SIC Codes Research Articles

Moderating effect of gross domestic production on the association between share ownership of CEO and strategic risk-taking of airlines

Purpose: The purpose of this research is to examine the moderating effect of gross domestic product on the relation-ship between share ownership and strategic risk-taking of airlines. Theoretical foundation of this work is upper echelon theory and contingency theory. Design/methodology/approach: The sample of this work is publicly traded airlines in the US stock market: NASDAQ, AMEX, and NYSE. For data collection, this work employed Annual 10-K, Bureau of economic analysis, Compustat, and Execucomp using SIC code 4512. 15 airlines were used and the study period is 1999-2019. For testing the research hypotheses, this study implemented multiple linear regression analysis. Findings: The results indicated that gross domestic product positively moderates the relationship between share ownership of CEO and strategic risk-taking of airlines. Research limitations/implications: The practical implication of this work is for the information provision of share-holders and potential investors for airline stocks. Originality/value: This research is worthy by elucidating the moderating effect of gross domestic product regarding top managers' decision making domain.

Dynamic factor copula models with estimated cluster assignments

This paper proposes a dynamic multi-factor copula for use in high-dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

Lack of small business participation (small fishing companies SIC Code 13100) in the Transport Education Training Authority-Supported Schemes

The study focusses on the transport sector, where company participation level is measured at approximately 20% of levy paying enterprises, and this level is mostly based on relatively high levels of participation from large and medium-sized companies. The study explores the relationship between Sector Education and Training Authorities (SETAs) and the companies within the sectors they are mandated to serve by researching the Transport Education Training Authority (TETA) maritime subsector. The research focus is on small companies and the focus is specifically on the low rate of participation of smaller entities in the skills development landscape. The consequences of the lack of participation are investigated and the study concludes that the SETA’s ability to effectively research its sector skills, as well as the SETAs ability to provide effective skills planning in support of the national agenda, are both negatively affected by current levels of poor participation. The research was conducted on small fishing companies registered with TETA, in order to determine the reasons for low participation in the government’s mandatory grant scheme. The research is survey-based across participating and non-participating small companies. In addition, it is proposed that the elements contained herein are transferable to other subsectors of the transport economy (e.g. air, road and rail), and to the multitude of other SETAs that have small companies registered with them. Succinctly, there is no financial incentive to a small company to participate, the SETAs should consider incentives schemes to increase participation that is project-based and allows for subsidised training. The value of an improved Sector Skills Plan (SSP) that matches the skills needs of the sector increases the likelihood of projects having a meaningful impact on the sector and reduces fruitless and wasteful expenditure in the sector. The small business environment has huge potential to assist in skills development, a skilled workforce, improved productivity and reducing unemployment. The findings and solutions are important tools for taking the maritime development agenda forward.

A study of Silicon Valley firms' accounting losses

PurposeSmall firms, which represent much of the Silicon Valley region, tend to experience losses due to their small scale, small customer base and lack of diversification. The authors study the impact of accounting conservatism and losses on firm value and as such this study is an appropriate addition to this growing field of financial management.Design/methodology/approachThe authors use methodology developed in prior literature to examine Silicon Valley and non-Silicon Valley firms' and their behavior when facing losses and the factors, which might play a role in their valuation. The authors focus particularly on earnings and accounting conservatism. Accounting conservatism captures how fast firms record losses relative to gains. The faster losses are recognized than gains the more accounting conservatism is exhibited. The authors examine the seemingly unrelated estimation of differences in means for our independent variables of interest across the two samples of Silicon Valley and non-Silicon Valley firms, both earnings and accounting conservatism. The authors use matched sample analysis of these firms based on four digit SIC code, size and date. In robustness, the authors run a more in-depth propensity score matched sample analysis.FindingsThe authors document that market values of Silicon Valley firms with accounting losses are affected less by negative earnings than other firms with accounting losses in the United States outside of the Silicon Valley region, noting the “lose big, win bigger” sentiment of Silicon Valley. Additionally, the authors document that accounting conservatism does play a role in influencing valuations of companies with accounting losses both in Silicon Valley and the rest of the United States, marginally more for Silicon Valley firms.Originality/valueThis study would be of interest to fund managers who need to consider smaller firms for inclusion in their portfolios. A lot of small firms have experienced losses ever since going public, especially Silicon Valley start-up firms.

