Using Survey Information for Improving the Density Nowcasting of U.S. GDP

We provide a methodology that efficiently combines the statistical models of nowcasting with the survey information for improving the (density) nowcasting of U.S. real GDP. Specifically, we use the conventional dynamic factor model together with stochastic volatility components as the baseline statistical model. We augment the model with information from the survey expectations by aligning the first and second moments of the predictive distribution implied by this baseline model with those extracted from the survey information at various horizons. Results indicate that survey information bears valuable information over the baseline model for nowcasting GDP. While the mean survey predictions deliver valuable information during extreme events such as the Covid-19 pandemic, the variation in the survey participants' predictions, often used as a measure of "ambiguity,” conveys crucial information beyond the mean of those predictions for capturing the tail behavior of the GDP distribution.

Comparison of the Performances for CAPM and Fama-French models During COVID-19

Since the outbreak of COVID-19, it has caused a startling stun to both society and economy in numerous nations, where different industries suffered unequally. This paper reviews the various performance of the Capital Asset Pricing Model (CAPM), and the Fama-French three-factor model and the five-factor model in different regions and industries. To metric the performance, various statistics models and scaling are applied including Pearson correlation, linear regression, R2 scores, t-test, etc. Specifically, this paper demonstrates the different performances of the CAPM model on the US and Egyptian stock markets, whereas using generalized method of moments in a panel data analysis to evaluate the performance in the U.S. market and the paired sample t-test and Wilcoxon signed-rank to evaluate the performance in the Egyptian market. The Fama-French three-factor model and five-factor model are both based on the U.S. market and analyze the model's performance (measured by significant level) in the U.S. market in general and in individual sectors, respectively. Whereas, in terms of three-factors model, the OLS estimation and relapse expected excess return are used onto the variables and multiple linear regression method was used to study the significance of factors in three sub-industries. Regarding to five-factors model, a multivariate regression with covariates and OLS estimation are the method for evaluation. These results shed light for deeply understanding the model and recognizing the impact on the security market of the COVID-19. © 2023 SPIE.

US weekly economic index: Replication and extension

We revisit the US weekly economic index (WEI) put forth by Lewis, Mertens, Stock and Trivedi (2021). In a narrow sense, we replicate their main results with data gathered from its original sources. In a wide sense, we apply the methodology established in Wegmüller, Glocker and Guggia (2023) to adjust the weekly input series for seasonal patterns, calendar day effects, and excess volatility. In a long sense, we show that our proposed data adjustment significantly improves the nowcasting performance of the WEI. © 2023 John Wiley & Sons, Ltd.

The role of personality traits and the ability to detect fake news in predicting information avoidance during the COVID-19 pandemic

PurposeThe objective of this study was to investigate the impacts of personality traits and the ability to detect fake news on information avoidance behavior. It also examined the effect of personality traits on the ability to detect fake news.Design/methodology/approachThe sample population included Shiraz University students who were studying in the second semester of academic year 2021 in different academic levels. It consisted of 242 students of Shiraz University. The Big Five theory was used as the theoretical background of the study. Moreover, the research instrument was an electronic questionnaire consisting of the three questionnaires of the ability to detect fake news (Esmaeili et al., 2019, inspired by IFLA, 2017), the Big Five personality traits (Goldberg, 1999) and information avoidance (Howell and Shepperd, 2016). The statistical methods used to analyze the data were Pearson correlation and stepwise regression, which were performed through SPSS software (version 26).FindingsThe results showed that from among the five main personality factors, only neuroticism had a positive and significant effect on information avoidance. In addition, the ability to detect fake news had a significant negative effect on information avoidance behavior. Further analyses also showed positive and significant effects of openness to experience and extraversion on the ability to detect fake news. In fact, the former had more predictive power.Practical implicationsFollowing the Big Five theory considering COVID-19 information avoidance and the ability to detect COVID-19 fake news, this study shifted the focus from environmental factors to personality factors and personality traits. Furthermore, this study introduced the ability to detect fake news as an influential factor in health information avoidance behaviors, which can be a prelude for new research studies.Originality/valueThe present study applied the five main personality factors theory in the context of information avoidance behavior and the ability to detect fake news, and supported the effect of personality traits on these variables.

