Search results for: investor sentiment.

Authors: Francesca Radice

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It has been observed that violent or coercive behaviour has been apparent from initial conversations on dating apps like Tinder. Child pornography, stalking, and coercive control are some criminal offences from dating apps, including women murdered after finding partners through Tinder. Police databases and predictive policing are novel approaches taken to prevent crime before harm is done. This research will investigate how police databases can be used in a privacy-preserving way to characterise users in terms of their potential for violent crime. Using the COPS database of NSW Police, we will explore how the past criminal record can be interpreted to yield a category of potential danger for each dating app user. It is up to the judgement of each subscriber on what degree of the potential danger they are prepared to enter into. Sentiment analysis is an area where research into natural language processing has made great progress over the last decade. This research will investigate how sentiment analysis can be used to interpret interchanges between dating app users to detect manipulative or coercive sentiments. These can be used to alert law enforcement if continued for a defined number of communications. One of the potential problems of this approach is the potential prejudice a categorisation can cause. Another drawback is the possibility of misinterpreting communications and involving law enforcement without reason. The approach will be thoroughly tested with cross-checks by human readers who verify both the level of danger predicted by the interpretation of the criminal record and the sentiment detected from personal messages. Even if only a few violent crimes can be prevented, the approach will have a tangible value for real people.

Keywords: Sentiment Analysis, data mining, predictive policing, virtual manipulation.

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Authors: Radek Malinský, Ivan Jelínek

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This article deals with the popularity of candidates for the president of the United States of America. The popularity is assessed according to public comments on the Web 2.0. Social networking, blogging and online forums (collectively Web 2.0) are for common Internet users the easiest way to share their personal opinions, thoughts, and ideas with the entire world. However, the web content diversity, variety of technologies and website structure differences, all of these make the Web 2.0 a network of heterogeneous data, where things are difficult to find for common users. The introductory part of the article describes methodology for gathering and processing data from Web 2.0. The next part of the article is focused on the evaluation and content analysis of obtained information, which write about presidential candidates.

Keywords: Sentiment Analysis, Web 2.0, Webometrics.

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Authors: Sarah O. Alhumoud, Mawaheb I. Altuwaijri, Tarfa M. Albuhairi, Wejdan M. Alohaideb

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Large-scale data stream analysis has become one of the important business and research priorities lately. Social networks like Twitter and other micro-blogging platforms hold an enormous amount of data that is large in volume, velocity and variety. Extracting valuable information and trends out of these data would aid in a better understanding and decision-making. Multiple analysis techniques are deployed for English content. Moreover, one of the languages that produce a large amount of data over social networks and is least analyzed is the Arabic language. The proposed paper is a survey on the research efforts to analyze the Arabic content in Twitter focusing on the tools and methods used to extract the sentiments for the Arabic content on Twitter.

Keywords: Big Data, Social Networks, Sentiment Analysis.

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Authors: Ivan C. Mustakerov, Daniela I. Borissova

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The paper proposes an approach to ranking a set of potential countries to invest taking into account the investor point of view about importance of different economic indicators. For the goal, a ranking algorithm that contributes to rational decision making is proposed. The described algorithm is based on combinatorial optimization modeling and repeated multi-criteria tasks solution. The final result is list of countries ranked in respect of investor preferences about importance of economic indicators for investment attractiveness. Different scenarios are simulated conforming to different investors preferences. A numerical example with real dataset of indicators is solved. The numerical testing shows the applicability of the described algorithm. The proposed approach can be used with any sets of indicators as ranking criteria reflecting different points of view of investors. 

Keywords: Combinatorial optimization modeling, economics investment attractiveness, economics ranking algorithm, multi-criteria problems.

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Authors: Aya Salama

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Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.

Keywords: Human Digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification and clustering.

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Authors: Colette Malyack, Pius Egbelu

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Business-to-Customer (B2C) delivery options have improved to meet increased demand in recent years. The change in end users has forced logistics networks to focus on customer service and sentiment that would have previously been the priority of the company or organization of origin. This has led to increased pressure on logistics companies to extend traditional B2B networks into a B2C solution while accommodating additional costs, roadblocks, and customer sentiment; the result has been the creation of the omnichannel delivery network encompassing a number of traditional and modern methods of package delivery. In this paper the many solutions within the omnichannel delivery network are defined and discussed. It can be seen through this analysis that the omnichannel delivery network can be applied to reduce the complexity of package delivery and provide customers with more options. Applied correctly the result is a reduction in cost to the logistics company over time, even with an initial increase in cost to obtain the technology.

Keywords: Network planning, Last Mile Delivery, LMD, omnichannel delivery network, omnichannel logistics.

