Proprietary Information Research Articles

Does the Disclosure of Proprietary Information About Loan Losses Pose a Threat to Financial Stability?

AbstractConcerns have been expressed that public disclosure of outcomes from regulatory inspections of banks could lead to contagion and instability in the financial systems. This paper exploits Denmark's system of open inspection reports to analyze whether announcements of required increases in loan loss reserves by the Danish supervisory authorities, following an ordinary bank inspection, lead to contagion in financial markets. We find strong evidence of negative returns for the announcing banks over the period 2009–2020, suggesting that inspection reports containing proprietary information about loan losses provide value-relevant news to investors. We also find statistically significant contagion effects in the share price reactions among non-announcing peer banks. However, the reactions of other, non-affected banks are mild and confined to smaller banks, which is reassuring from a systemic risk perspective. Overall, our findings support the use of accounting disclosures as a policy tool for bank supervisors.

Monitoring and Loan Pricing: Do Microfinance Institutions Extract Rents from Entrepreneurs?

Microfinance institutions (MFIs) have been criticized for charging high interest rates on loans. Building on multiple-principal agency theory, we argue that when an MFI acquires proprietary information about its clients through monitoring, it gains an information advantage over other lenders enabling it to extract rents by charging higher interest rates. Using data from 712 MFIs across 62 countries from 2010 to 2018, we find this to be the case. Furthermore, we find that MFIs that make more relationship-based loans, operate in less competitive markets, and those driven by for-profit commercial banking logic are more likely to extract even greater rents.

Disclosure, Patenting, and Trade Secrecy

AbstractPatent applications often reveal proprietary information to competitors, but does such disclosure harm firms or also benefit them? We develop and empirically support a theory showing that when firms patent enhancements to incumbent, nondisruptive technologies, they can cooperate more easily on these technologies, increasing their profitability. The downside of cooperating on nondisruptive technologies is that the investment in and commitment to disruptive technologies decline. To improve their commitment to disruptive technologies, some firms rely more on trade secrecy. We provide empirical support for these predictions. We document that after a patent reform that made information about patent applications widely accessible, firms cooperate more and charge higher markups. Furthermore, the nature of patented innovation has changed, with the proportion of nondisruptive patents increasing substantially. Finally, while some firms start patenting more, others patent less and rely more on trade secrecy, with the response depending on the attractiveness of firms' innovation prospects.

Improving Business Insurance Loss Models by Leveraging InsurTech Innovation

Recent transformative and disruptive advancements in the insurance industry have embraced various InsurTech innovations. In particular, with the rapid progress in data science and computational capabilities, InsurTech is able to integrate a multitude of emerging data sources, shedding light on opportunities to enhance risk classification and claims management. This article presents a collaborative effort as we combine real-life proprietary insurance claims information together with InsurTech data to enhance the loss model, a fundamental component of insurance companies’ risk management. Our study further utilizes a tree-based model and a conventional linear model to quantify the predictive improvement of the InsurTech-enhanced loss model over that of the insurance in-house model. The quantification process provides a deeper understanding of the value of InsurTech innovation and advocates potential risk factors that are unexplored in traditional insurance loss modeling. This study represents a successful undertaking of an academic–industry collaboration, suggesting an inspiring path for future partnerships between industry and academic institutions.

Internalizing Peer Firm Product Market Concerns: Supply Chain Relations and M&A Activity

ABSTRACTWe explore whether firms internalize the product market concerns of their economically linked peers by examining merger and acquisition decisions in the context of customer–supplier relations. Given the extensive transfer of capital, knowledge, and information between merging parties, we hypothesize that customers’ competition concerns discourage their suppliers from engaging in vertically conflicted transactions (i.e., acquisitions of their customers’ rivals or suppliers to those rivals). Consistent with our hypothesis, we find that suppliers are less likely to engage in such transactions when their customers are subject to higher product market competition. Moreover, the effect is more pronounced when suppliers and customers have greater relationship‐specific investments and when customers face heightened proprietary information concerns. Using plausibly exogenous variation in common ownership between customers and their rivals as a shock to customers’ competition concerns, we conclude that the link between customers’ competition concerns and supplier acquisitions is likely causal. Our findings suggest that firms alter their investment and strategic decisions in response to the product market competition concerns of their economically related peers.

