Economic News Impact Analysis
Using Causal-Chain Search from Textual Data
Kiyoshi Izumi1, Shintaro Suda2, and Hiroki Sakaji1
1School of Engineering, the University of Tokyo, 7-3-1 Hongo, Bunkyo, 113-8656 Tokyo, JAPAN2
Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd.
izumi@sys.t.u-tokyo.ac.jp

Abstract
In this research, we extract causal information from textual
data and construct a causality database in the economic field.
Furthermore, we develop a method to produce causal chains
starting from phrases representing specific events and offer
possible ripple effects and factors of particular events or situations. Using our approach to Japanese textual data, wedeveloped a method to select stocks related to specific news.
We created lists of related companies and measured impacts
on those stock prices for the two important news about awheat price in 2018. As a result, the market impacts appeared in the companies related to the ripple effects when
the news is about the obvious fact. In contrast, it appeared in
the companies related to the remote causes when the news isabout the opinion and decision.

Introduction
The impact of news on financial markets is not fixed. For
example, news “an increase in the US consumer price index” can lead to a weakening of the dollar in currency
markets from the perspective of purchasing power. The
same news, however, can lead to a stronger dollar from theperspective of inflation and rising interest rates. The market impact depends strongly on the economic and politicalsituation at the time the news is released. It also depends
on market participants' perceptions of the current economic
situation.
It is beneficial to construct a database of economic causality and analyze the relationship between causality forboth financial professionals and non-specialists. Suchtechnology can support the prediction of market impacts
caused by the news because market participants' percepCopyright © 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
The views expressed in this paper are those of the authors and do notnecessarily reflect the official views of the Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd.

tions mainly consist of economic causality information. It
is, however, difficult to analyze the causality between economic phenomena only by the statistical analysis of numerical data. That is because human activities produce a causal
relationship between economic events. Human activities
are determined by mental processes such as cognition,
thinking, and emotion. Thus, economic causality is influenced by social and cultural situations. It is almost impossible to extract objective and universal causality by statistical analysis of numerical data like natural scientific phenomena.
In this research, we propose a method to create an economic causal-chain network from economic text data inJapanese. The economic causal-chain refers to a cause andeffect network structure formed by extracting a description
indicating a causal relationship from the texts of financial
statement summaries. In other words, it is a network that
includes causal relationships that investors have recognizedfrom financial statement summaries. Using the causal
chain constructed by this method, we developed a method
to select stocks related to specific news.

Related works
Much work has been done on the extraction of causal information from texts. Inui et al. proposed a method forextracting causal relations (cause, effect, pre-cond, andmeans) from complex sentences containing the Japanese
resultative connective “ため (tame: because)” (Inui, T.;
Inui, K.; Matsumoto, Y. 2004), as this is a reliable indicator of causal information. Khoo et al. proposed a method to
extract cause-effect information from newspaper articles
by applying manually created patterns (Khoo, C.; Kornfilt,
J.; Oddy, R.; Myaeng, S.-H. 1998). They also obtained
causal knowledge from medical databases by applying
their graphical patterns (Khoo, C.; Chan, S.; Niu, Y. 2000).
Chang et al. proposed a method to extract causal relationships between noun phrases using cue expressions andword pair probabilities (Chang, D.-S., and Choi, K.-S.
2006). They defined as the probability that the pair forms acausal noun phrase. Girju proposed a method for automatic
detection and extraction of causal relations based on cue
phrases, where pairs of noun phrases express causal relationships (Girju, R. 2003). Girju used WordNet (Fellbaum,
C. 1998) to create semantic constraints for selecting candidate pairs, so her method cannot extract unknown phrases
that are not in WordNet. Bethard et al. proposed a method
for classifying verb pairs that have causal relationships
using an SVM for classification (Bthard, S., and Martin, J.
H. 2008). Sadek et al. proposed a method for extracting
Arabic causal relations using linguistic patterns represented
using regular expressions (Sadek, J., and Meziane, F.
2016). In contrast, our approach extract not only causeeffect expressions but also construct causal chains.
Ishii et al. proposed a method for constructing causal
chains using WordNet and SVO tuples (Ishii, H.; Ma, O.;
Yoshikawa, M. 2012). They employ the process of (Sakaji,
H.; Sekine, S.; Masuyama, S. 200) for extracting causeeffect expressions. Alashri et al. proposed a method to extract causal relations and construct causal chains from large
text corpora related to climate change (Alashri, S.; Tsai, J.Y.; Koppela, A. R.; Davulcu, H. 2018). However, theirapproach cannot build causal chains when expressions
consist of noun phrases. Because their method targets expressions that include Subjects, Verbs, and Objects (SVO).
On the other hand, our method can construct causal chainsfrom expressions that consist of noun phrases only.
Recently, several studies on extracting causal information from text data in the financial field have also been
published (Dasgupta, T.; Saha, R.; Dey, L.; Naskar, A.
2018)(Hassanzadeh, O.; Bhattacharjya, D.; Feblowitz, M.;
Srinivas, K.; Perrone, M.; Sohrabi, S.; Katz, M. 2019).
Nakagawa et al. proposed a method to exploit the lead-lag
effect in stock markets using the economic causal-chain(Nakagawa, K.; Sashida, S.; Sakaji, H.; Izumi, K. 2019).
There are, however, still a few studies on the application of
causal information in financial markets.

