acl acl2010 acl2010-138 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jochen Leidner ; Frank Schilder
Abstract: In the business world, analyzing and dealing with risk permeates all decisions and actions. However, to date, risk identification, the first step in the risk management cycle, has always been a manual activity with little to no intelligent software tool support. In addition, although companies are required to list risks to their business in their annual SEC filings in the USA, these descriptions are often very highlevel and vague. In this paper, we introduce Risk Mining, which is the task of identifying a set of risks pertaining to a business area or entity. We argue that by combining Web mining and Information Extraction (IE) techniques, risks can be detected automatically before they materialize, thus providing valuable business intelligence. We describe a system that induces a risk taxonomy with concrete risks (e.g., interest rate changes) at its leaves and more abstract risks (e.g., financial risks) closer to its root node. The taxonomy is induced via a bootstrapping algorithms starting with a few seeds. The risk taxonomy is used by the system as input to a risk monitor that matches risk mentions in financial documents to the abstract risk types, thus bridging a lexical gap. Our system is able to automatically generate company specific “risk maps”, which we demonstrate for a corpus of earnings report conference calls.
Reference: text
sentIndex sentText sentNum sentScore
1 com Abstract In the business world, analyzing and dealing with risk permeates all decisions and actions. [sent-5, score-0.694]
2 However, to date, risk identification, the first step in the risk management cycle, has always been a manual activity with little to no intelligent software tool support. [sent-6, score-1.294]
3 In addition, although companies are required to list risks to their business in their annual SEC filings in the USA, these descriptions are often very highlevel and vague. [sent-7, score-0.728]
4 In this paper, we introduce Risk Mining, which is the task of identifying a set of risks pertaining to a business area or entity. [sent-8, score-0.617]
5 We argue that by combining Web mining and Information Extraction (IE) techniques, risks can be detected automatically before they materialize, thus providing valuable business intelligence. [sent-9, score-0.658]
6 We describe a system that induces a risk taxonomy with concrete risks (e. [sent-10, score-1.347]
7 , interest rate changes) at its leaves and more abstract risks (e. [sent-12, score-0.57]
8 The taxonomy is induced via a bootstrapping algorithms starting with a few seeds. [sent-15, score-0.105]
9 The risk taxonomy is used by the system as input to a risk monitor that matches risk mentions in financial documents to the abstract risk types, thus bridging a lexical gap. [sent-16, score-2.887]
10 Our system is able to automatically generate company specific “risk maps”, which we demonstrate for a corpus of earnings report conference calls. [sent-17, score-0.161]
11 In business, companies are exposed to market risks such as new competitors, disruptive technologies, change in customer attitudes, or a changes in government legislation that can dramatically affect their profitability or threaten their business model or mode of operation. [sent-19, score-0.708]
12 Therefore, any tool to assist in the elicitation of otherwise unforeseen risk factors carries tremendous potential value. [sent-20, score-0.663]
13 Nassim Nicholas Taleb calls these “black swans” (Taleb, 2007). [sent-22, score-0.032]
14 Companies in the US are required to disclose a list of potential risks in their annual Form 10-K SEC fillings in order to warn (potential) investors, and risks are frequently the topic of conference phone calls about a company’s earnings. [sent-23, score-1.172]
15 These risks are often reported in general terms, in particular, because it is quite difficult to pinpoint the unknown unknown, i. [sent-24, score-0.603]
16 what kind of risk is concretely going to materialize. [sent-26, score-0.647]
17 On the other hand, there is a stream of valuable evidence available on the Web, such as news messages, blog entries, and analysts’ reports talking about companies’ performance and products. [sent-27, score-0.084]
18 Financial analysts and risk officers in large companies have not enjoyed any text analytics support so far, and risk lists devised using questionnaires or interviews are unlikely to be exhaustive due to small sample size, a gap which we aim to address in this paper. [sent-28, score-1.38]
