All risk items extracted formed the corpus of texts to be analyzed. This corpus reflected the structure of the risk items. Each risk item consists of one or two sentences describing a single risk. Many risks items reoccur over subsequent years. This corpus was analyzed by means of a sentence latent Dirichlet allocation (sLDA) algorithm that Bao and Datta (2014) developed for the analysis of the risk disclosure in Item 1.A. They adopted the original LDA by Blei et al. (2003) to exploit the unique structure of the risk items. As each risk
item deals with only one single topic, the sentence boundaries provide additional information on which words constitute one topic. As a result, instead of sampling words independently, all words of a sentence are sampled from the same topic (Bao and Datta, 2014). As the risk disclosure discusses a broad range of different topics, the sLDA was employed to identify supply- and demand related risk items and to quantify a firm’s exposure to such risks which allows to analyze the research question at finer granularity (George et al., 2016). The sLDA achieves high quality in assigning and quantifying common topics in the risk disclosure: It has highest predictive power measured by perplexity and best cluster quality measured by the silhouette coefficient (Bao and Datta, 2014). Results of their extensive numerical studies show that the sLDA has a comparable quality to supervised algorithms but is far more reliable. It has highest precision for 30 to 40 topics.
To build the topic model, the textual data was processed to extract the most meaningful words characterizing a distinctive topic. In addition to the steps outlined in Appendix B, a metric indicating the distinctiveness of a word was calculated. The purpose of the metric is to identify the words that are used by firms across different industries to capture rather broad themes of risk but not firm-specific ones. At the same time, the words should not be boilerplate (i.e., applicable to any situation). The computed metric is similar to the “term frequency inverse document frequency” (tf-idf). The tf-idf reflects the importance of a word in a corpus of documents. The counted number of appearances of a term in a document is divided by the number of documents in which the term occurs. The intuition of this calculation is that the more frequently a word occurs, the more important it is. However, if many documents use the word, then it is less distinctive. The metric applied in this study is calibrated to the data structure present. Other studies have also developed their metrics to distillate the most important words (e.g., Hasan et al., 2015). The nominator is the percentage of firms using a specific word. The denominator is the natural logarithm of the average fraction of a firm’s risk items that contain the word. The firm’s fraction of risk items containing a word is used instead of the absolute value in order to avoid distortions by very long risk disclosures. The intuition of the metric is as follows. The more firms use a specific word, the more likely it is to be relevant for a broad group of firms. However, the higher is the percentage of risk items of a firm that contain the word, the more likely it is that the word applies to a wide set of different
situations. Words that score high are used by several firms in only few risk items on average. These words are potentially relevant. In contrast, words that score low are either used by very few firms or in a large fraction of risk items. The former case excludes words on firm-specific risks while the latter case excludes words that are used in many situations. All words that score lower than two were excluded from the relevant words of the period. In total, the corpus of relevant words comprises 981 distinctive terms ranging from 344 terms in 2006 to 847 terms in 2016.
Figure 4-3: Computation of the scores for the exposure to supply- and demand- related risk
Figure 4-3 describes the computation of the supply and demand risk scores. After preparing the corpus of texts, the topic model is run with 34 topics. The algorithm simultaneously identifies the underlying topic structure of the documents and assigns each risk item to a topic (Bao and Datta, 2014). Its output is twofold: On the one hand, the topics are characterized by the most frequent words describing the topic. On the other hand, each risk item is assigned to a topic. The number of 34 topics serves as compromise between a higher granularity of topics (like 40 or 50) and the robustness of the assignment of risk item to topic. The key words per topic are robust to the number of topics. Two researchers manually labeled all topics based on each topic’s most frequent words and each topic’s compilation of risk items. Although automated labeling procedures exist, they are not applicable if solid background knowledge is required (Mei et al., 2007). All supply- and demand-related topics were then grouped into the two broader categories supply and demand, after discussions with other scholars in seminars and workshops. All other topics detected cover risks unrelated to supply chain management. Examples for these topics are the lack of human resources, volatility in the stock price, or lack of refinancing. The risk items assigned to these topics were not further considered.
Derive topic structure and assign risk item to
topic
Label all topics
Group topics into categories (supply, demand, irrelevant)
Count the number of risk items assigned to the respective category
Table 4-4: Supply- and demand-related topics extracted from 10-K reports’ Item 1.A using sLDA
Cat Topic Topic label Key words Sample
S u p p ly 0 Disruption in production natural facility production disaster manufacturing
The impact of natural disasters could negatively impact our supply chain and customers resulting in an adverse impact to our revenues and profitability.
