Twitter’s daily happiness sentiment,economic policy uncertainty,and stock index fluctuations

This research applies quantile Granger causality and impulse-response analyses to evaluate the causal linkages among Twitter’s daily happiness sentiment, economic policy uncertainty (EPU), and S&P 500 indices across the U.S. stock market cycles. We present notable evidence of bi-directional causality among cyclical components of Twitter’s daily happiness sentiment, economic policy uncertainty, and S&P 500 indices for most quantiles. The causal linkage of Twitter’s daily happiness sentiment and S&P 500 indices identified in this study reconciles the so-called Easterlin Paradox and Easterlin Illusion arguments from previous studies on income-happiness relationship. Moreover, given a high (low) EPU level, the positive (negative) impulse-response effects between the Twitter’s daily happiness sentiment and the S&P 500 indices are justified during a stock market bust cycle, but the signs of these correlations change to negative (positive) during a stock market boom cycle. These findings imply that investors’ hedging strategies can be linked to the surveillance of the Twitter’s daily happiness sentiment index.     

Research on the time-varying effects among green finance markets in China: A fresh evidence from multi-frequency scale perspective

Green finance is an essential instrument for achieving sustainable development. Objectively addressing correlations among different green finance markets is conducive to the risk management of investors and regulators. This paper presents evidence on the time-varying correlation effects and causality among the green bond market, green stock market, carbon market, and clean energy market in China at multi-frequency scales by combining the methods of Ensemble Empirical Mode Decomposition Method (EEMD), Dynamic Conditional Correlation (DCC) GARCH model, Time-Varying Parameter Vector Autoregression with Stochastic Volatility Model (TVP-VAR-SV), and Time-varying Causality Test. In general, the significant negative time-varying correlations among most green finance markets indicate a prominent benefit of risk hedging and portfolio diversification among green financial assets. In specific, for different time points and lag periods, the green finance market shock has obvious time-varying, positive and negative alternating effects in the short-term scales, while its time delay and persistence are more pronounced in the medium-term and long-term scales. Interestingly, a positive event shock will generate positive connectivity among most green finance markets, whereas a negative event including the China/U.S. trade friction and the COVID-19 pandemic may exacerbate the reverse linkage among green finance markets. Furthermore, the unidirectional causality of “green bond market - carbon market - green stock and clean energy markets” was established during 2018–2019.     

A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks

The unexpected emergence of the COVID-19 pandemic has changed how grocery shopping is done. The grocery retail stores need to ensure hygiene, quality, and safety concerns in-store shopping by providing “no-touch” smart packaging solutions for agri-food products. The benefit of smart packaging is to inform consumers about the freshness level of a packaged product without having direct contact. This paper proposes a data-driven decision support system that uses smart packaging as a smart product-service system to manage the sustainable grocery store supply chain during outbreaks to prevent food waste. The proposed model dynamically updates the price of a packaged perishable product depending on freshness level while reducing food waste and the number of rejected customers and maximising profit by increasing the inventory turnover rate of grocery stores. The model was tested on a hypothetical but realistic case study of a single product. The results of this study showed that stock capacities, freshness discount rate, freshness period, and quantity discounts significantly affect the performance of a grocery store supply chain during outbreaks.     

Assessment of the sustainable development of rural minority settlements based on multidimensional data and geographical detector method: A case study in Dehong,China

