The Business Review

Classification of Stock Market Price Change by Data Mining

Dr. Nursel Selver Ruzgar, Ryerson University, Toronto, Canada

ABSTRACT

In this paper, eight Canadian banks’ daily stock market price changes are examined by three data mining techniques, logistic regression, fuzzy-roughNN and genetic algorithms. Thirty-seven years of data from 1980 to 2017 obtained from NASDAQ for eight Canadian banks with 21 independent variables and one dependent variable, price, were used to classify the daily stock price changes. Daily price changes are divided into three classes, “up”, “down” and “same” according to the previous stock market daily close price. To determine which method makes the better classification, three methods run separately for each bank. Then predicted values for 2018 with each method for each bank, were compared the original 2018 data to see how the predicted values were compatible with the real values. It was seen that, among the three methods, the genetic programming algorithms classified the stock price changes well. This paper demonstrates that the genetic programming method is applicable to a wide range of practical problems pertaining to price changes. Moreover, the results show that the genetic programming is a promising alternative to the conventional methods for financial prediction. Data mining (DM) and knowledge discovery is a family of computational methods that aim at collecting and analyzing data related to the function of a system of interest to gain a better understanding of the system (Triantaphyllou, 2010). DM attempts to formulate, analyze and implement basic induction processes that help extract meaningful information and knowledge from unstructured data. DM that aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction is the process of using statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and knowledge assembled from large databases (Kusrini, 2009). DM can be used in different disciplines, such as engineering (Carrizosa and Morales, 2013), finance (Cheng, 2010), business, banking (Ferreira, 2018, Manurung, 2015), medicine (Ramamurthy and Chandran, 2011) and science (Singh, 2015). There are many DM methods to perform the analysis, such as clustering, classification, and association. Classification, which is a work of assessing a data object to include it in a certain class of available classes, is of the widely used DM method to extract information from various high-dimensional data sets.

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Learning from WhatsApp’s Business Model: The World of Messaging Apps

Dr. Nadeem M. Firoz, Baruch College, CUNY, New York, NY

Atif Noor, Founder & CEO, Adaptly-AI.com, Baruch College, CUNY, New York, NY

ABSTRACT

The objective of this proposal is to determine WhatsApp’s positioning within the social media marketplace, analyze its strengths and weaknesses, and propose actionable strategic marketing models which will allow the service to further increase its user base by curtailing weaknesses and exponentiating strengths. This proposal will follow the service from its inception as a startup in Mountain View California to becoming the most popular messaging app in the world after being acquired by Facebook. Its features will be dissected and its value to the marketplace will be closely analyzed. The demographics, geodemographics and user segmentation of the service will be ascertained; along with its business models, marketing positioning, and SWOT analysis. Through diligent and thoughtful analysis of WhatsApp’s SWOT, viable options for growth and further market capitalization will become evident, and recommendations for implementation will be established. WhatsApp is a cross-platform messaging and Voice over IP (VOIP) service that allows users to send text messages and video calls, along with other rich media such as audio, images, documents, and even video calls. (whatsapp.com) The service was created by WhatsApp Inc. in Mountain View, California by founders Brian Acton and Jan Koum, who previously worked for Yahoo! (Forbes.com) After leaving Yahoo! in 2007, they applied to jobs at Facebook, but failed to get hired. Ironically, after the they were rejected jobs at Facebook, their company was acquired by Facebook in February 2014 for close to $19.3 billion dollars (techcrunch.com). By early 2018, the product had accumulated over 1.5 billion users, making it the most popular messaging app in the market. (Forbes.com) According to the company, “More than 1 billion people in over 180 countries use WhatsApp to stay in touch with friends and family, anytime and anywhere. WhatsApp is free and offers simple, secure, reliable messaging and calling, available on phones all over the world.” (Whatsapp.com) The app was named WhatsApp because it sounded like “What’s up?” and had a viral component to it. It was officially incorporated on February 24, 2009 (forbes.com) and the founders visited RentACoder.com (now known as freelancer.com) to find a Russian iPhone developer named Igor Solomennikov. In October 2009, Brian Acton was able to secure $250,000 in seed funding from past colleagues at Yahoo!

