High-quality Opinion Research Articles

Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation

Financial analysts’ opinions are pivotal in investment decision-making, as they provide valuable expert knowledge. Aggregating these opinions offers a promising way to unlock their collective wisdom. However, existing opinion aggregation methods are hindered by their inability to effectively assess differences in opinion quality, resulting in suboptimal outcomes. This Study introduces a novel model called SmartMOA, which addresses this limitation by automatically evaluating the quality of each opinion and integrating this evaluation into the aggregation process. Our model begins with a novel Bayesian neural network that leverages the implicit knowledge embedded in the interactions between analysts and stock characteristics. This methodology produces an assessment of individual opinions that accounts for uncertainties. We then formulate a bi-objective combinatorial optimization problem to determine optimal weights for combining multiple analysts’ opinions, simultaneously minimizing the error and uncertainty of the aggregated outcome. Therefore, SmartMOA systematically highlights high-quality opinions during the aggregation process. Using a real dataset spanning eight years, we present comprehensive empirical evidence that demonstrates the superior performance of SmartMOA in heterogeneous analyst opinion aggregation.

How Service Quality Variability Hurts Revenue When Customers Learn: Implications for Dynamic Personalized Pricing

Most service settings involve some degree of variability in the quality of customers’ experiences. An understudied mechanism is analyzed by which this variability can reduce firm revenues when customers do not know true service quality but rather learn about that quality over time based on their experiences. Essentially, customers get stuck with low-quality opinions and low purchase likelihoods after unusually bad experiences. However, high-quality opinions after unusually good experiences are quickly corrected. The authors show that dynamic pricing can help mitigate this effect. Specifically, if the firm can set individual prices so that each customer perceives the same surplus, then the variability impact is entirely eliminated. They also demonstrate that simpler heuristics similar to those observed in practice (requiring less information and pricing flexibility) can partially mitigate the revenue loss.

Peer Prediction-Based Trustworthiness Evaluation and Trustworthy Service Rating in Social Networks

With the development of online applications based on social networks, many different approaches have emerged to evaluate the service that these applications provide. Reports made by end users regarding the consumer's experience or opinion are commonly used to rate the quality of different online services. Therefore, ensuring the authenticity of the users' reports, and the detection of malicious users' dishonest reports, have both become important issues to achieve accuracy in the rating of such services. In this paper, we propose and evaluate a private-prior peer prediction-based trustworthy service rating system, which requires users to report their prior and posterior beliefs regarding whether their peers will report a high-quality opinion of the service. The reports are made to a data processing center which evaluates the users' trustworthiness by applying a strictly proper scoring rule, and removes reports received from users whose trustworthiness rating is low. This peer prediction method is compatible with incentives to motivate users to report honestly. In addition, an unreliability index is proposed to identify malicious users, and malfunctioning or unreliable users who have a high error rate in making judgments about quality. Thus, reports with high unreliability values will also be excluded from the service rating system. By combining trustworthiness and unreliability, malicious users face the dilemma that they cannot receive both a high trustworthiness and low unreliability rating simultaneously when their reports are false. Simulation results indicate that the proposed peer prediction-based trustworthy service rating can identify malicious and unreliable behaviors effectively and motivate users to report truthfully, and that a relatively high service rating accuracy is achieved by the proposed system.

Current and future challenges facing medico-legal experts – An expert’s view

Recent notable changes in the legal landscape have resulted in a significant evolution of the expert witness role and created unique challenges. The number of clinical negligence cases continues to rise, with a greater public awareness of the litigation process, and additional clinical risk associated with an increased complexity of diagnoses/treatment and changes in clinical pathways. Many cases are high value and/or complex and following the Jackson reforms, urgent deadlines are more commonplace. Experts are now asked to provide a broader level of opinion and support to increasingly overworked and time pressured solicitors. To maintain high-quality opinion, experts are having to further professionalise their practices and work more closely and proactively with instructing parties while at the same time protecting their independence. Increased pressure to cut costs, the post-Jackson need for speed of response, changes in data protection laws and IT processes and loss of immunity have all led to a more resource intensive role. The challenge for experts is to develop sufficient medico-legal experience with adequate administrative support, alongside clinical work, to provide higher quality services and ultimately improved outcome for patients, doctors and Trusts. Through the opinion of an oncology expert, current and future challenges for the medico-legal expert are highlighted along with potential implications for patients, relatives, doctors and health care providers.

