Performance assessment of ensemble learning systems in financial data classification

Financial data classification plays an important role in investment and banking industry with the purpose to control default risk, improve cash and select the best customers. Ensemble learning and classification systems are becoming gradually more applied to classify financial data where outputs from different classification systems are combined. The objective of this research is to assess the relative performance of existing state‐of‐the‐art ensemble learning and classification systems with applications to corporate bankruptcy prediction and credit scoring. The considered ensemble systems include AdaBoost, LogitBoost, RUSBoost, subspace, and bagging ensemble system. The experimental results from three datasets: one is composed of quantitative attributes, one encompasses qualitative data, and another one combines both quantitative and qualitative attributes. By using ten‐fold cross‐validation method, the experimental results show that AdaBoost is effective in terms of low classification error, limited complexity, and short time processing of the data. In addition, the experimental results show that ensemble classification systems outperform existing models that were recently validated on the same databases. Therefore, ensemble classification system can be employed to increase the reliability and consistency of financial data classification task.     

A neural network based price sensitive recommender model to predict customer choices based on price effect

The impact of price and price changes should not be ignored while designing algorithms for predicting customer choice. Consumer preferences should be modeled with consideration of price effects. Businesses need to consider for efficient prediction of an individual's purchase behaviour. Personalized recommendation systems have been studied with machine learning algorithms. However, the price-aware personalized recommendation has received little attention. In this paper, we attempt to capture insightful economic results considered in the marketing and economics disciplines by employing modern machine learning architecture for predicting customer choice in a large-scale supermarket context. We extract personalized price sensitivities and examine their importance in consumer behaviour. The employed data collected from a supermarket chain in Germany consists of implicit feedback based on customer-product interactions and the price of every interaction. We propose a two-pathway matrix factorization (2way-MF) model that is price-aware and tries to memorize customer-product interaction's implicit feedback. The proposed models achieve better model performance than standard Matrix Factorization models widely used in the industry. The approach was re-validated with data from supermarket chain in Taiwan. Other industries can adopt the proposed framework of modeling customer's preferences based on price sensitivity. We suggest that further research and analyses could help understand the cross-price elasticities.     

个体主动性人格对学习能力影响的实证研究

探讨了员工个体的主动性人格特质对其学习能力的影响及其作用机制,以及员工的工作卷入和反馈寻求在这一关系中的中介作用。运用企业236个员工数据进行分析,得出以下结论:企业员工的主动性人格显著促进其学习能力;员工的主动性人格显著强化其工作卷入和反馈寻求;员工对内的工作卷入促进了其学习能力,且在主动性人格和学习能力的关系中起部分中介的作用;员工对外的反馈寻求促进其学习能力,且在主动性人格和学习能力的关系中起部分中介作用。研究结论为组织个人学习能力的提升提供了一个重要的前因变量,并对员工工作卷入与反馈寻求的中介作用机制进行了探讨,丰富了个人学习研究,并提出了相关的实践建议。     

Does online observational learning matter? Empirical evidence from panel data

The information presented on a product sales page plays an important role in consumers' purchase decisions. This study examines the persuasive effect, whether a customer's choice is heavily driven by information inferred from others' behavior, and how these impacts are moderated by market age and product type. Results show that online customer choice was significantly affected by historical cumulative sales and times saved. Positive cumulative sales and shop service quality have a significant positive impact on product sales. The times saved have no direct impact on product sales. For different types of products, the times saved of experiential products has a significant impact on product sales, while the shop service quality information of search products has a greater impact on product sales. The influence of online observation learning on product sales will be significantly moderated by a combination of product type and market age. These findings not only offer important theoretical contributions to e-commerce research but also provide practical implications for online sellers and managers of social commerce platforms.     

