Emerging research has examined individual differences in affective forecasting; however, we are aware of no published study to date linking psychopathology symptoms to affective forecasting problem...

_Purpose._ The paper attempts to investigate the constructivist approach to the social world and its implications for social forecasting. _Theoretical basis._ Social forecasting is mainly based on the idea that a human is "determined ontologically". Using the methodology of the natural sciences, most predictions and forecasts fail to encompass all the multiplicity and variability of the future. The postmodern interpretation of reality gave impetus to the development of the new approaches to it. A constructivist approach to social reality began to (...) compete with essentialism. Social constructivism asserts that reality is a set of mental constructs, that it is ultimately a text. Radical constructivism interprets reality as a specific system of meanings emphasizing the artifact aspects of our reality. An interpretation of the social actors’ behavior is based on the ways of understanding accepted in a given society/community and do not possess ontological universality. The creators of social space are also its creations. _Originality._ Within the framework of the postmodern approach to reality, the second-order forecasting, or forecasting of forecasting, is particularly relevant. That means that the observers-forecasters must be included in the forecast as a part of one-unified process. At this stage, a forecaster must realize that he/she is a part of a larger system, a part of the world he/she observes (and actually creates). The situation changes dramatically – the forecaster is forced to take responsibility for his/her own observations. This ultimately leads to the "humanization" of forecasting. Acting in our world full of uncertainty, unpredictability, and turbulence, modern researchers of the future should be mindful of powerful social constructs of reality. _Conclusions._ Social forecasting should be embedded in a wider context, which requires a joint effort of philosophers, foresight practitioners, historians, psychologists, sociologists, political scientists, religious scholars, anthropologists, etc. To develop promising visions and scenarios of the future, it is necessary to answer the question "Why?", which is the task of philosophically oriented research, because without this answer, we will deal with the consequences; and the implementation of the negative scenarios will reproduce itself in new socio-cultural and historical conditions. An in-depth understanding of this "Why?" provides opportunities to be in the flow of transformations. The study of the deep mental processes of the actors of social changes, the multidimensional influence on the transformation of social structures can gradually expand an answer to the question "Why?", that can cause positive changes and, accordingly, allow to create fruitful projects of the future and form effective behavioral strategies that correspond to the desired level of social development. (shrink)

The Securities and Exchange Commission requires publicly held US corporations to disclose all information, whether it is positive or negative, that might be relevant to an investor's decision to buy, sell, or hold a company's securities. The decisions made by corporate managers to disclose such information can significantly affect the judgments and decisions of investors. This paper examines academic accounting research on corporate managers' voluntary disclosures of earnings forecasts and non-GAAP earnings measures. Much of the evidence from this (...) class='Hi'>research indicates that some managers engage in opportunistic disclosure behavior that often benefits one group (managers and shareholders) at the expense of other groups (often other investors). The paper concludes by discussing the ethical implications of this behavior. (shrink)

Volatility is widely used in different financial areas, and forecasting the volatility of financial assets can be valuable. In this paper, we use deep neural network and long short-term memory model to forecast the volatility of stock index. Most related research studies use distance loss function to train the machine learning models, and they gain two disadvantages. The first one is that they introduce errors when using estimated volatility to be the forecasting target, and the second one is that (...) their models cannot be compared to econometric models fairly. To solve these two problems, we further introduce a likelihood-based loss function to train the deep learning models and test all the models by the likelihood of the test sample. The results show that our deep learning models with likelihood-based loss function can forecast volatility more precisely than the econometric model and the deep learning models with distance loss function, and the LSTM model is the better one in the two deep learning models with likelihood-based loss function. (shrink)

This research examined whether youth's forecasted risk taking is best predicted by a compensatory (namely, subjective expected utility) or non-compensatory (e.g., single-factor) model. Ninety youth assessed the importance of perceived benefits, importance of perceived drawbacks, subjective probability of benefits, and subjective probability of drawbacks for 16 risky behaviors clustered evenly into recreational and health/safety domains. In both domains, there was strong support for a noncompensatory model in which only the perceived importance of the benefits of engaging in a risky (...) behavior predicted youths' forecasted engagement in risky behavior. The study overcomes earlier methodological weaknesses by fully decomposing participants' assessments into importance and probability aspects for both benefits and drawbacks. As such, the findings provide clear evidence in support of a boundedrationality perspective on youth decision making regarding risk taking. (shrink)

The impact of incidental emotions on decision making is well established. Incidental emotions can be differentiated on several appraisal dimensions, including certainty–uncertainty. The present research investigates the effect of certainty–uncertainty of incidental emotions on hedonic forecasting. The results of four experimental studies indicate that uncertainty-associated incidental emotions, such as fear and hope, compared with certainty emotions, such as anger and happiness, amplify predicted utility. This amplification effect is confirmed for opposite utility types; uncertainty-associated emotions, when compared with their certainty (...) counterparts, lead to an overprediction of positive utilities and to an underprediction of negative utilities. This effect is mediated by the prediction task uncertainty, providing evidence for a carryover process of the incidental emotion. The effect of task uncertainty on predicted utility is, in turn, partly mediated by attention to the task, suggesting that an affective adaptation process lies behind the amplification of forecasts. Taken together, these findings extend the impact of certainty–uncertainty to the context of hedonic forecasting and further corroborate the impact of incidental emotions in judgment and decision making. (shrink)