In a developing country, paying attention to the sustainable development of rural areas is conducive to the development of the entire country. Ethnic minority areas are an important part of China's economic and social development. Owing to a lack of relevant statistical data, most previous studies in this area have focused on the sustainable development of rural areas or the development of ethnic minorities, but have not studied the sustainable development of rural ethnic minorities. The development of rural ethnic minorities is worthy of attention. In this study, we took Dehong as the study area. First used toponyms to accurately identify the rural minority areas and then calculated a grid of settlement density. Second, we considered the digital number (DN) value of the visible infrared imaging radiometer suite (VIIRS) as a measure of the development of the region and digital elevation model (DEM), net primary productivity (NPP), normalized difference vegetation index (NDVI), and gross domestic product (GDP) data as the indicators of terrain, climate, ecological, and economic factors, respectively. Finally, linear regression and the geographical detector method were used to determine the weight of the factors for constructing a sustainable development index (SDI) to quantitatively analyze the sustainable development and influencing factors of each minority nationality. The factors evaluated using linear regression and the geographical detector method were ranked as follows: NDVI > elevation > GDP > slope > NPP > settlement density. The results demonstrate that of the five main ethnic minorities in Dehong, Dai and Jingpo have higher SDI, followed by Achang, Lisu and De'ang. In addition, we provide some suggestions for ethnic minorities in Dehong.     

Shocks derived from mining windfalls and horizontal transfers: Exploring the permanent income hypothesis in Chilean municipalities from a spatial competition approach

We explore how spatial interaction affects the strategic use of municipal income when deciding between 1) an optimal long-run expenditure strategy versus 2) using the current income to finance current activities, a phenomenon known as the permanent income hypothesis. Even when this hypothesis is grounded in temporal logic, insufficient attention has been given to the impact of spatial dependence on this type of budget decision. Therefore, we present two reasons why spatial interaction adds new insight to this discussion. First, subnational governments located inside larger functional areas have lower average costs due to the population concentration, allowing for coordination between jurisdictions to achieve more power of negotiation and to potentially exploit economies of scale. Second, local government decision-making is not independent of other jurisdictions as municipalities would constantly evaluate the others’ actions regarding local tax effort, spending, and debt. While this spatial consideration remains a challenge for theoretical modeling, we offer empirical evidence to evaluate how robust the permanent income hypothesis is when geography is incorporated. Our empirical approach uses dynamic panel data with spatial dependence on debt, expenditure, and the error term. To evaluate our hypothesis, we exploit panel data from 320 Chilean municipalities between 2008 and 2020 and use two sources of income: non-matched grants via mining windfalls and horizontal fiscal transfers among cities. The evidence indicates that jurisdictions make backward-looking decisions regarding spending; that is, there are no significant differences between the short and long run. The results for debt, however, are not robust. Policy pertaining to the use of public resources should consider the spatial dependence between municipalities which should be a crucial factor in budgetary decision-making.     

Advancing purchasing as a design science: Publication guidelines to shift towards more relevant purchasing research

Due to rapidly changing business environments, purchasing and supply management (PSM) organisations are constantly confronted with new problems impacting organisational performance. PSM research can address these problems through design science research. Design science is also regarded as the science of the artificial. Design science research is a methodology that aims to systematically generate knowledge for the design, synthesis, testing, and evaluation of human-made artefacts (e.g., tools, interventions, policies) that solve practical problems. PSM artefacts such as the purchasing portfolio matrix invented by Kraljic (1983) represent a valuable opportunity to solve problems in the PSM discipline. However, our artificial-intelligence (AI)-based analysis of the discipline's flagship journal, the Journal of Purchasing and Supply Management (JPSM), indicates that design-oriented publications in PSM are underrepresented, accounting for less than 4% of the total publications. We argue that existing PSM research should be complemented with more design-oriented research, and address the following research question: How can PSM scholars publish more design-oriented research? Our objectives are to (1) provide arguments for advancing PSM as a design science, (2) nurture a better understanding of design science research as a methodology, and (3) propose publication guidelines that enable researchers to present design-oriented research in a management journal.     

