Forecasting the direction and trend of stock price is an important task which helps investors to make prudent financial decisions in the stock market. Investment in the stock market has a big risk associated with it. Minimizing prediction error reduces the investment risk. Machine learning (ML) models typically perform better than statistical and econometric models. Also, ensemble ML models have been shown in the literature to be able to produce superior performance than single ML models. In this work, we compare the effectiveness of tree-based ensemble ML models (Random Forest (RF), XGBoost Classifier (XG), Bagging Classifier (BC), AdaBoost Classifier (Ada), Extra Trees Classifier (ET), and Voting Classifier (VC)) in forecasting the direction of stock price movement. Eight different stock data from three stock exchanges (NYSE, NASDAQ, and NSE) are randomly collected and used for the study. Each data set is split into training and test set. Ten-fold cross validation accuracy is used to evaluate the ML models on the training set. In addition, the ML models are evaluated on the test set using accuracy, precision, recall, F1-score, specificity, and area under receiver operating characteristics curve (AUC-ROC). Kendall W test of concordance is used to rank the performance of the tree-based ML algorithms. For the training set, the AdaBoost model performed better than the rest of the models. For the test set, accuracy, precision, F1-score, and AUC metrics generated results significant to rank the models, and the Extra Trees classifier outperformed the other models in all the rankings.

Corporate social responsibility (CSR) continues to receive greater attention in the current business world. Many studies on CSR focus on manufacturing or industrial companies by examining external CSR activities from external stakeholders’ perceptions. However, academic institutions such as higher education institutions (HEIs) remain highly unexplored in the context of internal corporate social responsibility (ICSR). Employees are the most valuable and vital assets for every business organization. Therefore, this study focuses on CSR’s internal dimensions to determine its impact on social performance in HEIs in Ghana. Recognizing the social exchange theory (SET), we specifically examined the effects of five internal CSR dimensions (i.e., health and safety, human rights, training and development, workplace diversity, and work-life balance) on social performance. We used a multi-case approach to assess internal CSR activities in private and public Ghanaian universities. We purposely selected three public universities and one private university because of their varying contexts and academic mandates. We used structured questionnaires to collect data from both teaching and non-teaching staff of the selected universities. Structural equation modeling (SEM) was used to assess the data. We found that health and safety, workplace diversity, and training and development positively and significantly impact social performance. At the same time, human rights and work-life balance have an insignificant effect on social performance. Thus, ICSR practices have a substantial influence on both employees’ and organization’s performance, and hence this study gives important implications for both researchers and practitioners

The stock market is one of the key sectors of a country's economy. It provides investors with an opportunity to invest and gain returns on their investment. Predicting the stock market is a very challenging task and has attracted serious interest from researchers from many fields such as statistics, artificial intelligence, economics, and finance. An accurate prediction of the stock market reduces investment risk in the market. Different approaches have been used to predict the stock market. The performances of Machine learning (ML) models are typically superior to those of statistical and econometric models. The ability of Gaussian Naïve Bayes ML algorithm to predict stock price movement has not been addressed properly in the existing literature, hence this attempt to fill that gap in the literature by evaluating the performance of GNB algorithm when combined with different feature scaling and feature extraction techniques in stock price movement prediction. The performance of the GNB models set up were ranked using the Kendall's test of concordance for the various evaluation metrics used. The results indicated that, the predictive model based on integration of GNB algorithm and Linear Discriminant Analysis (GNB_LDA) outperformed all the other models of GNB considered in three of the four evaluation metrics (i.e., accuracy, and AUC). Similarly, the predictive model based on GNB algorithm, Min-Max scaling, and PCA produced the best rank using the specificity results. In addition, GNB produced better performance with Min-Max scaling technique than it does with standardization scaling techniques Povzetek: Predstavljena je metoda Gausovega naivnega Bayesa za borzne napovedi.

Access control has become problematic in several organizations because of the difficulty in establishing security and preventing malicious users from mimicking roles. Moreover, there is no flexibility among users in the participation in their roles, and even controlling them. Several role-based access control (RBAC) mechanisms have been proposed to alleviate these problems, but the security has not been fully realized. In this work, however, we present an RBAC model based on blockchain technology to enhance user authentication before knowledge is accessed and utilized in a knowledge management system (KMS). Our blockchain-based system model and the smart contract ensure that transparency and knowledge resource immutability are achieved. We also present smart contract algorithms and discussions about the model. As an essential part of RBAC model applied to KMS environment, trust is ensured in the network. Evaluation results show that our system is efficient.Information 2020, 11, 111 2 of 15 stored-in files to complex, heterogeneous array of systems with sophisticated options, there are several components of a KMS, and these KMSs deploy KM portals as central points of access for its component systems.The role-based access control (RBAC) framework has been the mechanism employed by most KMSs to achieve access control [4,5]. With roles and titles or statuses, instead of users attributed to access rights, many of today's organizations adopt such a model to implement their access control mechanisms [6][7][8][9][10]. With this contention in the existing literature, both researchers and practitioners over the years continued to extend the core RBAC to include features that: (1) protect and wholly secure knowledge assets; (2) align with KM initiatives; and (3) maintain the determination and motivation to share and transfer knowledge. It is important to emphasize that KM initiatives embrace numerous socio-technical elements, processes, structures, and business models in a broader spectrum. Social entities such as individuals, projects teams, collaborative groups, and inter-organizational relationships use sophisticated technological tools to function in organizations. The dynamics associated with systems and human interactions in KMS environment require critical security attention as users generate, store and utilize knowledge assets. A challenge to such KMS strategic initiatives is thus contingent on the appropriateness of the extension of RBAC deployable in KMS without compromising the overall knowledge sharing or transfer agenda.As with other information systems, users of KMS occupy roles through specific portals and platforms, and these roles are associated with permissions. At different access levels, users can access knowledge items (or objects) and perform operations on them according to their defined specific tasks. With roles associated with a set of sessions, users are allowed to share or transfer knowledge packages across different hierarchical levels. Since knowledge gives organizations competitive advantage [1...

Forecasting stock market behavior has received tremendous attention from investors and researchers for a very long time due to its potential profitability. Predicting stock market behavior is regarded as one of the extremely challenging applications of time series forecasting. While there is divided opinion on the efficiency of markets, numerous empirical studies which are widely accepted have shown that the stock market is predictable to some extent. Statistical based methods and machine learning models are used to forecast and analyze the stock market. Machine learning (ML) models typically perform better than those of statistical and econometric models. In addition, performance of ensemble ML models is typically superior to those of individual ML models. In this paper, we study and compare the efficiency of treebased ensemble ML models (namely, Bagging classifier, Random Forest (RF), Extra trees classifier (ET), AdaBoost of Bagging (ADA_of_BAG), AdaBoost of RandomForest (ADA_of_RF), and AdaBoost of ExtraTrees (ADA_of_ET)). Stock data randomly collected from three different stock exchanges were used for the study. Forty technical indicators were computed and used as input features. The data set was spilt into training and test sets. The performance of the models was evaluated with the test set using accuracy, precision, recall, F1-score, specificity and AUC metrics. Kendall W test of concordance was used to rank the performance of the different models. The experimental results indicated that AdaBoost of Bagging (ADA_of_BAG) model was the highest performer among the tree-based ensemble models studied. Also, boosting of the bagging ensemble models improved the performance of the bagging ensemble models. Povzetek: Z Adaboost algoritmi na osnovi dreves je analizirano dogajanje na borzah.