Search

Collecting large asset managers' capital market assumptions, we revisit the relationships between subjective equity premium expectations, equity valuations, and financial portfolios. In contrast to the well-documented extrapolative expectations of retail investors, asset managers' equity premium expectations are countercyclical: they are high (low) when valuations are low (high). We find that asset managers' portfolios reflect their heterogeneous expectations: allocation funds of asset managers with larger U.S. equity premium expectations invest significantly more in U.S. equities. The sensitivity of portfolios to expectations seems to be muted by investment mandates and is smaller than the one predicted by a standard portfolio choice model. [ABSTRACT FROM AUTHOR]

Analyst forecasts outperform econometric forecasts in the short run but underperform in the long run. We decompose these differences in forecasting accuracy into analysts' information advantage, forecast bias, and forecast noise. We find that noise and bias strongly increase with forecast horizon, while analysts' information advantage decays rapidly. A noise increase with horizon generates a mechanical reversal in the sign of the error-revision (Coibion-Gorodnichenko) regression coefficient at longer horizons, independently of over-/underreaction. A parsimonious model with bounded rationality and a noisy cognitive default matches the term structures of noise and bias jointly. [ABSTRACT FROM AUTHOR]

This article analyzes the optimal allocation of losses via a Central Clearing Counterparty (CCP) in the presence of counterparty risk. A CCP can hedge this risk by mutualizing losses among its members. This protection, however, weakens members' incentives to manage counterparty risk. Delegating members' risk monitoring to the CCP alleviates this tension in large markets. To discipline the CCP at minimum cost, members offer the CCP a junior tranche and demand capital contribution. Our results endogenize key layers of the default waterfall and deliver novel predictions on its composition, collateral requirements, and CCP ownership structure. [ABSTRACT FROM AUTHOR]

Combining experimental data sets from seven individual studies, including 255 asset markets with 2,031 participants, and 36,326 short-term price forecasts, we analyze the role of heterogeneity of beliefs in the organization of trading behavior by reproducing and reconsidering earlier experimental findings. Our results confirm prior evidence that price expectations affect trading behavior. However, heterogeneity in beliefs does not seem to drive overpricing and asset market bubbles, as suggested by earlier studies, and we find no indication of short-term beliefs being better determinants of trading behavior than longer-term beliefs. [ABSTRACT FROM AUTHOR]

Drawing upon more than 12 million observations over the period from 1996 to 2020, we find that allowing for nonlinearities significantly increases the out-of-sample performance of option and stock characteristics in predicting future option returns. The nonlinear machine learning models generate statistically and economically sizable profits in the long-short portfolios of equity options even after accounting for transaction costs. Although option-based characteristics are the most important standalone predictors, stock-based measures offer substantial incremental predictive power when considered alongside option-based characteristics. Finally, we provide compelling evidence that option return predictability is driven by informational frictions and option mispricing. Authors have furnished an Internet Appendix , which is available on the Oxford University Press Web site next to the link to the final published paper online. [ABSTRACT FROM AUTHOR]

Supply chain instabilities and inflated material prices have had a disruptive impact on cost estimating of construction projects. While several research efforts used national macroeconomic indicators to forecast the prices of domestically produced construction materials, none of the existing studies investigated whether the lagged macroeconomic indicators of the main trading partners could enhance the predictability of the prices of cement, steel, and lumber in the US construction sector. This paper fills this knowledge gap. The authors adopted a multi-step methodology that included: (1) collecting data on the target variables and the candidate leading indicators; (2) identifying the structural breaks in the collected data sets; (3) conducting causality tests to identify short-term associations and cointegration tests to examine long-term relationships; (4) developing vector error correction (VEC) models to forecast the prices in the short and long terms; and (5) evaluating the performance of the proposed models against existing forecasting models in the literature. Results of the Granger test and Johansen test indicate that Canada's overall producer price index (PPI) is a consistent leading indicator of the prices of cement, and Mexico's overall PPI is a consistent leading indicator of the prices of steel. Findings indicate no statistical evidence to suggest that neither Canada's PPI nor Mexico's PPI can be leading indicators of lumber prices. Over an 18-month ahead of sample horizon, the presented VEC models of cement and steel prices outperformed existing models, particularly beyond the 1-year-ahead forecasts. Utilization of the proposed forecasting models can significantly enhance the accuracy of cost estimates and feasibility studies of construction projects. This provides proactive financial planning for construction contractors and project owners through improved short- and long-term forecasting of the prices of main construction materials. [ABSTRACT FROM AUTHOR]