19 To this end, we propose to use a combination of Web Mining (WM) and Information Eextraction (IE) to assist humans interested in risk (with respect to an organization) and to bridge the gap between the general language and concrete risks. [sent-29, score-0.688]
20 We describe our system, which is divided in two main parts: (a) an offline Risk Miner that facilitates the risk identification step ofthe risk management process, and an online (b) Risk Monitor that supports the risk monitoring step (cf. [sent-30, score-1.996]
21 In addition, a Risk Mapper can aggregate and visualize the evidence in the form of a risk map. [sent-32, score-0.647]
22 Our risk mining algorithm combines Riloff hyponym patterns with recursive Web pattern bootstrapping and a graph representation. [sent-33, score-0.78]
23 We do not know of any other implemented endto-end system for computer-assisted risk identification/visualization using text mining technology. [sent-34, score-0.688]
24 IE systems have been applied to the financial domain on Message Understanding Contest (MUC) like tasks, ranging from named entity tagging to slot filling in templates (Costantino, 1992). [sent-38, score-0.133]
25 , 2008), which was designed to extract hyponymy, but they did so at the expense of recall, using longer dual anchored patterns and a pattern linkage graph. [sent-45, score-0.126]
26 Also, they create a set of pairs, whereas our approach creates a taxonomy tree as output. [sent-52, score-0.105]
27 Most importantly though, our approach is not driven by frequency, and was instead designed to work especially with rare occurrences in mind to permit “black swan”-type risk discovery. [sent-53, score-0.647]
28 , 2009) study the correlation between share price volatility, a proxy for risk, and a set of trigger words occurring in 60,000 SEC 10-K filings from 1995-2006. [sent-56, score-0.072]
29 Since the disclosure of a company’s risks is mandatory by law, SEC reports provide a rich source. [sent-57, score-0.605]
30 Their trigger words are selected a priori by humans; in contrast, risk mining as exercised in this paper aims to find risk-indicative words and phrases automatically. [sent-58, score-0.721]
31 Kogan and colleagues attempt to find a regression model using very simple unigram features based on whole documents that predicts volatility, whereas our goal is to automatically extract patterns to be used as alerts. [sent-59, score-0.056]
32 , 2004) found that sub-string matching of 14 pre-defined string literals outperforms an SVM classifier using bag-of-words features in the task of speculative language detection in medical abstracts. [sent-63, score-0.042]
33 They use a bi-partite graph-based approach, where one kind of node (content node) represents things people wish for (“world peace”) and the other kind of node (template nodes) represent templates that extract them (e. [sent-66, score-0.069]
34 3 Data We apply the mined risk extraction patterns to a corpus of financial documents. [sent-70, score-0.803]
35 In particular, we are dealing with 170k earning calls transcripts, a text type that contains monologue (company executives reporting about their company’s performance and general situation) as well as dialogue (in the form of questions and answers at the end of each conference call). [sent-72, score-0.073]
36 Participants typically include select business analysts from investment banks, and the calls are published afterwards for the shareholders’ benefits. [sent-73, score-0.112]
37 We randomly took a sample of N=6,185 transcripts to use them in our risk alerting experiments. [sent-75, score-0.704]
38 For demonstration purposes, we add a (c) Risk Mapper, a visualization component. [sent-79, score-0.026]
39 We describe how a variety ofrisks can be identified given a normally very high-level description of risks, as one can find in earnings reports, other finan- cial news, or the risk section of 10-K SEC filings. [sent-80, score-0.73]
40 Also, the three Lewisburg area warehouses will be consolidated as we assess the logistical needs of the casegood group’s existing warehouse operations at an appropriate time in the future to minimize any disruption of service to our customers. [sent-83, score-0.041]
41 This will result in the loss of 425 jobs or approximately 15% of the casegood group’s current employee base. [sent-84, score-0.041]
42 Idon’t know the net equipment sales number last quarter and this quarter. [sent-86, score-0.072]
