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The challenge of predicting changes in aggregate income and stock prices is one that has occupied the research agendas of economists. This paper aims to use the consumption–income ratio and the dividend–price ratio to predict future income and stock prices.

To examine the stability of the consumption–income ratio and the dividend–price ratio, the authors run a two-variable, two-lag reduced-form VAR in the vein of Cochrane (1994), using a lag of each respective ratio as exogenous to the VAR. Additionally, the authors estimate an AR(4) model for income and prices.

The consumption–income ratio and the dividend–price ratio remain key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. The dividend–price ratio does not significantly predict future price growth. Consumption and dividend shocks have lasting impacts on income and prices.

The consumption–income ratio and the dividend–price ratio are still key to understanding future movements in income and stock prices. The consumption–income ratio significantly predicts future income in the USA, and aggregate income is easier to predict than consumption in the VAR model. However, the dividend–price ratio does not significantly predict future price growth, a change from previous research from the 1990s, despite the increasing complexity of stock markets. Consumption and dividend shocks have lasting impacts on income and prices and appear to be significant drivers in both the short- and long-run variance in income and prices.

The onset of the COVID-19 pandemic certainly exacerbated the teacher shortage in the United States for all racial/ethnic groups, but especially for Black teachers. Black teachers account for 7–8% of the total teacher population and this percentage is the direct result of decades of systemic and structural barriers set against Black teachers in the form of racism. Still, Black teachers who enter the profession do so with the willingness to support all students and uplift Black students who often go years without seeing a teacher that looks like them. Black teachers often face different expectations than their white counterparts and these expectations, without the proper support, lead to Black teachers burning out at higher rates. In an effort to understand Black teachers' and the experiences that contribute them remaining in the classroom, the researchers explored Black teachers' working conditions through a phenomenological approach. The findings of this study suggest that Black teachers deserve working conditions that nurture who they are culturally and professionally, that reject actions of oppression toward them – both implicitly and explicitly, and offer spaces for Black teachers to be authentically heard.

The Arsenale was the largest medieval industrial enterprise, famous for its assembly line. Management faced extreme variations between peace-time and war-time demands. Satisfying these unpredictable and sudden demands for a large, complex product with a multiple years–long production cycle was challenging. The purpose of this study is to analyze the Arsenale’s operations and supply chain arrangements, and to identify and assess their management policies. We also track its development and investigate its influence on other countries.

The research methodology used is archival research with content analysis of text and graphic representations of production processes.

These reveal that Venice’s supply chain management evolved from simply exploiting woodlands as needs arose, to a managed forest with planned planting, cultivation and harvesting, ending with the active modification of growing trees so their natural growth was artificially shaped to satisfy production requirements. Instead of fabricating components in their factory, the Venetians formed them by shaping the trees while they were still growing. These arboriculture techniques then provided a planned and regular supply of high-quality components that purely natural processes provided only randomly.

There may be undiscovered archival documents despite the authors’ best efforts. The development of this historic supply chain reflects modern managerial concerns.

Modern restorations of historic ships and buildings use some of the fabrication methods identified, although the more intensive techniques would require higher volume production.

This reveals historical forestry practices emphasised long-term needs and sustainable use.

To the best of the authors’ knowledge, this is a unique long-term investigation of an integrated production system and considers its influence on Iberian, French, British and American forestry and ship building. The close integration of production requirements with forestry practices was a novel finding.

The purpose of this study is to examine the impact of a recent innovation in the uranium market, the Global X Uranium Exchange-Traded Fund (URA), on the trading characteristics of constituent and non-constituent stocks.

The authors analyse bid-ask spread measures, relative effective spreads and adverse selection costs to assess changes in information asymmetry among uranium stocks. The authors also study abnormal returns to assess the impact of URA on the market.

Over a three-month period, following the introduction of URA, the authors find uranium stocks display decreased bid-ask spread measures, driven by reductions in information asymmetry. Relative effective spreads decrease by 36% after the introduction of URA, and adverse selection costs decline by 24% over the same period. Uranium stocks experience a significant positive abnormal return of 5.0% the day after the introduction of URA with subsequent price reversals. These suggest that the introduction of URA prompted uninformed traders to rebalance portfolios and migrate to the less information-sensitive Exchange-Traded Fund (ETF), causing temporary deviations in trading characteristics.

The authors demonstrate that the introduction of new financial securities to the market can have a significant impact on the trading characteristics of related equities. As URA is the only ETF in the uranium sector, the authors thereby avoid the influence of multiple ETFs that may have impacted previous studies.

Critics say cryptocurrencies are hard to predict and lack both economic value and accounting standards, while supporters argue they are revolutionary financial technology and a new asset class. This study aims to help accounting and financial modelers compare cryptocurrencies with other asset classes (such as gold, stocks and bond markets) and develop cryptocurrency forecast models.

Daily data from 12/31/2013 to 08/01/2020 (including the COVID-19 pandemic period) for the top six cryptocurrencies that constitute 80% of the market are used. Cryptocurrency price, return and volatility are forecasted using five traditional econometric techniques: pooled ordinary least squares (OLS) regression, fixed-effect model (FEM), random-effect model (REM), panel vector error correction model (VECM) and generalized autoregressive conditional heteroskedasticity (GARCH). Fama and French's five-factor analysis, a frequently used method to study stock returns, is conducted on cryptocurrency returns in a panel-data setting. Finally, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to a portfolio makes a difference.