The Screen Advertising Production Industry: SIC Codes and Screen Industries Mapping

The screen advertising production sector as a distinctive sector of film and television production supplying content to the advertising industry. Due to different employment practices, legal and production processes, it has a distinctively different set of issues to theatrical and high end drama. An analysis of a number of case studies of this sector using company accounts and SIC codes, however, shows that it has been rendered invisible in Government commissioned reports. That could be corrected through partnership work between the sector’s trade association the Advertising Producers’ Association. Recognition of the sector’s integration within the UK independent film and television production sector would increase knowledge and understanding of the sector as a whole. This article recommends a theoretical model of horizontal integration of vertical value chains to progress new research in the area and identifies some of the consequences for existing scholarship.

Dynamic Factor Copula Models with Estimated Cluster Assignments

This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

Value drivers of development stage biopharma companies

ObjectiveScholars previously estimated research and development (R&D) costs of the internal drug development process. However, little is known about the costs and value arising from externally acquired therapeutics. This study identifies and estimates the magnitude of factors associated with Biopharma acquisition value.MethodsSDC Thomson Reuter and S&P Capital IQ were screened for majority acquisitions of US and EU Biopharma companies developing new molecular entities for prescription use (SIC code: 2834) from 2005 to 2020. Financial acquisition data were complemented with variables characterizing the target’s product portfolio extracted from clinicaltrials.gov, Drugs@FDA database, US SEC filings, and transaction announcements. A multivariate regression assesses the association of firm value with extracted variables.Results311 acquisitions of companies developing prescription drugs were identified over the study period. Acquirers paid 37% (p < 0.05) more for companies with biologics and gene therapeutics than small-molecule lead drugs. Multi-indication products were acquired for a 12% premium per additional indication (p < 0.01). No significant valuation difference between companies developing orphan and non-orphan designated lead products was observed (18%, p = 0.223). Acquisition value positively correlated with the total number of further products, headquarter location in the US, underlying market conditions, and acquirer market capitalization (p < 0.05).ConclusionsInternal and external drug development consumes many financial and human resources, yet it is important for entrepreneurs, regulators, and payers to understand their precise magnitude and value drivers. This information permits the design of targeted pricing and industrial policies that incentivize the development of novel drugs in areas with high unmet needs.

Valuation and Returns of Drug Development Companies: Lessons for Bioentrepreneurs and Investors

ObjectivesThis study evaluates the association of Biopharma company valuation with the lead drug’s development stage, orphan status, number of indications, and disease area. We also estimated annual returns Bioentrepreneurs and investors can expect from founding and investing in drug development ventures.MethodsSDC Thomson Reuter and S&P Capital IQ were screened for majority acquisitions of US and EU Biopharma companies developing new molecular entities for prescription use (SIC code: 2834). Acquisition data were complemented with drug characteristics extracted from clinicaltrials.gov, the US Food and Drug Administration (FDA), and deal announcements. Thereafter, company valuations were combined with previously published clinical development periods alongside orphan-, indication-, and disease-specific success rates to estimate annual returns for investments in drug developing companies.ResultsBased on a sample of 311 Biopharma acquisitions from 2005 to 2020, companies developing orphan, multi-indication, and oncology drugs were valued significantly higher than their peers during later development stages (p < 0.05). We also estimated significantly higher returns for shareholders of companies with orphan relative to non-orphan-designated lead drugs from Phase 1 to FDA approval (46% vs. 12%, p < 0.001). Drugs developed across multiple indications also provided higher returns than single-indication agents from Pre-Clinic to FDA approval (21% vs. 11%, p < 0.001). Returns for oncology drugs exceeded other disease areas (26% vs. 8%, p < 0.001).ConclusionsClinical and economic conditions surrounding orphan-designated drugs translate to a favorable financial risk-return profile for Bioentrepreneurs and investors. Bioentrepreneurs must be aware of the upside real option value their multi-indication drug could offer when negotiating acquisition or licensing agreements.