43 But it sounded like from your comments that if you exclude these fees, that equipment sales were probably flattish. [sent-87, score-0.054]
44 CEO: We’re not breaking out the origination fee from the equipment fee, but Ithink in total, Iwould say flattish to slightly up. [sent-89, score-0.063]
45 Figure 1: Example sentences from the earnings conference call dataset. [sent-90, score-0.083]
46 ; and eventually more concrete, candidates, and relate them to risk types via a transitive chain of binary IS-A relations. [sent-93, score-0.647]
47 Contrary to the related work, we use a base NP chunker and download the full pages returned by the search engine rather than search snippets in order to be able to extract risk phrases rather than just terms, which reduces contextual ambiguity and thus increases overall precision. [sent-94, score-0.691]
48 The taxonomy learning method described in the following subsection determines a risk taxonomy and new risks patterns. [sent-95, score-1.427]
49 architecture The second part of the system, the Risk Monitor, takes the risks from the risk taxonomy and uses them for monitoring financial text streams such as news, SEC filings, or (in our use case) earnings reports. [sent-96, score-1.55]
50 Using this, an analyst is then able to identify concrete risks in news messages and link them to the high-level risk descriptions. [sent-97, score-1.288]
51 He or she may want to identify operational risks such as fraud for a particular company, for instance. [sent-98, score-0.634]
52 The risk taxonomy can also derive further risks in this category (e. [sent-99, score-1.322]
53 Iceland) can be directly linked to the risk as stated in earnings reports or security filings. [sent-106, score-0.765]
54 2 Taxonomy induction method Using frequency to compute confidence in a pattern does not work for risk mining, however, because mention of particular risks might be rare. [sent-110, score-1.269]
55 Instead of frequency based indicators (n-grams, frequency weights), we rely on two types of structural confidence validation, namely (a) previously identified risks and (b) previously acquired structural patterns. [sent-111, score-0.57]
56 Note, however, that we can still use PageRank, a popularity-based graph algorithm, because multiple patterns might be connected to a risk term or phrase, even in the absence of frequency counts for each (i. [sent-112, score-0.734]
57 The first step is used to extract a list of risks based on high precision patterns. [sent-117, score-0.586]
58 However, it has been shown that the use of such patterns (e. [sent-118, score-0.04]
59 Ideally, we want to retrieve specific risks by re-applying the the extract risk descriptions: 2http : / /www . [sent-121, score-1.233]
60 (a) Take a seed, instantiate " < SEED > such as * " pattern with seed, extract candidates: Input: risks Method: apply pattern " < SEED > such as < INSTANCE > ", where < SEED > = risks Outpnuetn:ts l)ist of instances (e. [sent-124, score-1.26]
61 , faulty compo(b) For each candidate from the list of instances, we find a set of additional candidate hyponyms. [sent-126, score-0.108]
62 Input: faulty components Method: apply pattern " < SEED > such as < INSTANCE > ", where < SEED > = faulty components Output: list of instances (e. [sent-127, score-0.188]
63 Since the Risk Candidate extraction step will also find many false positives, we need to factor in information that validates that the extracted risk is indeed a risk. [sent-131, score-0.647]
64 We do this by constructing a possible pattern containing this new risk. [sent-132, score-0.052]
65 (a) Append " * risks " to the output of 1(b) in order to make sure that the candidate occurs in a risk context. [sent-133, score-1.237]
66 Input: brake(s) Pattern: "brake ( s ) * risk ( s ) " Output: a list of patterns (e. [sent-134, score-0.687]
67 We have now reached the point where we constructed a graph with risks and patterns. [sent-139, score-0.57]
68 Risks are connected via IS-A links; risks and patterns are connected via PATTERN links. [sent-140, score-0.67]
69 Note that there are links from risks to patterns and from patterns to risks; some risks back-pointed by a pattern may actually not be a risk (e. [sent-141, score-1.919]
70 However, this node is also not connected to a more abstract risk node and will therefore have a low PageRank score. [sent-144, score-0.713]
71 Risks that are connected to patterns that have a high authority (i. [sent-145, score-0.07]
72 The risk black Swan, for example, has only one pattern it occurs in, but this pattern can be filled by many other risks (e. [sent-148, score-1.36]