Causal Chain Search from Textual Data
In this study, we analyze Japanese economic text data that
seems to contain causality recognized by humans and construct a database of causality related to the economic field.
For example, the Japanese sentence“円高のため、日本経済は悪化した。(endaka no tamenihonkeizai ha akkashita: Because of the yen's appreciation,
the Japanese economy deteriorated.)” includes cause expression “円高 (endaka: the yen's appreciation)'' and effect expression “日本経済は悪化した。(nihonkeizai haakkashita: the Japanese economy deteriorated.).” Our
method extracts these cause-effect expressions using clue
expressions. In this case, the clue expression is “ため(tame: because).”Furthermore, we develop a method to search for causal
chains derived from phrases representing specific events.
Using this method, we implement a system that can display
causal chains for user's input words and select appropriate
sequences or delete inappropriate sequences. Our method
consists of the following steps.
1. Step 1 extracts sentences that include cause-effectexpressions (causal sentences) from Japanese financial statement summaries using a support vector machine.
2. Step 2 obtains cause-effect expressions from theextracted sentences using syntactic patterns.
3. Step 3 constructs economic causal-chains by connecting each cause-effect expression.
Step 1 and Step 2 are implemented by a method of (Sakaji,
H.; Murono, R.; Sakai, H.; Bennett, J.; Izumi, K. 2017).

Step 1: Extraction of Causal Sentences
We developed a technique for extracting causal sentencesfrom economic texts. It may be challenging to distinguishcausal sentences. For instance, the Japanese clue expression "ため (tame: because)" is essential for extracting
cause-effect expressions, but it can also be used to mean an
objective. For example, the Japanese sentence"あなたのために、花を買った。(anata no tame ni hanawo katta: I bought some flowers for you)" includes the clue
expression "ため (tame: because)," but it does not have acausal meaning in this context. We will, therefore, develop
a method for extracting sentences that include cause andeffect expressions that can cope with such situations.
Since this method uses a support vector machine (SVM)
for extraction, we explain how to acquire features fromfinancial statement summaries. Our approach uses thefeatures shown in Table 1 to extract causal sentences. We
employ both syntactic and semantic features.
Table 1. Features for the extraction of causal sentences.
Kinds of features
Syntactic features

Features
Pairs of particles

Semantic features

Extended language ontology
Part of speech of morphemes
just before clue expressions
Morpheme unigrams

Other features

Morpheme bigrams

We aim to use expressions that are frequently used incause and effect expressions in sentences as syntactic features. For example, the Japanese sentence“半導体の需要回復を受けて半導体メーカーが設備投資を増やしている。(handoutai no jyuyoukaifuku woukete hanndoutaime-ka-ga setsubitoushi wo fuyashiteiru: As
semiconductor demand recovers, semiconductor manufac-

turers are increasing their capital investment.)” has thefollowing pattern of particles and clue expressions: “...の ... を受けて ... を ... ( ... no ... woukete ... wo ...).” Thispattern indicates that it is highly likely to be a cause-effectexpression. Our method, therefore, acquires particles that
relate to clue expressions using a Japanese syntactic parser.
Besides, our method acquires words indicating causality
using an extended language ontology (Kobayashi, A.; Masuyama, S.; Sekine, S. 2010). Fig. 1 shows our process ofobtaining a semantic feature. Here, a core phrase is thelast part of a phrase that includes a clue expression, anda base point phrase is a phrase that is modified by thecore phrase. First, our method acquires words that modify
the core phrase or the base point phrase. Then, a tuple ofconcept words that are obtained by tracing extended language ontology using the words is acquired as a syntactic
feature. Here, each tuple consists of two concept words,
one based on the core phrase and the other based on thebase point phrase.

Fig. 2. A syntactic patterns list.