Balancing Diversity in analyst teams: Examining the impact of informational and social Diversity on analyst team Performance

Using detailed information on team analysts' background, we study how diversity and the type of diversity influence overall effectiveness and performance of an analyst team. Informational diversity reflects skills-based differences among group members, for instance, education background and work experience, while social diversity captures innate characteristics that individuals are born with, such as gender and ethnicity. We find that informational diversity is associated with less timely, but more accurate, forecasts. Social diversity has no impact on team performance. We find that group longevity moderates the negative association between informational diversity and timeliness. Further analysis indicates that the benefits of diversity is more pronounced when the forecast firm is more complex, but moderated when the brokerage house has greater access to proprietary information. Informational diversity decreases a team member's probability of gaining star status, thereby confirming the benefits of a homophilic environment. Our findings highlight that diversity is a double-edged sword for employees in the financial markets.

Strategic Alliances and Lending Relationships

ABSTRACT We study how proprietary information flows in strategic alliances facilitate banks’ information collection in private debt markets. We argue that lenders that have previously worked with a borrower’s alliance partners have an information advantage and show that firms entering a strategic alliance receive a lower interest spread on loans from banks that have previously lent to their strategic partners than loans from other banks. Cross-sectional tests on alliances’ economic importance and participants’ information environment support our hypothesis that the loan price effect is driven by reduced information asymmetry between borrowers and their partners’ relationship banks. Last, we find borrowers are more likely to obtain debt financing from alliance-related banks than from other banks. Overall, our findings are consistent with lenders that have previously worked with an alliance counterparty possessing debt contracting-relevant information about the soft nature of alliance value and the partners’ commitment to alliances. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G10; G21; G32.

Product market competition and disclosure content differentiation: A topic modeling analysis

AbstractWe examine how product market competition affects firms’ disclosure content. Theory suggests that competition leads firms to strategically disclose to avoid proprietary costs; however, the empirical evidence has been mixed. We investigate whether firms manage disclosure content to protect proprietary information, a disclosure strategy under‐researched in the literature. We use a topic‐modeling technique to extract and compare thematic topics that industry peers disclose in management discussion and analysis (MD&A) in 10‐K filings. Exploiting large reductions in US import tariff rates as an exogenous shock to product market competition, we find that firms differentiate disclosure topics in response. This effect is more pronounced when disclosures incur higher proprietary costs, as theoretically predicted, and stronger for firms with greater product similarity to industry peers and those with longer MD&A disclosure texts.

Enhancing Clinical Relevance of Pretrained Language Models Through Integration of External Knowledge: Case Study on Cardiovascular Diagnosis From Electronic Health Records.

