Search results

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.

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。

This paper aims to develop and present a new planning framework of social marketing, known as consumer research, segmentation, design of the social programme, implementation, evaluation and sustainability (CSD-IES).

The proposed framework is based on recent theoretical developments in social marketing and is informed by the key strengths of existing social marketing planning approaches.

The CSD-IES planning framework incorporates emerging principles of social marketing. For example, sustainability in changed behaviour, ethical considerations in designing social marketing programmes, the need for continuous research to understand the changing needs of the priority audience during the programme and the need for explicit feedback mechanisms.

The CSD-IES framework is a dynamic and flexible framework that guides social marketers, other practitioners and researchers to develop, implement and evaluate effective and sustainable social marketing programmes to influence or change specific behaviours based on available resources.

This paper makes an important contribution to social marketing theory and practice by integrating elements of behaviour maintenance, consideration of ethical perspectives and continuous feedback mechanisms in developing the CSD-IES framework, bringing it in line with the global consensus definition of social marketing.

Wicked problems are causally complex, lack definite solutions, and re-emerge in different guises. This paper discusses how new ways of organizing emerge to tackle changing manifestations of wicked problems. Focusing on the wicked problem of poverty, we conducted a longitudinal study of Fe y Alegria (FyA), one of the world’s largest non-governmental organization, which provides education for the poor across 21 countries in Latin America and Africa. Drawing on archival and ethnographic data, we trace the historical narratives of how FyA defined poverty as a problem and developed new ways of organizing, from its foundation by a Jesuit priest in 1955 to its current networked structure. Our findings reveal the ongoing cycle of interpretive problem definition and organizing solutions for wicked problems. First, since there is no “true” formulation of a wicked problem, actors construct narrative explanations based on their understanding of the problem. Second, organizational solutions to a wicked problem are thus reflections of these narrative constructions. Third, emerging and changing narratives about what the problem is inspire new organizational responses. Our findings provide insights into the dynamic relationship between organizing for wicked problems, narratives, and the changing manifestations of wicked problems and grand challenges more broadly.

The purpose of this paper is to offer a response to expressions in the literature concerning the limitations of critical reflection, using Rancière’s exposition of the role of values and reasonableness to examine how forms of negotiated work-based learning can support learners’ pathways to impact in their organisation. The implications for work applied management in terms of enabling these employees to make an impact are considered.

Vignettes illuminate and articulate Rancière’s (1991, 2010) ideas, the vignettes constructed through events experienced and narrated, perhaps imagined, tutorial conversations, assignments and work practices. Such construction of “multiple layers of fiction and narrative imaginings” draws on Sparkes (2007, p. 522). They consider individuals’ negotiation of working practices using ideas developed during their studies, and personal and professional development prompted by unexpected insights into their capabilities, interests, and possible roles.

Negotiated work-based learning appears to offer the individual opportunity to take responsibility for action in his/her learning and in his/her workplace, but effect depends on several factors, and can be perceived in different ways. Students’ encounter with autonomy in their studies resonates with Rancière’s belief in equality. In the workplace (becoming “citizens” alongside “reasonable” individuals) their agency might, at best, lead to “reasonable moments”, as they encounter both negative and positive challenges of work applied management.

Successful utilisation of agency in learning prompts expectations of responsibility and equality in the workplace. Such equality can lead to diverse, unpredicted insights and consequent opportunities for changes in practice.

This is the first paper to utilise Ranciére’s ideas to offer a critical consideration of both learning provision and workplace practice. Consideration of his profound stance on individuals’ freedom and agency provides rich (but challenging) prompts for analysis of one’s own practice, and the potential for impact when the manager is “ignorant”.

This study examines the effect of stock liquidity on the magnitude of the accrual anomaly.

This paper examines the relation—both time-series and cross-sectional—between stock liquidity and the magnitude of the accrual anomaly and use the 2001 minimum tick size decimalization as a quasi-experiment to establish causality.

There is both cross-sectional and time-series evidence that stock liquidity is negatively related to the magnitude of the accrual anomaly. Moreover, the extent to which investors overestimate the persistence of accruals decreases with stock liquidity. Results from a difference-in-differences analysis conducted using the 2001 minimum tick size decimalization as a quasi-experiment suggest that the effect of stock liquidity on the accrual anomaly is causal. The findings of this study are consistent with the enhancing effect of stock liquidity on pricing efficiency.

