Optimistic Forecasts Research Articles

Long-term probabilistic forecasts of activity mitigation in English hospitals: a national elicitation exercise providing an outside view based on judgements of experts in support of the New Hospital Programme

ObjectivesThe planning process for a new hospital relies on assumptions about future levels of demand. Typically, such assumptions are characterised by point estimates, the flaw-of-averages, base-rate neglect and overoptimism from...

Manifestation of national identity in the socio-political values of student youth

The problem of national identity is one of the most controversial, not losing scientific and socio-political relevance for many centuries. National identity is based on a system of value guidelines that unites individuals into a single historical, cultural and social community. The formation of national self-awareness occurs in the process of internalization by an individual of the value system of his country. At the same time, the self-perception of belonging to a certain nation is not predetermined by formal citizenship and is not given once and for all; throughout the life of an individual there is the possibility of choice and change of national identity. This choice is inevitable when the country’s value guidelines change. The modern period in Russian history, and indeed the whole world, is a kind of bifurcation point; the values of the globalizing world, the so-called Western values, come into conflict with traditional Russian values. For the preservation of Russian national identity, the choice of young people is of particular importance. The future of the country depends on what value guidelines form the basis of the national self-determination of today's Russian youth. The article presents the results of a study of the relationship between socio-political values and the choice of national identity of Russian students. It is not by chance that students are identified as the object of observation. As the avant-garde, the most educated part of young people with high ambitions and a desire for social independence, students are the most receptive to change. For the analysis, empirical data from a study by the Center for Political Science of the Institute of Socio-Political Research of FCTAS RAS “Students of Russia: civic culture and life strategies” were used. Using the method of factor analysis, based on the results of an empirical study, three basic types of identity were identified: “Russian, person of his city”, “person of the world, the Internet”, “Asian”. It has been proven that the prevailing type of student identity is closely related to his socio-political attitudes and guidelines. The types that differ most in their views are “Russian, person of the city” and “person of the world, the Internet.” The first type is characterized by pronounced positive assessments of Russia's situation on the world stage, optimistic forecasts for the development of both the economy and the development of the country as a whole in 10-15 years. Representatives of this type see the main national idea in the unity of the peoples of Russia in order to revive the country as a great power. Representatives of the “person of the world, Internet” type are characterized by less positive assessments of the present and future of Russia. The main national idea for them is to create a legal and modern democratic state, the path of development is a free market economy. Among the values, such concepts as human rights, freedom and democracy are more significantly represented. But the most important difference between the second type is the pronounced migration sentiment.

Improving Project Forecasting Accuracy by Developing the Normalised Project Management Baseline

Periodic project reporting using Earned Value Management (EVM) and Earned Duration Management (EDM) uses performance metrics to communicate current and forecast future project performance. The likely accuracy ranges of schedule and cost forecasts are commonly expressed as a simple plus or minus percentage or value. This paper introduces the Normalised Project Management Baseline (NPMB), a single (x, y) plot of the normalised EDM and EVM Project Management Baselines to identify whether periodic project forecasts are optimistic (low) or pessimistic (high), as well as informing the magnitude of variation in the forecast. The NPMB is tested using six real-life completed projects and it is shown to be highly suited to identifying schedule forecast optimism, pessimism, and variations, whilst results for cost are highly promising and warrant further investigation. For project practice the NPMB increases awareness of performance variations, improving forecasting accuracy and confidence in final forecast ranges. For academia, the NPMB introduces new research directions by improving forecasting accuracy and early warning processes by improving the accuracy of periodic project performance observations. This paper contributes to project management literature as the NPMB is the first attempt to improve forecasting accuracy by identifying variations in the periodic project performance observations themselves.

