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This study evaluates the Federal Reserve (Fed) initial and final forecasts of the unemployment rate for 1983Q1-2018Q4. The Fed initial forecasts in a typical quarter are made in the first month (or immediately after), and the final forecasts are made in the third month of the quarter. The analysis also includes the private forecasts, which are made close to the end of the second month of the quarter.

In evaluating the multi-period forecasts, the study tests for systematic bias, directional accuracy, symmetric loss, equal forecast accuracy, encompassing and orthogonality. For every test equation, it employs the Newey–West procedure in order to obtain the standard errors corrected for both heteroscedasticity and inherent serial correlation.

Both Fed and private forecasts beat the naïve benchmark and predict directional change under symmetric loss. Fed final forecasts are more accurate than initial forecasts, meaning that predictive accuracy improves as more information becomes available. The private and Fed final forecasts contain distinct predictive information, but the latter produces significantly lower mean squared errors. The results are mixed when the study compares the private with the Fed initial forecasts. Additional results indicate that Fed (private) forecast errors are (are not) orthogonal to changes in consumer expectations about future unemployment. As such, consumer expectations can potentially help improve the accuracy of private forecasts.

Unlike many other studies, this study focuses on the unemployment rate, since it is an important indicator of the social cost of business cycles, and thus its forecasts are of special interest to policymakers, politicians and social scientists. Accurate unemployment rate forecasts, in particular, are essential for policymakers to design an optimal macroeconomic policy.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

This paper investigates the static and dynamic directional return spillovers and dependence among green investments, carbon markets, financial markets and commodity markets from January 2013 to September 2020.

This study employed both the quantile vector autoregression (QVAR) and time-varying parameter VAR (TVP-VAR) technique to examine the magnitude of static and dynamic directional spillovers and dependence of markets.

Results show that the magnitude of connectedness is extremely higher at quantile levels (q = 0.05 and q = 0.95) compared to those in the mean of the conditional distribution. This connotes that connectedness between green bonds and other assets increases with shock size for both negative and positive shocks. This further indicates that return shocks spread at a higher magnitude during extreme market conditions relative to normal periods. Additional analyses show the behavior of return transmission between green bond and other assets is asymmetric.

The findings of this study offer significant implications for portfolio investors, policymakers, regulatory authorities and investment community in terms of carefully assessing the unique characteristics offered by each markets in terms of return spillovers and dependence and diversifying the portfolios.

The study, first, uses a relatively new statistical technique, the QVAR advanced by Ando et al. (2018), to capture upper and lower tails’ quantile price connectedness and directional spillover. Therefore, the results possess adequate power against departure from mean-based conditional connectedness. Second, using a portfolio of green investments, carbon markets, financial markets and commodity markets, the uniqueness of this study lies in the examination of the static and dynamic dependence of the markets examined.

This paper aims to focus on identifying key health-care issues amenable to digital twin (DT) approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare facilities management (FM) using four different scenarios. The scenario planning focused on monitoring and controlling the heating, ventilation, and air-conditioning system in real-time; monitoring indoor air quality (IAQ) to monitor the performance of medical equipment; monitoring and tracking pulsed light for SARS-Cov-2; and monitoring the performance of medical equipment affected by radio frequency interference (RFI).

The importance of a healthcare facility, its systems and equipment necessitates an effective FM practice. However, the FM practices adopted have several areas for improvement, including the lack of effective real-time updates on performance status, asset tracking, bi-directional coordination of changes in the physical facilities and the computational resources that support and monitor them. Consequently, there is a need for more intelligent and holistic FM systems. We propose a DT which possesses the key features, such as real-time updates and bi-directional coordination, which can address the shortcomings in healthcare FM. DT represents a virtual model of a physical component and replicates the physical data and behavior in all instances. The replication is attained using sensors to obtain data from the physical component and replicating the physical component's behavior through data analysis and simulation. This paper focused on identifying key healthcare issues amenable to DT approach. It starts with a description of the concept and enabling technologies of a DT and then discusses potential applications of DT solutions in healthcare FM using four different scenarios.

The scenarios were validated by industry experts and concluded that the scenarios offer significant potential benefits for the deployment of DT in healthcare FM such as monitoring facilities’ performance in real-time and improving visualization by integrating the 3D model.

In addition to inadequate literature addressing healthcare FM, the study was also limited to one of the healthcare facilities of a large public university, and the scope of the study was limited to IAQ including pressure, relative humidity, carbon dioxide and temperature. Additionally, the study showed the potential benefits of DT application in healthcare FM using various scenarios that DT experts validated.

The study shows the practical implication using the various validated scenarios and identified enabling technologies. The combination and implementation of those mentioned above would create a system that can effectively help manage facilities and improve facilities' performances.