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange market has become an important focus of both academic and practical research. There are many reasons why FX is important, but one of most important aspects is the determination of foreign investment values. Therefore, FX serves as the backbone of international investments and global trading. Additionally, because fluctuations in FX affect the value of imported and exported goods and services, such fluctuations have an important (...) impact on the economic competitiveness of multinational corporations and countries. Therefore, the volatility of FX rates is a major concern for scholars and practitioners. Forecasting FX volatility is a crucial financial problem that is attracting significant attention based on its diverse implications. Recently, various deep learning models based on artificial neural networks have been widely employed in finance and economics, particularly for forecasting volatility. The main goal of this study was to predict FX volatility effectively using ANN models. To this end, we propose a hybrid model that combines the long short-term memory and autoencoder models. These deep learning models are known to perform well in time-series prediction for forecasting FX volatility. Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index as a measure of FX volatility. In particular, the three major FXVIX indices from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model. Our hybrid model utilizes an LSTM model as an encoder and decoder inside an autoencoder network. Additionally, we investigate FXVIX indices through subperiod analysis to examine how the proposed model’s forecasting performance is influenced by data distributions and outliers. Based on the empirical results, we can conclude that the proposed hybrid method, which we call the autoencoder-LSTM model, outperforms the traditional LSTM method. Additionally, the ability to learn the magnitude of data spread and singularities determines the accuracy of predictions made using deep learning models. In summary, this study established that FX volatility can be accurately predicted using a combination of deep learning models. Our findings have important implications for practitioners. Because forecasting volatility is an essential task for financial decision-making, this study will enable traders and policymakers to hedge or invest efficiently and make policy decisions based on volatility forecasting. (shrink)

Tversky and Kahneman (1974) originally discussed three main heuristics: availability, representativeness, and anchoring-and-adjustment. Research on judgemental forecasting suggests that the type of information on which forecasts are based is the primary factor determining the type of heuristic that people use to make their predictions. Specifically, availability is used when forecasts are based on information held in memory; representativeness is important when the value of one variable is forecast from explicit information about the value of another variable; and anchoring-and-adjustment is (...) employed when the value of a variable is forecast from explicit information about previous values of that same variable. Although there has been increased emphasis on the adaptiveness of heuristics and increased interest in specifying their use in terms of computational models, this way of structuring our knowledge about judgemental forecasting continues to be a useful one. I use it to frame discussion of some recent debates in the area. (shrink)

Affective forecasting often drives decision-making. Although affective forecasting research has often focused on identifying sources of error at the event level, the present investigation draws upon the “realistic paradigm” in seeking to identify factors that similarly influence predicted and actual emotions, explaining their concordance across individuals. We hypothesised that the personality traits neuroticism and extraversion would account for variation in both predicted and actual emotional reactions to a wide array of stimuli and events (football games, an election, Valentine's Day, (...) birthdays, happy/sad film clips, and an intrusive interview). As hypothesised, individuals who were more introverted and neurotic anticipated, correctly, that they would experience relatively more unpleasant emotional reactions, and those who were more extraverted and less neurotic anticipated, correctly, that they would experience relatively more pleasant emotional reactions. Personality explained 30% of the concordance between predicted and actual emotional reactions. Findings suggest three purported personality processes implicated in affective forecasting, highlight the importance of individual-differences research in this domain, and call for more research on realistic affective forecasts. (shrink)

The integration of the global economy has led to an increasingly strong connection between the futures and spot markets of commodities. First, based on one-minute high-frequency prices, this paper applies the thermal optimal path method to examine the lead-lag relationship between Chinese crude oil futures and spot from March 2018 to December 2021. Second, we apply the Mixed Frequency Data Sampling Regression model and indicators such as deviation degree to test the degree of prediction of high-frequency prices in the futures (...) market to the spot market. The experimental results show that the futures markets lead the spot market most of the time, but the lead effect reverses when major events occur; 60-minute futures high-frequency prices are the most predictive of daily spot data; crude oil futures’ predictive power declined after the Covid-19 outbreak and is more predictive when night trading is available. This study has important implications, not only to guide investors but also to provide empirical evidence and valid information for policy makers. (shrink)

This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...) their research through a stronger collective identity. The overarching aim is to set the background for a collaborative project organising, systematising, and ultimately forging an identity for, European philosophy of science by creating research structures and developing research networks across Europe to promote its development. (shrink)

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks (...) have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article. (shrink)

Solar energy has become an integral part of the economy of developed countries, so it is important to monitor the pace of its development, prospects, as well as the largest companies that produce solar panels since the supply of solar energy in a particular country directly depends on them. The study analyzes the shares of Canadian Solar Inc. and First Solar Inc. The purpose of the study is to study the possibility of forecasting the stock price of solar energy companies (...) using neural networks for the purpose of subsequent investment. The recurrent neural network LSTM is used in the article and this approach is based on complexity theory. Machine learning technologies are now being actively implemented in various sectors of the economy and are considered effective. The program used assigns different significance to the data of the last months and the data for the first months of the 1st year. The first year of the last 5 years of the company’s activity is taken as the first year since more distant data no longer have significant significance for the forecast. In the course of the study, a forecast of the stock price of Canadian Solar Inc. and First Solar Inc. for 245 days was obtained. Based on the results obtained, the following conclusions were made: 20 neurons of the network is not enough to make an accurate forecast, but the level of confidence in such a forecast is high enough, neural network forecasts are applicable in investing and are accurate enough to determine medium- and long-term trends, but these forecasts are not applicable for traders. The direction of improving the accuracy of neural network predictions is promising for further research. (shrink)