To illustrate the operation of our method, we now workthrough examples using two of the five syntactic patterns(Patterns A and C). Fig. 3 shows our method of extracting
cause and effect expressions using Pattern A. It first identifies core and base point phrases using the clue expression“を背景に (wohaikeini: with).” Then, the cause expression “半導体メーカーの設備投資の拡大 (handoutaimeka-nosetsubitoushinokakudai: expansion of capital investment by semiconductor manufacturers)” is extracted bytracking back through the syntactic tree from the corephrase. Finally, the effect expression``半導体製造装置向け制御システムの販売が伸びた。
(handoutaiseizousouchimukeseigyosisutemunohannbaiganobita: sales of control systems for semiconductor
manufacturing equipment increased)'' is extracted by tracking back through the syntactic tree from the base point
phrase.

Fig. 1. An example of a semantic feature.

Step 2: Extracting Cause-Effect Expressions
We employ a method by [3] to extract cause-effect expressions using four syntactic patterns. We analyzed sentence
structures and used a pattern matching method with syntactic patterns is shown in Fig. 2. In Fig. 2, "Cause" indicatesa cause expression, "Effect" denotes an effect expression,and "Clue" indicates a clue expression. Sakaji et al. [3]used a Japanese dependency analyzer [4] to analyze Japanese syntax, but the recall of that method is too low for ourneeds. However, the technique is not enough about therecall. Therefore, to improve the recall of the method, we
add a new syntactic pattern (Pattern E).
Pattern A is the most basic pattern for expressing causality in Japanese. The others derive from Pattern A to emphasize either cause or effect, as represented by the arrows
in Fig. 2.

Fig. 3. An example of pattern A.

Fig. 4 shows our method extracting cause and effect expressions using Pattern C. It first identifies core and basepoint phrases using the clue expression“ためだ。(tameda: because).” Then, the cause expression“国際線が好調なのは (kokusaisengakoutyounanoha}:International airlines are doing well)” is extracted by tracking back through the syntactic tree from the core phrase.
Finally, the effect expression“欧米路線を中心にビジネス客が増えた (oubeirosenwotyushinnibijinesukyakugafueta}: the number of businesscustomers increased mainly in Euro-American airlines)” isextracted by tracking back through the syntactic tree fromthe base point phrase.

In Fig. 5, "Company" indicates the company that issues
the financial statement summary from which the causeeffect expression has been extracted. Additionally, "Date"is the date the financial statement summary was published.
In Fig. 5 getSimilarity 𝑒 ; 𝑐 is a function that calculates
the similarity between the effect expression ei and thecause expression cj. Our method estimates the similarities
based on vectors of word embedding. First, our method
obtains word embedding average of the words included in
the expressions. Here, we define the average obtained fromthe effect expression ei as 𝑊 and the average obtained
from the cause expression cj as 𝑊 . 𝑊 , 𝑊 ∈ 𝑅 and mis the dimension size of word embedding. Then, our method calculates a cosine similarity between 𝑊 and 𝑊 andemploys the similarity as the similarity between the effectexpression ei and the cause expression cj. Finally, our approach acquires pairs of cause-effect expressions as a causal chain when the similarities are larger than a threshold.
That method can be applied to both the forward searchand backward search. In the forward search, a user can findthe ripple effects of an input phrase (Fig.6a). The input text
is considered the first causal expression, and our algorithm
tries to finds cause-effect expressions as a chain in order ofcause and effect. In the backward search, a user can findremote causes of an input phrase (Fig.6b). The input text is
considered the first effect expression, and our algorithm
tries to finds cause-effect expressions as a chain in order ofeffect and cause.

Fig. 4. An example of pattern C.

Step 3: Constructing Causal Chains
To construct causal chains, our method (Sakaji, H.; Sekine,S.; Masuyama, S. 2008) connects an effect expression of acause-effect expression and a cause expression of anothercause-effect expression. We show an algorithm of constructing causal chains in Fig. 5.

Fig. 6. Forward and backward causal chains.

Empirical Study: News Impact Analysis
Dataset and Methodology

Fig. 5. Construction of causal chains.