Dynamic Factor Copula Models with Estimated Cluster Assignments

&lt;ns2:p&gt;This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.&lt;/ns2:p&gt;

The Impact of Market Timing on European Firms’ Capital Structure: RLBOs vs. IPOs

Our study compares the impact of market timing on the capital structure of reverse leveraged buyouts (RLBOs) and initial public offerings (IPOs). Our sample is made up of 210 RLBOs and 210 public companies listed between 1995 and 2015 and linked by size (turnover) and industry (based on the first two digits of the SIC code). Our results show that the impact of market timing measures on capital structure is different between RLBOs and public companies. In accordance with Baker and Wurgler (2002) and others, these measures have a negative and significant effect on the capital structure of the two types of companies. This significance is persistent ten years after the IPO for public companies and only three years after the IPO for RLBOs. RLBOs rebalance the market timing effect on their capital structures much more quickly and therefore move toward the target debt ratio more quickly than their counterparts. These results challenge the robustness and generality of Baker and Wurgler’s (2002) market timing theory. The capital structure of RLBOs seems to be better explained by the characteristic variables of companies suggested by the theory of trade-off.

Who Gains from M&amp;A? The Effect of Industry Life Cycle on Post-M&amp;A Innovation Performance

Mergers and acquisitions (M&A) are popular strategies for firms seeking innovations, but previous studies show contradicting results about the effect of M&A on post-M&A innovation performance. I argue that the interplay between industry life cycle and firms’ incentives for innovation activities plays a critical role in determining the post-M&A innovation performance of acquiring firms. In this study, using datasets of granted patents and M&A transactions between 1980 and 2000, I analyze the effect of M&A on post-M&A innovation performance of acquiring firms. In particular, I focus on industries in the declining stage in which firms are desperate for but not able to create fundamental innovation due to their rigid structure. Industries are identified based on 3-digit SIC codes, and declining industry is defined through the sales growth rate, firm entering rate, and annual stock return. The results show that M&A transactions in declining industries lead to better post-M&A innovation performance of acquiring firms. Furthermore, diversifying M&A has more positive effect on innovation performance than consolidating M&A in declining industries. However, as an industry becomes more concentrated, the positive effect of M&A in declining industry decreases significantly. The study contributes to the existing literature on M&A by clarifying the key sources of conflicting findings with regard to post-M&A performance

Examining the stock performance of acquirers where the acquirer or target hold patents

We investigate the stock returns for acquirers of firms holding registered patents (“innovative targets”). We find that acquiring innovative targets is associated with significantly positive stock performance relative to acquisitions of non-innovative targets. Specifically, acquirers of innovative targets enjoy higher announcement and interim period abnormal returns. Acquirers of innovative targets also enjoy higher post-acquisition returns, but only in settings where the acquirer’s and target’s industry is unambiguously close (i.e., they share the same 3-digit primary SIC code), suggesting that when the acquirer has familiarity and expertise in that industry the acquirer is able to better exploit the target firm’s patents. Additionally, we find that an acquirer’s innovation level can be critical to their post-acquisition stock performance; that is, acquirers holding at least one patent of their own prior to the acquisition enjoy significantly higher post-acquisition returns, and this benefit does not depend on the innovativeness of the target firm.