Regional economic development objectives are improved economic, political, and social conditions in a certain area. Investment, production, innovation, wealth, and affluence are the four stages of regional economic development that may be identified in each selected area. It becomes more reliant on technological advancements in the industry and less on locally sourced production inputs as the economy region grows. The regional economic growth issue is inequality in the rates of improvement of subnational geographic regions and inequalities in the distribution of wealth. Intellectual algorithms or enhanced and hybrid algorithms based on machine learning, such as Fuzzy C-means clustering (FCM), principal component analysis, and algorithm, can newly achieve more appropriate solutions to practical issues of discrete, non-linear, non-differentiable, and various constraints. A hybrid algorithm combines two or more other algorithms that solve the same problem. Hence, this paper proposes a Principal Component Analysis for the Sustainable Regional Economic Development (PCA-SRED) model to enhance the efficiency in examining regional economic changes and industrial development zones. The data are taken from the Organization for Economic Cooperation and Development (OECD) regional statistics dataset. Using PCA, industries may be categorized based on shared criteria, and the whole spatial distribution law of datasets and common patterns can be uncovered. To create a long-lasting regional economic development plan, it is crucial to categorize, compare, and evaluate the economic growth level of several areas. The research outcomes illustrate that the hybrid algorithms have high accuracy and a fast convergence rate because they can replicate the smart behavior of some clusters in nature while examing the variances in regional economic growth. The experimental outcomes illustrate that the recommended PCA-SRED model enhances the accuracy ratio by 98.2%, industry production ratio by 95.6%, regional economic change prediction ratio by 96.4%, and economic efficiency ratio by 97.8% compared to other popular models. [ABSTRACT FROM AUTHOR]

This study harnesses advanced time series models ARIMA, ETS, and SARIMA, coupled with neutrosophic statistics, to forecast unemployment trends through interval-based predictions. Transforming these predictions into neutrosophic forms enables the quantification of indeterminacy, providing a nuanced interpretation of potential economic scenarios. The integration of neutrosophic statistics enhances the interpretative power and accuracy of these models, offering a deeper insight into the inherent uncertainties of economic forecasting. The approach reveals not only the variabilities and potential outcomes within the unemployment rates but also strengthens the decision-making processes by presenting data that encompass both precision and indeterminacy. This paper underscores the importance of advanced statistical methods in economic predictions, suggesting further exploration into other economic metrics and advocating for a broader application of neutrosophic statistics to enhance the reliability of economic forecasting across diverse contexts. [ABSTRACT FROM AUTHOR]

In this paper, we study the predictive power of electricity consumption data for regional economic activity. Using unique high‐frequency electricity consumption data from industrial firms for the second‐largest German state, the Free State of Bavaria, we conduct a pseudo out‐of‐sample forecasting experiment for the monthly growth rate of Bavarian industrial production. We find that electricity consumption is the best performing indicator in the nowcasting setup and has higher accuracy than other conventional indicators in a monthly forecasting experiment. Exploiting the high‐frequency nature of the data, we find that the weekly electricity consumption indicator also provides good predictions about industrial activity in the current month with only 2 weeks of information. Overall, our results indicate that regional electricity consumption is a promising avenue for measuring and forecasting regional economic activity. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production. Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting. Results: The forecasting accuracy on four provincial companies has reached over 90%. Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development. [ABSTRACT FROM AUTHOR]

Economic growth is essential for regional economic performance, with gross regional domestic product (GRDP) being a key indicator of economic development over time. In this research case, the GRDP data of various provinces on Java Island from 2010 to 2023 will be used as the variable being studied. The data obtained from the GRDP variable contain spatial and temporal information, requiring an appropriate model to forecast spatiotemporal data, namely, the Generalized Space-Time Autoregressive (GSTAR) model. However, in estimating the parameters, the GSTAR model is unable to detect correlated residuals between equations, resulting in inefficient estimators. Therefore, an appropriate estimation method is needed to address correlated residuals within the seemingly unrelated regression (SUR) framework, namely, the Generalized Least Square (GLS) estimation method. The GSTAR-SUR method is applied to forecast the economic growth rate of Java Island. The optimal model, GSTAR-SUR (1[sub 1])-I(1) with inverse distance location weights, demonstrates high accuracy with a mean absolute percentage error (MAPE) of 8.451%. Forecasts for Banten, DKI Jakarta, West Java, Central Java, East Java, and DI Yogyakarta predict consistent monthly GRDP increases through December 2024. [ABSTRACT FROM AUTHOR]

