Table Of ContentManaging the Asian Century
Purnendu Mandal
John Vong Editors
Entrepreneurship
in Technology for
ASEAN
Managing the Asian Century
Series editor
Purnendu Mandal, College of Business, Lamar University, Beaumont, TX, USA
Managing the Asian Century provides a platform for scholastic discussions and
stresses the need for a holistic framework to understand Asia as an emerging
economic global powerhouse. Books published in this series cover Asia-centric
topics in economics, production, marketing, finance, entrepreneurship, education,
culture, technology, as well as other areas of importance to Asian economics. The
series will publish edited volumes based on papers submitted to international and
regionalconferencesthatfocusonspecificAsia-Pacificthemes,suchasinvestment
in education, women’s rights, entrepreneurship, climate change, wage inequality,
challengesingovernance,andcorruption.Booksinthisseriesareofkeeninterestto
researchers and policy planners around the world and will be used by universities
for graduate and doctoral level studies.
More information about this series at http://www.springer.com/series/13579
Purnendu Mandal John Vong
(cid:129)
Editors
Entrepreneurship
in Technology for ASEAN
123
Editors
Purnendu Mandal JohnVong
Collegeof Business Lee KuanYewSchool of PublicPolicy
Lamar University National University ofSingapore
Beaumont, TX Singapore
USA Singapore
ISSN 2364-5857 ISSN 2364-5865 (electronic)
Managingthe AsianCentury
ISBN978-981-10-2280-7 ISBN978-981-10-2281-4 (eBook)
DOI 10.1007/978-981-10-2281-4
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Preface
The word entrepreneurship brings forth thoughts and images that are associated
with resources, risk taking, opportunity seeking, monetizing of ideas, and seed
capital.Theenterprisesarefar-reachingandwide-ranging.Theentrepreneurscould
wellpitchtheirproductsandservices,toandin,theindustriesandmarketssuchas
finance, education, manufacturing, healthcare and hospitality, mining, and the
public sector.
Technology conjures ideas on thoughts and thinking towards automation,
mechanization, computerization, convergence of hardware and software, noble
technology, connectivity, and a borderless world. Indeed the onslaught of tech-
nologyinthelastthreedecadeshasbeenrapidandithasmadesignificantchanges
that impacted our lives, living and livelihoods across the globe.
Combining the words of entrepreneurship and technology burst forth Microsoft
and Bill Gates, Facebook and Mark Zuckerberg, Apple and the late Steve Jobs,
AlibabaandJackMa,DellandMichaelDell,GoogleandSergeyBrin,andYahoo
and Jerry Yang, and many more can be included in the list. There is a common
themeintheseindividualsandtheirglobalbrands.Theyhavenovelideas.Theyare
entrepreneurs who are able to marshal the resources and add deep technology
researchanddevelopmenttoconjureproductsandservicesthatsurroundtheirideas
toreachthecoreofconsumermarkets.Throughthatprocess,theyalsoachievedthe
monetization of both technology and ideas.
This book expounds technology research and development that fuels
entrepreneurship in ASEAN. It extrapolates technological approaches to big data
analysis, healthcare, trends in intellectual property, port management, manufactur-
ing,landadministration,andtheinfluenceofConfucianismonentrepreneurship.In
short,there willbeinterestingessays onthecombinedforce oftechnologicalideas
andideologicalforcesthatisdrivingentrepreneurshipinASEANandawiderAsia
today.
Beaumont, USA Purnendu Mandal
Singapore, Singapore John Vong
v
Contents
Modern Approaches of Financial Data Analysis
for ASEAN Entrepreneurs... ..... .... .... .... .... .... ..... .... 1
Insu Song, Bryan Anselme, Purnendu Mandal and John Vong
Business Models for Entrepreneurs: Commercializing,
Leveraging, and Monetizing Intellectual Property Rights
in India and ASEAN Countries.... .... .... .... .... .... ..... .... 15
K.V. Nithyananda
New Patent Market Analysis Technology for ASEAN
Entrepreneurs. .... .... .... ..... .... .... .... .... .... ..... .... 33
Priyanka Rana, Insu Song, Purnendu Mandal and John Vong
Factors Influencing Implementation of Lean Manufacturing:
Case on Manufacturing in Indonesia.... .... .... .... .... ..... .... 47
Hendro Lukman and Susanto Salim
Global Health Surveillance Approaches: New Opportunities. ..... .... 59
Insu Song, Dominic Hayes, Purnendu Mandal and John Vong
An Integrated Approach for Healthcare Systems Management
in India .. .... .... .... .... ..... .... .... .... .... .... ..... .... 73
Pradip Kumar Ray
An Overview of Impact of Healthcare Inventory Management
Systems on Entrepreneurship. ..... .... .... .... .... .... ..... .... 83
Esha Saha and Pradip Kumar Ray
Gender Equality in Performance Assessment to Nurture
the ASEAN Entrepreneurial Spirit—An Exploratory Study
in Vietnam.... .... .... .... ..... .... .... .... .... .... ..... .... 95
Philip Michael Ross Smith, Helga Nagy, Christine Bilsland
and Dinh Thi Hong Nhung
vii
viii Contents
Software Development Productivity in Different Sourcing
Arrangements . .... .... .... ..... .... .... .... .... .... ..... .... 111
Niharika Dayyala, Kallol Bagchi and Purnendu Mandal
Advancements in Technology and Potential Impacts on Port
Automations Decisions: The Case of Port of Singapore. .... ..... .... 127
Kelly Weeks, Purnendu Mandal and Kabir Sen
Impacts of Land Dereliction: Classification and Basic Information
for Entrepreneurs.. .... .... ..... .... .... .... .... .... ..... .... 139
Bela Das
Confucianism and Entrepreneurship in ASEAN Context ... ..... .... 161
Larry Allen
Modern Approaches of Financial Data
Analysis for ASEAN Entrepreneurs
Insu Song, Bryan Anselme, Purnendu Mandal and John Vong
Abstract Short-andmedium-termpredictionsofstockpriceshavebeenimportant
problems in financial analysis. In the past, various different approaches have been
used including statistical analysis, fundamental analysis, and more recently
advanced approaches that use machine learning and data mining techniques.