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Collaborative Strategies in the Context of the Tourism Cluster in the Azores: A Qualitative Analysis

Antonia Canto, University of the Azores, Portugal

Anhelina Bykova, University of the Azores, Portugal

Dr. Joao Couto, University of the Azores, Portugal

Abstract

The main objective of this study is to discuss the collaborative strategies within the tourism cluster on the Azores. A qualitative research framework was developed and responses obtained through interviews with 30 regional stakeholders were analyzed using the MaxQDA and NVivo programs. The results highlighted the existence of dynamics and collaboration between the regional tourism partners. The study reveals that the most dynamic partners are car rental companies, restaurants, tours companies, and hotels. Promoting collaboration is crucial for developing a tourist destination. For collaboration to be successful, it is important to establish collaborative strategies. There are several definitions of the concept, according to Child, Faulkner, Tallman, and Tallman (2005); collaborative strategies are an attempt by organizations to achieve their goals through cooperation with other organizations rather than competing with them. This work attempts to delineate a framework of collaborative strategies among several tourism cluster actors in the Azores region. A thorough analysis of the interviews collected from the various actors in this sector is undertaken. Particular attention is paid to the type of activity and to the location of such activity, so as to reasonably cover the tourism cluster in the Azores. First, we present the concepts of collaborative strategies and the tourism cluster, observing their evolution and complexity, and highlight the various contributions of several researchers in this field of study. Second, the method and instruments for gathering information are presented, together with the procedures undertaken in the elaboration of this study. The final section includes the results and discussion, which evaluates the existence and importance of collaborative strategies in the region. We first analyze the definition of the central concepts; this study follows Canto and Couto (2018), who define the concept of a tourism cluster and collaborative strategies, and observe the importance, dynamics, efficiency, and sustainability of the tourism cluster in the Azores. According to Beni (2012), a tourism cluster means a group of similar products or activities that developed together to “highlight that this concept” has a strong and significant connotation of junction, union, aggregation, and interaction. However, according to the same author, it is possible to deepen the concept by considering that the tourism cluster constitutes a permanent set of dynamic actions and reiterates community effort, social mobilization, entrepreneurship in economic investments, efficient inter-organizational communication, engagement of social actors and institutional agents, and interaction of all segments of the supply for the necessary and indispensable synergy in the productive arrangement for the consolidation of its sustainable development (Beni ,2012), While the definition of a tourism cluster refers to a group/union of activities and similar products that evolve together through the efforts and synergies of its collaborators, its objectives, according to Beni (2012), can be enumerated as follows: reducing operating costs and transactions between companies (1); harnessing and enhancing synergies for the production, marketing, and distribution of products and services (2); sharing technical, productive, and marketing information (3), and disseminating innovation (4).

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Efficiency Changes in the Home and Community-based Services of Long-term Care in Taiwan

Chia-Mei Shih, Department of Resources Engineering, National Cheng Kung University, Tainan, Taiwan

Yu-Hua Wang, Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan

Dr. Li-Fan Liu, Professor, Institute of Gerontology, National Cheng Kung University, Tainan, Taiwan

Jung-Hua Wu, Department of Resources Engineering, National Cheng Kung University, Tainan, Taiwan