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Authors: Chien-Hung Chen, Yan-Chun Hsing, Yung-Chun Chang

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The Internet has grown into a powerful medium for information dispersion and social interaction that leads to a rapid growth of social media which allows users to easily post their emotions and perspectives regarding certain topics online. Our research aims at using natural language processing and text mining techniques to explore the public emotions expressed on Twitter by analyzing the sentiment behind tweets. In this paper, we propose a composite kernel method that integrates tree kernel with the linear kernel to simultaneously exploit both the tree representation and the distributed emotion keyword representation to analyze the syntactic and content information in tweets. The experiment results demonstrate that our method can effectively detect public emotion of tweets while outperforming the other compared methods.

Keywords: Public emotion recognition, natural language processing, composite kernel, sentiment analysis, text mining.

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as physiological vital signs, images and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful to influence the judgement of clinical sentiment in ICU clinical notes. This paper presents two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of a clinical transformer model that can reliably predict clinical sentiment for notes of COVID patients in ICU. We train the model on clinical notes for COVID-19 patients, ones not previously seen by Bio_ClinicalBERT or Bio_Discharge_Summary_BERT. The model which was based on Bio_ClinicalBERT achieves higher predictive accuracy than the one based on Bio_Discharge_Summary_BERT (Acc 93.33%, AUC 0.98, and Precision 0.96). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and Precision 0.92).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation.

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Authors: Zhi-Wei Zheng, Zhong-Qi Liu, Jia-Ling Qiu, Shan-Qing Guo, Zhong-Wei Jia, Chun Hao

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Objectives: The purpose of this study was to understand and examine the association between diurnal mood variation and sexual/health-related status among men who have sex with men (MSM) using data from MSM Chinese Twitter messages. The study consists of 843,745 postings of 377,610 MSM users located in Guangdong that were culled from the MSM Chinese Twitter App. Positive affect, negative affect, sexual related behaviors, and health-related status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotions score were also calculated. Linear regression models based on a permutation test were used to assess associations between affective states and sexual/health-related status. In the results, 5,871 active MSM users and their 477,374 postings were finally selected. MSM expressed positive affect and joy at 8 a.m. and expressed negative affect and negative emotions between 2 a.m. and 4 a.m. In addition, 25.1% of negative postings were directly related to health and 13.4% reported seeking social support during that sensitive period. MSM who were senior, educated, overweight or obese, self-identified as performing a versatile sex role, and with less followers, more followers, and less chat groups mainly expressed more negative affect and negative emotions. MSM who talked more about sexual-related behaviors had a higher positive sentiment score (β=0.29, p < 0.001) and a higher positive emotions score (β = 0.16, p < 0.001). MSM who reported more on their health status had a lower positive sentiment score (β = -0.83, p < 0.001) and a lower positive emotions score (β = -0.37, p < 0.001). The study concluded that psychological intervention based on an app for MSM should be conducted, as it may improve mental health.

Keywords: Affect, men who have sex with men, sexual-related behaviors, health-related status, social media.

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Authors: G. Erkomaishvili, M. Kobalava, T. Lazariashvili, N. Damenia

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Current state of foreign direct investment (FDI) in Georgia is analyzed and evaluated in the paper, the existing legislative background for regulating investments and stimulating policies to attract investments are shown. It is noted that in developing countries encouragement of investment activity, support and implementation are of the most important tasks, implying a consistent investment policy, investor-friendly tax regime and the legal system, reducing administrative barriers and restrictions, fare competitive conditions and business development infrastructure. The work deals with the determining factor of FDIs and the main directions of stimulation, as well as prospective industries where new investments are needed. Contributing and hindering factors and stimulating measures are analyzed. As a result of the research, the direct and indirect factors attracting FDI have been identified. Facilitating factors to FDI inflow are as follows: simplicity of starting business, geopolitical location, low taxes, access to credit, ease of ownership registration, natural resources, low burden of regulations, low level of corruption and low crime rates. Hindering factors to FDI inflow are as follows: small market, lack of policy for attracting investments, low qualification of the workforce (despite the large number of unemployed people it is difficult to find workers with necessary special skills and qualifications), high interest rates, instability of national currency exchange rate, presence of conflict zones within the country and so forth.

Keywords: Foreign direct investment, investment attracting policies, investor, reinvestment.

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Authors: Chimaobi V. Okolo, Afamefuna J. Ani, Ebere U. Okolo

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This paper seeks to assess the implications of insurance to foreign direct investment inflow in Nigeria. Multiple linear regression technique and correlation matrix test were employed to measure the extent to which foreign direct investment was influenced. The result showed that insurance premium (IP), asset size of insurance industry (AS), and total investment of the industry (TI) impacted significantly and positively on foreign direct investment inflow in Nigeria. There should be effective risk transfer mechanism and financial intermediation, which gives the investor confidence in the risk management strength of the host country.