The deterministic economic system will also produce chaotic dynamic behaviour, so economic chaos is getting more and more attention, and the research of economic chaos forecasting methods has become an important topic at present. The traditional economic chaos forecasting models are mostly based on large samples, but in actual production activities, there are a large number of small-sample economic chaos problems, and there is still no effective solution. This paper proposes a combined forecasting model based on the traditional economic (...) chaos forecasting method. First of all, through the decision tree classification, priority selection of features, rough prediction is achieved. Secondly, we use BP neural network to make secondary prediction. Because the initial weight is randomly selected, it is easy to fall into the defect of local minimum. This paper optimizes the BP neural network. Finally, the decision tree model and the BP neural network model optimized by the improved genetic algorithm are combined, and the combined model is optimized by the improved GA. This method can take advantage of many prediction models and combine the prediction information of multiple different prediction models to effectively improve the fitting ability of the model and improve the prediction accuracy. (shrink)

The big-fish-little-pond effect (BFLPE) is the tendency for students to evaluate themselves more favourably when they have high rank in a low rank school than low rank in a high rank school. Research has documented the BFLPE on experienced emotions. We conducted three studies that examined forecasts of how the BFLPE influences other people’s emotions (i.e. empathic forecasts). In Study 1, participants received performance feedback about themselves or another person and reported their own affect or anticipated the other person’s (...) affect. Results extended the BFLPE to empathic forecasting. Moreover, forecasters anticipated that the BFLPE had a stronger influence on negative emotion than it actually did. Study 2 tested whether neglect of group rank underlies the BFLPE in empathic forecasting. Empathic forecasts were strongly influenced by another person’s rank in their group, but only weakly influenced by group rank. Study 3 tested whether extremity of group ranks exacerbates the BFLPE in empathic forecasting. Empathic forecasts were especially favourable (unfavourable) when a target had very high (low) rank in a very low (high) rank group. These data support the BFLPE in empathic forecasting, but also illustrate ways in which it is both similar to and different from actual experience of the effect. (shrink)

The relationship between analysts' forecasts and corporate fraud is a vital theoretical and practical question that needs to be clarified. Based on a strict distinction between negative performance gaps relative to analyst forecasts (negative forecast gaps hereinafter) and analyst coverage, this study investigates the influence of analyst forecasts on corporate fraud from a panoramic perspective. Using panel data on listed companies in China from 2008 to 2019, we find that short-term performance pressure caused by negative forecast gaps is significantly positively (...) correlated with firms’ possibility for fraudulent behavior. However, this positive correlation is weaker in state-owned enterprises than in their non-state-owned counterparts. Further, as a key external governance mechanism, analyst coverage enhances the negative moderator effect of state ownership on the positive relationship between negative forecast gaps and the likelihood of corporate fraud. (shrink)

The objective of this research is to develop accurate forecasting models for chlorophyll-α concentrations at various depths in El Mar Menor, Spain. Chlorophyll-α plays a crucial role in assessing eutrophication in this vulnerable ecosystem. To achieve this objective, various deep learning forecasting techniques, including long short-term memory, bidirectional long short-term memory and gated recurrent uni networks, were utilized. The models were designed to forecast the chlorophyll-α levels with a 2-week prediction horizon. To enhance the models’ accuracy, a sliding window (...) method combined with a blocked cross-validation procedure for time series was also applied to these techniques. Two input strategies were also tested in this approach: using only chlorophyll-α time series and incorporating exogenous variables. The proposed approach significantly improved the accuracy of the predictive models, no matter the forecasting technique employed. Results were remarkable, with |$\overline{\sigma}$| values reaching approximately 0.90 for the 0.5-m depth level and 0.80 for deeper levels. The proposed forecasting models and methodologies have great potential for predicting eutrophication episodes and acting as decision-making tools for environmental agencies. Accurate prediction of eutrophication episodes through these models could allow for proactive measures to be implemented, resulting in improved environmental management and the preservation of the ecosystem. (shrink)

As one of the key technologies for accelerating the construction of the ubiquitous Internet of Things, demand response not only guides users to participate in power market operations but also increases the randomness of grid operations and the difficulty of load forecasting. In order to solve the problem of rough feature engineering processing and low prediction accuracy, a short-term load forecasting model of LSTM neural network considering demand response is proposed. First of all, in view of the strong randomness and (...) complexity of input features, the weighted method is used to process multiple input features to strengthen the contribution of effective features and tap the potential value of features. Secondly, an improved genetic algorithm is used to obtain the best LSTM parameters; finally, the special gate structure of the LSTM model is used to selectively control the influence of input variables on the model parameters and perform load forecasting. The experimental results show that the research has high prediction accuracy and application value and provides a new way for the development of power load forecasting. (shrink)