The impact of stock market liberalization on management forecast precision–Evidence from Mainland-Hong Kong Stock Connect Programs in China

This study examines the effects of stock market liberalization, specifically through the Shanghai-Hong Kong Stock Connect and the Shenzhen-Hong Kong Stock Connect initiatives (jointly referred to as MHKSCPs), on the precision of management forecasts and the underlying mechanisms. We find that post MHKSCPs, management forecasts become less precise for bad news and more precise for good news, leading to an overall decrease in precision. The channels include litigation risk and media/analyst attention. The results are particularly pronounced in firms without foreign investors, within industries with higher proprietary costs, or in smaller firms. Moreover, there is a decrease in the information content of management forecasts, management forecast optimism, and financial report readability post MHKSCPs.

2023 Industry Perceptions Survey on AI Adoption and Return on Investment.

This SIIM-sponsored 2023 report highlights an industry view on artificial intelligence adoption barriers and success related to diagnostic imaging, life sciences, and contrasts. In general, our 2023 survey indicates that there has been progress in adopting AI across multiple uses, and there continues to be an optimistic forecast for the impact on workflow and clinical outcomes. This report, as in prior years, should be seen as a snapshot of the use of AI in imaging. Compared to our 2021 survey, the 2023 respondents expressed wider AI adoption but felt this was behind the potential. Specifically, the adoption has increased as sources of return on investment with AI in radiology are better understood as documented by vendor/client use case studies. Generally, the discussions of AI solutions centered on workflow triage, visualization, detection, and characterization. Generative AI was also mentioned for improving productivity in reporting. As payor reimbursement remains elusive, the ROI discussions expanded to look at other factors, including increased hospital procedures and admissions, enhanced radiologist productivity for practices, and improved patient outcomes for integrated health networks. When looking at the longer-term horizon for AI adoption, respondents frequently mentioned that the opportunity for AI to achieve greater adoption with more complex AI and a more manageable/visible ROI is outside the USA. Respondents focused on the barriers to trust in AI and the FDA processes.

Epidemic experience, analyst sentiment, and earnings forecasts: Evidence from SARS exposure

This study examines whether exposure to dangerous infectious diseases affects how analysts assess risks. We use the outbreaks of the severe acute respiratory syndrome (SARS) at analysts' previous office locations across China as a plausibly exogenous shock in the analysts' life experience. We show that compared to their less-affected counterparts, analysts in provinces with more SARS cases issue more optimistic forecasts for firms. This effect is stronger for affected analysts in provinces perceived as more salient during the SARS epidemic period. Mechanism tests show a high level of unexpected economic growth and positive media reports can motivate optimistic forecast bias induced by SARS exposure. Further heterogeneity tests indicate that our findings are particularly pronounced among busier analysts, those with less industry specialization, and female analysts. Overall, these findings suggest that exposure to the SARS epidemic influences the information intermediaries’ judgment.

Under the dome: Air pollution and analyst forecast optimism

In this study, we investigate whether unpleasant mood caused by air pollution leads to lower analyst forecast optimism. Using a sample of 103,768 observations from 2014 to 2021, we show that there is a negative association between air pollution and analyst forecast optimism. Furthermore, we find that analysts become more optimistic forecasters after moving to a city with lower level of air pollution. Our results hold through various robustness tests including alternative measures of air pollution, controlling for various fixed effects, 2SLS by instrumenting for air pollution using thermal inversions and a placebo test by randomizing the headquarter locations of brokerage firms in our sample. Our paper provides plausibly causal evidence that air pollution lowers analyst forecast optimism.

Friends in media: Implications of media connections for analyst forecast optimism

We investigate the effect of corporate executives' connections with financial media on analyst forecast optimism. Some studies document that managers use their connections to benefit the firm. Alternatively, other studies show that managers use their connections to benefit themselves at the expense of the firm. Thus, it is difficult to predict ex-ante whether executives use their media connections to enhance information efficiency or distort information environment. We propose two hypotheses on the impact of financial media connections: information efficiency and manipulation. We estimate managerial media connection by using the BoardEx database, and apply standard OLS regression, mediation analysis, propensity score matching, 2-SLS instrumental variable regressions, and difference-in-difference regressions to test the two alternate hypothesis. Consistent with the manipulation hypothesis, our results suggest that corporate executives use media connections to exacerbate analyst forecast optimism. Mediation analysis suggests that media connections are related to not only more news coverage but also more positive news sentiment. We further find that managerial incentives and board monitoring moderates the relationship between media connections and analyst forecast optimism. Our study makes several contributions to academic literature and is interesting to regulators and investors. As corporate executives tend to exploit their friendship with the media to achieve enhanced analyst forecast optimism, it would have a negative impact on the efficiency of the capital markets. Our results suggest that regulators and investors should more closely scrutinize the social ties of corporate executives.