Many countries, especially those with a high energy demand but insufficient renewable resources are currently investigating the role that imported low carbon hydrogen may play in meeting future energy requirements and emission reduction targets. A future hydrogen economy is uncertain and predicated on reduced price of hydrogen delivered to customers. Current hydrogen production, steam reforming of natural gas or coal gasification, is co-located to its end-use as a chemical feedstock. Large-scale multi-source value chains of hydrogen needed to support its use for energy are still at concept phase. This research investigates the combination of technical and economic factors which will determine the viability and competitiveness of two competing large scale renewable hydrogen value chains via ammonia and liquid hydrogen. Using a techno-economic model, an evaluation of whether green hydrogen exports to Germany from countries with low-cost renewable electricity production, but high-costs of storage, distribution and transport will be economically competitive with domestic renewable hydrogen production is conducted. The model, developed in Python, calculates costs and energy losses for each step in the value chain. This includes production from an optimised combination of solar and/or wind generation capacity, optimised storage requirements, conversion to ammonia or liquid hydrogen, distribution, shipping, and reconversion. The model can easily be applied to any scenario by changing the inputs and was used to compare export from Chile, Namibia, and Morocco with production in Germany using a 1 GW electrolyser and 2030 cost scenario in each case. • Electrolyser utilisation is more important than renewable energy storage when determining the LCOH. • The lowest cost LCOE for green hydrogen production does not always result in the LCOH. • The reconversion of ammonia back to hydrogen significantly increases the cost of energy to the end user. [ABSTRACT FROM AUTHOR]

Social sustainability, alongside environmental and economic sustainability, is one of the three key dimensions of sustainability. Unlike environmental and economic sustainability, social sustainability is often missing from research and practice related to sustainability efforts and policies. Using in-depth qualitative interviews and official document review this article studies the COVID-19 pandemic response of the small island developing state, Mauritius. Using the Opp, Susan M. [2017. “The forgotten pillar: a definition for the measurement of social sustainability in American cities.” Local Environment 22 (3): 286–305] framework for social sustainability, this research finds that Mauritius had both successes and failures in maintaining and promoting social sustainability on the island nation and are at a crossroads with respect to the future of social sustainability on the island. Although the island nation holds an impressive record in engaging in social sustainability efforts, longstanding inequalities were exacerbated during the pandemic and the changed economic outlook may jeopardize the small island's momentum towards their 2030 sustainability goals. [ABSTRACT FROM AUTHOR]

A growing number of industries have started to adapt to the circular economy since the concept's introduction. Therefore, in order to accurately evaluate the development level of circular economy, the circular economy prediction model based on support vector machine-Gaussian K-nearest neighbor is proposed. This model first uses the improved K-nearest neighbor algorithm based on Gaussian function to classify the index data of various levels, and then uses Support Vector Machine to make predictions based on relevant data. According to the experimental findings, the model's average prediction accuracy for each level of indicator was approximately 98.1%, 98.8%, 94.9%, and 95.9% for the levels of industrial development, resource consumption, ecological protection, and resource recycling and reuse, respectively. This prediction accuracy was higher than that of the multi-vector autoregressive model and the grey prediction model. The average prediction accuracy of the multi-vector autoregressive model, the grey prediction model, and the support vector machine-Gaussian K-nearest neighbor-based model in predicting the overall development level of the circular economy were about 94.3%, 96.2%, and 99.3%, respectively, with average recalls of about 86.6%, 87.7%, and 89.1%, and the average F1-measure was about 0.88, 0.89, and 0.92. Moreover, the average relative error based on the support vector machine-Gaussian K-nearest neighbour model was only approximately 0.6%, which was lower than the 3.7% and 2.8% for the multi-vector autoregressive model and the grey prediction model, respectively. Meanwhile, compared with the existing time series analysis techniques, the proposed SVM-Gaussian K nearest neighbor model fitted up to 0.95, which achieved good prediction performance. According to the above data, the support vector machine-Gaussian K-nearest neighbour model has the highest accuracy in predicting the amount of development of the circular economy. [ABSTRACT FROM AUTHOR]

