Download Reading All the News at the Same Time: Predicting Mid

2013 46th Hawaii International Conference on System Sciences
Reading all the news at the same time: Predicting mid-term stock price
developments based on news momentum
Michael Hagenau
University of Freiburg
michael.hagenau
@is.uni-freiburg.de
Matthias Hauser
Karlsruhe Institute of
Technology
matthias.hauser
@student.kit.edu
Michael Liebmann
University of Freiburg
michael.liebmann
@is.uni-freiburg.de
end of the trading day. Additionally, these studies only
focus on single news messages, but do not include the
momentum triggered by combinations of news into
their prediction.
In this paper, we focus on investment decisions that
are built on news momentum to predict medium-term
stock price index developments. We calculate the news
momentum by aggregating tone values of single news
over the past weeks. Based on news momentum of up
to 12 weeks of the past, we predict future stock price
index developments over one to ten weeks horizons.
Based on our news momentum, we formulate two rules
deciding whether a long or short strategy should be
pursued during the investment horizon. We
demonstrate that profitable stock index trading can be
established based on news momentum for two different
news sources. We calculate several performance
metrics and pay special attention to the stability of our
results. To demonstrate the superiority of our trading
strategy, we also provide a momentum and buy-andhold benchmark to reflect typical alternative strategies.
In contrast to intraday short-term predictions,
medium-term predictions have two advantages: First,
medium-term investments allows for significantly
higher investment volumes than low-latency trades [5]
as trades do not have to happen within milliseconds,
but can be spread out over the day or even be placed in
the very liquid open and close auctions of the trading
day. Second, the news momentum may not be the sole
criteria for the investment decision, but be part of a
complex decision process factoring in qualitative and
quantitative information as well as expert judgments.
We forecast a stock price index instead of single
stocks as the higher number of news contributing to the
momentum ensures more stable results. Additionally,
indices offer a wide variety of financial instruments
(i.e. index futures) which are popular among
algorithmic traders.
Our findings are also supported by financial
literature. Despite contradicting the efficient market
hypothesis (“semi strong form”, [4]) in the sense that
news is not fully reflected in stock prices immediately
after news is transmitted, more recent financial
literature supports the view that news may need more
Abstract
This paper investigates whether news momentum
can predict medium-term stock index developments.
News momentum can be built by aggregating tone of
news over the past weeks. We find that news
momentum can predict future stock price developments
and establish profitable trading strategies that beat
buy-and-hold and momentum benchmarks. Trades are
issued for significant changes in momentum between
current and prior weeks. We ensure stability of our
results by using two different news data sets and by
analyzing both different investment horizons and
aggregation times for our news momentum. Compared
to intraday news trading, medium-term momentum
trading allows higher investment volumes and can
contribute to complex investment decisions also
incorporating other qualitative and quantitative
factors.
1. Introduction
The amount of available information to financial
investors has substantially increased over the last
decades due to improved information intermediation.
The available information consists of qualitative and
quantitative information of different kinds and from
various sources, e.g. corporate disclosures, independent
opinions, news articles and analyst reports. Since the
sheer amount of information is vast and the
information is mostly unstructured, there is an
increasing demand for development of automated
methods to understand and draw inferences from this
information. As news carries information about the
company’s fundamentals and qualitative information
influencing expectations of market participants, it plays
an important role in the valuation process of investors
and analysts.
Thus, in recent years, many studies have focused on
stock price prediction based on financial news (e.g.
[12], [17], [14]). However, these predictions are only
short-term oriented: They predict the immediate stock
price reaction following the transmission of the news
and measure the stock prices between minutes and the
1530-1605/12 $26.00 © 2012 IEEE
DOI 10.1109/HICSS.2013.460
Dirk Neumann
University of Freiburg
dirk.neumann
@is.uni-freiburg.de
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time to be fully reflected in stock prices ([1], [7], [9],
[18]). Moreover, news momentum addresses
explanations of financial literature for the prolonged
incorporating of news into stock prices. As objective
automated measure, news momentum is not affected
by subjective behavioral aspects (c.f. [3]) and
maintains oversight over all relevant news for the asset
(c.f. [9]). Further, we presume that there is an
additional
value
by
processing
all
news
simultaneously. The strong relation to comprehensive
stock indices indicates that news momentum may
capture general sentiment changes in a country.
The remainder of the paper is structured as follows:
In section 2, we conduct a review of relevant research
on momentum in capital markets and the prediction of
stock price effects based on qualitative information.
Section 3 describes how we build our news momentum
based on existing research about sentiment metrics and
develop our trading strategy. In section 4, we evaluate
the performance of our trading strategy and compare
our approach to popular benchmarks. Section 5
summarizes and describes the extension of our current
news momentum model.