The era of Omni-learning: Frameworks and practices of the expanded human resource development

According to the ever-changing organizational environment, we also adopt an ever-expanding HRD in contents and scope. Focusing on the drivers of the recent HRD reforms, the growing demand for organizational agility and holistic capabilities of human resources is driving the need for change, and the pandemic crisis is pushing the revolutionary changes of HRD. Such trends of the expanded HRD can be characterized as a ‘march toward Omni-learning’. In specific, there are at least four noticeable and intertwined waves of HRD reforms toward Omni-learning: (1) embracing holistic capabilities such as benchmarking, modeling, forecasting, and backcasting (BMFB); (2) integrating working and learning by promoting on-the-job learning (OJL), on-the-life learning (OLL), and on-the-life training (OLT); (3) standardizing communication tools such as LMF (logic tree; multi-dimensional matrix/map; flowchart) and EEOSP (everything/everyone on the same page); and (4) diversifying communication space-time across diverse places (close; remote) and times (synchronized; a-synchronized). And all the HRD waves are commonly facilitated and promoted by technological breakthroughs of artificial intelligence (AI) and the metaverse. Beyond the current innovations of HRD, no one would be certain about the answer to the question “What’s next?”. But what is certain is that HRD will continue to be deepened and widened as long as human resources are needed to respond to the ever-changing organizational environment.     

Performance of microfinance institutions in Europe—Does social capital matter?

This paper investigates performance drivers of microfinance suppliers in Europe. As such suppliers, in contrast to advanced microfinance suppliers in developing economies, typically focus on uncollateralized microcredit services to individuals at the margins of society and of labor markets, we draw on the theory of social capital and empirically investigate the role that social capital may play in the overall performance of European microfinance suppliers. We build a unique, unbalanced panel data set of 302 microfinance service providers in Europe covering the years 2008–2015, and measure their performance in terms of credit risk, financial and social performance, and efficiency. Pursuing an econometric approach, we test a series of hypotheses using various measures of conditions conducive to building social capital on both the institutional and the country level, such as the client base of a microfinance supplier and the level of cultural fractionalization in a society. Our findings confirm that a higher intensity of social capital is positively associated with all areas of the performance of microfinance suppliers in Europe. Our conclusions could help in the design and launch of microfinance institutions in those European countries in which microfinance markets are developed not at all or only to a very limited extent. Our paper thus contributes to the nascent literature on microfinance in developed economies by applying and extending the theoretical framework and empirical models on social capital and microfinance that were originally elaborated for developing economies.     

Orchestrating distributed data governance in open social innovation

Open Social Innovation (OSI) involves the collaboration of multiple stakeholders to generate ideas, and develop and scale solutions to make progress on societal challenges. In an OSI project, stakeholders share data and information, utilize it to better understand a problem, and combine data with digital technologies to create digitally-enabled solutions. Consequently, data governance is essential for orchestrating an OSI project to facilitate the coordination of innovation. Because OSI brings multiple stakeholders together, and each stakeholder participates voluntarily, data governance in OSI has a distributed nature. In this essay we put forward a framework consisting of three dimensions allowing an inquiry into the effectiveness of such distributed data governance: (1) openness (i.e., freely sharing data and information), (2) accountability (i.e., willingness to be held responsible and provide justifications for one's conduct) and (3) power (i.e., resourceful actors' ability to impact other stakeholder's actions). We apply this framework to reflect on the OSI project #WirVsVirus (“We versus virus” in English), to illustrate the challenges in organizing effective distributed data governance, and derive implications for research and practice.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

Individual stock sentiment beta and stock returns

Using the data in Chinese stock market, we measure the individual stock sentiment beta, which is defined as the sensitivity of individual stock returns to the individual stock sentiment changes. We demonstrate that stocks in the highest individual stock sentiment beta portfolio have significantly higher excess returns, CAPM alpha, Fama-French three-factor alpha and Fama-French five-factor alpha. Besides, we find that the high individual stock sentiment beta stocks are smaller, younger, more volatile stocks with higher price and higher market beta. After controlling for firm characteristic, the returns of High-Low individual stock sentiment beta portfolios are still significantly positive. Moreover, we show the effect of the individual stock sentiment beta on stock returns is positive and significant in different stock markets, in different sample periods, and in bull and bear market. Besides, the results of the Bayes-Stein individual stock sentiment beta are still stable.     