Ethics has been identified as an important factor that potentially affects auditors’ professional skepticism. For example, prior research finds that auditors who are more concerned with professional ethics exhibit greater professional skepticism. Further, the literature suggests that professional skepticism may lead the auditor to more vigilantly resist the client’s position in financial reporting disputes. These reporting disputes are generally resolved through negotiations between the auditor and client to arrive at the final reported amounts. To date, the role that professional (...) skepticism potentially plays in the negotiation process has been relatively unexplored. The literature prior to the enactment of Sarbanes–Oxley (SOX) suggests that auditors are more likely to approve a client position when the matter in dispute is relatively ambiguous and when changing the client’s position will result in the client failing to meet analysts’ expectations. However, changes resulting from SOX have led auditors to be more vigilant and therefore results found in the pre-SOX environment may not hold in the current environment where auditors are held more accountable for their actions. Results from an experiment with experienced audit managers and partners suggest that in the post-SOX climate, auditors’ negotiations do not appear to be substantively influenced by management being able to meet or beat forecasts. Moreover, we find that when auditors exhibit heightened professional skepticism, they are more ethical by being conservative and they stand more resolute than when auditors do not exhibit heightened professional skepticism. Finally, although we do not find a main effect for the influence of earnings forecast, we do find a significant interaction between earnings forecast and heightened professional skepticism. Implications for practice and research are then presented. (shrink)

This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...) their research through a stronger collective identity. The overarching aim is to set the background for a collaborative project organising, systematising, and ultimately forging an identity for, European philosophy of science by creating research structures and developing research networks across Europe to promote its development. (shrink)

This volume is a serious attempt to open up the subject of European philosophy of science to real thought, and provide the structural basis for the interdisciplinary development of its specialist fields, but also to provoke reflection on the idea of ‘European philosophy of science’. This efforts should foster a contemporaneous reflection on what might be meant by philosophy of science in Europe and European philosophy of science, and how in fact awareness of it could assist philosophers interpret and motivate (...) their research through a stronger collective identity. The overarching aim is to set the background for a collaborative project organising, systematising, and ultimately forging an identity for, European philosophy of science by creating research structures and developing research networks across Europe to promote its development. (shrink)

Ultra-short-term wind speed prediction can assist the operation and scheduling of wind turbines in the short term and further reduce the adverse effects of wind power integration. However, as wind is irregular, nonlinear, and nonstationary, to accurately predict wind speed is a difficult task. To this end, researchers have made many attempts; however, they often use only point forecasting or interval forecasting, resulting in imperfect prediction results. Therefore, in this paper, we developed a prediction system integrating an advanced data preprocessing (...) strategy, a novel optimization model, and multiple prediction algorithms. This combined forecasting system can overcome the inherent disadvantages of the traditional forecasting methods and further improve the prediction performance. To test the effectiveness of the forecasting system, the 10-min and one-hour wind speed sequences from the Sotavento wind farm in Spain were applied for conducting comparison experiments. The results of both the interval forecasting and point forecasting indicated that, in terms of the forecasting capability and stability, the proposed system was better than the compared models. Therefore, because of the minimum prediction error and excellent generalization ability, we consider this forecasting system to be an effective tool to assist smart grid programming. (shrink)

The research fieldwork dedicated to trend analysis and foresight/forecast scenarios building, represents an unusual raid in an area where economic and social sciences have invested many efforts. Nevertheless, the semiotic dimension of this subject is not sufficiently thorough; many issues are at stake:We are going to investigate this area of analysis, for its relevance in post-Greimassian studies, for its interface with social sciences, as well as for its relevance for the legitimacy of semiotics in contemporary discussions of innovation and (...) evolution. (shrink)

In this work, we study and analyze the aggregate death counts of COVID-19 reported by the United States Centers for Disease Control and Prevention (CDC) for the fifty states in the United States. To do this, we derive a stochastic model describing the cumulative number of deaths reported daily by CDC from the first time Covid-19 death is recorded to June 20, 2021 in the United States, and provide a forecast for the death cases. The stochastic model derived in this (...) work performs better than existing deterministic logistic models because it is able to capture irregularities in the sample path of the aggregate death counts. The probability distribution of the aggregate death counts is derived, analyzed, and used to estimate the count’s per capita initial growth rate, carrying capacity, and the expected value for each given day as at the time this research is conducted. Using this distribution, we estimate the expected first passage time when the aggregate death count is slowing down. Our result shows that the expected aggregate death count is slowing down in all states as at the time this analysis is conducted (June 2021). A formula for predicting the end of Covid-19 deaths is derived. The daily expected death count for each states is plotted as a function of time. The probability density function for the current day, together with the forecast and its confidence interval for the next four days, and the root mean square error for our simulation results are estimated. (shrink)