Investment recommendation by discovering high-quality opinions in investor based social networks

Investor based social networks, such as StockTwist, are gaining increasing popularity. These sites allow users to post their investment opinions in the form of microblogs. Given the growth of the posted data, a significant and challenging research problem is how to utilize the personal wisdom and different viewpoints in these opinions to help investment. A typical way is to aggregate sentiments related to stocks and generates buy or hold recommendations for stocks obtaining favorable votes while suggesting sell or short actions for stocks with negative votes. However, considering the fact that there always exist unreasonable or misleading posts, sentiment aggregation should be improved to be robust to noise. In our work, we study how to estimate qualities of investment opinions in investor based social networks. To predict the quality of an investment opinion, we use multiple categories of factors generated from the author information, opinion content and the characteristics of stocks to which the opinion refers. With predicted qualities of investment opinions, we perform two types of investment recommendation. The first is recommending high-quality opinions to users and the second is recommending portfolios generated by sentiment aggregation in a quality-sensitive manner. Experimental results on real datasets demonstrate the effectiveness of our work in recommending high-quality investment opinions and profitable portfolios.

What Do Federal District Judges Want? An Analysis of Publications, Citations, and Reversals

Evidence from a data set of federal district judges from 2001 and 2002 suggests that district judges adjust their opinion-writing practices to minimize their workload while maximizing their reputation and chance for elevation to a higher court. District judges in circuits with politically uniform circuit judges are better able to predict what opinions will get affirmed by the circuit court, leading to higher publication rates and a higher affirmance rate. In contrast, district judges in circuits with politically diverse circuit judges are less able to predict the preferences of the reviewing circuit court panel, leading district judges to publish fewer but higher-quality opinions in an effort to maximize their affirmance rate (JEL K40, K41, K49).

What Do Federal District Judges Want? An Analysis of Publications, Citations, and Reversals

We report evidence from a dataset of federal district judges from 2001 to 2002 that district judges adjust their opinion-writing practices to minimize their workload while maximizing their reputation and chance for elevation to a higher court. District judges in circuits with politically uniform circuit judges are better able to predict what opinions will get affirmed by the circuit court, leading to higher publication rates and a higher affirmance rate. In contrast, district judges in circuits with politically diverse circuit judges are less able to predict the preferences of the reviewing circuit court panel, leading district judges to publish fewer but higher quality opinions in an effort to maximize their affirmance rate.

Bargaining and Opinion Assignment on the US Supreme Court

We formulate a new game-theoretic model of bargaining on the US Supreme Court. In the model, a degree of monopoly power over policy endogenously accrues to the assigned writer despite an “open rule” permitting other justices to make counteroffers. We assume justices are motivated ultimately by a concern for judicial policy, but that the policy impact of an opinion depends partly on its persuasiveness, clarity, and craftsmanship—its legal quality. The effort cost of producing a high-quality opinion creates a wedge that the assignee can exploit to move an opinion from the median without provoking a winning counteroffer. We use this bargaining model as the foundation for a formal analysis of opinion assignment. Both the bargaining and opinion assignment models display rich and tractable comparative statics, allowing them to explain well-known empirical regularities, as well as to generate new propositions, all within a unified and internally consistent framework.

Bargaining and Opinion Assignment on the U.S. Supreme Court

We formulate a new game-theoretic model of bargaining on the U.S. Supreme Court. In the model, a degree of monopoly power over policy endogenously accrues to the assigned writer despite an open rule for the other justices. We assume justices are motivated ultimately by a concern for judicial policy, but that the policy impact of an opinion depends partly on its persuasiveness, clarity, and craftsmanship - its legal quality. The effort-cost of producing a high quality opinion creates a wedge that the assignee can exploit to move an opinion from the median without provoking a winning counter-offer. We use this bargaining model as the foundation for a formal analysis of opinion assignment. Both the bargaining and opinion assignment models display rich and tractable comparative statics, allowing them to explain well-known empirical regularities as well as generate new propositions, within a unified and internally consistent framework.