Intraday Volatility Spillover between the Shanghai and Hong Kong Stock Markets—Evidence from A+H Shares after the Launch of the Shanghai-Hong Kong Stock Connect

Using minute data of eligible A+H stocks under the Shanghai-Hong Kong Stock Connect (SHHKSC), we investigate the volatility spillover between the Shanghai and Hong Kong stock markets based on a generalized autoregressive conditional heteroskedasticity-X (GARCH-X) model with four exogenous variables, namely, volatilities of the corresponding stocks on the other market, volatilities of the indexes of both stock markets, and volatilities of the correlated stocks, which are selected using the dynamic conditional correlation model and bootstrap approach. Results show that after the launch of the SHHKSC, volatility spillovers are significant in both directions almost all the time, and the volatility spillover between the two stock markets tends to be larger when bidirectional capital flows under the SHHKSC increase or when important financial events occur. We also analyze the influences of the volatilities of correlated stocks and industries on the volatility spillover and volatilities of A+H stocks. The bidirectional volatility spillovers between Shanghai and Hong Kong stock markets do not change qualitatively after incorporating the volatilities of correlated stocks and industries in the GARCH-X model. Moreover, the average volatilities of the correlated stocks are shown to have significant influences on the volatilities of individual A+H stocks, and the influences increase when the local stock market shows a sharp rise or fall. Compared with the market indexes, the correlated stocks could be regarded as a more important and indispensable factor for individual A+H stocks’ volatilities modeling, which may carry more information than the industry.     

Evaluating mutual funds on a small market: is benchmark selection crucial?

This paper focuses on mutual fund performance analysis on a small market during a turbulent period. Firstly, we address the question of proper measures and benchmarks. A potentially critical issue on a small market concerns large market weights for individual stocks, which in combination with mutual fund legislation may prevent funds from following the index. Secondly, our investigation period was characterized by persistent bull and bear markets, which makes it interesting to investigate whether successful market timing was possible. Different measures of performance are compared and benchmark sensitivity is analysed with the help of various market timing and multiple index models, including benchmarks such as the FOX and HEX indices, a small-firm index, and a bond index. Contrary to many studies that record great benchmark sensitivity, we find markedly similar ranking for different benchmarks. Performance measures are also related to certain fund characteristics such as fund expenses, and fund size. We find that fund characteristics (especially fund expenses) are significantly related to fund performance, a relationship that may be used to increase the power of tests of fund performance.     

Investor co-attention and stock return co-movement: Evidence from China’s A-share stock market

This study attempts to link investor co-attention to stock return co-movement in China's A-share stock market. On the one hand, stock price will co-move for stocks within the same industry and within the same market, which is labelled “return co-movement”. On the other hand, investor attention will also co-move as investors systematically search for relevant information for stocks of similar characteristics or as the stocks experience common information shocks, which is termed “investor co-attention”. The empirical evidence suggests that stock return co-movement can be explained by investor co-attention to a great extent, even after controlling for stock fundamentals and firm characteristics, and this effect is more salient for stocks with lower institutional ownership. Moreover, we employ large national lottery jackpots as exogenous shocks to investor attention. The empirical findings show that the co-movement of both investor attention and stock return increase on large lottery jackpot days, while investor co-attention contributes less to return co-movement on large lottery jackpot days. In summary, we offer an alternative explanation for return co-movement by observing the causal relationship between investor co-attention and stock return co-movement.     