Introduction: Coastal erosion is widely distributed globally, with sandy coasts being particularly prominent, causing significant economic losses to coastal areas. This study focuses on the coastal areas of Liaoning Province, China, which represent typical sandy beach erosion. Methods: Based on the assessment of coastal erosion disaster losses, a multivariate variable-weight combination prediction model is utilized to predict the losses caused by coastal erosion disasters, providing an effective mathematical modeling approach for predicting such losses. Furthermore, the cluster analysis method is employed to delineate vulnerability risk zones for coastal erosion along the Liaoning Province coast. Results and discussion: The research findings indicate the following: (1) From 2023 to 2025, the losses due to coastal erosion disasters in Liaoning Province are projected to decrease significantly froma scale of 36 million RMB yuan to 5million RMB yuan; (2) Based on themagnitude of disaster vulnerability risk, the coastal areas of Liaoning Province are categorized into four risk zones: high-risk zones (Jinzhou District), moderately high-risk zones (Zhuanghe City, Wafangdian City, Ganjingzi District), medium-risk zones (Lvshunkou District, Bayuquan), and low-risk zones (Gaizhou City, Suizhong County, Xingcheng City). Recommendations are proposed accordingly: (1) Effective measures should be implemented to mitigate the losses incurred by coastal erosion on land, buildings, infrastructure, agricultural output; (2) In the selection of disaster prevention and control measures, economic factors should be carefully considered in line with the specific circumstances of the losses. Specifically, the economic efficiency of disaster prevention andmitigationmeasures should be taken into account; (3) Precise efforts should be made to implement coastal erosion disaster prevention and control measures in different regions. [ABSTRACT FROM AUTHOR]

This paper examines the effects of macroeconomic and financial uncertainty on professional economic forecasts. Using the predictions of four variables sourced from the Bloomberg survey, we find that macroeconomic uncertainty, a proxy for the complexity of the forecasting task, is associated with high disagreement and low accuracy. By contrast, the results show that financial uncertainty is associated with low disagreement and high accuracy, which suggests that financial uncertainty encourages forecasters to adhere to the consensus to avoid large forecast errors. Furthermore, we find that the forecaster rank moderates the effect of macroeconomic uncertainty, indicating that high-ability forecasters can better navigate periods of high uncertainty. This study advances the understanding of forecasting behavior under uncertainty. [ABSTRACT FROM AUTHOR]

The ontology behind quantum mechanics has been the subject of endless debate since the theory was formulated some 100 years ago. It has been suggested, at one time or another, that the objects described by the theory may be individual particles, waves, fields, ensembles of particles, observers, and minds, among many other possibilities. I maintain that these disagreements are due in part to a lack of precision in the use of the theory's various semantic designators. In particular, there is some confusion about the role of representation, reference, and denotation in the theory. In this article, I first analyze the role of the semantic apparatus in physical theories in general and then discuss the corresponding ontological implications for quantum mechanics. Subsequently, I consider the extension of the theory to quantum fields and then analyze the semantic changes of the designators with their ontological consequences. In addition to the classical arguments to rule out a particle ontology in the case of non-relativistic quantum field theory, I show how the existence of black holes makes the proposal of a particle ontology in general spacetimes unfeasible. I conclude by proposing a provisional pluralistic ontology of fields and spacetime and discussing some prospects for possible future ontological economies. [ABSTRACT FROM AUTHOR]

In the ambitiously evolving construction industry of Kuwait, characterised by its vision 2035 and rapid technological integration, there exists a pressing need for advanced analytical frameworks. The pressing need for advanced analytical frameworks in the Kuwait Construction Market arises from the necessity to identify inefficiencies, predict market trends, and enhance decision-making processes. For instance, these frameworks can be used to detect anomalies in investment patterns, forecast the impact of economic changes on project timelines, and optimise resource allocation by analysing labour and material supply data. By leveraging deep learning techniques, such as autoencoder neural networks, stakeholders can gain deeper insights into the market's complexities and improve strategic planning and operational efficiency. This research paper introduces a deep learning approach utilising an autoencoder neural network to analyse the complexities of the Kuwait Construction Market and identify data irregularities. The construction sector's significant investment influx and project expansion make it an ideal candidate for deploying sophisticated analytical techniques to detect anomalous patterns indicating inefficiencies or unveiling potential opportunities. Our approach leverages the capabilities of autoencoder architectures to delve into and understand the prevalent patterns in market behaviours. This analysis involves training the autoencoder on historical market data to learn the normal patterns and subsequently using it to identify deviations from these learned patterns. This allows for the detection of anomalies that may lead to operational or financial consequences. We elucidate the mathematical foundations of autoencoders, highlighting their proficiency in managing the complex, multidimensional data typical of the construction industry. Through training on an extensive dataset—comprising variables like market sizes, investment distributions, and project completions—our model demonstrates its ability to pinpoint subtle yet significant anomalies. The outcomes of this study enhance our understanding of deep learning's pivotal role in construction and building management. Empirically, the model detected anomalies in transaction volumes of lands and houses, highlighting unusual spikes that correlate with specific market activities. These findings demonstrate the autoencoder's effectiveness in anomaly detection, emphasising its importance in enhancing operational efficiency and strategic planning in the construction industry. [ABSTRACT FROM AUTHOR]