Climate change is confronting African farmers with growing uncertainties. Advances in seasonal climate predictions offer potential for assisting farmers in dealing with climate risk. Experimental cases of forecast dissemination to African rural communities suggest that participatory approaches can facilitate understanding and use of uncertain climate information. But few of these studies integrate critical reflections on participation that have emerged in the last decade which reveal how participatory approaches can miss social dynamics of power at the community level and in the (...) broader context. Furthermore, neither climate application research nor theoretical critiques of participation fully examine the culturally constructed nature of participation. Drawing on sociolinguistic analysis, in-depth interviews, and ethnographic observation, this paper examines how Ugandan farmers engage in participation in the context of discussions of seasonal climate forecasts. Forecasts were presented to farmers groups whose members were then asked to discuss the forecast among themselves. In doing so, groups sought to develop a common understanding of the forecast and consensual plans for response strategies. Focusing on one particular group meeting as an example, we show how different cultural styles of participation affect the interpretation of the forecast and the formulation of response strategies. Group interaction is shown to be mostly structured around two styles of participation. On the one hand, there is the “Western” style advocated by NGOs and the government, which centers on ensuring that all individuals who are present have opportunities to speak during discussion and to vote on group decisions. On the other hand, a “Kiganda” style of participation emphasizes the importance of affirming ties to a collectivity, respect for social hierarchy, deployment of good manners, and consensus building. The case study illuminates how the performance of different styles of participation is grounded in localized frameworks of language and culture but also draw on political and policy discourses at the national level. Although a cultural high value on consensus may work in favor of prominent members, the availability of multiple styles of participation also enables group members to exercise their agency in positive ways. Attention to the interplay of different styles of participation throws light on the subtle social processes that shape how knowledge is assessed, which sources are trusted, which and whose interpretations prevail, what options are deemed viable, how costs and benefits are calculated, and whose resources are mobilized in the effort to reduce vulnerability to climate risk. These are key questions for an assessment of the role of boundary organizations, such as farmer associations, in the communication and application of climate forecasts in agriculture. (shrink)

Accurate estimation of risk and benefit is integral to good clinical research planning, ethical review, and study implementation. Some commentators have argued that various actors in clinical research systems are prone to biased or arbitrary risk/benefit estimation. In this commentary, we suggest the evidence supporting such claims is very limited. Most prior work has imputed risk/benefit beliefs based on past behavior or goals, rather than directly measuring them. We describe an approach – forecast analysis – that would enable (...) direct and effective measure of the quality of risk/benefit estimation. We then consider some objections and limitations to the forecasting approach. (shrink)

The International Research Institute for Climate Prediction was created in 1996 with an “end-to-end” mission to engage in climate research and modeling on a seasonal-to-interannual time scale and to provide the results of this research in a useful way to farmers, fishermen, public health officials, and others capable of making the best of the predicted climate conditions. As a boundary organization, IRI straddles the divides between the production and use of research and between the developed world (...) and the developing world. This article describes the institutional history of IRI, examining how the end-to-end mission evolved over time, how it is becoming institutionalized in IRI as a boundary organization, and the ongoing challenges it presents to managing the boundary between climate variability research and societal applications. (shrink)

Affective forecasting refers to the ability to predict future emotions, a skill that is essential to making decisions on a daily basis. Studies of the concept have determined that individuals are often inaccurate in making such affective forecasts. However, the mechanisms of these errors are not yet clear. In order to better understand why affective forecasting errors occur, this article seeks to trace the theoretical roots of this theory with a focus on its multidisciplinary history. The roots of affective forecasting (...) lie mainly in economics, with early claims positing that utility (i.e. satisfaction) played a role in decision-making. Furthermore, the philosopher Jeremy Bentham’s descriptions of utilitarianism played a major role in our understanding of whether to define utility as a hedonic quality. The birth of behavioural economics resulted in a paradigm shift, introducing the concept of cognitive biases as influences on the accuracy of predicted utility. Daniel Gilbert and Timothy Wilson, the earliest researchers of affective forecasting errors, have proceeded with the concept of the accuracy of predicted affective utility to conduct experiments that seek to determine why our predictions of future affect are inaccurate and how such errors play a role in our decision-making. (shrink)

Stock price data have the characteristics of time series. At the same time, based on machine learning long short-term memory which has the advantages of analyzing relationships among time series data through its memory function, we propose a forecasting method of stock price based on CNN-LSTM. In the meanwhile, we use MLP, CNN, RNN, LSTM, CNN-RNN, and other forecasting models to predict the stock price one by one. Moreover, the forecasting results of these models are analyzed and compared. The data (...) utilized in this research concern the daily stock prices from July 1, 1991, to August 31, 2020, including 7127 trading days. In terms of historical data, we choose eight features, including opening price, highest price, lowest price, closing price, volume, turnover, ups and downs, and change. Firstly, we adopt CNN to efficiently extract features from the data, which are the items of the previous 10 days. And then, we adopt LSTM to predict the stock price with the extracted feature data. According to the experimental results, the CNN-LSTM can provide a reliable stock price forecasting with the highest prediction accuracy. This forecasting method not only provides a new research idea for stock price forecasting but also provides practical experience for scholars to study financial time series data. (shrink)

The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper (...) uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry. (shrink)