The study's findings are well aligned with the mispricing-based explanation for the accrual anomaly, suggesting that the improvement in market-wide stock liquidity drives the contemporaneous decline in the magnitude of the accrual anomaly, at least to a great extent.

The literature on ‘mixed’ families (in which members are socially viewed as ‘different’ due to their varying ethnicities and/or nationalities) identifies several stakes of mixedness. One of them arises from childbirth, after which parents need to give name(s) to their offspring. How does the parent–child dyad understand the giving of names in their mixed family? What does naming children unveil regarding interpersonal interactions and the value of children within this social unit? The chapter delves into these questions through a case study of forenaming children in Filipino-Belgian families in Belgium. Interview data analysis reveals two modes of forenaming in these families: individualisation through single forenames and reinforcement of collective affiliation through compound forenames. Through the analytical framework of social relatedness, this chapter uncovers the way the act of naming a child bridges families based on biological and social ties, generations, and parents' nations of belonging in their transnational spaces. The complex process of naming reflects the power dynamics not only within the parental couple but also within the wider set of social relations. Although the use of forename(s) in everyday life and in legal terms differ, the value of children in the mixed families studied lies in their symbolic role as social bridges linking generations and non-biological relationships, the then and now, and the here and there.

To comply with the adopted Paris Agreement, global finance flows must be measured against climate scenarios consistent with possible pathways towards limiting global warming to 2°C or less. For this, there must be proven and accepted accounting principles for assessing financial plans of climate relevant actors against climate models. As there are a variety of data sources describing the financial plans of relevant actors, these principles must accommodate a variety of reported information, while still yielding relevant metrics to different stakeholders. The paper aims to discuss these issues.

A set of accounting principles tested by governments, financial supervisory bodies and both institutional investors and mangers, covering global-listed equity and corporate bond investment is described.

The application illustrates that a common set of accounting principles can act across both asset classes and provide relevant metrics to multiple stakeholders.

The principles require data of varying quality and are ultimately unverified. Thus, the definitive quality of the output metrics is uncertain and is yet to be characterized. The principles are yet to be applied to the credit market as the information is seldom publicly available, but it too plays an important role in the required market transition and therefore must be incorporated into these guiding principles of analysis.

The principles allow for standardised assessment of financial flows of equity and corporate debt with global climate scenarios.

It illustrates the acceptance of a common set of accounting principles that is relevant across different actors and asset classes and summarizes the principles underlying the first climate finance scenario analyses.

The retention and success of engineering undergraduates are increasing concern for higher-education institutions. The study of success determinants are initial steps in any remedial initiative targeted to enhance student success and prevent any immature withdrawals. This study provides a comprehensive approach toward the prediction of student academic performance through the lens of the knowledge, attitudes and behavioral skills (KAB) model. The purpose of this paper is to aim to improve the modeling accuracy of students’ performance by introducing two methodologies based on variable selection and dimensionality reduction.

The performance of the proposed methodologies was evaluated using a real data set of ten critical-to-success factors on both attitude and skill-related behaviors of 320 first-year students. The study used two models. In the first model, exploratory factor analysis is used. The second model uses regression model selection. Ridge regression is used as a second step in each model. The efficiency of each model is discussed in the Results section of this paper.

The two methods were powerful in providing small mean-squared errors and hence, in improving the prediction of student performance. The results show that the quality of both methods is sensitive to the size of the reduced model and to the magnitude of the penalization parameter.

First, the survey could have been conducted in two parts; students needed more time than expected to complete it. Second, if the study is to be carried out for second-year students, grades of general engineering courses can be included in the model for better estimation of students’ grade point averages. Third, the study only applies to first-year and second-year students because factors covered are those that are essential for students’ survival through the first few years of study.

The study proposes that vulnerable students could be identified as early as possible in the academic year. These students could be encouraged to engage more in their learning process. Carrying out such measurement at the beginning of the college year can provide professional and college administration with valuable insight on students perception of their own skills and attitudes toward engineering.

This study employs the KAB model as a comprehensive approach to the study of success predictors. The implementation of two new methodologies to improve the prediction accuracy of student success.