Compared to heat pumps established in the building sector, high‐temperature heat pumps (HTHPs) suitable for industrial use still face challenges, not only from a mechanical engineering point of view but also regarding integration into existing processes. They need to supply larger amounts of heat at higher temperatures. The mere replacement of a gas boiler by an HTHP will rarely deliver an energy‐efficient solution. Instead, these units must often be tailored to the application and vice versa, making specifications a complex task. This paper intends to link thermodynamic and economic basics of HTHPs to the process engineering of the actual production process. It also ventures an outlook on the economic and regulatory boundary conditions in the EU as corner stones for a sustainable investment decision. [ABSTRACT FROM AUTHOR]

An econometric model was developed to analyze the contribution of various factors to the gross value added to the agricultural, manufacturing, and service sectors in the United States. The study found that variables such as rare earth element mining, the employment it generates, the domestic consumption, imports, and prices of certain elements significantly impact economic sectors. The models showed a good fit and met the necessary statistical assumptions. Rare earth elements are essential for a wide range of technological products, with China being the leading producer and consumer. This has raised concerns about the dependence on other countries. These elements significantly impact the economy's primary, secondary, and tertiary sectors used in agriculture, manufacturing, and services. Rare earth elements' mining and processing are complex and expensive processes, and demand is expected to continue to increase in the future. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of machine learning models, specifically gradient boosting machine (GBM) and random forest (RF), against the traditional vector autoregressive (VAR) model for forecasting economic growth in Algeria. By utilizing a dataset comprising key macroeconomic indicators—Gross Domestic Product (GDP), money supply (M), and inflation (I)—we aim to evaluate the predictive accuracy and robustness of these models. Our findings indicate that the RF model outperforms both GBM and VAR in terms of accuracy and reliability, providing a valuable understanding of the economic dynamics of Algeria. These results highlight the potential of advanced machine learning techniques in improving economic forecasting and informing policy decisions in emerging economies. [ABSTRACT FROM AUTHOR]

The article explores the concept of Industry 5.0, which focuses on sustainability, human-centric design, and collaboration. It discusses the technologies shaping Industry 5.0, such as AI, machine learning, IoT, collaborative robots, and additive manufacturing. The article specifically looks at the potential of Industry 5.0 in Africa, addressing challenges, opportunities, and the importance of equitable access to technology. It emphasizes the need for Africa to embrace these technologies to drive economic diversification, workforce empowerment, and innovation. The author, Dr. Blessing Takawira, is an experienced business and supply chain management expert and academic lecturer. [Extracted from the article]

The UK relationship between nominal wage inflation and the unemployment rate is unstable. Over sub‐periods of the last 160 years of turbulent data, Phillips curve slopes range from strongly negative, slightly negative, flat, slightly positive and strongly positive. Our constant‐parameter congruent model of real wages explains these instabilities, yet also implies a constant negative relationship between nominal wage inflation and the unemployment rate when corrected by its regressors. Disentangling these effects reveals that structural breaks in the real‐wage model's variables do not explain the instabilities, which instead occur during sub‐periods when some of its explanatory variables are insignificant. [ABSTRACT FROM AUTHOR]

We forecast US inflation with a new Keynesian Phillips curve (NKPC) in the frequency domain. Our method consists of decomposing the time series of inflation and its NKPC predictors into several frequency bands, forecasting separately each frequency component of inflation, and then summing up those forecasts to obtain the forecast for aggregate inflation. We find that (i) accurately forecasting the low frequency of inflation is, on average, crucial to successfully forecast inflation; (ii) our NKPC low‐frequency forecast model consistently and significantly outperforms the time‐series NKPC and standard benchmark models; (iii) the low frequencies of inflation expectations and unemployment are the key predictors; and (iv) optimally switching on / off the forecasts of each frequency components of inflation at each period allows to outstandingly track inflation and show that all frequencies of inflation matter. [ABSTRACT FROM AUTHOR]