This article provides a comparative institutional analysis between El Niño/southern Oscillation forecasting systems in the Pacific and southern Africa with a focus on how scientific information is connected to the decision-making process. With billions of dollars in infrastructure and private property and human health and well-being at risk during ENSO events, forecasting systems have begun to be embraced by managers and firms at multiple levels. The study suggests that such systems need to consciously support the coproduction of knowledge. A critical (...) component of such coproduction seems to be managing the boundaries between science and policy and across disciplines, scale, and knowledges to create information that is salient, credible, and legitimate to multiple audiences. This research suggests institutional mechanisms that appear to be useful in managing such boundaries, including mechanisms for structuring convening, translation, collaboration, and mediation functions. (shrink)

When forecasting events, multiple types of uncertainty are often inherently present in the modeling process. Various uncertainty typologies exist, and each type of uncertainty has different implications a scientist might want to convey. In this work, we focus on one type of distinction betweendirect quantitative uncertaintyandindirect qualitative uncertainty. Direct quantitative uncertainty describes uncertainty about facts, numbers, and hypotheses that can be communicated in absolute quantitative forms such as probability distributions or confidence intervals. Indirect qualitative uncertainty describes the quality of knowledge (...) concerning how effectively facts, numbers, or hypotheses represent reality, such as evidence confidence scales proposed by the Intergovernmental Panel on Climate Change. A large body of research demonstrates that both experts and novices have difficulty reasoning with quantitative uncertainty, and visualizations of uncertainty can help with such traditionally challenging concepts. However, the question of if, and how, people may reason with multiple types of uncertainty associated with a forecast remains largely unexplored. In this series of studies, we seek to understand if individuals can integrate indirect uncertainty about how “good” a model is (operationalized as a qualitative expression of forecaster confidence) with quantified uncertainty in a prediction (operationalized as a quantile dotplot visualization of a predicted distribution). Our first study results suggest that participants utilize both direct quantitative uncertainty and indirect qualitative uncertainty when conveyed as quantile dotplots and forecaster confidence. In manipulations where forecasters were less sure about their prediction, participants made more conservative judgments. In our second study, we varied the amount of quantified uncertainty (in the form of the SD of the visualized distributions) to examine how participants’ decisions changed under different combinations of quantified uncertainty (variance) and qualitative uncertainty (low, medium, and high forecaster confidence). The second study results suggest that participants updated their judgments in the direction predicted by both qualitative confidence information (e.g., becoming more conservative when the forecaster confidence is low) and quantitative uncertainty (e.g., becoming more conservative when the variance is increased). Based on the findings from both experiments, we recommend that forecasters present qualitative expressions of model confidence whenever possible alongside quantified uncertainty. (shrink)

In the last seven years, philosophers have discussed the topic of transformative experiences. In this paper, we contribute to a crucial issue that is currently under-researched: transformative experiences' influence on cognitive modelling. We argue that cognitive modelling can be operationalized as affective forecasting, and we compare transformative and non-transformative experiences with respect to the ability of affective forecasting. Our finding is that decision-makers’ performance in cognitively modelling transformative experiences does not systematically differ from decision-makers’ performance in cognitively modelling non-transformative experiences. (...) This claim stands in strict opposition to L.A. Paul’s main argument. (shrink)

The financial market is a highly complex and dynamic system that has great commercial value; thus, many financial elite are drawn to research on the subject. Recent studies show that machine learning methods perform better than traditional statistical ones. In our study, based on the characteristics of financial sequence data, we propose a wrapped ensemble approach using a supervised learning algorithm to predict stock price volatility of China’s stock markets. To check our new approach, we developed an intelligent financial (...) forecast system and used the Hushen 300 index data to test our model; it proves that our model performs better than a single algorithm. We also compared our model with the famous ensemble approach of bagging, and the result shows that our model is better. (shrink)

Global Cellular Health Screening Market Size research report offers in-depth assessment of revenue growth, market definition, segmentation, industry potential, influential trends for understanding the future outlook and current prospects for the market. -/- Get a Sample Copy of the Report at – -/- Also, the growing importance of healthy life expectancy (HALE) and the use of home diagnostic tests are remarkably increasing globally. As a result, there is increasing demand for cellular health screening kits and services due to greater (...) patient awareness. Moreover, home diagnostic tests are convenient for patients because it helps to save time and cost associated with visiting hospitals and clinics. -/- Drivers & Restraints -/- The growing awareness for personalized medicine across the world due to the emergence of new disease conditions increased the rate of clinical studies conducted in countries such as the U.S., the U.K., and China. This led to the adoption of cellular health screening tests for an overview of health at a cellular level. -/- This study provides information about the sales and revenue during the historic and forecasted period of (2024 to 2030). Understanding the segments helps in identifying the importance of different factors that aid market growth. Estimations about the CAGR value for specific forecast period, market drivers, market restraints, and competitive strategies are assessed in this Cellular Health Screening Market report. -/- This report focuses on Cellular Health Screening Market volume and value at the global level, regional level and company level. From a global perspective, this report represents overall Cellular Health Screening Market market size by analysing historical data and future prospect. Regionally, this report focuses on several key regions: North America, Europe, Asia-Pacific, Latin America and Middle East and Africa. -/- Competitive Strategic Window: -/- The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period. -/- An Overview of the Impact of COVID-19 on this Market: -/- The advancement of COVID-19 has conveyed the world to a stop. We fathom that this prosperity crisis amazingly influences associations across ventures. Nevertheless, everything fortunate or unfortunate should reach a conclusion. There are a couple of organizations that are fighting and some are thriving. By and large, essentially every region is relied upon to be impacted by the pandemic. -/- We are advancing incessant endeavors to help your business support and create during COVID-19 pandemics. Considering our experience and expertise, we will offer you an impact examination of Covid episode across ventures to help you with preparing for what's to come. -/- Key players covered in the global Cellular Health Screening Market research report: -/- . (shrink)