Despite their growing use in health care, pretrained language models (PLMs) often lack clinical relevance due to insufficient domain expertise and poor interpretability. A key strategy to overcome these challenges is integrating external knowledge into PLMs, enhancing their adaptability and clinical usefulness. Current biomedical knowledge graphs like UMLS (Unified Medical Language System), SNOMED CT (Systematized Medical Nomenclature for Medicine-Clinical Terminology), and HPO (Human Phenotype Ontology), while comprehensive, fail to effectively connect general biomedical knowledge with physician insights. There is an equally important need for a model that integrates diverse knowledge in a way that is both unified and compartmentalized. This approach not only addresses the heterogeneous nature of domain knowledge but also recognizes the unique data and knowledge repositories of individual health care institutions, necessitating careful and respectful management of proprietary information. This study aimed to enhance the clinical relevance and interpretability of PLMs by integrating external knowledge in a manner that respects the diversity and proprietary nature of health care data. We hypothesize that domain knowledge, when captured and distributed as stand-alone modules, can be effectively reintegrated into PLMs to significantly improve their adaptability and utility in clinical settings. We demonstrate that through adapters, small and lightweight neural networks that enable the integration of extra information without full model fine-tuning, we can inject diverse sources of external domain knowledge into language models and improve the overall performance with an increased level of interpretability. As a practical application of this methodology, we introduce a novel task, structured as a case study, that endeavors to capture physician knowledge in assigning cardiovascular diagnoses from clinical narratives, where we extract diagnosis-comment pairs from electronic health records (EHRs) and cast the problem as text classification. The study demonstrates that integrating domain knowledge into PLMs significantly improves their performance. While improvements with ClinicalBERT are more modest, likely due to its pretraining on clinical texts, BERT (bidirectional encoder representations from transformer) equipped with knowledge adapters surprisingly matches or exceeds ClinicalBERT in several metrics. This underscores the effectiveness of knowledge adapters and highlights their potential in settings with strict data privacy constraints. This approach also increases the level of interpretability of these models in a clinical context, which enhances our ability to precisely identify and apply the most relevant domain knowledge for specific tasks, thereby optimizing the model's performance and tailoring it to meet specific clinical needs. This research provides a basis for creating health knowledge graphs infused with physician knowledge, marking a significant step forward for PLMs in health care. Notably, the model balances integrating knowledge both comprehensively and selectively, addressing the heterogeneous nature of medical knowledge and the privacy needs of health care institutions.

Generative Ai in Software Development : an Overview and Evaluation of Modern Coding Tools

Generative AI has significantly transformed software development by leveraging advanced machine learning models to automate coding tasks, generate code, and enhance productivity. This paper provides an overview and evaluation of modern AI-powered coding tools, including GitHub Copilot, OpenAI Codex, DeepCode, Amazon CodeGuru, TabNine, Kite, and IntelliCode, which use large language models (LLMs) to offer real-time code suggestions, automated error detection, and intelligent code completions. Despite their benefits, these tools face challenges related to accuracy, contextual understanding, security, privacy, and ethical considerations, necessitating thorough review and testing of AI-generated code by developers. The integration of AI in coding also raises concerns about proprietary information protection and ethical implications such as job displacement. This paper explores the capabilities, applications, and limitations of current generative AI tools, highlighting their impact on software development and discussing future directions. Emphasis is placed on the need for improved model training, enhanced contextual understanding, secure AI training methods, and ethical AI usage. By addressing these challenges, the industry can maximize the potential of generative AI, creating more accurate, reliable, and ethically sound tools that support a collaborative and innovative software development environment.

Effects of information asymmetry on green advertising for remanufacturing within a closed-loop supply chain

Manufacturers invest in Green Advertising for Remanufacturing (GAR) to attract consumers by promoting the eco-friendly benefits of their remanufacturing. Despite this, the production of remanufactured products is often influenced by Third-Party Collectors (3PCs) which handle the collection of used products. Unfortunately, market responsiveness to GAR levels is undisclosed due to proprietary information, which leaves 3PCs to unreasonably set pricing and collection quantities, potentially leading to profit loss. To explore how a two-part tariff (TPT) contract menu facilitates 3PCs in acquiring information regarding the manufacturer’s GAR effectiveness, a closed-loop supply chain comprising one manufacturer and one 3PC is taken into consideration. The optimal decisions are analyzed in three distinct decision-making scenarios: the centralized scenario, the complete information scenario, and the asymmetric information scenario. The research findings suggest that in the presence of information asymmetry, manufacturers with high GAR effectiveness (H-type manufacturers) are inclined to masquerade as manufacturers with low GAR effectiveness (L-type manufacturers). Consequently, H-type manufacturers gain additional profits due to information asymmetry. However, for the 3PC, information asymmetry leads to reduced expected profit when the manufacturer has high GAR effectiveness and increased expected profit when the manufacturer has low GAR effectiveness. Finally, our analysis demonstrates that information asymmetry does not consistently hurt consumer surplus.