Techno-economic analysis of the effect of a novel price-based control system on the hydrogen production for an offshore 1.5 GW wind-hydrogen system

The cost of green hydrogen production is very dependent on the price of electricity. A control system that can schedule hydrogen production based on forecast wind speed and electricity price should therefore be advantageous for large-scale wind-hydrogen systems. This work presents a novel price-based control system integrated in a techno-economic analysis of hydrogen production from offshore wind. A polynomial regression model that predicts wind power production from wind speed input was developed and tested with real-world datasets from a 2.3 MW floating offshore wind turbine. This was combined with a mathematical model of a PEM electrolyzer and used to simulate hydrogen production. A novel price-based control system was developed to decide when the system should produce hydrogen and when it should sell electricity to the grid. The model and control system can be used in real-world wind-hydrogen systems and require only the forecast wind speed, electricity price and selling price of hydrogen as inputs. 11 test scenarios based on 10 years of real-world wind speed and electricity price data are proposed and used to evaluate the effect the price-based control system has on the levelized cost of hydrogen (LCOH). Both current and future (2050) costs and technologies are used, and the results show that the novel control system lowered the LCOH in all scenarios by 10–46%. The lowest LCOH achieved with current technology and costs was 6.04 $/kg H2. Using the most optimistic forecasts for technology improvements and cost reductions in 2050, the model estimated a LCOH of 0.96 $/kg H2 for a grid-connected offshore wind farm and onshore hydrogen production, 0.82 $/kg H2 using grid electricity (onshore) and 4.96 $/kg H2 with an off-grid offshore wind-hydrogen system. When the electricity price from the period 2013–2022 was used on the 2050 scenarios, the resulting LCOH was approximately twice as high.

Changes in Analysts’ Accrual-Related Forecast Optimism after the Disappearance of the Accrual Anomaly

ABSTRACT We investigate changes in analysts’ accrual-related forecast optimism after the disappearance of the accrual anomaly. We find that such optimism persists in the no-anomaly period, particularly when analysts’ incentives are aligned with greater optimism (i.e., for firms with high external financing). By contrast, the accrual-related forecast optimism declines when analysts’ incentives for optimism are low and in circumstances where analysts would have been more aware of accruals information after the anomaly became publicized (i.e., when analysts issued a cash flow forecast). These findings suggest that analysts’ incentives can offset forecast improvements that their heightened accrual awareness would otherwise allow. They also highlight the importance of considering both analysts’ incentives and knowledge when interpreting their forecasts. Data Availability: Data are available from sources identified in the text. JEL Classifications: G14; G29; M41.

Institutional ownership and analysts’ earnings forecasts

This paper selects the data of China's A-share market from 2011 to 2022 to explore the impact of institutional shareholding on analysts’ earnings forecasts. The findings indicate the following: institutional shareholding enhances analysts’ earnings forecast accuracy and reduces analysts’ forecast optimism bias; institutions with small geodesic pits will be more helpful for analysts to improve the quality of earnings forecasts; and institutional shareholding improves the quality of analysts’ earnings forecasts by promoting the quality of firms’ information disclosure.

Observation of Multiply Imaged Quasars with the 4-m ILMT

Gravitationally lensed quasars (GLQs) are known to potentially provide an independent way of determining the value of the Hubble-Lemaître parameter H0, to probe the dark matter content of lensing galaxies and to resolve tiny structures in distant active galactic nuclei. That is why multiply imaged quasars are one of the main drivers for a photometric monitoring with the 4-m International Liquid Mirror Telescope (ILMT). We would like to answer the following questions: how many multiply imaged quasars should we be able to detect with the ILMT? And how to derive accurate magnitudes of the GLQ images? Our estimation of the possible number of multiply imaged quasars is 15, although optimistic forecasts predict up to 50 of them. We propose to use the adaptive PSF fitting method for accurate flux measurements of the lensed images. During preliminary observations in spring 2022 we were able to detect the quadruply imaged quasar SDSS J1251+2935 in the i’ and r’ spectral bands.

