Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

Uncertainty,story-telling and transformative learning: An instructor’s experience of TEFI’s Walking Workshop in Nepal

ABSTRACT

This article recounts three stories from TEFI’s walking workshop in Nepal: the construction of a road through what was once a trekking path; a dance-floor encounter at a Himalayan party; and the arrival of one participant, fatigued by jet lag and disoriented by the new surroundings. These stories of confusion, discomfort and fear are linked by one common theme: the potential of uncertainty to foster deep reflection and nuanced conclusions. The premise that uncertainty is to be valued and even cultivated has been explored in educational theory, spiritual traditions, and research on transformative learning. These sources affirm the role of uncertainty in the process of knowledge creation. However, accepting this role can be challenging for educators because it requires they assume a new identity, one which they may perceive as being at odds with their status as “teacher” the identity of learner. One way for the educator to address this challenge may be through recounting their own stories of uncertainty. Stories of being immersed in unfamiliar situations that challenge, confuse and even frighten – stories, in other words of being a tourist – can foster reflection on an intellectual, emotional and spiritual level, engaging the “whole” person, and thus initiating the educator/learner’s transformative journey.     

组织间学习与绿色创新:绿色吸收能力的调节作用

尽管组织间学习已经获得较多关注,但鲜有研究探讨它对绿色创新的影响。从组织间关系和双元性理论视角,提出了组织间的纵向利用式学习和横向探索式学习,基于组织学习理论和吸收能力理论,探究了两种组织间学习方式与绿色创新的关系,并检验了绿色吸收能力的调节作用。基于203家中国制造企业的调查数据,实证结果表明:纵向利用式学习和横向探索式学习都能显著正向影响绿色创新;相比横向探索式学习,纵向利用式学习对绿色创新的影响作用更强。绿色吸收能力在横向探索式学习与绿色创新之间也起调节作用,但它并不调节纵向利用式学习与绿色创新的关系。研究提出了不同的组织间学习方法与绿色创新的关系,丰富并拓展了绿色创新的相关研究。     

An explainable machine learning approach to improve take-off time predictions

Accurate aircraft trajectory predictions are necessary to compute exact traffic demand figures, which are crucial for an efficient and effective air traffic flow and capacity management. At present, the uncertainty of the take-off time is one of the major contributions to the loss of trajectory predictability. In the EUROCONTROL Maastricht Upper Area Control Centre, the predicted take-off time for each individual flight relies on the information received from the Enhanced Traffic Flow Management System. However, aircraft do not always take-off at the times reported by this system due to several factors, which effects and interactions are too complex to be expressed with hard-coded rules. Previous work proposed a machine learning model that, based on historical data, was able to predict the take-off time of individual flights from a set of input features that effectively captures some of these elements. The model demonstrated to reduce by 30% the take-off time prediction errors of the current system one hour before the time that flight is scheduled to depart from the parking position. This paper presents an extension of the model, which overcomes this look-ahead time constraint and allows to improve take-off time predictions as early as the initial flight plan is received. In addition, a subset of the original set of input features has been meticulously selected to facilitate the implementation of the solution in an operational air traffic flow and capacity management system, while minimising the loss of predictive power. Finally, the importance and interactions of the input features are thoroughly analysed with additive feature attribution methods.     

Factors affecting classroom participation and how participation leads to a better learning

The purpose of this paper is twofold. First, we analyze various course- and student-related factors that affect participation grades within several college accounting classes. The second purpose is to determine whether in-class participation grades have an effect on exam performance in these classes. We find that, unlike in liberal arts and sciences courses, in accounting courses, participation grades for female students do not differ from participation grades for their male counterparts, regardless of class level. Overall, course level and student major have no direct effect on participation grades; however, the accounting majors and students in the lower level courses have better attendance than do non-accounting majors and students in more advanced courses. We observe that class schedule, in particular class duration and semester, affects students’ participation performance. Lastly, we find that students who participate more in classroom discussions perform 25% better on exams than do those with lower participation grades. This relationship holds for the three elements of participation grading: frequency of participation, consistency of participation, and attendance.     

Firm-specific assets and the internationalization–performance relationship in the U.S. movie studio industry

We study the role that firm-specific assets (FSAs) play in the processes underlying the internationalization–performance relationship. International business scholars have begun studying the interrelationship between FSAs, internationalization, and performance; however, this research is still emergent, and has produced inconclusive results. We believe that this may be due, in part, to research designs involving the same FSAs across many industries, even though individual industries may rely on different FSAs in their internationalization strategies. We address this issue in a single industry study of U.S. movie studios, which typically rely on blockbuster production capabilities as FSAs in their internationalization efforts. We show how these FSAs co-evolve with firms’ degree of internationalization, and how each factor mediates the positive effect of the other on performance. Our results highlight the importance of studying specific industries and their salient FSAs when assessing their role of such resources in the internationalization–performance relationship.     

Is brain hemisphericity a correlate of performance in problem-solving courses? A case study from an HBCU

The authors set out to determine the brain dominance characteristics of students enrolled in business statistics courses in a historically Black university in a major southeastern Texas city. Thereafter, the authors investigated the relationship between a student’s brain hemispheric preference and academic performance in college courses, which emphasize problem solving. The results reported in this study reveal that left brain– and right brain–dominant students are at parity when it comes to performance in a problem-solving course. Moreover, both left brain– and right brain–dominant learners perform better than whole-brain dominant learners in a problem-solving course.     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.