Hotel employee resilience during a crisis: conceptual and scale development

This research aims to address the lack of research on hotel employee resilience during a crisis (HERC) and the absence of a measurement scale to assess it. A mixed-method approach was used to conceptualize HERC, identify its dimensions, and build a measurement scale. In Study 1, an online survey of 69 employees from upscale hotels was conducted, revealing a five-factor HERC model comprising resistance, adaptability, cooperation, restoration, and thriving. Study 2 developed preliminary measurement items for HERC, which were refined through exploratory factor analysis (EFA). Study 3 conducted another round of surveys and used a confirmatory factor analysis (CFA) to verify the factors generated from the second study. This research provides a comprehensive five-factor model of employee resilience during a crisis and a corresponding measurement scale, offering a theoretical foundation for hotel managers to develop effective strategies to manage crises. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

How can hesitation in hotel live-streaming payment be overcome?: Examine the role of entrepreneurial performance and viewers' personality traits

After the outbreak of COVID-19, E-commerce lives streaming (ELS) has become popular as a marketing tool to reduce spatial restrictions and control the spread. However, there are concerns about the hesitance to pay after purchasing through ELS. This study examined the psychological factors from a consumer's perspective on reluctance to pay at the end of a hotel reservation through ELS. The conceptual framework of the study integrates the concepts of the stimulus-organism-response (SOR) framework in conjunction with the Five-Factor Model (Big Five). Data were collected from 401 online questionnaires collected by users with no more than three months of ELS experience and causal relationships were analyzed using the partial least squares structural equation modeling (PLS-SEM) technique. The study found that promotion (PROM), Content Quality (CTQ) and Content Valence (CTV) played a crucial role in driving viewer interest in booking through hotel live-streaming (HLS). Viewers with the Extraversion personality type (EXT) show interest in booking accommodations through the hotel e-commerce live streaming platform. Financial and privacy concerns influence the assessment of payment options. In addition, finances and dissatisfaction are essential factors that encourage viewers to hesitate to pay after booking. This study is crucial for applying ELS in the tourism and hospitality industry to create new travel experiences post-COVID-19.

Exploring digital natives' mobile addiction syndrome in Taiwan: psychological issues and beyond

PurposeThis study seeks to explore digital natives' mobile usage behaviors and, in turn, develop an analytic framework that helps articulate the underlying components of mobile addiction syndrome (MAS), its severity levels and mobile usage purposes.Design/methodology/approachThe investigation adopts a survey method and a case study. The results of the former are based on 411 random classroom observations and 205 questionnaire responses, and the insights of the latter are derived from 24 interviews and daily observations.FindingsThe findings validate five distinctive signs that constitute MAS and their significant correlations with each of the Big Five personality traits. Classroom observations confirm the prevalence of addiction tendency among digital natives in the research context. Seven levels of MAS and six different mobile usage purposes further manifest themselves from case analysis. There appears to be a sharp contrast between the addicted and non-addicted groups in their mobile purposes and behavioral patterns. Additionally, family relationships seem influential in shaping non-addictive mobile usage behaviors.Research limitations/implicationsPsychological perspectives on MAS may be important but insufficient. Empirical investigation on a global scale, especially with distinctive cross-cultural comparisons, will be highly encouraged. How MAS evolves over time should also serve as future research interests.Practical implicationsTeaching pedagogy of college education might need certain adjustments to intrigue digital natives' learning interests. Future managers might also need to adopt better performance measurements for digital natives who barely separate work from personal matters in their mobile devices.Social implicationsParents and healthcare institutions may need to develop response mechanism to tackle this global issue at home and in society. The long-term effects of the COVID-19 pandemic on MAS might also deserve global attention.Originality/valueThe analytic framework developed provides an original mechanism that can be valuable in identifying MAS severity and associated behavioral patterns.