2.2 Stock price prediction based on text mining
Numerous studies in financial text mining research
focused on the explanation of future returns and
prediction of short-term stock price reactions based on
financial news. Thereby, two approaches for stock
price prediction were used: One set of scholars rely on
self-developed text metrics quantifying the tone of
each news message (e.g. [17], [11], [10]). The
classification of news messages into positive and
negative is based on the text metric. The different
methods vary in complexity: While Tetlock et al. only
base their decision on the fraction of negative words
from a psychosocial dictionary, Liebmann et al., for
example, design dynamic word lists and assign weights
for each word based on statistical analyses.
The other set of scholars directly use machine
learning approaches for the prediction of stock price
reactions following a financial news message (e.g.
[12], [14], [8], [15]). Most of the studies make use of a
Support Vector Machine (SVM) to classify news
messages into positive and negative.
However, independent of the approach, previous
literature is mainly focused on short-term stock price
prediction, i.e. only intraday returns immediately
succeeding the transmission of single news messages
are considered. Fung et al. [6] investigate price
reactions of up to one week which is still short
compared to the investment horizons considered in
financial literature (i.e. several weeks up to 12 months,
[7]). The corresponding algorithms may be
implemented in real-time trading systems, but are not
suitable for medium or long term investment decisions.
To the best of our knowledge, so far, only one
working paper focuses on medium to long-term stock
price prediction based on an aggregation of financial
news [16]. The study uses company related news from
the Reuters NewsScope data set where each news
message is provided with Reuters’ proprietary tone
score. As the methodology for this proprietary tone
score is not published, it can neither be reviewed nor
reproduced. Based on these tone scores, Sinha
calculates a “qualitative information” measure over the
past three months by unweighted averaging of the tone
scores. He finds that the stock market under-reacts to
information contained in news articles. When using for
investment decisions for an up to five months horizon,
his measure has predictive abilities and generates an
excess return of 34 basis points per month.
In contrast to [16], we make use of a publicly
available and reproducible metric to base our
aggregation and investment decision upon. While
Sinha is only interested in contributions to the field of
finance, we transform our findings into a decision
support system with practical focus. Whereas Sinha
2. Related Work
2.1 Momentum in capital market research
Since the late ‘80s, financial literature supports the
view that price responses to new information might be
delayed [1]. Consequently, the field of behavioral
finance emerged and an increasing number of scholars
found behavioral anomalies, such as stock price drifts
and excess returns in medium to long-term trading
after news (e.g. [7], [18]). The main assumption of this
research stream is that there are positive future returns
if there were positive returns in the past (and vice
versa): There is “momentum”. They attributed their
findings to the fact that “the market responds gradually
to new information” and “prolonged adjustment of
analyst forecasts” [18]. Other behavioral explanations
attribute the slower incorporation of news into stock
prices by overconfident investors [3] who value their
own information higher than external signals. Recent
literature also suggests that high numbers of concurrent
news limit investor’s attention and lead to delayed
stock price reactions [9].
This delayed information processing exhibits
potential of news-based medium-term trading.
Automated analysis of news momentum also addresses
two behavioral aspects from financial literature:
Automation may mitigate investor’s inattention and the
objectivity of an automated algorithm may compensate
for investor’s overconfidence.
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simulates the trading of a subsample of stocks (i.e. top
and bottom deciles of tone), we simulate the prediction
of a comprehensive stock price index. As a large
number of stocks are represented in an index, forecasts
are based on more news than for single stocks reducing
noise and leading to higher more stable results. Major
indices also offer futures which are very popular
among algorithmic traders. Moreover, comprehensive
stock indices may represent whole countries. Similar
analyses can be performed for industries or
economically dependent regions (e.g. emerging
markets). Thus, forecasting indices is also forecasting
pulse and development of economic regions.
will make use of the statistical tone measure “Tonality”
of [10].
For this approach, all words in the document corpus
have to be extracted first. Assuming that words with
the same word stem convey the same or similar
meaning, we employ the Porter Stemmer, as in [13],
which reduces inflected words to their stem. We also
remove stopwords (e.g. “the”, “it”, “like”, “or”) as they
are of low informative value. Subsequently, the most
informative words for sentiment analysis out of the
remaining words have to be selected. Most informative
refers in particular to those words which help to
discriminate between positive and negative news
messages. If, for example, a word frequently occurs in
both, positive and negative news messages, it is
considered to contain less information. However, if a
word occurs less frequently in the overall data set, but
concentrates in either positive or negative news
messages, it is considered to be informative. To
determine whether a news message is positive or
negative in an objective manner, abnormal stock price
returns on the day the news was disclosed can be used
to label the messages. Thus, actual market returns are
used to identify the most informative words. The
market feedback reflects the average interpretation of
all acting market participants which significantly differ
from using a predetermined dictionary as all words are
selected that play a discriminating role in the given text
corpus and thus do not miss out on relevant terms that
the creator of a predetermined dictionary has not
thought of. The concept is similar to the information
gain [10].
The just mentioned step of selecting the most
informative words and assigning tonality values is very
similar to the training step of a machine learning
approach. Consequently, a separation of data into
training and validation set is needed.