Financial statement comparability and analysts’ optimism for accruals

This study examines the role of financial statement comparability in mitigating analysts' optimism for accruals. Prior research shows that sell-side analysts do not fully incorporate accrual information in anticipating future earnings and thus tend to issue more optimistic forecasts for firms with a higher level of accruals. We posit that financial statement comparability allows analysts to use industry peers' information in assessing the persistence of accruals, thereby lowering their optimism for accruals. We find that analysts' accrual-related optimism is significantly moderated for firms with a high degree of financial statement comparability. Further, we disaggregate financial statement comparability into accrual comparability and cash flow comparability and find that the inverse relation between comparability and analysts' optimism for accruals is driven by accrual comparability rather than cash flow comparability. Moreover, exploiting the mandatory adoption of IFRS as an exogenous increase in comparability, we find that analysts’ optimism for accruals decreases significantly following the IFRS adoption while it does not show a significant change in non-IFRS-adopting countries during the same period. Collectively, the results suggest that financial statement comparability helps analysts process and interpret accrual information, thereby improving their forecasting activities.

Exploring Book-Tax Differences, Analyst Coverage, and Forecast Optimism: An Empirical Study

This research explores the relationship between book-tax differences (BTD) and the coverage of financial analysts, as well as how these two factors relate to optimistic forecasts. By evaluating how BTD affects analyst coverage, this research expands on earlier studies and finds a negative link. Additionally, it demonstrates that greater BTD indicates a higher probability of earnings manipulation, leading analysts to have a more pessimistic view of their predictions for such organizations. This research contributes to a better understanding of financial markets by shedding light on analyst behavior, financial reporting, and the complex dynamics underlying BTD in determining analyst estimates.

Stock market reactions and optimism bias in analysts’ earnings forecasts: An analysis of China's stock markets

This paper examines analysts’ catering behavior to current investor demand, proxied by the unbalanced stock market reaction toward optimistic forecasts and non-optimistic forecasts (i.e., optimism premium). Using data on earnings forecasts issued by sell-side analysts in China from 2014 to 2018, we show that the optimism premium significantly increases analysts’ tendency to issue optimistic forecasts—in short, that analysts do cater to investor demand. The implications of our findings for theory and practices are discussed.

Local government official competition and financial analyst forecasts

AbstractWe examine how financial analyst forecasts could be influenced by local political forces. Using Chinese data, we find that financial analysts from brokerage houses controlled by provincial governments issue more optimistic forecasts for state‐owned enterprises (SOEs) headquartered in the home province (i.e., home SOEs) than for nonhome SOEs, and this effect is more pronounced for nonhome SOEs located in competing provinces. Our results remain unchanged in a battery of robustness checks. Further, local officials are more likely to enjoy political career advancement when they pressure analysts to issue optimistic forecasts for home SOEs relative to nonhome SOEs from competing provinces.

Designing of new proposed technique for the multi-attribute choice problems: Application to the selection of renewable energy technologies in the Tunisian Electricity and Gas Society case study

The economic importance of the selection of alternative energies in the national scenario tends to be maintained with the optimistic forecast of an increase in production for the years to come in Tunisia. The problem of choosing multiple attributes for the selection of renewable energies is a set of concepts whose purpose is to help the user in a decision-making process. This help provided to the user is designed to be the most online with his preferences. The objective of this study is to answer the following question: How to decide to determine the optimal alternative energies from a large set of available outcomes for this case study according to the decision maker’s preferences? The purpose of this article was to solve multi-attribute choice problems by simultaneously using the choice to essentially define comparative superiority, inferiority, and the flow of non-inferiority as indicators of goodness/poverty evidence. We have developed a new method called Multi-Attribute Superiority, Inferiority and Non-Inferiority Selection Approach (MASINISA) based on the successive elimination Multi-Attribute Decision Making (MADM) to solve the selection of renewable energies in a certain environment: the case of the STEG Company in Tunisia. We can ascertain that the renewable energy technologies belonging to the screened set produced by the proposed MASINISA version were all judged admissible by the experts of the STEG.