Data augmentation for predictive maintenance: Synthesising aircraft landing gear datasets

AbstractIn the aviation industry, predictive maintenance is vital to minimise Unscheduled faults and maintain the operational availability of aircraft. However, the amount of open data available for research is limited due to the proprietary nature of aircraft data. In this work, six time‐series datasets are synthesised using the DoppelGANger model trained on real Airbus datasets from landing gear systems. The synthesised datasets contain no proprietary information, but maintain the shape and patterns present in the original, making them suitable for testing novel PdM models. They can be used by researchers outside of the industry to explore a more diverse selection of aircraft systems, and the proposed methodology can be replicated by industry data scientists to synthesise and release more data to the public. The results of this study demonstrate the feasibility and effectiveness of using the DoppelGANger model from the Gretel.ai library to generate new time series data that can be used to train predictive maintenance models for industry problems. These synthetic datasets were subject to fidelity testing using six metrics. The six datasets are available on the UWE Library service.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Stock exchange comment letters and MD&A tone management

This paper investigates the impact of stock exchange comment letters on MD&A tone management by Chinese listed firms. Our findings are twofold. First, abnormal positive MD&A tone is significantly reduced for firms receiving comment letters from stock exchanges. Second, comment letters have made abnormal positive MD&A tone more informative about future accounting earnings. Further analysis suggests that the treatment effect of stock exchange comment letters on MD&A tone management is more pronounced when the comment letters make stronger demands, or firms face greater concerns over proprietary information or attract more media attention. These findings are consistent with the view that stock exchange comment letters can restrain uninformative tone management leading to enhanced financial reporting quality.

Adaptive Critic Design for Event-Triggered Tracking Control in Discrete-Time on Linear Systems with Observers

This research pioneers an innovative methodology for the bespoke manufacturing of SquidSkin safety products, meticulously tailored to the distinct requisites of diverse industries encompassing medical, military, and defense sectors. The triple DES algorithm is a three instance of DES on same plain text in blocks of 64 bits and converts them to cipher text using keys of 168 bits. The triple DES encryption process creates cipher text, which is an unreadable, effectively indecipherable conversion of plaintext data, the version of information that humans can read and understand. The output of the encryption process, the triple DES cipher text, cannot be read until a secret triple DES key is used to decrypt it. The delineation of industry- specific manufacturing parameters mandates a nuanced approach to material composition and production processes, culminating in a paradigmatic advancement in safety product customization. Integral to this methodology is a rigorous analysis of industry-specific requirements, guiding the selection of optimal material compositions. This selection is underpinned by a meticulously designed material testing phase, ensuring adherence to exacting industry standards and performance benchmarks. Augmenting this paradigm is an imperative emphasis on fortifying the security framework underpinning the handling of proprietary manufacturing data. The proposal introduces an advanced cryptographic architecture, featuring encryption and decryption protocols. Approach assures heightened efficiency, unwavering reliability, and the preservation of proprietary information across varied industry sectors.

Smart grid public datasets: Characteristics and associated applications

AbstractThe development of smart grids, traditional power grids, and the integration of internet of things devices have resulted in a wealth of data crucial to advancing energy management and efficiency. Nevertheless, public datasets remain limited due to grid operators' and companies' reluctance to disclose proprietary information. The authors present a comprehensive analysis of more than 50 publicly available datasets, organised into three main categories: micro‐ and macro‐consumption data, detailed in‐home consumption data (often referred to as non‐intrusive load monitoring datasets or building data) and grid data. Furthermore, the study underscores future research priorities, such as advancing synthetic data generation, improving data quality and standardisation, and enhancing big data management in smart grids. The aim of the authors is to enable researchers in the smart and power grid a comprehensive reference point to pick suitable and relevant public datasets to evaluate their proposed methods. The provided analysis highlights the importance of following a systematic and standardised approach in evaluating future methods and directs readers to future potential venues of research in the area of smart grid analytics.

