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We provide complete source code for building a fundamental industry classification based on publicly available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssrn.com/abstract=2600798) built using such industry classifications. Our source code includes various stand-alone and portable modules, e.g., for downloading/parsing web data, etc.

Background: The objective of this study was to estimate the risk of severe osteoarthritis, with the need for arthroplasty, in the knee and/or hip accroding to body mass index (BMI) both within a normal range and in persons with high BMI. Furthermore, we wanted to study the significance of smoking. Methods: This study identifies male construction workers participating in a national health control program (n = 320,192). The incidence rate for joint replacement was found by matching with the Swedish hospital discharge register between 1987 and 1998. BMI and smoking habit was registered at the time of the health examination. Results: In total 1495 cases of osteoarthritis of the hip and 502 cases of osteoarthritis of the knee were identified and included in this analysis. The incidence rate was found to increase linearly to the BMI even within low and 'normal' BMI. The relative risk for osteoarthritis of the hip was more than two times higher in persons with a BMI of 20-24 than in men with a BMI 17-19. There was almost a doubling of the risk of severe knee osteoarthritis with an increase in BMI of$5\\ {\\rm kg/m}^{2}$. Smoker had a lower risk of osteoarthritis than non-smokers and ex-smokers. Conclusions: BMI is an important predictor of osteoarthritis even within normal BMI. A decreased risk of osteoarthritis of the hip was found in smokers, but the effect was weak compared to that of BMI or age. Contrary to studies of radiographic osteoarthritis our study indicates higher risk of hip than of knee osteoarthritis.

BackgroundMapping job titles to standardised occupation classification (SOC) codes is an important step in identifying occupational risk factors in epidemiological studies. Because manual coding is time-consuming and has moderate reliability, we developed an algorithm called SOCcer (Standardized Occupation Coding for Computer-assisted Epidemiologic Research) to assign SOC-2010 codes based on free-text job description components.MethodsJob title and task-based classifiers were developed by comparing job descriptions to multiple sources linking job and task descriptions to SOC codes. An industry-based classifier was developed based on the SOC prevalence within an industry. These classifiers were used in a logistic model trained using 14 983 jobs with expert-assigned SOC codes to obtain empirical weights for an algorithm that scored each SOC/job description. We assigned the highest scoring SOC code to each job. SOCcer was validated in 2 occupational data sources by comparing SOC codes obtained from SOCcer to expert assigned SOC codes and lead exposure estimates obtained by linking SOC codes to a job-exposure matrix.ResultsFor 11 991 case–control study jobs, SOCcer-assigned codes agreed with 44.5% and 76.3% of manually assigned codes at the 6-digit and 2-digit level, respectively. Agreement increased with the score, providing a mechanism to identify assignments needing review. Good agreement was observed between lead estimates based on SOCcer and manual SOC assignments (κ 0.6–0.8). Poorer performance was observed for inspection job descriptions, which included abbreviations and worksite-specific terminology.ConclusionsAlthough some manual coding will remain necessary, using SOCcer may improve the efficiency of incorporating occupation into large-scale epidemiological studies.

We develop a method for defining groups of peer firms on the basis of joint analyst coverage. Besides industry boundaries, analysts’ coverage choices reflect other aspects of firm relatedness such as business model. We find that the analyst-based method produces substantially more homogeneous groups of firms compared to common industry classifications, and has a number of other desirable properties. The paper has two broader implications. First, it demonstrates the advantages of a self-organizing approach to classification, as opposed to a hierarchical system. Second, it illustrates a new positive information production externality generated by the institution of security market analysis.

This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.

The paper uses text mining and semantic algorithms to tag innovative firms and offer an alternative perspective to classify industrial activities. Instead of referring to firms’ standard industrial classification codes, we gather information from companies’ websites and corporate purposes, extract keywords and generate tags concerning firms’ activities, specializations, and competences. Evidence is interesting because allows us to understand ‘what firms do’ in a more penetrating and updated way than referring to standard industrial classification codes. Moreover, through matching firms’ keywords, we can explore the degree of closeness between the firms under observation, a measure by which researchers can derive industrial proximity. The analysis can provide policymakers with a detailed and comprehensive picture of the innovative trajectories underlying the industrial structure in a geographic area.

The Professional Pest Management Industry (PPMI) dates back over a century in the United States. Stakeholder calls for economic studies of the PPMI include, in the 1980s, the National Research Council, although there has been little to no progress on that topic. US Census and Bureau of Labor Statistics data indicate that revenue and employment for the PPMI in Georgia increased 117% from 1997 to 2021. We determined the revenue, employment, and economic contributions for the PPMI in Georgia, United States, using 2 methodologies applied to IMPLAN: primary survey data in combination with an open records request and publicly available Federal Economic data. Estimates of average revenue for the Georgia PPMI in 2021 were $833–$988 million, using the survey/open records and publicly available data, respectively. We utilized an economic modeling program, IMPLAN, to estimate the economic contributions by the Georgia PPMI in 2021 to be between $1.7 and $2.0 billion, with 13,000–14,000 jobs for the 2 respective data sets. We describe the methods and provide tutorials for other states or national organizations to follow to generate justifiable, comparable economic information on the PPMI. In addition, we discuss the unique position of the PPMI as heavily regulated by State Departments of Agriculture to advocate for including the PPMI economic values when reporting agricultural economic contributions.

This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard ( GICS)SMsystem, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross-sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.

With high penetration of renewable energy sources (RESs) in modern power systems, system frequency becomes more prone to fluctuation as RESs do not naturally have inertial properties. A conventional energy storage system (ESS) based on a battery has been used to tackle the shortage in system inertia but has low and short-term power support during the disturbance. To address the issues, this paper proposes a new synthetic inertia control (SIC) design with a superconducting magnetic energy storage (SMES) system to mimic the necessary inertia power and damping properties in a short time and thereby regulate the microgrid (µG) frequency during disturbances. In addition, system frequency deviation is reduced by employing the proportional-integral (PI) controller with the proposed SIC system. The efficacy of the proposed SIC system is validated by comparison with the conventional ESS and SMES systems without using the PI controller, under various load/renewable perturbations, nonlinearities, and uncertainties. The simulation results highlight that the proposed system with SMES can efficiently manage several disturbances and high system uncertainty compared to the conventional ESS and SMES systems, without using the PI controller.

ObjectivesTo use industry-specific denominators to more accurately examine trends in prevalence rates for occupational cases of elevated blood lead levels (eBLLs) in Pennsylvania.MethodsWe used adult (aged ≥16 years) blood lead level data from Pennsylvania (2007–2018) and industry-specific denominator data from the US Census Bureau’s County Business Patterns to calculate prevalence rates for eBLLs, defined as ≥25 µg/dL.ResultsOf the 19 904 cases with eBLLs, 92% were due to occupational lead exposure, with 83% from workers in the battery manufacturing industry. In 2018, the prevalence rate of eBLLs for battery manufacturing (8036.4 cases per 100 000 employed battery manufacturing workers) was 543 times the overall Pennsylvania prevalence rate. The prevalence rate for battery manufacturing steeply declined 71% from 2007 to 2018.ConclusionsThe battery manufacturing industry had the highest burden of occupational lead exposure in Pennsylvania, illustrating the importance of using industry-specific denominators to accurately identify sources of lead exposure. Although the prevalence rate of eBLLs declined over time, lead exposure remains a major concern among battery manufacturing workers.