The role of text-extracted investor sentiment in Chinese stock price prediction with the enhancement of deep learning

Whether investor sentiment affects stock prices is an issue of long-standing interest for economists. We conduct a comprehensive study of the predictability of investor sentiment, which is measured directly by extracting expectations from online user-generated content (UGC) on the stock message board of Eastmoney.com in the Chinese stock market. We consider the influential factors in prediction, including the selections of different text classification algorithms, price forecasting models, time horizons, and information update schemes. Using comparisons of the long short-term memory (LSTM) model, logistic regression, support vector machine, and Naïve Bayes model, the results show that daily investor sentiment contains predictive information only for open prices, while the hourly sentiment has two hours of leading predictability for closing prices. Investors do update their expectations during trading hours. Moreover, our results reveal that advanced models, such as LSTM, can provide more predictive power with investor sentiment only if the inputs of a model contain predictive information.     

Pains and gains of environmental management system certification for the sustainable development of manufacturing companies: Heterogeneous effects of industry peer learning

Environmental sustainability is a growing global concern. Environmental management systems (EMS) could be an effective strategic tool to help firms deal with their sustainable development. However, whether EMS certification pays off financially and how it takes effect can be debated. Thus far, these questions remain largely under‐researched. In particular, the effects of EMS certification on financial performance are inconclusive, and the reasons explaining the effects are underdeveloped. This study aims to enrich the current research by exploring the mediating and moderating roles from the perspective of cost‐efficiency trade‐offs to reveal how EMS certification affects financial performance. Applying a PROCESS procedure analysis and causal mediation analysis to a sample of 1,751 Chinese listed manufacturing firms from 2008 to 2016, this study shows that the effect of EMS certification on firms' financial performance is insignificant because their operating costs burden increases while their marketing efficiency and managerial efficiency improve. For the first time, this study demonstrates the moderating role of industry peer learning, as the mediating effects decrease with the growth of industry peer learning.     

Market drivers of sustainability and sustainability learning capabilities: The moderating role of sustainability control systems

This study examines (i) the impact of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the moderating role of sustainability control systems (SCS) on the relationship between market drivers of sustainability and sustainability learning capabilities. Drawing on the levers of control framework, stakeholder theory and organisational learning literature, survey data were collected from 175 large scale local and multinational companies operating in Sri Lanka. Findings reveal that market drivers of sustainability have a significant positive impact on sustainability learning capabilities. Whereas the interactive use of SCS shows a positive moderating impact, the diagnostic use of SCS shows a negative impact. The study enhances our understanding of (i) the influence of market drivers of sustainability on the adoption of sustainability learning capabilities and (ii) the use of SCS in enabling sustainability learning capabilities. The study reveals novel insights for managers responding to changing market drivers of sustainability, on how to (re)align different uses of SCS to enable sustainability learning capabilities.     

Learning about the real-world in MICE education: The case of exhibition learning from Thailand

Abstract

The use of experiential learning in tourism and hospitality education is well-documented in literature. Experiential learning studies in this field may include, for example, internship experiences, field trip perceptions, conferences, and social events. However, there is still insufficient literature to understand students’ learning and their real-world experience in MICE education, especially in the exhibition sector. This study, therefore, addresses this gap by reporting the experiential learning of graduate students in an event course with the objectives to investigate student perceptions on academic learning experiences and the development of work-related skills by carrying out the exhibition project. Students are challenged to perform a complicated task as a real exhibition organizer, and to deal with other stakeholders of the exhibition industry (e.g., exhibition venue, exhibitors, contractors, and visitors). The experiential learning method is discussed through the Plan-Do-Check-Act (PDCA) process. The results indicate that students not only gained in-depth learning about the exhibition industry, but also developed important work skills (e.g., teamwork, planning, and coordinating skills). Moreover, classroom learning, industry visits, and real-world experience are found to be the important factors contributing to exhibition learning. The current study contributes to the limited exhibition learning literature and provides event educators new insights into the teaching and learning of exhibition-based projects in regard to how students plan, learn and carry out the exhibition event through the case of Thailand. Other similar courses may apply the learning processes and results of this study to develop effective experiential learning in MICE education.     

Object detection based on visual memory: a feature learning and feature imagination process

ABSTRACT

Visual memory plays an important role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. We simulate the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method is tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.