ABSTRACT

Global aging trends have led to dilemmas in resource allocation. Most developed or OECD countries are struggling to ensure the sustainability of their long-term care systems, and Taiwan, a developing country, is not an exception. After years of effort, Taiwan has established home and community-based services in a formal long-term care infrastructure and has developed a nationwide database on long term care. However, the cost-effectiveness of the large amount of resources invested in this system has not yet been analyzed. This study sought to examine the performance of Taiwan’s long-term care system from 2011-2016, using the Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) approach. The results showed a regression in average total factor productivity over 6 years (-5.5%), mostly affected by deteriorating technological change (-6.6%). During that same period of time there was an ascending trend in technical efficiency due to a dramatic increase in financial investment since 2014, which produced an overall growth of 1.1%. Long-term care is a labor-intensive industry. Our study’s findings show that, while change factors within long-term care did help to improve the efficiency of the system to some degree, what really made a difference were factors that impacted the system from exogenous factors, such as improved technology. To sustain the productivity of the long-term care system we must focus on investment in innovations. The sustainability of long-term care (henceforth LTC) is currently a prominent policy priority in many countries since the aging trend is causing major fiscal issues (Mosca, van der Wees, Mot, Wammes, & Jeurissen, 2017). In recent decades, economic growth has been outpaced by the growth in public funded health expenditures in Organization for Economic Co‐operation and Development (OECD) countries. This trend is shown in the increasing proportion of health expenditure accounted for in calculations of gross domestic product (GDP) (Angelis, Tordrup, & Kanavos, 2017). A new set of public health and long-term care expenditure projections reaching to 2060 suggest that public spending on health and LTC in OECD countries and in the BRIICS (Brazil, Russia, India, Indonesia, China and South Africa) will rise rapidly over the next 50 years despite cost-containment efforts ( through policy action) or downward cost-pressures (occurring without implicit policy actions). In the prediction, the total health and LTC expenditure across OECD countries will increase by 3.3-7.7 percent of GDP on average till 2060. For the BRIICS, these costs are projected to increase even more steeply by 2.8-7.3 percent of GDP over the same period overall (Maisonneuve & Martins, 2014).

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The Impact of the Tohoku-Oki Earthquake on Tourism Share Prices in Taiwan

Dr. Chun-Huang Liao, Zhao Qing University, Guangdong Province, China

ABSTRACT

This study uses the event study method and the recursive Chow test to investigate the impact of the Tohoku-Oki earthquake on tourism share prices in Taiwan. Cumulative abnormal returns and structural changes in return relationships were estimated and tested. The findings show the Tohoku-Oki earthquake had a short-term negative impact on the stock returns of Taiwan’s tourism companies. Significant negative abnormal returns lasted for about 19 trading days, and the structure of the return relationships between the tourism index and market index changed in the short term after the earthquake occurred. By using hierarchical multiple regression analysis, this study found the variables of firm size, debt ratio, ratio of stock market value relative to assets, margin trading of stocks, and percentage of hotel revenue relative to sales can significantly account for the cumulative abnormal returns. To prevent such unexpected event impacts, suitable strategies of risk diversification should be undertaken by hoteliers and tourism operators. International travel between Taiwan and Japan is very popular, not only because of the close proximity, but also to some extent because of the history of colonization. However, on March 11, 2011, the Tohoku-Oki earthquake (Tajima et al., 2013; Ito et al., 2012) suddenly shut down this busy travel line. International tourism markets between both countries encountered a situation of chaos; many tourists canceled or changed their original itineraries. This unexpected earthquake influenced Taiwan’s tourism revenue and share prices slumped suddenly. Large seismic events are rare in modern history, so they are a natural experiment and a valuable case study that cannot be reproduced (Kollias et al., 2011b; Shan and Gong, 2012). However, literature on this kind of case is still rare. The Tohoku-Oki earthquake occurred at 14:46 on March 11, 2011, in the northeast area of Japan, just around the Miyagi, Fukushima, and Iwate prefectures. The earthquake of 9.0 Richter on the scale triggered tsunami and nuclear disasters, resulting in 86,000 deaths, more than 13,000 missing persons, and more than 550,000 people to flee disaster areas. The disasters caused economic losses amounting to USD 235 billion, much higher than other countries’ cases: the economic losses of USD 9.5 billion from the south Asian tsunami in 2004, USD 81.2 billion loss from U.S. hurricane Katrina in 2005 (Park et al., 2013), USD 1.340 billion losses from the China Wenchuan earthquake in 2008, and USD 8 billion losses from the Haiti earthquake in 2010 (Gao et al., 2012).

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