After each informative word has received a tonality
value, the sum of tonality values over each word in the
news message is calculated and standardized by the
total number of words. Thus, it ranges between -1 and
+1 while negative tone values indicate negative
messages and positive tone values indicate positive
messages.
3. Methodology
In this section, we first select a suitable text metric
for the tone of news from literature. Subsequently, we
describe two approaches for calculating news
momentum. The first approach aggregates the tone of
single messages. The second approach aggregates the
weekly proportion of positive news messages. Lastly,
we formulate decision rules to be used for actual
trading of our stock price index.
3.1 Selecting a suitable text metric for the news tone
Analyzing unstructured information in the shape of
text requires a machine readable representation. This
step includes the identification of those words, which
are most relevant and the counting of the occurrences
within each news message.
Although combinations of words are representing
the content of text messages better than single words
([8],[14]), we make use of single words due to better
availability of well-researched text metrics for the tone
of messages. In contrast to the mentioned machine
learning approaches, we need to rely on tone metrics to
be able to aggregate the tone value of each message
into a longer term news momentum.
The set of available text metrics ranges from
dictionary to statistical approaches which develop their
word lists and weights based on actual feedback from
the stock market.
While dictionary-based approaches (as e.g. in [17],
[11]) are striking for their simplicity, they suffer from
the fix word set and a missing differentiation between
different words in the dictionary (i.e. the dictionary
does not come with a weighting scheme, thus, just the
weights +1 and -1 are assigned for positive and
negative words). Consequently, statistical approaches
showed higher explanatory power and predictability
for stock price returns [10]. Thus, in the following, we
3.2 Trading based on news momentum
In this section, we present two approaches for
calculating news momentum: The first approach is
based on the aggregation of tonality values. Thus, it
can directly measure changes in the average tone of
news. The second approach is based on aggregating the
proportion of positive news (i.e. not the actual tone
values). Thus, it is independent from the level of tone
values and, thus, could be more resistant to
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proportion aggregate value is equivalent to an
increased number of weighted positive news. The
portfolio is bought (i.e. long position) if the current
proportion aggregate is greater than the past value and
short-sold otherwise (i.e. short position). Thus, we
trade on differences of proportion aggregates which we
denote as “news proportion momentum”. To reduce
noise and avoid trading on minimal differences, we do
not trade for very small news momenta.
Building the proportion aggregate based on positive
news only is fully equivalent to building it on
proportion of negative news (i.e. both proportions sum
up to one) as we use relative comparisons between two
aggregates.
The analogy to the term “momentum” in financial
literature is obvious: “Momentum” in financial
literature denotes the continuation of returns (i.e.
changes in stock prices). “News momentum” denotes
continued changes in the average tone of news
messages (i.e. changes in tone aggregates).
It is important to note that the computation of
tonality values for each news message and their
aggregation runs in polynomial complexity.
To demonstrate the behavior of our aggregates, we
plot the stock price index CDAX in comparison to the
12-week aggregate for both of our approaches (Figure
1). Our measure seems to follow economic cycles very
well. Thus, we presume that news momentum could
help to understand the economic situation and
development
of
regions
and
countries.
exaggerations in single news messages. For each
approach, we describe how to build an aggregate (a)
and how calculate the news momentum from changes
in the aggregate to make trading decisions (b).
1. News tone momentum:
a.
Aggregation of tonality values: As companies
with higher market value have a stronger influence on
the considered stock price index, we first weight
tonalities of single news messages by the logarithm1 of
the market value of the underlying companies. The
weighted tonalities of the weekly news messages are
averaged to determine weekly tonality values. We
introduce this interim step to limit the influence of
weeks with very high numbers of news [16]. For
building a longer-term news aggregate, we summarize
these weekly tonality values over four to twelve weeks.
Consequently, e.g., the sum of 6 weeks will be a 6week aggregate.
b.
Trading on news tone momentum: Every week,
the current week’s aggregate is compared to the
aggregate four weeks prior. The portfolio is bought
(i.e. long position) if the current aggregate is greater
than the past value and short-sold otherwise (i.e. short
position). Thus, we trade on differences of tone
aggregates which we denote as “news tone
momentum”. To reduce noise and avoid trading on
minimal differences, we do not trade for very small
news momenta. We chose differences of four weeks as
suggested by financial literature [18]. Pilot studies also
confirmed four weeks to be a reasonable time span.
Significantly shorter time spans (e.g. one week) could
not capture news momentum well as the two compared
aggregates would have been too similar (e.g. too few
news in the one week making the difference).
CDAX
800
Aggregates
0.8
CDAX
2. News proportion momentum:
a.
Aggregation of proportion of positive news: We
first classify news messages into positive and negative
based on their tonality value (c.f. [10]). Second, we
calculate the weekly proportion factors of positive
news by relating the sum of the market-value weighted
frequencies of positive news to the market-value
weighted frequencies of all news. With these
proportion values, proportion aggregates are built in
the exact same manner as for the tone aggregates, i.e.
summed up for 4 to 12 weeks to build a proportion
aggregate.
b.