Alpha-factor integrated risk parity portfolio strategy in global equity fund of funds

This study proposes a principal alpha-style factor integrated risk parity strategy that can diversify style risk factors and the stock selection risk of external managers in Fund-of-Funds (FoFs) portfolios. First, we separated the style risk factors and stock-specific sources held by each individual fund. Stock-specific sources, referred to as principal alpha portfolios, are extracted through principal component analysis, where the sources are utilized for risk parity in the alpha division. As the parity portfolio was integrated into both the alpha and style factor divisions, we used a Basin-Hopping two-phase optimization technique, which can mitigate the local optimal trap by exploring the surroundings of the sequential quadratic programming solution secondarily. Through this, a more stable integrated risk parity portfolio can be realized. Finally, the suggested integrated risk parity portfolios were simulated with a global fund dataset. The simulation results from 2006 through June 2022 show a more stable risk-return profile than an independently constructed strategy using style risk factors or principal alpha sources, especially in high volatility and down-market periods, such as a global financial crisis or unexpected events like COVID-19. This study can be applied to various areas covering other FoFs and asset allocation strategies by integrating alpha and factor divisions. © 2023 Elsevier Inc.

Global Confidence, Uncertainty, and Business Cycles

This paper investigates the role of global confidence cycles, measured as the common factor across a wide range of survey-based business or consumer confidence indicators in global macroeconomic fluctuations over 1985–2019. We estimate a factor-augmented vector autoregression model, where global confidence shocks are identified through recursive restrictions. We report three main results. First, the global confidence cycles—in particular, that of consumer confidence—have played a key role in global business cycle fluctuations, explaining over a third of total variations. Second, while global business confidence shocks are in nature demand driven, global consumer confidence seems to reflect both demand and supply shocks, in line with "animal spirit” and "news” views on the relationship between confidence and economic activities. Third, the shifts in global confidence are not necessarily accounted for by uncertainty shocks. Instead, confidence acts as an important channel in the transmission of uncertainty shocks. The results are robust to alternative identification using a novel set of external instruments, alternative variable orderings, and different uncertainty measures. © 2023, European Central Bank. All rights reserved.

Weekly economic activity: Measurement and informational content

We construct a composite index to measure the real activity of the Swiss economy on a weekly frequency. The index is based on a novel high-frequency data set capturing economic activity across distinct dimensions over a long time horizon. We propose a six-step procedure for extracting precise business cycle signals from the raw data. By means of a real-time evaluation, we highlight the importance of our proposed adjustment procedure: (i) our weekly index significantly outperforms a comparable index without adjusted input variables;and (ii) the weekly index outperforms established monthly indicators in nowcasting GDP growth. These insights should help improve other recently developed high-frequency indicators. © 2021 International Institute of Forecasters

Forecasting real activity using cross-sectoral stock market information

Stock prices declined abruptly in the wake of the Covid-19, reflecting both the deterioration of investors' expectations of profitability as well as the surge in risk aversion. In the following months however, economic activity remained sluggish while equity markets bounced back. This disconnect between equity values and macro-variables can be partially explained by other factors, namely the decline in risk-free interest rates, and -for the US- the strong earnings of the IT sector. As a result, an econometrician forecasting economic activity with aggregate stock market variables during the Covid-crisis is likely to get poor results. Our main contribution is thus to rely on sectorally disaggregated equity variables within a factor model in order to predict US economic activity. We find, first, that the factor model better predicts future economic activity compared to aggregate equity variables, or to conventional benchmarks used in the literature, both in-sample and out-of-sample. Second, we show that the strong performance of the factor model comes from the fact that it filters out the "expected returns” component of the sectoral equity variables as well as the foreign component of aggregate future cash flows. The constructed factor overweights upstream and "value” sectors that are found to be closely linked to the future state of the business cycle. © 2023 Elsevier Ltd

Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms

The ability to estimate current GDP growth before official data are released, known as "nowcasting”, is crucial for the Chinese government to effectively implement economic policy and manage economic uncertainties;however, there is limited research on nowcasting China's GDP in a data-rich environment. We evaluate the performance of various machine learning algorithms, dynamic factor models, static factor models, and MIDAS regressions in nowcasting the Chinese annualised real GDP growth rate in pseudo out-of-sample exercise, using 89 macroeconomic variables from years 1995 to 2020. We find that some machine learning methods outperform the benchmark dynamic factor model. The machine learning method that deserves more attention is ridge regression, which dominates all other models not only in terms of nowcast error but also in effective recognition of the impacts of the Global Financial Crisis and Covid-19 shocks. Policy-wise, our study guides practitioners in selecting appropriate nowcasting models for China's macroeconomy.