Global Medical Robotic Systems Market Size research report offers in-depth assessment of revenue growth, market definition, segmentation, industry potential, influential trends for understanding the future outlook and current prospects for the market. -/- Get a Sample Copy of the Report at – -/- Robots used in the medical industry, ranging for various applications surgical interventions to rehabilitation are known as medical robots. The ever increasing efficiency of these robots in performing tasks which include surgeries have been pivotal in the (...) rapid adoption of these robots in the medical industry. -/- Drivers & Restraints -/- The global medical robotic systems market size stood at USD 2,257.8 Million in 2018 and is projected to reach USD 10,710.6 Million in 2026, exhibiting at a CAGR of 21.5% in the forecast period -/- This study provides information about the sales and revenue during the historic and forecasted period of (2024 to 2030). Understanding the segments helps in identifying the importance of different factors that aid market growth. Estimations about the CAGR value for specific forecast period, market drivers, market restraints, and competitive strategies are assessed in this Medical Robotic Systems Market report. -/- This report focuses on Medical Robotic Systems Market volume and value at the global level, regional level and company level. From a global perspective, this report represents overall Medical Robotic Systems Market market size by analysing historical data and future prospect. Regionally, this report focuses on several key regions: North America, Europe, Asia-Pacific, Latin America and Middle East and Africa. -/- Competitive Strategic Window: -/- The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period. -/- An Overview of the Impact of COVID-19 on this Market: -/- The advancement of COVID-19 has conveyed the world to a stop. We fathom that this prosperity crisis amazingly influences associations across ventures. Nevertheless, everything fortunate or unfortunate should reach a conclusion. There are a couple of organizations that are fighting and some are thriving. By and large, essentially every region is relied upon to be impacted by the pandemic. -/- We are advancing incessant endeavors to help your business support and create during COVID-19 pandemics. Considering our experience and expertise, we will offer you an impact examination of Covid episode across ventures to help you with preparing for what's to come. -/- Key players covered in the global Medical Robotic Systems Market research report: -/- Ekso Bionics Intuitive Surgical Inc. Medtronic Stryker CYBERDYNE INC. BIONIK Smith & Nephew Zimmer Biomet Other Prominent Players. (shrink)

The Bayesian vector autoregressive model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive model, which can overcome the problem of too little freedom. The BVAR model established in this paper can overcome the problem of short time series data by using prior statistical information. In theory, it should have a good effect in China’s regional economic forecasting. Most regional forecasting model literature lacks out-of-sample forecasting error evaluation research in the (...) real sense, but our early forecasts of major economic indicators provide an excellent opportunity for this paper to evaluate the actual forecast errors of the BVAR model in detail. The analysis in this paper shows that the prediction error of the BVAR model is very small and the prediction ability is very satisfactory. At the same time, this article also analyzes and points out the direction of efforts to further improve the prediction accuracy of the BVAR model. (shrink)

ArgumentThis paper is the first part of a two-part examination of computer modeling practice and philosophy. It discusses electrical engineer Jay Forrester's work on Industrial Dynamics, later called System Dynamics. Forrester developed Industrial Dynamics after being recruited to the newly-established School of Industrial Management at the Massachusetts Institute of Technology (MIT), which had been seeking a novel pedagogical program for management for five years before Forrester's arrival. We argue that Industrial Dynamics should be regarded in light of this institutional context. (...) Unlike economics, as well as operations research and management science (twin fields which were also gaining traction at that time), Industrial Dynamics was not meant to be applied by mathematicians and technical specialists in consultation with managers, but by managers themselves. This concern shifted the emphasis in modeling from the specialist act of analysis of a crucial problem to the managerial act of choosing which problems were crucial and developing effective policies around them. (shrink)

The purpose of the research is to study the feasibility of using the combined modeling method in evaluation of business value. Modern approaches and methods of evaluating business value and the possibilities of combining them are explored. The peculiarities of the methodology of evaluating the business value by methods of Gordon Growth Model and Exit Multiple are disclosed. During the research the fair value of Luxoft company and the reasons for its deviation from the cost of sale are (...) found. Luxoft’s business value forecast is calculated by 2023. Recommendations for further development of the field of knowledge of modern business analyst are given. (shrink)