Trading on news proportion momentum: Every
week the value of the proportion aggregate of the
current week is compared with the proportion
aggregate four weeks prior. A higher current
600
0.4
400
0
200
-0.4
Tonality aggregate
0
2000
2001
CDAX
2002
2003
2003
2004
2005
Tonality Aggregate (moving avg.)
2006
2007
2008
2009
2010
-0.8
2011
Proportion Aggregate (moving avg.)
Figure 1. CDAX index vs. aggregates
In the following section, we will analyze the
relations between CDAX and our news momentum
approaches in detail.
4. Evaluation
4.1 Data set and evaluation setup
Our data set comprises financial news from two
different data sources: DGAP (“Deutsche Gesellschaft
1
We use the logarithm as there are extreme differences in market
values between small and large companies
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für Adhoc-Publizität”) between 2000 and 2011 and
Reuters between 2003 and 2009.
Regulatory requirements in many countries (e.g. US,
UK, and Germany) oblige listed companies to publish
any material facts that are expected to affect the stock
price by an authorized intermediate publisher, such as
the DGAP. Thus, our first news set forms a preselection of relevant news from the set of available
financial news. These typically include facts on
deviations of financial results from earlier
expectations, management changes, M&A transactions,
dividends, major project wins or losses, litigation
outcomes, and other types. With data coverage from
2000 to 2011, the DGAP data set features two big
economic crises (i.e. dot-com and financial crisis) and
is thus a suitable robustness check for trading
strategies. The second data set from Reuters differs
significantly from the first news set. Whereas DGAP
only features corporate disclosures emitted by
companies, the Reuters set comprises company related
news written by Reuters correspondents. While the
DGAP news set is focused on material facts required to
be published by law, the Reuters set also contains other
news as well. Actually, despite focusing on the same
companies, only ~33% of the news actually overlaps.
From both data sets, we removed penny stocks and
required each message to have a minimum of 50 words
in total. We impose these filters to limit the influence
of outliers and avoid messages that only contain tables.
Finally, we obtained 10,490 corporate disclosures
from our first source DGAP eligible for our
experiment. Thereof, we randomly (i.e. without
temporal distinction) selected 10% of the messages for
training of our tonality metric and the remaining 90%
for building the news momentum. We deliberately
chose the low number of training messages to increase
the news base for the news momentum. As many news
messages per week are essential for calculating the
news momentum, we chose the training set to be as
small as possible. Pilot tests showed that the smaller
training set did not significantly decrease predictability
for short-term subsequent stock price reactions on
validation set (i.e. the remaining 90% of the data set
dedicated for the news momentum). From our second
source Reuters, we obtained 4,766 news articles which
were used to build a separate news momentum for
evaluating to what extent our approach can be
generalized and to confirm our results.
For quantification of prediction accuracy and
achievable returns, we use a comprehensive stock price
index, i.e. the CDAX index which constitutes all 584
relevant2 stock-listed companies in Germany. We
chose the CDAX as it contains the same companies as
both of our data sets. CDAX index values were
obtained from DataStream.
Despite the extensive news data set over 12 years,
there are only a limited number of data points for
performance evaluation due to the long prediction
horizon (i.e. 624 weeks and 104 six-week horizons).
Thus, it is very important not to optimize for a local
performance maximum, but to ensure more stable
results which can be reproduced in other data sets and,
most important, in practice. Consequently, we
x evaluate our approaches on two different data sets
x analyze stability exhaustively for different
aggregation time spans and prediction horizons
x evaluate performance for two different
approaches to calculate news momentum
x employ three different performance measures, i.e.
prediction accuracy, implied return when trading
according to the evaluated strategy, and OLS
regressions using an extensive set of control
variables
x analyze stability over years
We also provide standard benchmarks reflecting
conditions of the stock market. As there is a long-term
tendency for stock-prices to increase, benchmarks are
important to ensure that a proposed approach actually
generated value. If, for example, the stock market went
up by 4% p.a. with going up in 54.5% of the weeks, an
approach delivering 5% p.a. with 55% prediction
accuracy generated only marginal value. Our first
benchmark reflects a buy-and-hold strategy where the
portfolio is bought at the beginning of the evaluation
horizon and sold at the end. This is similar to investing
in the index and a typical way how investment funds
benchmark themselves.
Our second benchmark follows a simple
momentum strategy based on findings in literature (e.g.
[18]). The portfolio is bought when the index
developed positively over the past weeks and shortsold when development was negative.
The buy-and-hold benchmark for the Reuters data
sets is a good challenge as our data set happened to set
a favorable entry point. The beginning of 2003 marked
the six-year-low of the index after the dot-com-crises
and the tensions around the upcoming Iraq-war and,
thus, was an almost perfect entry point for a buy-andhold strategy.