In this section, we examine the news impact analysis using
the economic causal-chain of the previous section.
Textual data: As a source of causal information, weused about 20,000 financial statement summaries from
October 2012 to May 2018. They were published by about
2,300 companies listed on the Tokyo Stock Exchange. A
financial statements summary is a common form reportthat is disclosed when a listed company announces its financial performance (balance sheet, cash flows, operating
results, and business risks). It contains various causal in-formation. For example, "full-year same-store sales declined due to issues with product mixes and volume planning," and "lower estimated future cash flows or a higher
discount rate would cause further impairment losses." We
extracted 1,078,542 pairs of cause-effect expressions fromthose Japanese documents using the method as mentioned
earlier.
Selection of companies: In this study, we focused onnews about wheat prices in commodity markets. First, wecreated a causal chain starting from the phrase “wheat
price,” using the method mentioned in fig. 5. The maximum number of causal chain layers is 3. The threshold ofsimilarity is 0.65. Fig. A shows a part of a forward causalchain starting from “wheat price.” Each cause-effect expression relates to a particular company that published afinancial statement summary, including the expression. In
each layer of a causal chain, we listed up related companies that appeared for the first time in that layer. Thosecompanies are considered to be affected by news about
wheat prices in commodity markets. The related company
lists are created for both forward and backward causalchains.
Event study: During the out-of-sample period after May
2018, we focused on the following two days. On these days,
important news about wheat prices was published in the
Nihon Keizai Shimbun, Japan's most significant economic
newspaper.
 Case 1: (July 24 2018) “International wheat price of ishigh for the first time in a month with the prospect of
reduced production in France.”
 Case 2: (September 6 2018) “Wheat international pricedrops by 4%. Russia says, “No export restrictions.”
To measure an impact on the financial market, we used anabsolute value of an excess return.
𝑟,
𝑅 ,where 𝑟 , is a price return of a company i,
𝑝,
𝑝,
𝑟,
.
𝑝,
𝑝 , is a closing price of company i at day t. 𝑡 is a day
when the important news was released (July 24, 2018 or
September 6, 2018). 𝑅 is a benchmark,
𝑃 𝑃
𝑅
.
𝑃
𝑃 is a closing price of TOPIX, a stock index of the Tokyo
Stock Exchange, at day t. t ranges from 20 (20 days before 𝑡 ) to 20 (20 days after 𝑡 ).
In this study, we did not distinguish between positive andnegative relationships in a cause-effect expression andconnection between expressions. Thus, the current method
cannot deal with the direction (positive and negative) of an
impact. It can measure only an amplitude of a market impact. Then, we did not use a raw value of an excess return,but an absolute value.

Results and Discussion
We created lists of related companies and measured impacts on those stock prices for the two important news
about a wheat price (Case 1 and 2).
Selection of companies: As mentioned, first we constructed 3-layer causal-chains starting from “wheat price”
for each direction (forward and backward) like Fig. A. Tables 2 and 3 show the companies that appeared at the firsttime in each layer of the forward and backward causalchain, respectively.
Table 2. Related companies in the forward causal-chain from

“wheat price.”
Layer
1st

2nd

3rd

Ticker code (company name)2002 (Nisshin Seifun Group Inc.)
2003 (Nitto Fuji Flour Milling Co., Ltd.)
2212 (Yamazaki Baking Co., Ltd.)
3306 (The Nihon Seima Co., Ltd.)4042 (Tosoh Corporation)
4185 (JSR Corporation)
5632 (Mitsubishi Steel Mfg. Co., Ltd.)
2802 (Ajinomoto Co., Inc.)
4569 (Kyorin Holdings, Inc.)
4726 (SB Technology Corp.)
5104 (Nitto Kako Co., Ltd.)
5406 (Kobe Steel, Ltd.)5542 (Shinhokoku Steel Corporation)
5697 (Sanyu Co., Ltd.)
5801 (Furukawa Electric Co., Ltd.)6334 (Meiji Machine Co., Ltd.)7965 (Zojirushi Corporation)

In Table 2, the depth of the company’s layer reflects thedeepness of the relationship between a wheat price and thatcompany. For example, the companies in the first layer
deal with products made of wheat, such as flour (2002, 2003,
and 3306) and bread (2212). As the layers deepen, the relationship with wheat becomes indirect. Chemical makers(4042 and 4185) appeared in the second layer. Steel companies (5632, 5406, and 5542) appeared in the second andthird layers. Chemical and steel companies are indirectly
linked to wheat prices in terms of raw material prices, including crude oil. Besides, the condiment maker (2802)
and the pharmaceutical company (4569) appeared in thethird layer. Notably, the machine manufacturers (6334) inthe third layer specializes in grain milling machines, so thetrend of grain prices, including wheat, affects its demand
trend and performance. The cooking device manufacturer(7965) in the third layer produces oven toasters, home bakery, and hot plates. Sales of those devices are influenced bya decline in consumer demand with increased grain prices.

Table 3. Related companies in the backward causal-chain from

8

1st
2nd
3rd

Ticker code (company name)2002 (Nisshin Seifun Group Inc.)
2003 (Nitto Fuji Flour Milling Co., Ltd.)
2215 (First Baking Co., Ltd.)
2001 (Nippon Flour Mills Co., Ltd.)
6250 (Yamabiko Corporation)
8031 (Mitsui & Co., Ltd.)