Dynamic and asymmetric effects between carbon emission trading,financial uncertainties,and Chinese industry stocks: Evidence from quantile-on-quantile and causality-in-quantiles analysis

This paper investigates the dynamic and asymmetric effects between carbon emission trading (CET), financial uncertainties, and Chinese stocks in different industries over the period from 19th December 2013 to 21st March 2022. We utilized a novel quantile framework including rolling window quantile regression method, quantile-on-quantile method, and causality-in-quantiles method to implement this research more comprehensively and accurately. Our contributions and findings, empirical in nature, are as follows: (i) In the early establishing stage of the carbon market, with a bullish market situation, carbon emission trading has a negative impact on most industry stocks. In the developing and improving stage of the carbon market, different industries have different impact situations. (ii) We find that the effects of financial uncertainty on stocks are stronger than CET on stocks. We also find that the dependence structures between CET, financial uncertainty, and industry stocks are asymmetric in most industries, and there are many mutation structures with significant risks in extreme situations. (iii) Carbon emissions trading, crude oil volatility, and US stock volatility all have strong causal relationships with Chinese industry stocks. (iv) We also provide policy suggestions to relevant countries to balance carbon market and stock markets and avoid risks from financial uncertainty in different industries.     

The stock–bond comovements and cross-market trading

We propose an asset pricing model with heterogeneous agents allocating capital to the stock and bond markets to optimize their portfolios, utilizing the dynamic interaction between the two markets. While some agents focus on the stock market and have more expertise in it, the others specialize in the bond market. Based on their comparative advantages in a particular market, heterogeneous agents constantly revise their investment portfolios by taking into account the time-varying stock–bond return comovements and the changing market conditions. Agents׳ collective investment behavior shapes the stock–bond interlinkage, which feedbacks on their subsequent capital allocations. Using monthly US stock and bond data from January 1990 to June 2014, we estimate the vector autoregression model with threshold and Markov switching mechanisms. We find evidence in support of flight-to-quality and show that it is mainly driven by the technical traders who actively sell stocks and buy bonds during periods of high market uncertainty.     

The relationship between oil prices,the stock market and the exchange rate: Evidence from Mexico

This paper analyzes the variables of oil price, exchange rate and stock market index to explain how they interact with each other in the Mexican economy. The examined period includes monthly data from January 1992 to June 2017. A Vector Autoregressive Model (VAR) is implemented that includes oil prices, the nominal exchange rate, the Mexican stock market index, and the consumer price index. Results indicate that the exchange rate has a negative and statistically significant effect on the stock market index; this indicates that an appreciation of the exchange rate is related to an increase in the stock market index. It is also found that the consumer price index has a positive effect on the exchange rate and a negative effect on the stock market index. The results also indicate that oil prices are statistically significant against the exchange rate, concluding that an increase in oil prices creates an appreciation of the exchange rate. In addition, the impulse-response functions show that the effects found tend to disappear over time.     

Hedge funds in portfolios of risk-averse investors

We find that adding a hedge fund to an optimally weighted portfolio of stocks and T-bills generally increases the utility of an investor. From a sample of hedge funds with returns from 1996 to 2005, the certainty equivalent was an average of five basis points (monthly) higher with a ten percent allocation into a hedge fund. Funds from different style categories require different allocations into the stock market, but nearly all funds improved performance. Contrary to popular opinion, we find that highly risk-averse investors gain even more than less risk-averse investors by adding a hedge fund into their portfolio.     

上海A股市场股价波动相互影响能力实证研究

股票市场中股票价格的波动是相互影响的,但不同的股票其价格波动时对其他股票价格行为的影响能力是不同的。本文通过对我国上海A股市场的实证分析表明,有少数股票其价格波动时对其他股票价格行为有很强的影响能力,而大多数股票的这种影响能力很小。此外,单个股票价格波动时引起其他股票价格涨、跌的能力一般都有显著差异。     

股票指数期货：工具与监管

本文从股票指数期货在金融交易中发挥的作用、股票指数期货交易的开设对股票市场波动性的影响、以及我国当前的股票市场交易制度与监管三个层面上对开设股票指数期货交易进行分析研究,并指出:就股票指数期货作为一种金融衍生工具而言,我国应该及早引入股票指数期货,以提高股票市场的运作效率。同时考虑到我国经济正在进行深层次的结构性调整,市场交易制度有其自身的特点,当前更应着重加强资本市场的制度建设,提高监管力度,为早日开设股票指数期货交易创造条件。