Did ESG portfolio augment investors' wealth during Covid19? Evidence from Indian stock market

PurposeThis paper aims to empirically examine the performance of the high-ESG (environment, social and governance) portfolio vis-a-vis the low-ESG portfolio at the Indian stock market before and during the Covid19 pandemic. Design/methodology/approachThe absolute rate of return and several risk-adjusted performance measures, for instance, Sharpe ratio, Modigliani-Modigliani measure, Treynor ratio, Jensen's alpha, information ratio, Fama's decomposition measure and Fama and French's three-factor model, have been used in this study along with the t-test. FindingsAll three indices (CARBONEX, GREENEX and BSE 500) had better returns during Covid19 period as compared to the pre-Covid19 period. However, these returns were not statistically significant. During Covid19, the risk of the indices also rose, but they provided better returns for the additional risk taken. Finally, it is concluded that the performance of high-ESG and low-ESG stock portfolios did not differ significantly in both periods. Practical implicationsThe study is relevant to individual and institutional investors, financial advisors, portfolio managers, corporations, policymakers, market regulators and society at large. Social implicationsThis study emphasized the need to expand the role of ESG investment in India for the benefit of people, communities and society as a whole. Originality/valueThis research is the first of its kind, to the best of the authors' knowledge, that compares the performance of a high-ESG portfolio with a low-ESG portfolio both before and during the Covid19, particularly in the Indian context.

Forecasting Real Activity using Cross-Sectoral Stock Market Information

Stock prices declined abruptly in the wake of the Covid-19, reflecting both the deterioration of investors' expectations of profitability as well as the surge in risk aversion. In the following months however, economic activity remained sluggish while equity markets bounced back. This disconnect between equity values and macro-variables can be partially explained by other factors, namely the decline in risk-free interest rates, and -for the US- the strong earnings of the IT sector. As a result, an econometrician forecasting economic activity with aggregate stock market variables during the Covid-crisis is likely to get poor results. Our main contribution is thus to rely on sectorally disaggregated equity variables within a factor model in order to predict US economic activity. We find, first, that the factor model better predicts future economic activity compared to aggregate equity variables, or to conventional benchmarks used in the literature, both in-sample and out-of-sample. Second, we show that the strong performance of the factor model comes from the fact that it filters out the "expected returns” component of the sectoral equity variables as well as the foreign component of aggregate future cash flows. The constructed factor overweights upstream and "value” sectors that are found to be closely linked to the future state of the business cycle.

Lecturers' personality traits and affective states amidst the COVID-19 pandemic: estimating a theory-driven longitudinal model using the novel PLSe2 methodology

Purpose - This longitudinal study aims at assessing the impact of openness to experience and neuroticism on affective states experienced by the academics from the Malaysian public universities during the first strict COVID-19 lockdown in 2020. Design/methodology/approach - The author collected data for openness to experience and neuroticism at the beginning of the lockdown, and for positive and negative affect, when the lockdown ended. The author used the efficient partial least squares structural equation modeling (PLSe2-SEM) methodology to fit the model to the screened data (N = 291). Findings - The results showed that openness to experience had a negative effect on negative affect and a positive effect on positive affect. The author also observed that neuroticism had a positive effect on negative affect and a negative effect on positive affect. These findings provided support for the proposition of the impact of personality traits on affective states amidst the COVID-19 pandemic in academic settings. Practical implications - The study shows that careful assessment of lecturers' personality traits should be considered during the process of selection and recruitment since these factors, theoretically and empirically, trigger affective states which, in turn, lead to behaviors and attitudes. Originality/value - This is the first study on examining the impact of academics' personality traits on their affective states. Also, it is amongst the few longitudinal studies on evaluating personality traits during the COVID-19 pandemic. As a methodological novelty, the author used the PLSe2 methodology to test the model and compared the results with maximum likelihood (ML) results.

Portfolio management for typical stocks and precious metal assets

This paper examines and analyses the volatility of precious metal assets and the portfolios management. The innovative study of precious metal assets and equity portfolios is instructive and forward-looking under the background of global COVID-19. We use GARCH model, Markowitz model, CAPM Model and Fama-French Three-Factor Model to study volatility, asset forecasts, and the relationship between asset prices and markets. The results show that, firstly, precious metals are less volatile than stocks;secondly, precious metals, especially gold, make up a large percentage of rational risk-hedged portfolios;and finally, the stock prices have an almost linear relationship with the market, but precious metal prices do not have a linear relationship with the market. Our conclusion suggests that investors should consider precious metal assets, especially gold, in addition to stocks when considering their portfolios, which helps to hedge investment risk and provides some guidance to the market. © 2022 SPIE.