4.2 Results
We measure performance by prediction accuracy
and implied return. As the stock price index return of
CDAX over the investment horizon may be either
2
All companies listed in the Prime and General Standard (as
of March 2012)
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have invested into a CDAX portfolio over the time of
our data coverage (e.g. 2000 to 2011 for DGAP).
Momentum represents a stock price momentum
strategy with a 6 weeks investment horizon. To give a
comprehensive overview, we present results for
various investment horizons and aggregation times to
select the optimal values for these parameters and
analyze stability.
Best achieved accuracy values are up to 60%,
highest returns are around 20% p.a. We observe
highest and most stable accuracies and returns for
investment horizons of 4-6 weeks. These horizons are
in line with literature, but at the lower end [18]. Using
shorter horizons also results in good, but less consistent
performance (i.e. news proportion momentum
performs not as well for shorter investment horizons;
accuracies of news tone momentum are also reduced to
below 54%). This can be attributed to the fact that
effects from the news momentum are not fully
‘positive’ or ‘negative’, prediction accuracy is the
percentage of positive or negative returns we predicted
correctly, i.e. the percentage of correct investment
decision out of all investment decision. While
prediction accuracy is a binary measure, the implied
return measures the actual accumulated annual return
that would have been achieved when trading according
to our trading rules based on the recent news
momentum. Table 1 presents prediction accuracy and
implied return for our two news momentum
approaches based on DGAP news. We list accuracy
and implied return for different aggregation times and
investment horizons. Aggregation time denotes the
number of weeks in the past which were used for
building the news momentum. Investment horizon
denotes the number of weeks in the future which are
used to calculate accuracy and return. The last two
columns show our two benchmarks: Buy & Hold
represents the average annual return if someone would
Table 1. Prediction accuracy and implied return for evaluations based on DGAP data set
Accuracy of news tone momentum
Benchmark I
Aggregation time
1 week 2 weeks 4 weeks 6 weeks 8 weeks 10 weeks
Buy & Hold
4 weeks
48.5%
49.5%
50.7%
54.3%
55.8%
55.6%
6 weeks
50.2%
51.4%
55.7%
58.8%
58.6%
59.5%
54.6%
8 weeks
55.0%
54.5%
59.0%
60.4%
60.2%
57.4%
10 weeks
54.6%
55.2%
56.5%
57.8%
58.3%
54.7%
12 weeks
48.8%
52.2%
55.5%
55.2%
56.8%
55.2%
Benchmark II
Momentum
54.6%
54.4%
56.7%
58.3%
58.8%
Return of news tone momentum
Aggregation time
1 week
2 weeks 4 weeks 6 weeks 8 weeks 10 weeks
4 weeks
-5.1%
-3.8%
0.8%
8.0%
9.8%
7.4%
6 weeks
1.0%
4.5%
7.9%
12.0%
13.6%
9.6%
8 weeks
20.4%
17.9%
18.7%
15.4%
13.0%
9.2%
10 weeks
25.1%
21.6%
15.6%
14.6%
9.4%
6.9%
12 weeks
6.7%
8.8%
8.2%
8.4%
6.8%
4.4%
Benchmark I
Buy & Hold
Benchmark II
Momentum
1.4%
0.9%
6.2%
7.6%
10.3%
Accuracy of news proportion momentum
Aggregation time
1 week 2 weeks
4 weeks
48.7%
50.8%
6 weeks
49.5%
50.4%
8 weeks
52.5%
53.3%
10 weeks
53.6%
52.9%
12 weeks
49.8%
53.2%
Benchmark I
Buy & Hold
Return of news proportion momentum
Aggregation time
1 week
2 weeks
4 weeks
-4.9%
1.5%
6 weeks
-3.3%
2.2%
8 weeks
8.6%
10.2%
10 weeks
16.3%
13.6%
12 weeks
4.4%
10.8%
4 weeks
51.6%
55.8%
56.1%
55.3%
53.4%
4 weeks
3.8%
7.8%
13.5%
13.4%
8.0%
6 weeks
53.9%
58.4%
58.6%
57.5%
54.9%
6 weeks
8.5%
13.6%
9.2%
11.6%
7.3%
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8 weeks
53.8%
57.6%
58.1%
57.1%
56.8%
8 weeks
7.6%
8.7%
8.5%
9.3%
9.1%
10 weeks
53.1%
56.4%
56.7%
54.6%
54.4%
10 weeks
6.8%
5.7%
6.4%
5.6%
1.8%
-0.4%
54.6%
Benchmark I
Buy & Hold
-0.4%
Benchmark II
Momentum
54.6%
54.4%
56.7%
58.3%
58.8%
Benchmark II
Momentum
1.4%
0.9%
6.2%
7.6%
10.3%
pronounced yet. Longer investment horizons than 6
weeks are also possible, but also result in less stable
accuracies and returns. This might be driven by other
market influences. The longer the investment horizon
the more likely are major market events which are not
covered by our set of company news (like e.g.