However, most of existing algorithms do not incorporate all available information
of the market. Using more informative and relevant data, prediction results will
better reflect market reality. This would benefit in reducing the inaccuracy of
predictingduetorandomnessinstockprices,usingtrendratherthanasinglestock
price variation. For instance, some stock prices are correlated and/or dependent
with/on each other and market mood. In this paper, we review the existing tech-
niques of stock prices and time series predictions, and the classification and clus-
tering methods. Based on the literature analysis, we propose a method for
incorporating-related stock trend information: clustering-related companies using
machine learning approaches. We report on a preliminary analysis results using
monthly adjusted closing prices of 100 companies collected over a 15-month
period.
I.Song(&)
SchoolofBusiness/IT,JamesCookUniversity,Singapore,Singapore
e-mail:[email protected]
B.Anselme
SchoolofBusiness/IT,JamesCookUniversity,Singapore,Singapore
e-mail:[email protected]
B.Anselme
EcoleNationaleSuperieurdel’Informatiquepourl’Industrieetl’Entreprise(ENSIIE),
Évry,France
P.Mandal
CollegeofBusiness,LamarUniversity,Beaumont,TX,USA
e-mail:[email protected]
J.Vong
LeeKuanYewSchoolofPublicPolicy,NationalUniversityofSingapore,
Singapore,Singapore
e-mail:[email protected]
©SpringerScience+BusinessMediaSingapore2017 1
P.MandalandJ.Vong(eds.),EntrepreneurshipinTechnologyforASEAN,
ManagingtheAsianCentury,DOI10.1007/978-981-10-2281-4_1
2 I.Songetal.
(cid:1) (cid:1) (cid:1) (cid:1)
Keywords Clustering Financial analyses Stock price prediction Prediction
Classification
1 Introduction
Todaymostoftheglobalmonetarymassisinvestedinfinancialplaces,oncoupons,
debt financing, raw materials, features, or stocks. Optimizing investment strategies
in these markets has become one of today’s most important research topics. The
most common methodologies are: portfolio management that aims to reduce the
risktakenininvestmentbydiversifyingtherangeofinvestment(Paranjape-Voditel
and Deshpande 2013), arbitrage by detecting anomaly in prices and take a free
lunch, pricing by calculating the real value of stocks, and finally standard trading
withtwomajorstypes,whicharethefundamental(LevandThiagarajan1993)and
the technical/quantitative (Lo et al. 2000) analysis approaches. The
technical/quantitativeanalysisapproachesincludeusingmathematicaltoolsinorder
to predict trends, discovering patterns for machine-based trading, and predicting
medium-/short-term trends. The fundamental analysis approaches include using
micro- and macroeconomics indicators, news and financial data of companies in
order to predict trend for the concerned stocks for medium- and long-term views.
Recentadvancementindataminingtechnologies,suchasclustering(Esteretal.
1996), ANN (Artificial Neural Network) (Xi et al. 2014; Lei et al. 2014), SVM
(Support Vector Machines) (Zhang et al. 2012; Song and Marsh 2012), decision
tree (Chang 2011; Patel et al. 2015), and rough set theory (Cheng et al. 2010),
opened up new approaches that allow analysts to incorporate more relevant
information (Rajanish 2006; Song et al. 2014; Song and Vong 2013; Vong et al.
2012; Vong and Song 2015; Song 2015a, b). These new technologies allow ana-
lysts to consider much larger amounts of data and build prediction models auto-
matically using computers with less training (Rajanish 2006).
In this paper, we will review existing techniques of financial analysis and
machinelearningapproachesinordertoidentifynewopportunities.Inaddition,we
propose a new method for predicting stock prices in a more accurate way using
clusteringapproaches.Thisproposalisbasedonthefactthatmanystockpricesare
correlated, and the awareness of those correlations can allow us to improve the
previous models. This allows us to take into account more data and to diversify
theirsourcesinordertoreducetheinaccuracyofpredictions.Infacttouseonlythe
historicaldataofonlyonestockpriceissometimesveryriskyandalthoughleadto
bad forecasting.
The rest of the paper is organized as follows. In the next section, we review
existingtechniquesoffinancialanalysisandmachinelearningapproaches.Wethen
report on identified opportunities for researchers. In Sect. 3, we propose a new
clustering method that determines the optimal number of clusters of related com-
panies. In Sect. 4, we report on the analysis result and conclude the paper with
remarks in Sect. 5.
