strategy
based on the filtered news-sentiment data works, we
want to test whether the signals also hold an econometric
analysis. We therefore run the following ordinary leastsquares (OLS) regressions:
Δlog(
o
t
) = c + α1CUSUM t + β1CUSUM t −11 + εt (4)
where Δlog(MSCIt ) refer to log returns of the MSCI
World stock market index, CUSUM refers to the
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news-sentiment CUSUM signal, ε is the error term,
and
Δlog(
o
t
) = c + α1CompNSt +β1MacroNSt −11 + εt
(5)
where CompNSt refers to weekly raw company news sentiment and MacroNSt refers to weekly raw macro newssentiment data. Exhibit 9, column 1 shows the regression
results from Equation (4). The CUSUM signals are statistically significant at the 5% level, confirming the good
results of the trading strategy. Exhibit 9, column 2, shows
the regression results with the raw news-sentiment data
for the sub-components company and the macro news
sentiment. The coefficients of the news-sentiment variables are also statistically significant. In sum, this simple
econometric analysis shows that the news-sentiment signals generated with the CUSUM filter technique are
both statistically significant and useful for creating alpha
with a trading strategy.
When comparing these results with those of Leinweber and Sisk [2011], the news-sentiment strategy
outperforms their strategy in 2010 by around 1 percentage point. More importantly, the news-sentiment
strategy delivers higher average returns over a longer
time frame that has been tested both out-of-sample
and even live.
Furthermore, the news-sentiment strategy’s maximum drawdown of 11% is lower by far than the 60%
that Leinweber and Sisk [2011] achieved. Additionally, the news sentiment strategy also switches less.
Therefore, the news-sentiment strategy presented
here is more robust and more feasible for tactical asset
allocators.
EXHIBIT 9
Regression Analysis
Note: Robust t-statistics in parentheses, coefficients in bold are statistically significant.
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CONCLUSION
News and sentiment in news often influence financial markets and asset prices. Although this is well-recognized by investors, only a few studies have used news
sentiment to predict future financial market developments as a way to formulate alpha-generating strategies,
let alone create a best-practice approach for tactical asset
allocation. In particular, strategies for tactical asset allocators are only suitable if they switch at most 12 times per
year. In order to fill this gap, we combine company- and
macro-specific news sentiment from around 100,000
news pieces per week and use the CUSUM filter method
to calculate momentum in news sentiment.
Our results show that this method provides solid
outperformance in most years during the period from
2004 to 2013, even after transaction costs. Furthermore,
the risk metrics that we achieve with the news-sentiment
strategy are solid, as the proposed strategy has an information ratio of 0.8 and a maximum drawdown of −11%,
while only switching as little as eight times per year
(on average). These findings suggest that longer-term
news-sentiment cycles exist that can be exploited with
investment strategies that are based on news-sentiment
momentum, which are feasible for professional investors
and tactical asset allocators.
7
The topics range from financial market to economic
and political news, categorized into topic codes. See Reuters
Codes—A Quick Guide, available at https://customers.reuters.
com/training/trainingCRMdata/promo_content/ReutersCodes.pdf.
8
The database software that was used for these operations is kdb+.
9
See also Page [1961], Ellaway [1978], and Severo and
Gama [2006] for more information on change detection.
10
See Lam and Yam [1997], Blondell et al. [2002], and
Philips et al. [2003].
11
Hagenau et al. [2013] obtained 10,490 corporate disclosures from DGAP (“Deutsche Gesellschaft fuer AdhocPublizitaet”) between 2000 and 2011, and 4,766 news articles
from Reuters from 2003 to 2009.
12
The calculations for obtaining the investment strategy
signals with the CUSUM filter technique were programmed
in MATLAB.
13
The Sharpe ratio indicates that the annual return is
1.5 times higher than the strategy’s annual volatility. See also
Sharpe [1994] for more information on the Sharpe ratio. The
information ratio sets the alpha of the strategy versus the
benchmark into perspective vis a vis the tracking error.
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ENDNOTES
1
See Goldman Sachs study “Economic News and the
Equity Market,” U.S. Economics Analyst, Issue No. 12/30,
July 27, 2012.
2
See speech by Mario Draghi, president of the European Central Bank, at the Global Investment Conference
in London, ECB website, July 26, 2012, http://www.ecb.
europa.eu/press/key/date/2012/html/sp120726.en.html.
3
See Antweiler and Frank [2004], Cao and Wei [2005],
Edmans et al. [2007], Hirshleifer [2001], Hirshleifer and
Shumway [2003], Kamstra et al. [2003], Maier [2005], and
Mullainathan and Shleifer [2005], among others.
4
See Hong and Stein [1999] for a detailed discussion on
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5
For example, the UBS CIO WM Global Investment
Office manages roughly $130 billion of assets in discretionary
mandates and advises on roughly $1600 billion of client assets
on the advisory side.
6
See Thomson Reuters News Analytics, http://thomsonreuters.com/products_services/financial/financial_products/
quantitative_research_trading/news_analytics.
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To order reprints of this article, please contact Dewey Palmieri
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