International Diversified Portfolio Optimization With Artificial Neural Networks

Investing in developed markets offers investors the opportunity to diversify internationally by investing in foreign firms. In other words, it provides the possibility of reducing systematic risk. For this reason, investors are very interested in developed markets. However, developed are more efficient than emerging markets, so the risk and return can be low in these markets. For this reason, developed market investors often use machine learning techniques to increase their gains while reducing their risks. In this chapter, artificial neural networks which is one of the machine learning techniques have been tested to improve internationally diversified portfolio performance. Also, the results of ANNs were compared with the performances of traditional portfolios and the benchmark portfolio. The portfolios are derived from the data of 16 foreign companies quoted on NYSE by ANNs, and they are invested for 30 trading days. According to the results, portfolio derived by ANNs gained 10.30% return, while traditional portfolios gained 5.98% return.

Forecasting Software Vulnerabilities Using Time-Series Techniques

This chapter discusses the concepts of time-series applications and forecasting in the context of information systems security. The primary objective in such formulation is the training of the models followed by efficient prediction. Although economic and financial forecasting problems extensively use time-series, predicting software vulnerabilities is a novel idea. The chapter also provides appropriate guidelines for the implementation and adaptation of univariate time-series for information security. To achieve this, the authors focus on the following techniques: autoregressive (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and exponential smoothing. The analysis considers a unique data set consisting of the publicly exposed software vulnerabilities, available from the U.S. Dept. of Homeland Security. The problem is presented first, followed by a general framework to identify the problem, estimate the best-fit parameters of that model, and conclude with an illustrative example from the above dataset to familiarize readers with the business problem.

Big Data Analytics in Healthcare Sector

The importance of big data over analytics made the process of solving various real-world problems simpler. The big data and data science tool box provided a realm of data preparation, data analysis, implementation process, and solutions. Data connections over any data source, data preparation for analysis has been made simple with the availability of tremendous tools in data analytics package. Some of the analytical tools include R programming, python programming, rapid analytics, and weka. The patterns and the granularity over the observed data can be fetched with the visualizations and data observations. This chapter provides an insight regarding the types of analytics in a big data perspective with the realm in applicability towards healthcare data. Also, the processing paradigms and techniques can be clearly observed through the chapter contents.

Multivariate Analysis

Multivariate analysis is based on the statistical principle of multivariate statistics, which includes observation and analysis of statistical output variables in case of more than one output variable at a time. The technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest in design and analysis. This chapter includes the theoretical concepts of multivariate analysis including factor and discriminant analyses. It is also gives examples to understand and apply them correctly.

Knowledge Discovery From Evolving Data Streams

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

Tree-Based Modeling Techniques

Tree-based learning techniques are considered to be one of the best and most used supervised learning methods. Tree-based methods empower predictive models with high accuracy, stability, and ease of interpretation. Unlike linear models, they map non-linear relationships pretty well. These methods are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being widely used in all kinds of machine learning and data science problems. Hence, for every data analyst, it is important to learn these algorithms and use them for modeling. This chapter guide the learner to learn tree-based modeling techniques from scratch.

An Exploratory Analysis Using Co-Authorship Network

Social network analysis has been widely used by organizational behavior researchers to stress the importance of the context, social connections, and social structure on human behavior. In the last decade, social network analysis has emerged as one of the most useful techniques for exploring online social networks, world wide web, e-mail traffic, and logistic operations. In this chapter, the authors present an application of social network analysis techniques for academic research. The authors choose Kahneman and Tversky's prospect theory as the focus of their analysis and, based on that, develop a co-authorship structure that depicts in a clear manner the key authors and/or the researchers that dominate and bridge different sub-fields in the field of management. The authors discuss the implications of this study for academic research and management discipline.

Association Rules-Based Gray Relational Approach for E-Commerce Recommender System

Recommender systems cannot provide healthy results in case of similar products that cannot be identified in e-commerce sites. Insufficient information about users or items is one of the most crucial problems, especially with adding new users or products. The inability to perform relational analysis in the system is due to insufficient data. In this case, the system cannot recommend or bring the non-related items to the users. This chapter suggests the gray relational approach to identify more healthy recommendation lists when there are few relational items. The data was obtained from an e-commerce company and apriori algorithm was applied to the dataset that a randomly chosen user purchased. Gray relational analysis was applied for the most suitable recommendation by using support, confidence, number of likes, adding favorite, deleting from basket, and return information of the products in the dataset. In addition, the most appropriate product sequencing of the recommendation list was realized by gray relational degrees.

Big Data Analytics in Social Media

The rapid development of online social media is the method of collaboratively produced content material presents new possibilities and challenges to both producers and patrons of knowledge. The term big data refers to large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques. In the current scenario, social media has gained amazing attention within the last decade. Accessing social media platforms and websites such as Facebook, Twitter, YouTube, LinkedIn, Instagram, and Google+, web technologies have become more responsible. People are becoming more fascinated about and relying on social media platform for records, news, and opinion of other customers on diverse topics. Hence, these situations produce a large volume of data. The main objective of this chapter is to provide knowledge about big data analytics in social media. A brief overview of big data and social media are discussed. Research challenges in social media are also discussed.