Appellate Courts Inside and Out

In Inside Appellate Courts: The Impact of Court Organization on Judicial Decision Making in The United States Courts of Appeals (Michigan 2002), Jonathan Matthew Cohen, a sociologist and practicing attorney, asks a question that has received scant attention in the academic commentary on appellate judging: If we accept the dominant conception of appellate court judging as a process of atomistic contemplation, how do federal circuit court judges continue to maintain high quality opinions in the face of pervasively growing judicial dockets? Cohen advances the provocative thesis that increasing workloads have not prevented appellate judges from producing high quality outputs, but rather, that the dominant image of appellate judging as an isolated contemplative task is conceptually flawed. A better approach, Cohen argues, is to compare the task of appellate court judging to production within a multi-divisional private firm. While Cohen recognizes the inherent limits of his analogy, and in particular, that unlike private firms, circuit courts lack a central coordinating authority, he nonetheless contends that it is more fruitful to consider the judges in the manner of workers in a complex organization than as autonomous actors reflecting in isolation on the legal issues presented on appeal. In this review essay, Stearns considers three complementary methodologies for analyzing appellate courts that yield insights of particular interest to lawyers and legal scholars. Such questions include how appellate courts transform preferences into doctrine; the nature of cases that are likely susceptible to further appellate process through en banc, mini-en banc, or Supreme Court review; and how best to evaluate appellate court opinions. While organizational theory provides a useful starting point, Stearns contends that insights drawn from other methodologies, including economics (demonstrating how decentralized informational processes can provide more meaningful data), probability analysis (demonstrating the quality of data drawn from subsets of a larger group), and social choice (demonstrating the nature and limits of group decision making), might prove more fruitful in evaluating at least some of these questions. Stearns concludes that a comprehensive understanding of federal appellate judging requires not only an understanding of the circuit courts' internal organizational structure, but also an analysis of the edifice of circuit court decision making from inside and out.

Appellate Courts Inside and Out

In Inside Appellate Courts: The Impact of Court Organization on Judicial Decision Making in The United States Courts of Appeals (Michigan 2002), Jonathan Matthew Cohen, a sociologist and practicing attorney, asks a question that has received scant attention in the academic commentary on appellate judging: If we accept the dominant conception of appellate court judging as a process of atomistic contemplation, how do federal circuit court judges continue to maintain high quality opinions in the face of pervasively growing judicial dockets? Cohen advances the provocative thesis that increasing workloads have not prevented appellate judges from producing high quality outputs, but rather, that the dominant image of appellate judging as an isolated contemplative task is conceptually flawed. A better approach, Cohen argues, is to compare the task of appellate court judging to production within a multi-divisional private firm. While Cohen recognizes the inherent limits of his analogy, and in particular, that unlike private firms, circuit courts lack a central coordinating authority, he nonetheless contends that it is more fruitful to consider the judges in the manner of workers in a complex organization than as autonomous actors reflecting in isolation on the legal issues presented on appeal. In this review essay, Stearns considers three complementary methodologies for analyzing appellate courts that yield insights of particular interest to lawyers and legal scholars. Such questions include how appellate courts transform preferences into doctrine; the nature of cases that are likely susceptible to further appellate process through en banc, mini-en banc, or Supreme Court review; and how best to evaluate appellate court opinions. While organizational theory provides a useful starting point, Stearns contends that insights drawn from other methodologies, including economics (demonstrating how decentralized informational processes can provide more meaningful data), probability analysis (demonstrating the quality of data drawn from subsets of a larger group), and social choice (demonstrating the nature and limits of group decision making), might prove more fruitful in evaluating at least some of these questions. Stearns concludes that a comprehensive understanding of federal appellate judging requires not only an understanding of the circuit courts' internal organizational structure, but also an analysis of the edifice of circuit court decision making from inside and out.

An Analysis of the Market for Systems Reliability Assurance Services

As the accounting profession enters new assurance markets, many decisions as to the best manner in which such services can be provided must be addressed. For instance, the traditional audit/attest-reporting model may not provide adequate information to justify the cost of assurance services in some situations. Alternatively, potential clients may turn to other assurance providers who are willing to provide better quality services and/or more informative assurance reporting. In this study, we examine the impact of a two-tier reporting model on the market demand for assurance services on software product reliability. One tier uses the traditional binary report common with audit/attest services where a standard report notes whether a software product achieves a minimum standard, while the other tier of reporting provides for a graded assessment of multiple levels of quality. The results indicate that when a graded report that differentiates high-quality vendors from moderateor low-quality vendors is available (1) high-quality vendors are willing to pay for the report, (2) buyers are willing to pay a premium for the reduced risk associated with a highquality opinion, and (3) high-quality vendors are able to drive other vendors with inferior products out of the market. When only a binary report is available, the market is more confused and chaotic. Vendors are not able to signal quality clearly; the high-quality vendor is not able to dominate the market; there is less vendor demand for the assurance report; buyers are unable to determine their desired product and make less total profit. These results have several implications for the accounting profession and researchers as they work on the design of new assurance service models.