Fukushima nuclear disaster, Lehman bankruptcy). For
a 4-6 weeks investment horizon, best and most stable
results are achieved based on the news momentum of
the past 8-10 weeks. Shorter aggregations were less
stable due to the lower amount of included news.
Longer aggregations also reduced performance as they
consider news that is too old. We highlight this
investment horizon and aggregation time in Table 1
and 2 and will further denote it as a focus window.
When analyzing the optimal focus window, we
considered both performance metrics for both
momentum approaches to ensure robustness of our
results.
Comparing the different momentum approaches,
the news tone momentum reaches a better performance
than the news proportion momentum for the DGAP
data set. We attribute this to the fact that news
proportion momentum only captures the proportion of
positive news, but not actual tonality values. Thus,
news tone momentum might capture finer changes in
news momentum. Still, robustness of results is
confirmed by both momentum approaches reaching
their optima for the same horizons (i.e. 4-6 weeks) and
aggregation times (i.e. 8-10 weeks). Also both
momentum approaches perform significantly better
than their benchmarks.
Analogously to Table 1, Table 2 presents prediction
accuracy and implied return based on Reuters news.
Best performance for the Reuters data set (i.e. 20.7%
implied return p.a.) is achieved for 1 week prediction
horizon and 10 weeks aggregation time for the news
proportion momentum. However, this optimum is
Table 2. Prediction accuracy and implied return for evaluations based on Reuters data set
Accuracy of news tone momentum
Aggregation time
1 week
2 weeks
4 weeks
6 weeks
8 weeks
10 weeks
4 weeks
47.0%
48.2%
48.7%
50.1%
50.1%
51.0%
6 weeks
51.5%
50.3%
52.5%
51.1%
53.0%
51.1%
8 weeks
51.4%
52.7%
58.8%
56.0%
55.9%
53.8%
10 weeks
53.3%
53.9%
57.6%
52.8%
53.9%
50.2%
12 weeks
51.2%
52.3%
52.9%
53.3%
51.4%
49.8%
Benchmark I Benchmark II
Buy & Hold
Momentum
58.3%
59.5%
56.8%
62.0%
64.4%
61.6%
Return of news tone momentum
Aggregation time 1 week
2 weeks
4 weeks
-8.4%
-6.7%
6 weeks
2.3%
-1.5%
8 weeks
1.3%
10.9%
10 weeks
12.5%
14.4%
12 weeks
6.3%
9.5%
Benchmark I Benchmark II
Buy & Hold
Momentum
1.9%
2.7%
8.3%
8.1%
11.7%
10.5%
Accuracy of news proportion momentum
Aggregation time
1 week
2 weeks
4 weeks
48.6%
48.3%
6 weeks
48.8%
47.5%
8 weeks
51.5%
50.8%
10 weeks
57.3%
55.0%
12 weeks
56.2%
54.1%
Return of news proportion momentum
Aggregation time 1 week
2 weeks
4 weeks
-7.5%
-9.9%
6 weeks
-8.7%
-10.4%
8 weeks
-0.4%
5.2%
10 weeks
20.7%
17.0%
12 weeks
16.8%
16.4%
4 weeks
-5.5%
2.5%
12.3%
11.3%
10.5%
4 weeks
45.8%
47.8%
55.5%
57.6%
54.1%
4 weeks
-9.6%
-3.5%
7.6%
15.1%
11.1%
6 weeks
0.4%
5.4%
12.3%
8.6%
5.1%
6 weeks
47.5%
51.4%
57.3%
56.9%
54.9%
6 weeks
-1.7%
4.1%
14.2%
15.4%
9.5%
1285
1283
8 weeks
1.2%
5.2%
7.3%
5.2%
3.9%
8 weeks
49.4%
53.9%
58.1%
58.9%
55.9%
8 weeks
0.0%
6.8%
14.1%
15.2%
9.1%
10 weeks
2.7%
5.8%
9.1%
5.2%
3.9%
10 weeks
49.9%
53.0%
58.0%
57.3%
52.5%
10 weeks
0.1%
6.7%
12.7%
12.2%
8.0%
Benchmark I Benchmark II
Buy & Hold
Momentum
58.3%
59.5%
56.8%
62.0%
64.4%
61.6%
Benchmark I Benchmark II
Buy & Hold
Momentum
1.9%
2.7%
8.3%
8.1%
11.7%
10.5%
neither confirmed by the accuracy metric nor the news
tone momentum. Thus, we still focus on the 4-6 weeks
investment horizon at 8-10 weeks aggregation time.
An important note relates to the stability of results
for different investment horizons. For longer
investment horizons, we witness lower stability of
results as they are based on less data points. For
example, for the 10-week horizon, we base our results
on only one tenth of the data points as only every tenth
week is invested.
We also look at stability over years to ensure that
results can be generalized and are not a local optimum
(Table 3). To provide convincing results that can be
applied in practice, it is important that performance is
delivered in almost every year.