3 Dependence on contract manufacturing party rely development manufacture delay
We have no capacity to manufacture clinical or commercial supplies of our product candidates and intend to rely solely on third parties to manufacture clinical and commercial supplies of all of our product candidates. 17 Dependence on joint development license agreement contract development right
We are dependent on technology systems and third- party content that are beyond our control.
22 Supply issues supplier supply component party raw
As we rely on a limited number of third parties to manufacture, assemble and test our IC products and to supply required parts and materials, we are exposed to significant supplier risks. 23 International risks foreign currency international fluctuation rate
We manufacture a significant portion of our products outside the United States, and political, societal or economic instability may present additional risks to our business.
D ema n d 4 Market competition competition competitive industry compete competitor
We face intense competition and rapid technological change that could result in products superior to the products we are developing.
9 Product approval approval regulatory obtain requirement regulation
We may be unable to complete our BTT study or obtain regulatory approvals, which will prevent us from selling our products and generating revenue.
25 Market acceptance party reimbursement marketing revenue acceptance
MelaFind may not be commercially viable if we fail to obtain an adequate level of reimbursement by Medicare, Medicaid and other third party payers.
26 Product approval approval regulatory delay development clinical
Ethical and other concerns surrounding the use of stem cells may negatively affect regulatory approval or public perception of our product candidates.
31 Industry demand economic industry demand global downturn
Current uncertainty in global economic conditions makes it particularly difficult to predict demand for our products and forecast revenues, and makes it more likely that our actual results could differ materially from expectations.
Both the supply-related risk category and the demand-related risk category consist of five topics each from the sLDA. As a result, every risk item is either assigned to the category of supply-related risk, to the category of demand- related risk or discarded. Table 4-4 describes the relevant sLDA-topics and their mapping to the categories of supply- and demand-related risk. Other studies in the field of accounting that have applied the LDA to annual reports have also aggregated the number of topics to broad categories (e.g., Dyer et al., 2016).
Let 𝑅𝐷𝑒𝑚 and 𝑅𝑆𝑢𝑝 denote the sets of demand- and supply-related risk items
respectively. 𝟏𝑅𝐼𝑖𝑡𝑘∈𝑅𝐷𝑒𝑚 is an indicator function indicating the membership of
the 𝑘th risk item of firm 𝑖 in year 𝑡 (𝑅𝐼𝑖𝑡𝑘) to the set of demand-related risk
items 𝑅𝐷𝑒𝑚.
1𝑅𝐼𝑖,𝑡,𝑘∈𝑅𝐷𝑒𝑚 = {
1 𝑖𝑓 𝑅𝐼𝑖,𝑡,𝑘∈ 𝑅𝐷𝑒𝑚
0 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
The risk exposure to demand-related risk of firm 𝑖 in year 𝑡 is then calculated as the sum of the indicator function values for the 𝐾𝑖,𝑡 risk items 𝑅𝐼𝑖𝑡𝑘 of firm 𝑖 in
year 𝑡.
𝐸𝑥𝑝𝐷𝑒𝑚,𝑖,𝑡= ∑ 1𝑅𝐼𝑖𝑡𝑘∈𝑅𝐷𝑒𝑚 𝐾𝑖,𝑡
𝑘=1
The exposure to supply-related risk is calculated analogously based on the set of supply-related risk items 𝑅𝑆𝑢𝑝. The measurements for the exposure to
supply- and demand-related risk as well as the total number of extracted risk items are then compared to the Standard & Poor’s (S&P) credit rating that is available in the Compustat database. In the case of S&P credit rating in Compustat there are seven rating categories, the highest credit quality being A+, and the lowest C. The credit ratings reflect the default probability of the bonds issued (A+: low default probability, C: high default probability). A bond is in default (D), if the issuer is not able to redeem either a coupon or the underlying principal. The literal ratings are recoded as numeric values with 1 corresponding to the A+ rating and 8 corresponding to C rating. Hence, the lower the value of the credit rating the lower is the default probability. The Spearman’s rank correlation coefficient between the measurement of demand- and supply-related risk as well as the total number of risk items and the credit rating is computed because all variables are not continuous. The correlation of
0.25 between the total number of risk items and the S&P credit rating indicates that the number of risk items reflects some information of the credit rating. This reinforces the view that the number of risk items disclosed is a meaningful proxy for the risk exposure of a firm. The correlation between the credit rating and supply- and demand-related risk is 0.15 and 0.12, respectively. The results illustrate that while supply- and demand-related risk items are associated with bankruptcy probability, exposure to them is not as severe as it is to the full bundle of risk items including stock market risk, refinancing risk, or regulation risk.