The only identifiable social solution is that the proposed system in this study can manually be overridden to prevent absolute autonomous control of the smart system in cases when needed.

To the best of the authors’ knowledge, this is the only study that has addressed healthcare FM using the DT approach. This research is an excerpt from an ongoing dissertation.

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.

Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.

This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

This paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.

The authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.

The results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.

This study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.

This finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

This paper aims to introduce a new perspective on long-term stock return predictability by focusing on the relative (individual and hybrid) informative power of a wide range of accounting (firm-related), technical and macroeconomic factors while considering the past performance of the stocks using machine learning algorithms.

The sample includes a panel data set of 94 non-financial firms listed in Egyptian Exchange 100 index from 2014: Q1 to 2019: Q4. Relativity has been investigated by comparing relevant factors’ individual and combined informative power and differentiating between losers and winners based on historical stock returns. To predict the quarterly stock returns, Gaussian process regression (GPR) has been used. The robustness of the results is examined through the out-of-sample test. This study also uses linear regression (LR) as a benchmark model.

The past performance and the presence of other predictors influence the informative power of relevant factors and hence their predictive ability. The out-of-sample results show a trade-off between GPR and LR with proven superiority to GPR in limited experiments. The individual informative power outperforms the hybrid power, in which macroeconomic indicators outperform the remaining sets of indicators for losers, while winners show mixed results in terms of various performance evaluation metrics. Prediction accuracy is generally higher for losers than for winners.

This study provides interesting insight into the dynamic nature of the predictor variables in terms of stock return predictability. Hence, this study also deepens the understanding of asset pricing in a way that directly contributes to practitioners’ portfolio diversification strategies.

In concern of the chaos of factors in the literature and its accompanying misleading conclusions, this study takes another look at the approach that studies stock return predictability. To the best of the authors’ knowledge, this is the first study in the Egyptian context that re-examines the predictive power of the previously discovered factors from a different perspective that highlights their relative nature.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of the paper is to propose an efficient and accurate force/torque (F/T) sensing method for the robotic wrist-mounted six-dimensional F/T sensor based on an excitation trajectory.

This paper presents an efficient and accurate F/T sensing method based on an excitation trajectory. First, the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity. Therefore, the sensing accuracy is improved. Then, the excitation trajectory with optimized poses is used for robot following and data acquisition. The data acquisition is not limited by poses and its time can be significantly shortened. Finally, the least squares method is used to identify parameters and sense contact forces/torques.

Experiments have been carried out on the self-developed robot manipulator. The results strongly demonstrate that the proposed approach is more efficient and accurate than the existing widely-adopted method. Furthermore, the data acquisition time can be shortened from more than 60 s to 3 s/20 s. Thus, the proposed approach is effective and suitable for fast-paced industrial applications.

The main contributions of this paper are as follows: the dynamic identification model is established by comprehensively considering inertial forces/torques, sensor zero-drift values, robot base inclination errors and forces/torques caused by load gravity; and the excitation trajectory with optimized poses is used for robot following and data acquisition.

The purpose of this study is to empirically examine the cause–effect relationships between workplace friendship and knowledge-sharing behavior, from a static perspective. Furthermore, it investigates the bi-directional relationship between the increase in both workplace friendship and knowledge-sharing behavior over same time periods, and also endeavors to identify whether there is a significant negative lagged effect of the increase in both workplace friendship on knowledge-sharing behavior, and vice versa, across time from a dynamic perspective.

The study conducts a three-wave questionnaire survey to test the research model. A latent change score approach was used to test the direct relationship between changes in workplace friendship and changes in knowledge-sharing behavior.

The findings reveal that knowledge-sharing behavior fosters workplace friendship and workplace friendship promotes the emergence of knowledge-sharing behavior. An increase in workplace friendship promotes an increase in knowledge-sharing behavior over same time periods. However, an increase in workplace friendship will lead to a lagged decrease of knowledge-sharing behavior across time, and vice versa.

The time interval in this study is a little short to capture the full changes in workplace friendship. Some important control factors and mediating mechanisms are not included in the research model.

This study guides managers to focus on various motivators to better strengthen workplace friendship and knowledge-sharing behavior and to consider and effectively respond to the negative side of workplace friendship and knowledge-sharing behavior across time.

This study emphasizes the predictivity of one important interaction patterns, namely, knowledge-sharing behavior on friendship at the workplace, from a static perspective. This study also shows the benefits of an increase in workplace friendship for the development of knowledge-sharing behavior in the same time period. Furthermore, the study presents a counterintuitive finding when taking the lag effect into consideration in exploring the relationship between changes both in workplace friendship and knowledge-sharing behavior, and identifies a negative side of both when viewed over longer periods.