We observe higher stability (i.e. lower volatility)
for news momentum based training. Returns of
benchmarks are relatively high in good years, but less
stable considering all covered years. While benchmark
I had five years of negative return and benchmark II
four years of negative, our DGAP-based news
momentum trading strategy had no year of negative
return. Further, our news momentum trading strategy
did not have any year with very negative returns (e.g.
less than -10%). Reuters-based trading again
performed worse than DGAP-based trading, but
performed more stable than the benchmarks.
Our news momentum predicts falling markets
better than rising markets. Thus, in years of falling
stock price (e.g. 2002, 2008), we achieve higher
accuracies and returns. This behavior is in line with the
financial literature finding that negative messages have
higher drifts after news [2].
In our final evaluation, we analyze how well news
momentum predicts actual return values of our index
(instead of the binary measure “accuracy”). By using
multivariate regressions, we can also examine the
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
influence of other variables on index returns. We still
use the investment horizon of 6 weeks which was
found best-performing when considering accuracies
and implied returns.
We use a set of eight ordinary least square (OLS)
regressions to investigate how the aggregated news
momentum over the past nine weeks predicts actual
CDAX returns over a six week investment horizon (see
Table 4). The news momentum is calculated for both
of our approaches: For each approach, it is the delta of
the actual 9-week aggregate and the 9-week aggregate
four weeks prior.
To analyze autocorrelations, we use the past three
4-week (i.e. monthly) CDAX returns as control
variables as suggested by [18]. We also control for the
market value of equity (ln(MV)). As we do not predict
single stock returns, but a comprehensive stock price
index, we aggregate the market values of index
participants according to the CDAX weighting. We
also include years as dummy variables to account for
cyclical effects. Furthermore, we tested for
heteroscedasticity and multicollinearity to ensure that
no abnormality confounds the experiments’ results.
The results are presented in Table 4: It shows
regression coefficients, t-statistics in parentheses and
adjusted R² for each of the regressions. Significance
level is indicated by stars: ‘***’ indicate D = 0.1%,
‘**’ indicate D = 1%, and ‘*’ indicates D = 5%.
Estimations (1), (2), and (3) examine the association
between CDAX returns of 6-week investment horizons
and our news tone momentum for our different data
sources. Estimations (4), (5), and (6) examine the same
association for news proportion momentum.
Both news tone momentum and news proportion
momentum is significant at D = 0.1%-level (indicated
by ***) at t-statistics of 4.13 and 3.57. Positive
coefficients indicate that a higher news momentum
Table 3. Stability over years for news tone momentum (8 weeks aggregation time, 4 weeks prediction)
Accuracy
Accuracy
Accuracy
Accuracy
Return
Return
Return
Return
DGAP
Reuters
Benchmark I Benchmark II
DGAP
Reuters
Benchmark I Benchmark II
71.5%
46.2%
55.8%
29.9%
-8.1%
13.0%
54.5%
49.0%
49.3%
8.4%
-17.9%
1.1%
64.4%
41.5%
54.9%
28.9%
-39.9%
22.8%
65.0%
63.4%
54.7%
77.3%
27.5%
26.6%
40.1%
26.6%
59.3%
68.4%
55.8%
51.9%
10.2%
17.1%
7.4%
-3.5%
56.2%
60.1%
67.3%
70.6%
0.2%
6.9%
28.2%
17.3%
67.6%
61.9%
66.0%
75.5%
6.7%
3.2%
23.1%
16.9%
51.6%
42.6%
67.3%
53.9%
0.4%
-6.0%
20.4%
4.1%
66.3%
51.9%
35.8%
46.7%
44.6%
17.0%
-40.5%
2.6%
57.2%
57.7%
56.0%
34.0%
23.0%
-9.3%
63.1%
61.5%
40.7%
14.4%
18.1%
-7.3%
58.5%
52.1%
46.1%
35.0%
-14.1%
-6.6%
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1284
Table 4. Regression results for prediction of 6-week CDAX return
(1)
(2)
(3)
(4)
Investment Horizon
6 weeks
6 weeks
6 weeks
6 weeks
News tone momentum 0.1746***
0.1484**
(DGAP)
(4.13)
(2.88)
News tone momentum
(Reuters)
0.1617***
(3.13)
(5)
6 weeks
(6)
6 weeks
(7)
6 weeks
0.1663
(1.92)
0.1505**
(3.12)
News proportion
momentum (DGAP)
(8)
6 weeks
0.0927
(1.56)
0.0423***
(3.57)
News proportion
momentum (Reuters)
0.0286
(1.96)
0.0471***
(3.50)
0.0292*
(2.64)
0.0026
(0.11)
0.0265*
(2.12)
CDAX Return
4 to 1 weeks ago
-0.2221
(-1.90)
0.0081
(0.05)
-0.0992
(-0.59)
-0.2065
(-1.73)
-0.0259
(-0.15)
-0.0859
(-0.51)
-0.2212
(-1.87)
-0.0123
(-0.07)
CDAX Return
8 to 5 weeks ago
-0.1472
(-1.29)
0.2087
(0.12)
0.0727
(0.45)
-0.1430
(-1.22)
-0.0794
(-0.45)
-0.0135
(-0.08)
-0.1474
(-1.28)
-0.0638
(-0.37)
CDAX Return
12 to 9 weeks ago
-0.0089
(-0.07)
-0.0324
(-0.17)
-0.1024
(-0.58)
-0.0442
(-0.34)
-0.0171
(0.09)
-0.0035
(-0.02)
-0.0108
(-0.08)
-0.0442
(-0.24)
ln(MV)
-0.1290
(-0.74)
-0.0656
(-0.28)
0.0119
(0.05)
-0.1731
(-0.91)
-0.0048