Cross-sector comovements and policy impact in the COVID-19 stock market: A dynamic factor approach

This study investigates the remarkable comovements in U.S. equity returns during the COVID-19 pandemic. It constructs a dynamic factor model (DFM) to illuminate the sources of the comovements and their implications. Using the Markov Chain Monte Carlo (MCMC) estimation method, the study finds that the comovements had a weak daily oscillation pattern during the pandemic. With that pattern, the study also finds significant monetary policy effects on the equity returns of several key sectors. In addition, it estimates the impact of news shocks, including monetary policy news, fiscal stimulus news, and unemployment news, on cross-sector equity returns. For any given sector, the conventional and unconventional monetary policy news shocked the sector in opposite directions. Among the positive monetary news shocks, the strongest were from interest rate policy surprises. Conversely, fiscal stimulus news had the most substantial positive impact and triggered all sectors to rebound from the bear market at the end of March 2020. Furthermore, by applying Natural Language Processing (NLP) sentiment analysis, this study sheds light on the positive correlation between comovements and news sentiment.

Forced transition to technology: role of self-efficacy and big five personality variables in the adoption of technology

Purpose The present study is mainly concerned with investigating the migration to online learning under the coronavirus disease 2019 (COVID-19) pandemic and analysing the adoption of technology in the context of Indian educational organisations. The purpose of the paper is to identify aspects that explain and predict the adoption propensity of new technology by users as a dependent variable, with perceived usefulness (PU) and perceived ease of use (PE) as independent variables and personality and self-efficacy as the moderator variables. Design/methodology/approach An online as well as offline survey is collected from N = 202 employees (teachers/faculty) from private (N = 97) and public (N = 105) educational organisations located in India. A conceptual model of technology adoption is developed and validated, measuring the impact of Big Five personality factors and self-efficacy on technology adoption. Findings Results of moderation analysis suggest that personality traits moderate the relationship between PU, PE and acceptance of technology (TAP). Originality/value The present research uniquely contributes to the limited literature on the role of personality and self-efficacy in adopting technology and the outcomes. Furthermore, the research captures the theoretical and practical understanding of the PU, PE and TAP link in educational organisation and COVID-19 context.

Nowcasting GDP with a pool of factor models and a fast estimation algorithm

We propose a novel mixed-frequency dynamic factor model with time-varying parameters and stochastic volatility for macroeconomic nowcasting and develop a fast estimation algorithm. This enables us to generate forecast densities based on a large space of factor models. We apply our framework to nowcast US GDP growth in real time. Our results reveal that stochastic volatility seems to improve the accuracy of point forecasts the most, compared to the constant-parameter factor model. These gains are most prominent during unstable periods such as the Covid-19 pandemic. Finally, we highlight indicators driving the US GDP growth forecasts and associated downside risks in real time.

Trust and Media: Reflection of the Big Five Factor Personality Traits on COVID-19 Pandemic Communication

The pandemic process, in general, is a process that creates anxiety due to reasons such as confusion, instability, misinformation and inadequate planning. Within the scope of this study, the assumption that the personality traits of individuals are effective in getting information and taking the required measures while carrying out the health communication during COVID-19 pandemic, and from the question of whether the individual differences can be associated with the inclination for information receipt or not. This scope of this study aims to measure how the communication of the COVID-19 epidemic towards the Turkish people affects the individuals' preferences for information, the relationships between the sources they trust, and their individual differences based on the Big-5 factors, within the framework of the five major personality traits. This quantitative study, firstly aims to investigate the information-seeking behavior of the Turkish public in relation to their perceptions of COVID-19 and the impact of messages received from the media. Secondly, it is aimed to measure how the COVID-19 pandemic communication affects the relationships among the media sources they trust in their choices of information. While the trust in health care professionals and independent health care associatons were the highest among the individuals who participated in the survey, those who have the responsibility personality trait, trust in the content of COVID-19 messages for government agencies is low.