Impact of 2008 Recession on Consumer Discretionary Sector

I examine the effect of the 2008 economic recession on the current assets, sales, COGS, and day’s sales of companies in consumer discretionary sector. The study focuses on 19 companies in the consumer discretionary sector selected from S&P 500. The study finds similar effects of recession on different groups of companies except for day’s sales. The companies are grouped with the same four-digit SIC codes within the consumer discretionary sector. All groups show increased sales, inventory, COGS, and current assets after recession. Interestingly, some groups show faster day’s sales after recession and some show slowdown in the consumer discretionary sector.

Dynamic Factor Copula Models with Estimated Cluster Assignments

This paper proposes a dynamic multi-factor copula for use in high dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.

TFP growth in Chinese cities: The role of factor-intensity and industrial agglomeration

This article estimates agglomeration effects via calculating EG (Elilsion & Glaeser) and TFP growth (Total Factor Production) by considering the undesired output of the industrial enterprise database and the entropy weight method. Using panel data of 207 county-level cities in China and 28 two-digit manufacturing industries from 2003 to 2013 based on SIC codes, this paper analyzes the relationship between agglomeration and TFP growth through the smooth transition model under different regions and factor-intensity. The results are as follows. (1) A negative relationship appears in manufacturing productivity. The agglomeration effect changes to the crowded effect. Environmental pollution is also generated by transportation and inadequate pollution treatment technology. (2) The excessive agglomeration phenomenon of developed areas (eastern region) is less than the less developed areas (central and western regions). (3) Resource-intensity industries present two thresholds that indicate complex regional features. For various intensive industries in different regions, the relationship between GML and agglomeration is different. High agglomeration does not always promote TFP growth. (4) At different levels of urban industrial agglomeration, the influences of efficiency change and technical change on GML are different. Overall, moderate agglomeration in all regions helps promote economic development.

Assessing UNGC pharmaceutical signatories stakeholders using big data

AbstractThis article aims to focus on how signatories versus nonsignatories in the U.S. pharmaceutical sector compare with respect to the internal and external stakeholders and principles of the United Nations Global Compact (UNGC). We seek to answer the question: Do signatories to the UNGC walk the talk better than nonsignatories as determined by a variety of published rankings and data? This research presents an innovative approach to the evaluation of UNGC signatories. It uses several objective and independent data sources to assess a matched group of signatories versus nonsignatories in the U.S. pharmaceutical sector. Signatory organizations in the same sector as determined by SIC codes were matched with nonsignatories on variables such as size and number of employees in U.S. pharmaceutical companies. Then both types of organizations were compared on the following data sources: Coalition for Environmentally Responsible Economies ratings, ratings by external stakeholders, ratings by employees, unionization data, financial measures, and annual reports to shareholders. Using nonparametric testing the research found there are differences between signatories and nonsignatories in the U.S. pharmaceutical sector for some of the external stakeholder measures and no differences were found for the internal stakeholder measures.

Logistics land use patterns in metropolitan Canada

This paper focuses on the spatial patterns of freight and logistics activities in Metropolitan Canada. The literature acknowledges that these activities, while fundamentally important to economic and social wellbeing in cities, also contribute negatively to aspects of urban life through their associated vehicular traffic for example. The at times sprawling nature of their pattern (Dablanc, Ogilvie, & Goodchild, 2014; Dablanc & Ross, 2012), driven by industry desires for larger footprints, can further exacerbate these negatives. However, the existence of sprawl is not common in all major centres, with local variations in influencing factors interacting with systematic developments in the logistics industry (e.g. larger facilities). The objectives of this paper are to analyse the pattern of logistics developments in Canadian cities and to explore the factors influencing the observed patterns. The methodology employs a case study approach (building on the authors previous work on the Greater Toronto Area - (Woudsma, Jakubicek, & Dablanc, 2015), obtaining fine-grained spatial data on logistics businesses and their employment levels across selected Canadian cities including Vancouver, Calgary, Montreal, Winnipeg and Halifax. The data used to assess these patterns were business listings from DMTI spatial, a Canadian provider of geospatial data. The data set used in this paper contains listings of business names, addresses, SIC codes in 2002, and NAICS and SIC codes in 2012. For analysis of this data, our approach uses basic spatial indicators, like mean centre of establishments and average distance to mean centre, along with statistical analysis of facility types and intra-regional changes. The exploration of influencing factors is conducted through in-depth review of industry publications, regional news sources, and conversations with experts in the commercial development industry in each case study city. Preliminary results indicate that in smaller metropolitan areas (Winnipeg, Halifax), the land supply pressures, and scope of logistics developments result in no evidence of sprawl. In the remaining cities, there is moderate evidence of sprawl, with noted influences including supply and local development controls. These results are discussed in the context of the current literature on urban logistics sprawl with directions for future work provided.

