Regulating Dynamic Risk in Changing Market Conditions

How successful are the SEC\u27s attempts to regulate dynamic risk in financial markets? Using mutual fund disclosure data from two financial shocks--the Puerto Rican debt crisis and COVID-19--this Article finds evidence that SEC open-ended regulations, like the obligation to disclose changing market conditions, are largely successful in capturing dynamic, future risk. Funds engage in widespread and, often, detailed disclosures for new risks--although these disclosures vary widely in specificity. But not all funds disclose new risks. This creates perverse incentives for funds to opt out of disclosure or downplay threats with boilerplate language when new risks are emerging. This Article recommends several SEC interventions to improve dynamic risk disclosures including empirically monitoring disclosures, issuing guidance when problematic variation is observed, and enforcing disclosure standards

The Impact of Remote Patient Monitoring on Chronic-Disease Readmissions: The Role of Disparities

While prior research has found evidence of the digital divide in health IT outcomes, we do not know the combination of conditions under which health IT is more or less effective at reducing readmissions. This study examines the average and heterogeneous treatment effect of Remote Patient Monitoring (RPM) adoption on chronic-disease related readmissions. We first apply a deductive, econometric-driven approach (difference-in-differences) and then an inductive, machine learning-driven approach (casual forest). We find that RPM reduces the excess readmission ratio of pneumonia, heart failure, and chronic obstructive pulmonary disease. Furthermore, we find heterogeneity in the impact of RPM on heart failure readmissions based on the levels of patient complexity and local competition. Our research contributes to the literature on health IT digital divide by highlighting the combination of conditions in which RPM effectively reduces chronic-disease related readmissions. This combined research approach also contributes methodological insights to IS literature

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Supply chain channel structure and disruption management

textThis dissertation contributes to supply chain management in two important areas: supply chain channel structure and disruption management. For the supply chain channel structure, I investigated both strategic and operational decisions for firms in a supply chain when these firms can invest either in cost reduction or demand-enhancing activities. For the supply chain disruption management, I developed a theoretic framework for managing disruptions in a supply chain, and then applied this framework to the economic production quantity model and a two-stage production and inventory model. Specifically, this dissertation research assists firms in making decisions when they have investment opportunities and also provides important guidelines to firms for recovering their optimal production and inventory schedule when facing disruptions. In short, this research not only develops models to provide managerial insights, but also has potential for developing a decision support system to manage supply chain disruptions.Managemen

The Payback of Effective Innovation Programs: Empirical Evidence from Firms that Have Won Innovation Awards

Despite the widely held belief of the importance of innovation, the connection between innovation and firm performance is empirically inconclusive, partially owing to the limitations of existing innovation measures, which tend to ignore the effectiveness of innovation programs. In this study, we use the winning of innovation awards as a proxy for the effective execution of innovation. We conducted event-study analyses based on data from more than 1000 publicly traded firms that won innovation awards between 1998 and 2003. Our statistical tests provide strong evidence that the performance of award-winning firms is significantly higher as compared with several sets of control firms. Over an 8-year period, starting from 4 years before to 3 years after the year of winning the first innovation award, the test sample’s mean (median) change in return on assets is nearly 33% (24%) higher than that of a control sample. The evidence also suggests that effective innovation programs can increase firms’ revenue, cost efficiency, and market valuation. Over the period, the control-adjusted mean (median) change in sales, cost per dollar of sales, and Tobin’s Q are 39.28% (20.71%), −5.52% (−3.80%), and 23.70% (3.16%), respectively. Panel data regression analysis provides additional insights on the performance impact of effective innovation programs. The results show that award winners are not only financially more successful but also enjoy an indirect benefit through better R&D execution, which increases firm profitability in both the short term and long term

Promise & Peril of Plain English: Mutual Fund Disclosure Readability

The SEC requires mutual funds to write disclosures for the average investor using plain English. These requirements make funds\u27 investment strategies and associated risks transparent and accessible to investors. Improved investor understanding furthers the SEC\u27s regulation-through-disclosure regime. But our examination of funds\u27 summary prospectuses--an abbreviated discussion of a fund\u27s strategies and risks--suggests that funds often fail to meet the plain English standard. Our analysis of all summary prospectuses filed between 2010 and 2020 reveals that mutual funds write long, hard-to-read, and complex disclosures. Importantly, we find that failure to draft disclosures in plain English is more than a technical error. Using a regression model, we find that positive past returns predict easier-to-read disclosures, but an increase in fund risk predicts harder-to-read disclosures. Further, we find that compliance with other metrics of plain English, like short sentences and active voice, predicts easier-to-read disclosures. In other words, compliance in one dimension of plain English writing suggests compliance in other aspects as well. Our results suggest several recommendations. The SEC should update their plain English guidance and adopt text mining measures to better monitor and enforce disclosure standards. Finally, given the incentives to draft overinclusive and exhaustive disclosures, the SEC should issue guidance on liability for summary prospectus risk omissions if full disclosure is made elsewhere

Promise & Peril of Plain English: Mutual Fund Disclosure Readability

The SEC requires mutual funds to write disclosures for the average investor using plain English. These requirements make funds\u27 investment strategies and associated risks transparent and accessible to investors. Improved investor understanding furthers the SEC\u27s regulation-through-disclosure regime. But our examination of funds\u27 summary prospectuses--an abbreviated discussion of a fund\u27s strategies and risks--suggests that funds often fail to meet the plain English standard. Our analysis of all summary prospectuses filed between 2010 and 2020 reveals that mutual funds write long, hard-to-read, and complex disclosures. Importantly, we find that failure to draft disclosures in plain English is more than a technical error. Using a regression model, we find that positive past returns predict easier-to-read disclosures, but an increase in fund risk predicts harder-to-read disclosures. Further, we find that compliance with other metrics of plain English, like short sentences and active voice, predicts easier-to-read disclosures. In other words, compliance in one dimension of plain English writing suggests compliance in other aspects as well. Our results suggest several recommendations. The SEC should update their plain English guidance and adopt text mining measures to better monitor and enforce disclosure standards. Finally, given the incentives to draft overinclusive and exhaustive disclosures, the SEC should issue guidance on liability for summary prospectus risk omissions if full disclosure is made elsewhere