A LITERATURE REVIEW ON RISK FACTORS ON INTERNATIONAL CONSTRUCTION PROJECTS

Internationalization is one of the most critical decisions for construction companies. Although expanding into new markets promises several benefits such as new job opportunities, increases in market share and profit margins, etc., it is exposed to risks and difficulties, which in turn result in poor project performance in terms of delays and cost overruns. Therefore, construction companies should thoroughly recognize the potential risk factors and develop proper risk management strategies against these risk factors. This study reviews the recent studies on risk factors on international construction projects, which might cause delays and cost overruns, in order to help construction companies that mostly undertake international construction projects in recognizing risk factors and developing their risk response strategies against them. For this purpose, semi-systematic literature review was conducted, 32 journal articles from 14 academic journals reviewed in depth, and 23 different risk factors were identified. “Host government related risk”, “Resource price fluctuations”, “Unavailability of resources in the host market”, and “Contractor’s lack of managerial skills” were found to be “highly cited risk factors”. On the other hand, “Adverse public attitude”, “Project specification and standard problems”, “Technology and proprietary information sharing problems”, and “Virtual Risk” were found to be “lowly cited risk factors”.

A LITERATURE REVIEW ON RISK FACTORS ON INTERNATIONAL CONSTRUCTION PROJECTS

Internationalization is one of the most critical decisions for construction companies. Although expanding into new markets promises several benefits such as new job opportunities, increases in market share and profit margins, etc., it is exposed to risks and difficulties, which in turn result in poor project performance in terms of delays and cost overruns. Therefore, construction companies should thoroughly recognize the potential risk factors and develop proper risk management strategies against these risk factors. This study reviews the recent studies on risk factors on international construction projects, which might cause delays and cost overruns, in order to help construction companies that mostly undertake international construction projects in recognizing risk factors and developing their risk response strategies against them. For this purpose, semi-systematic literature review was conducted, 32 journal articles from 14 academic journals reviewed in depth, and 23 different risk factors were identified. “Host government related risk”, “Resource price fluctuations”, “Unavailability of resources in the host market”, and “Contractor’s lack of managerial skills” were found to be “highly cited risk factors”. On the other hand, “Adverse public attitude”, “Project specification and standard problems”, “Technology and proprietary information sharing problems”, and “Virtual Risk” were found to be “lowly cited risk factors”.

Safeguarding proprietary information in the supply chain and relationship‐specific investments

AbstractWe examine whether the risk of losing proprietary information through the supply chain affects relationship‐specific investment (RSI) decisions by supply chain partners. Using state courts' staggered adoption of the Inevitable Disclosure Doctrine (IDD) as a shock to the firm's ability to protect proprietary information, we find that customers increase RSI when their supplier is headquartered in a state that adopts IDD. The effect is more substantial for customers who face a higher risk of losing proprietary information and with suppliers that are difficult to substitute. IDD also plays a more prominent role in the absence of alternate mechanisms that reduce contracting frictions, such as shared directors, common ownership, or joint ventures. Our results are robust to using cross‐citation measures of RSI and alternative estimations that mitigate potential biases arising from the staggered difference‐in‐difference approach. Our findings suggest that proprietary information protection enables firms to reduce contracting frictions arising in supply chain relations.

The Battle for Repairability: Navigating Intellectual Property Rights

The “right to repair” movement allows consumers freedom to choose how they repair their product, either by themselves or choosing any third party repair service provider, instead of being obligated to return to the original manufacturer for repairs. When a product becomes non-functional or obsolete, consumers may decide whether to repair or replace it. But this choice is influenced by different factors taking into account cost, quality of repair services, convenience of repair services, as well as consumer preferences for newer and better products. The movement aims to pressure manufacturers to make spare parts, tools, and repair guides available for their products, extending their lifespan with the goal of reducing electronic waste. Replacement with a new product and throwing away the old ones is the growing concern as it leads to generation of electronic-waste. Manufacturer’s opposition to the movement stems from concerns about intellectual property and proprietary information theft. This has sparked debates around protecting intellectual property while ensuring consumer rights and safety, promoting sustainability, and competition. The paper aims to explore the relationship between “Right to Repair” movement and intellectual property rights, and to find a harmonious construct between these competing interests. It posits that a collaborative effort is necessary to find a solution that strikes the right balance, and enables a sustainable future for generations to come.