In order to reduce the dimensionality of parameter space and enhance out-of-sample forecasting performance, this research compares regularization techniques with Autometrics in time-series modeling. We mainly focus on comparing weighted lag adaptive LASSO with Autometrics, but as a benchmark, we estimate other popular regularization methods LASSO, AdaLASSO, SCAD, and MCP. For analytical comparison, we implement Monte Carlo simulation and assess the performance of these techniques in terms of out-of-sample Root Mean Square Error, Gauge, and Potency. The comparison is assessed (...) with varying autocorrelation coefficients and sample sizes. The simulation experiment indicates that, compared to Autometrics and other regularization approaches, the WLAdaLASSO outperforms the others in covariate selection and forecasting, especially when there is a greater linear dependency between predictors. In contrast, the computational efficiency of Autometrics decreases with a strong linear dependency between predictors. However, under the large sample and weak linear dependency between predictors, the Autometrics potency ⟶ 1 and gauge ⟶ α. In contrast, LASSO, AdaLASSO, SCAD, and MCP select more covariates and possess higher RMSE than Autometrics and WLAdaLASSO. To compare the considered techniques, we made the Generalized Unidentified Model for covariate selection and out-of-sample forecasting for the trade balance of Pakistan. We train the model on 1985–2015 observations and 2016–2020 observations as test data for the out-of-sample forecast. (shrink)

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression, k-dependence Bayesian, and Random Forest. The Bayesian inference and classification methods allow (...) an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts, performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing. (shrink)

Мета. У статті зроблено спробу розглянути соціальне прогнозування крізь призму трансдисциплінарного підходу з урахуванням цілісної природи людини. Теоретичний базис. Складність соціального прогнозування полягає в тому, що воно має справу з багатовимірним феноменом людини – людини, яка є і творцем, і творінням соціальних світів, для якої всі економічні, соціальні, політичні, наукові, культурні досягнення, проблеми та перспективи набувають сенсу лише в контексті неї самої, її життя, її власної долі. Таким чином, феномен людини є ключем до розуміння динаміки сучасних трансформаційних процесів і створення (...) перспективних моделей майбутнього розвитку людської спільноти. Оскільки в процесі прогнозування ми намагаємося осягнути майбутнє, яке ще не є визначеним та може мати різні траєкторії розвитку, трансдисциплінарний підхід до прогнозування, який охоплює те, що наявне в межах дисциплін, на перетині дисциплін і поза межами всіх дисциплін, може стати найбільш плідним підходом. Наукова новизна. Людина привносить високий рівень непередбачуваності та невизначеності в усі соціальні прогнози. Сьогодні складна багатогранна природа людини як біологічної, психологічної та соціальної істоти потребує більш глибокого розуміння, що вимагає спільних зусиль представників різних наукових напрямів. Взаємозбагачуючись у межах трансдисциплінарної парадигми, представники різних наукових галузей та напрямів можуть створити свого роду путівник, покликаний формувати та пояснювати нову реальність, нове майбутнє. У такому підході на перший план має вийти не "конкуренція" теорій, методологій та протокольних рішень, а спільна мета, спільні мрії та прагнення кращого майбутнього людства. Висновки. Трансдисциплінарний підхід до соціального прогнозування має потенціал розглядати всі науки в гуманітарному контексті, зважаючи на складну багатогранну природу людини. Він виходить за рамки традиційних меж, надаючи можливість не лише синтезувати та інтегрувати варіанти розв’язання проблеми, а й височіти над нею. Трансдисциплінарність, визнаючи існування різних реальностей, забезпечує ширший погляд на світ, глибше розуміння явищ та процесів, що сприяє розробці нових проєктів кращого майбутнього людини та людства. (shrink)

Natural language processing is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including (...) document term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals. (shrink)

The amount of information that is produced on a daily basis in the financial markets is vast and complex; consequently, the development of systems that simplify decision-making is an essential endeavor. In this article, several intelligent systems are proposed and tested to predict the closing price of the IBEX 35 index using more than ten years of historical data and five distinct architectures for neural networks. A multi-layer perceptron was the first step, followed by a simple recurrent neural network, a (...) gated recurrent unit network and two long-short-term memory (LSTM) networks. The results of the analyses performed on these models have demonstrated a powerful capacity for prediction. Additionally, the findings of this research point to the fact that the application of intelligent systems can simplify the decision-making process in financial markets, which is a substantial advantage. Furthermore, by comparing the predicted outcome errors between the models, the LSTM presents the lowest error with a higher computational time in the training phase. The LSTM was able to accurately forecast the closing price of the day as well as the price for the following one and two days in advance. In conclusion, the empirical results demonstrated that these models could accurately predict financial data for trading purposes and that the application of intelligent systems, such as the LSTM network, represents a promising advancement in financial technology. (shrink)

There is growing recognition of the need to understand societal impacts of nanotechnology. Global interest in nanotechnology implies many nations will see a need to seek out advantages for military use. Militarization will inevitably include consideration of both offensive and defensive goals. This presents emerging implications for military forces in the near future, and will greatly influence the nature of warfare and peacekeeping in the distant future. The development of nanotechnology creates possibilities for both beneficial opportunities and adverse consequences as (...) a result of application to the military strategies of both large and small nations.The promise of nanotechnology for military and security roles derives from several innate characteristics. For example, further improvements in sensors are expected and already there are numerous reports of successful developments in research and development. Material properties can be tailored on a fine scale through nanomachining, scanning force microscopy, molecular self-assembly, and other techniques. (shrink)

This is the first volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The 78 authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. -/- Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, (...) inconsistent, and indeterminacy. -/- In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements. (shrink)