The seven findings in this analysis are summarized as follows: (1) VECM produces the best out-of-sample price forecast of cryptocurrency prices; (2) cryptocurrencies are unlike cash for accounting purposes as they are very volatile: the standard deviations of daily returns are several times larger than those of the other financial assets; (3) cryptocurrencies are not a substitute for gold as a safe-haven asset; (4) the five most significant determinants of cryptocurrency daily returns are emerging markets stock index, S&P 500 stock index, return on gold, volatility of daily returns and the volatility index (VIX); (5) their return volatility is persistent and can be forecasted using the GARCH model; (6) in a portfolio setting, cryptocurrencies exhibit negative alpha, high beta, similar to small and growth stocks and (7) a cryptocurrency portfolio offers more portfolio choices for investors and resembles a levered portfolio.

One of the tasks of the financial econometrics profession is building pro forma models that meet accounting standards and satisfy auditors. This paper undertook such activity by deploying traditional financial econometric methods and applying them to an emerging cryptocurrency asset class.

This paper attempts to contribute to the existing academic literature in three ways: Pro forma models for price forecasting: five established traditional econometric techniques (as opposed to novel methods) are deployed to forecast prices; Cryptocurrency as a group: instead of analyzing one currency at a time and running the risk of missing out on cross-sectional effects (as done by most other researchers), the top-six cryptocurrencies constitute 80% of the market, are analyzed together as a group using panel-data methods; Cryptocurrencies as financial assets in a portfolio: To understand the linkages between cryptocurrencies and traditional portfolio characteristics, an efficient frontier is produced with and without cryptocurrencies to see how adding cryptocurrencies to an investment portfolio makes a difference.

Invented in late 1890s, asbestos cement sheeting rose to prominence during the post-Second World War period as a building material for low-cost housing by state housing commissions and low-income families (“fibro homes”). The adverse health effects of asbestos fibres in the building industry and home renovation activities are well documented. Fibro homes of the 1950s and 1960s are increasingly coming under the gaze of heritage studies, which brings to the fore the question of how to deal with the asbestos cement sheeting most are clad with.

This paper provides the first systematic review to assess the literature (126 papers were identified in Google Scholar and scanned for content) on the conservation management of asbestos cement sheeting in heritage properties.

Overall, engagement with the conservation management of asbestos cement sheeting in heritage properties was low, with only two sources dealing with asbestos cement sheeting in any level of detail. The studies note that if asbestos cement sheeting is in good condition, it should be left alone. Numerous conservation and repair options do exist, in particular the application of (coloured) sealants that extend the life of asbestos cement sheets and asbestos cement roofing.

This paper represents the first systematic review to assess conservation management options for asbestos cement sheeting in heritage properties.

Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally intelligent and an emotionally unintelligent CEO, especially during a crisis? This chapter aims to answer this question.

40 CEOs of large corporations were asked to deliver a verbal address to their board members in reaction to a vignette describing a critical situation for the company. Participants were provided with the Schutte self-report emotional intelligence (EI) test. The verbal content of CEOs' closed-door discourses was analyzed using Cognitive-Discursive Analysis (CDA) and, subsequently, Geometric Data Analysis (GDA).

The results revealed that CEOs with low EI tend to evoke unpleasant emotions, talk about competition, and often blame some – or all – of the board members for their (poor) actions in comparison to CEOs with high or medium EI. In contrast, CEOs with high EI tend to use terms in relation to decision or realization and appear to be more cooperative than those with lower EI and were also ready to make decisions on behalf of team.

Previous research has mainly focused on CEOs' public speeches. But the content of CEOs' speeches within the boardroom might noticeably differ from what they would say in a public address. The results of our exploratory study can serve CEOs as a basis toward improving their closed-door rhetoric during a crisis.

It would be interesting to enlarge the size of our population in order to strengthen our statistical analyses as well as explore other cultural and linguistic environments and other channels through which emotions can be expressed (e.g., human face, gesture, vocal tone).

In this introduction to the special issue, ‘Marxist Thought in South Asia’, we detail the long history of Marxist politics and theorizing in South Asia and highlight the unique contributions and perspectives of South Asian Marxists to global Marxism. Three contributions we find particularly significant are (1) South Asian Marxists' approach to thinking about questions of capitalism, colonialism and imperialism, (2) the treatment of agrarian and feudal continuities in Marxist theories from South Asia and (3) unique South Asian contributions to theorizing caste from a Marxist perspective.

Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination.

This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables.

The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism.

This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.

2019冠狀病毒病引致消費者習慣有根本的改變; 這些改變顯示，根據歷史序列而運作的慣常需求預測技巧未必是正確的。這不確性尤以受到大流行極大影響的酒店服務需求為甚。因此，我們擬探討、若把在推特網站上的旅遊活動視為聖雅各之路 (一個重要的朝聖旅遊聖地) 酒店服務需求的預測器，這會否是合適的呢?

本研究比較 SARIMA 時間序列模型與附有外生變數 (SARIMAX)模型兩者在預測旅遊及酒店服務需求方面的表現。為此，研究人員收集在推特網站上發佈的資訊，作為外生變數進行研究。這個樣本涵蓋於2018年1月至2022年9月期間110,456個發佈資訊。

研究結果確認了傳統的時間序列模型，若涵蓋推特網站上的旅遊活動，則其對旅遊需求方面的預測會得到顯著的改善。推特網站的數據，就改善預測實時旅遊需求的準確度，或許可成為有效的工具; 而這發現對旅遊管理會有一定的意義。本研究亦讓我們進一步瞭解朝聖旅遊方面旅客的數碼足跡。

現存文獻甚少探討朝聖旅遊的數字化，而本研究不但在這方面充實了有關的文獻，還使用了一個根據推特網站上使用者原創內容嶄新的方法框架，進行分析和探討。這會幫助酒店從業人員把社交媒體數據轉變為可供酒店管理之用的合宜資訊。