Also, in the backward causal-chain, directly relatedcompanies (2002, 2003, and 2215) appeared in the firstlayer (Table 3). As you move back in the causal chain,companies that deal with a broader range of products appear. For example, an agricultural machinery manufacturer(6250) appeared in the second layer, and a trading firm(8031) appeared in the third layer. The backward causalchain is a good indicator of the spread of the remote causes.
Event study (Case 1): Fig. 7 shows the average of absolute excess returns in each layer of the forward causalchain in Case 1 (July 24 2018).
1
𝑟,
𝑅 ,
𝑛
where n is a number of companies in the layer.
The stock prices of related companies moved clearly
after the news release. The impact of stock prices at a
deeper layer was larger and lasted longer. Those results
show that the market impact of the news has amplified to abroader range of stocks over time.
14
12

2nd layer
3rd layer

5432‐20 ‐18 ‐16 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2

0

2

4

6

8

10 12 14 16 18 20

Days

Fig. 8. Average of absolute excess returns in each layer of the
backward causal-chain in Case 1 (July 24 2018).

Event study (Case 2): Fig. 9 shows the average of absolute
excess returns in each layer of the forward causal-chain in
Case 2 (September 6 2018). In this case, the stock prices of
the related companies in the forward causal-chain did notchange their price trends. In contrast, the stock prices of
the related companies in the backward causal-chain moved
after the news release (Fig. 10).
12
10

1st layer
2nd layer

8

3rd layer

6
4
2

1st layer0

3rd layer‐20 ‐18 ‐16 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2

0

2

4

6

8

10 12 14 16 18 20

Days

8

Fig. 9. Average of absolute excess returns in each layer of theforward causal-chain in Case 2 (September 6 2018).

6
4
2

14

0
‐20 ‐18 ‐16 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2

0

2

4

6

8

10 12 14 16 18 20

Days

Fig. 7. Average of absolute excess returns in each layer of theforward causal-chain in Case 1 (July 24 2018).

Fig. 8 shows the average of absolute excess returns ineach layer of the backward causal-chain in Case 1. In thiscase, the stock prices of the related companies did notchange their price trends.

12

1st layer
2nd layer

10

Absolute returns （％）

Absolute returns （％）

1st layer

60

2nd layer
1071

Absolute returns （％）

Layer

Absolute returns （％）“wheat price.”

3rd layer

8
6
4
2
0
‐20 ‐18 ‐16 ‐14 ‐12 ‐10 ‐8 ‐6 ‐4 ‐2

0

Days

2

4

6

8

10 12 14 16 18 20

Fig. 10. Average of absolute excess returns in each layer of the
backward causal-chain in Case 2 (September 6 2018).

Discussions about differences in Case 1 and 2: Case 1
and 2 showed the opposite results: There were impacts on
the companies related to the ripple effects in Case 1. There
were impacts on companies related to the remote causes inCase 2. Those differences would have been caused by differences in the content of the news. In Case 1, the news
was related to the apparent fact, “the reduced production in
France.” Market participants would focus only on the results caused by this fact. That was because the reasons forthis fact are not essential for financial markets. In Case 2,
the news was related to Russian decision, “no export restrictions.” Market participants would have tried to find theeconomic and political background of the decision. They
then focused on the companies related to the remote causesof the decision.

Conclusions
In this research, we propose a method to select companies
affected by certain news using economic causal-chains.
The economic causal chain refers to a cause and effectnetwork structure created by extracting a description indicating a causal relationship from the texts of the financial
statement summaries. We created lists of related companies and measured impacts on those stock prices for thetwo important news about a wheat price in 2018. As a result, the market impacts appeared in the companies related
to the ripple effects when the news is about the obvious
fact. In contrast, it appeared in the companies related to theremote causes when the news is about the opinion and decision.
There are some extensions of the proposed method as
future work. First, we are going to apply our method toEnglish textual data, such as financial news articles andfinancial reports. Besides, we are going to develop a method to add positive and negative relationships to the causal
chain. That makes it possible to analyze the direction of themarket impact. The economic causal-chain search algorithm can be used for various financial information services. We want to launch some of the following services incollaboration with financial institutions or financial information vendors.

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Fig. A. A part of a forward causal-chain starting from “wheat price”. A gray (white) box means a cause (effect, respectively) expression. A
solid arrow (dotted line) stands for causality (similarly, respectively). Code means a ticker code of a company that published the causeeffect expression. Original texts are written in Japanese.