73 Hence, the PageRank score of the black swan is high similar to well known risks, such as fraud. [sent-151, score-0.107]
74 3 Risk alerting method We compile the risk taxonomy into a trie automaton, and create a second trie for company names from the meta-data of our corpus. [sent-153, score-0.953]
75 The Risk Monitor reads the two tries and uses the first to detect mentions of risks in the earning reports and the second one to tag company names, both using case-insensitive matching for better recall. [sent-154, score-0.741]
76 Optionally, we can use Porter stemming during trie construction and matching to trade precision for even higher recall, but in the experiments reported here this is not used. [sent-155, score-0.041]
77 count for this hcompany; risk typei tuple, which we use foorr t graphic rendering purposes. [sent-157, score-0.647]
78 The second option also permits the user to explore the detected risk mentions per company and by risk type. [sent-160, score-1.389]
79 5 Results From the Web mining process, we obtain a set of pairs (Figure 4), from which the taxonomy is constructed. [sent-161, score-0.146]
80 In one run with only 12 seeds (just the risk type names with variants), we obtained a taxonomy with 280 validated leave nodes that are connected transitively to the risks root node. [sent-162, score-1.392]
81 Our resulting system produces visualizations we call “risk maps”, because they graphically present the extracted risk types in aggregated form. [sent-163, score-0.647]
82 A set of risk types can be selected for presentation as well as a set of companies of interest. [sent-164, score-0.7]
83 A risk map display is then generated using either R (Figure 5) or an interactive Web page, depending on the user’s preference. [sent-165, score-0.647]
84 We inspected the output of the risk miner and observed the follow- Figure 5: An Example Risk Map. [sent-167, score-0.715]
85 ing classes of issues: (a) chunker errors: if phrasal boundaries are placed at the wrong position, the taxonomy will include wrong relations. [sent-168, score-0.133]
86 that I -A indi re ct ris k s) beS fore we introduced a stop word filter that discards candidate tuples that contain no content words. [sent-171, score-0.261]
87 Another prominent example is “short term” instead of the correct “short term risk”; (b) semantic drift3 : due to polysemy, words and phrases can denote risk and non-risk meanings, depending on context. [sent-172, score-0.664]
88 is cash flow primarily an operational risk or a financial risk? [sent-179, score-0.8]
89 We also found that some classes contain more noise than others, for example operational risk was less precise than financial risk, probably due to the lesser specificity of the former risk type. [sent-187, score-1.447]
90 In this paper, we introduced the task ofrisk mining, which produces patterns that are useful in another task, risk alerting. [sent-189, score-0.687]
91 Both tasks provide com- putational assistance to risk-related decision making in the financial sector. [sent-190, score-0.116]
92 We described a specialpurpose algorithm for inducing a risk taxonomy offline, which can then be used online to analyze earning reports in order to signal risks. [sent-191, score-0.828]
93 how to match up terms and phrases in financial news prose with the more abstract language typically used in talking about risk in general. [sent-194, score-0.812]
94 We have described an implemented demonstrator system comprising an offline risk taxonomy miner, an online risk alerter and a visualization component that creates visual risk maps by company and risk type, which we have applied to a corpus of earnings call transcripts. [sent-195, score-2.924]
95 Extracted negative and also positive risks can be used in many applications, ranging from e-mail alerts to determinating credit ratings. [sent-197, score-0.62]
96 Our preliminary work on risk maps can be put on a more theoretical footing (Hunter, 2000). [sent-198, score-0.665]
97 After studying further how output of risk alerting correlates4 with non-textual signals like share price, risk detection signals could inform human or trading decisions. [sent-199, score-1.365]
98 4Our hypothesis is that risk patterns can outperform bag of words (Kogan et al. [sent-203, score-0.687]
99 May all your wishes come true: A study of wishes and how to recognize them. [sent-227, score-0.088]
100 Semantic class learning from the web with hyponym pattern linkage graphs. [sent-247, score-0.114]
wordName wordTfidf (topN-words)
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