(-0.02)
0.0300
(0.13)
-0.1319
(-0.70)
0.0204
(0.09)
N
Adjusted R²
104
22.2%
58
22.2%
58
32.8%
104
18.0%
58
25.4%
58
29.8%
104
20.5%
58
27.7%
predicts higher CDAX returns. Relations for news
momentum based on Reuters news remain slightly
weaker for both approaches, but are still significant
(i.e. t-statistics of 3.13 and 3.50). The number of
observations (‘N’) also varies with the data set as the
Reuters data set covers fewer years. The adjusted R²
for regressions based on the DGAP data set reaches
~18-22%. This good result confirms the strong visual
correlation between CDAX and our aggregates in
Figure 1. Regressions based on the Reuters data set
reach an even higher adjusted R² of ~22-25%.
However, as they are based on fewer data points, we
cannot infer that explanatory power is higher.
The inclusion of both data sets in one regression
demonstrates that it is beneficial to base decision on
information from both data sets (i.e. adjusted R²
increases from ~18-25% to ~29-32%). The comparison
of tone momentum and proportion momentum in
estimations (7) and (8) reveals slightly more
explanatory power for tone-based momentum on the
DGAP data set (vice versa for the Reuters data set).
The logarithmic market value and past CDAX returns
do not reach significance.
are reached and exceeded. Stability of results was
checked for different years on two different data sets.
Thus, news cannot only be used for short-term trading,
but also for medium-term investments. Evaluation
showed that news tone momentum overall performed
slightly better than news proportion momentum. We
attribute this to the fact that news proportion
momentum only captures the proportion of positive
news, but not actual tonality values. Thus, fine changes
in tonality value may only be captured by news tone
momentum.
News momentum supports investment managers to
maintain oversight and incorporate all relevant
available news into their decisions. Furthermore, news
momentum contributes to more objective decisions as
no subjectivity in interpretation of news articles is
involved.
Trading on medium-term horizons has several
advantages over short-term low-latency trading. First,
higher investment volumes are possible. Second, it also
allows for manual control and intervention. This is
very relevant for those fund managers who would not
trust a fully automated system. Even if not
implemented in a low-latency trading system, it might
be beneficial for investment managers as news
momentum is an objective measurement of all
available information. Lastly, as news momentum also
may predict stock price indices, trading on major index
futures is feasible.
Many stock price indices are representative for
national economies. As news momentum is a good
5. Conclusion
In summary, our research shows that a profitable
trading strategy can be established based on the news
momentum of the past weeks. Prediction accuracies of
up to ~60% are achieved, implied returns of 20% p.a.
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Earnings News”, The Journal of Finance, Volume 64(5),
2009, pp. 2289–2325
[10] M. Liebmann, M. Hagenau, M. Häussler, and D.
Neumann, "Effects Behind Words: Quantifying Qualitative
Information in Corporate Announcements”, FMA European
Conference, Porto (Portugal), 2011
[11] T. Loughran, and B. McDonald, “When Is a Liability
Not a Liability? Textual Analysis, Dictionaries, and 10-Ks”,
The Journal of Finance Volume 66, 2011, pp. 35–65
[12] M.A. Mittermayr, “Forecasting Intraday Stock Price
trends with Text Mining techniques”, Proceedings of the 37th
Annual Hawaii International Conference on System
Sciences, Waleia (Hawaii), 2004
[13] M.F. Porter, “An Algorithm for Suffix Stripping”,
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predictor for stock indices, it also measures pulse and
sentiment of the respective country. The relationship
between economic development of a country and the
aggregated tone was already indicated by Figure 1.
Consequently, it could help investment funds which
choose their assets from different countries or invest in
indices from these countries. Similar analyses are
possible for single industries or economically
dependent regions (e.g. emerging markets, Europe).
Our current setup was limited to company specific
news. However, as also economic news or news from
other countries may influence stock index
developments, our approach should be extended to also
incorporate this kind of news. While company news
reflect the actual situation of companies and, thus, have
the advantage to be more based on fundamentals, they
are also slower in adapting to new trends and
developments. This is because it takes more time for
new developments to be reflected in companies’
fundamentals.
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