Hot streak! Inferences and predictions about goal adherence

When do people make optimistic forecasts about goal adherence? Nine preregistered studies find that a recent streak of goal-consistent behavior increases the predicted likelihood that the individual will persist, compared to various other patterns holding the rate of goal adherence constant. This effect is due to perceiving a higher level of commitment following a streak. Accordingly, the effect is larger when the behavior requires commitment to stick with it, compared to when the same behavior is enjoyable in its own right. Furthermore, the effect is weaker in the presence of another diagnostic cue of commitment: when the individual has a high historic rate of goal adherence. People also behave strategically in ways consistent with these inferences (e.g., are less likely to adopt costly goal support tools following a streak, choose partners with recent streaks for joint goal pursuit). Together, these results demonstrate the significance of streaky behavior for forecasting goal adherence.

Тривалий COVID у дітей: частота та виклики діагностики

Purpose - to analyze the prevalence of symptoms of long COVID-19 in the pediatric population and the methods of their diagnosis. Electronic search of scientific research using known databases from PubMed, SCOPUS, ResearchGate, Wiley Online Library and Google Scholar from 2019 to February 2023. The keywords for this review: long COVID, post COVID, COVID-19, pediatrics, children, adolescents, post-acute sequelae of SARS‐CoV‐2 infection (PASC). Exclusion criteria were: duplicated, dedicated exclusively to adults, analyzed only acute COVID-19. In the analysis were included research from the post-Covid period in children and adolescents, which contained the results of the assessment of their state of health and displayed certain clinical manifestations that remained after the end of the acute infection within 4-12 weeks. Optimistic forecasts regarding the course of SARS-CoV-2 infection in the child population at the beginning of the pandemic quite quickly passed into the stage of uncertainty in forecasts of the course of the post-Covid period and the consequences of the transferred disease. Most children infected with COVID-19 recover, but some of them have persistent symptoms after an infection. The true prevalence of “long-term COVID” is under investigation study. Reports on the range of its manifestations are very diverse and differ in conclusions about the intensity of their impact on the quality of children’s life. Hence, there is an obvious need for long-term clinical observations with a mandatory comparison with the data of control groups appropriate age. Because this category of convalescents will need of a multidisciplinary approach in monitoring them, and therefore they will bear a significant burden on the health care system. No conflict of interests was declared by the authors.

Unpacking human systems in data science innovations: Key innovator perspectives

Despite optimistic forecasts, industry innovations in data science have extraordinarily high rates of failure. It is essential to minimise the failure of data science projects, for both businesses and data professionals. Human systems are critical to the success of data science innovations. However, the human aspects of innovation management are often neglected or omitted in most guidelines and frameworks for data science. This provides limited guidance about the necessary human conditions for successful data science innovations. In this article we address this concern by developing a systematic framework for understanding human systems that support data science innovations.We first reviewed the elements of human system at different levels of analysis and how they contribute to innovation. Substantial research and theory indicate successful innovation requires supports at different levels of organisations, which combine to create the organisation’s innovation capability. The review provided an initial framework for integrating human systems with data science innovations. Then, we drew on a series of interviews with key innovators engaged in developing current data science innovations. The interviews generated a more complete picture of human systems in practice. The findings support a range of practices to energise and facilitate innovation as an integral part of strategic planning and business processes. This study contributes to the advancement of innovation management theories and calls attention to guiding and engaging individuals through providing support and removing barriers to data science at different organisational levels. More practically, innovation managers could use this as a guide to optimise work systems and inform pathways to improve organisation data science efforts.