Emerging specializations, competences and firms' proximity in digital industries: The case of London

AbstractIn order to overcome the limitations of defining industrial specializations in digital industries through SIC codes, this paper suggests measuring the specializations and competences of these industries on the basis of the degree of digital technologies present in the products and services supplied. Metadata from CrunchBase are employed, as proxies of firms' specializations and competences which are defined as the fields of activity in which firms are involved. Applying a network analysis, these specializations and competences are linked to the recognition of emerging digital technologies and the strongest combinations of products and services. We tested the proposed methodology on London, a leading centre for the digital economy.

Managing strategic intellectual property assets in the fuzzy front end of new product development process

The strategic use of intellectual property (IP) is crucial for technology‐based companies to gain competitive advantage. The recent transformation of the US patent system brings new challenges and opportunities in this arena. In this regard, this study attempts to identify techniques which can help with IP evaluation and selection in the fuzzy front end (FFE) of new product development (NPD) process. This study combines data collection methods such as mining the literature, conducting in‐depth interviews, surveying questionnaires, and analyzing cases. This research serves as an analysis of modern literature and identifies a multicriteria weighted scoring model that can be employed to help with the patent decision process. The criterion to discern patent eligibility is a contended discussion. For this survey administration, 300 companies, as the targeted sample, were randomly selected to be reached from LexisNexis database. Consequently, this paper identifies the key decision criteria to incorporate into this model and obtains weights gathered from surveying IP professionals and R&amp;D managers in US‐based electronics manufacturing firms (SIC code: 36). This study proposes a structured approach to identify ideas that should be patented in the FFE of NPD process by way of an analysis of pertaining literature and case studies. The technique we present in this paper could be essential for many firms to achieve IP success as their strategic means. Moreover, this tool can help R&amp;D managers not only speed up the FFE of NPD process but also make more informed and target‐worthy decisions for IP filing.

Exploring the decline in trade credit investment

Purpose The purpose of this paper is to identify three factors leading to the observed decline in trade credit offered from publicly traded firms. Design/methodology/approach The study conducts firm fixed effect regressions testing the relationship between cash flow volatility and firm investment in trade credit. The relationship is further examined with all firms separated into two groups, based on SIC codes, designating if they are in industries that traditionally offer higher amounts of trade credit. Findings The proportion of US firms that has traditionally extended the most trade credit has been decreasing over time, contributing to part of the decline in trade credit offered. Increases in cash flow volatility have also contributed to decreasing investment in trade credit. The negative relationship with cash flow volatility is greatest amongst firms that traditionally place the highest value on trade credit. Firms with access to credit, proxied by investment grade debt ratings, do not experience the same decline in trade credit offered. Practical implications Firms that value the ability to extend trade credit may maintain their level of investment in trade credit, even with increased risk of cash flow volatility, by maintaining a comparative advantage in access to credit. Originality/value This study extends prior findings by providing three previously unexplored explanations for the decline in offered trade credit seen in the USA. The changing make-up of publicly traded firms, a market-wide increase in cash flow volatility, and access to credit all play an important role in observed declines of trade credit investment.