Keywords: Foreign direct investment, insurance.

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Authors: Claudiu Brandas

Abstract:

Several recent studies have shown that the transparency of financial reporting have a significant influence on investor-s decisions. Thus, regulation authorities and professional organizations (IFAC) have emphasized the role of XBRL (eXtensible Business Reporting Language) and interactive data as a means of promoting transparency and monitoring corporate reporting. In this context, this paper has as objective the analysis of interactive reporting through XBRL and its use as a support in the process of taking decisions in corporate governance, namely the potential of interactive reports in XBRL to increase the transparency and monitoring process of corporate governance.

Keywords: Corporate Governance, decision, financial reporting, transparency, XBRL.

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Authors: E. Mike Dison, T. Pathinathan

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Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.

Keywords: Appositive, computing with words, PRUF, semantic sentiment analysis, set theoretic interpretations.

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Authors: Shashwat Gangwal

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This paper analyses the effect of adding Bitcoin, to the portfolio (stocks, bonds, Baltic index, MXEF, gold, real estate and crude oil) of an international investor by using daily data available from 2^nd of July, 2010 to 2^{nd of} August, 2016. We conclude that adding Bitcoin to portfolio, over the course of the considered period, always yielded a higher Sharpe ratio. This means that Bitcoin’s returns offset its high volatility. This paper, recognizing the fact that Bitcoin is a relatively new asset class, gives the readers a basic idea about the working of the virtual currency, the increasing number developments in the financial industry revolving around it, its unique features and the detailed look into its continuously growing acceptance across different fronts (Banks, Merchants and Countries) globally. We also construct optimal portfolios to reflect the highly lucrative and largely unexplored opportunities associated with investment in Bitcoin.

Keywords: Portfolio management, Bitcoin, optimization, Sharpe ratio.

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Authors: Maria Damian, Carmen G. Bonaci, Jiří Strouhal, Razvan V. Mustata, Dumitru Matis, Jiřina Bokšová

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In the current context of globalization, a large number of companies sought to develop as a group in order to reach to other markets or meet the necessary criteria for listing on a stock exchange. The issue of consolidated financial statements prepared by a parent, an investor or a venture and the financial reporting standards guiding them therefore becomes even more important. The aim of our paper is to expose this issue in a consistent manner, first by summarizing the international accounting and financial reporting standards applicable before the 1st of January 2013 and considering the role of the crisis in shaping the standard setting process, and secondly by analyzing the newly issued/modified standards and main changes being brought

Keywords: Consolidated financial statements, control, IFRS 10, financial crisis.

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Authors: Radek Malinský, Ivan Jelínek

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Web 2.0 (social networking, blogging and online forums) can serve as a data source for social science research because it contains vast amount of information from many different users. The volume of that information has been growing at a very high rate and becoming a network of heterogeneous data; this makes things difficult to find and is therefore not almost useful. We have proposed a novel theoretical model for gathering and processing data from Web 2.0, which would reflect semantic content of web pages in better way. This article deals with the analysis part of the model and its usage for content analysis of blogs. The introductory part of the article describes methodology for the gathering and processing data from blogs. The next part of the article is focused on the evaluation and content analysis of blogs, which write about specific trend.

Keywords: Blog, Sentiment Analysis, Web 2.0, Webometrics

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Authors: Pulak Swain, A. K. Ojha

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Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of  E- constraint method.

Keywords: Portfolio optimization, multi-objective optimization, E-constraint method, box uncertainty, robust optimization.

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Authors: Margareta Ilkova, Petr Teply

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The article deals with dividends and their distribution from investors from a theoretical point of view. Some studies try to analyzed the reaction of the market on the dividend announcement and found out the change of dividend policy is associated with abnormal returns around the dividend announcement date. Another researches directly questioned the investors about their dividend preference and beliefs. Investors want the dividend from many reasons (e.g. some of them explain the dividend preference by the existence of transaction cost; investors prefer the dividend today, because there is less risky; the managers have private information about the firm). The most controversial theory of dividend policy was developed by Modigliani and Miller (1961) who demonstrated that in the perfect and complete capital markets the dividend policy is irrelevant and the value of the company is independent of its payout policy. Nevertheless, in the real world the capital markets are imperfect, because of asymmetric information, transaction costs, incomplete contracting possibilities and taxes.

Keywords: dividend distribution, taxation, payout policy, investor, Modigliani and Miller theorem

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Authors: L. Tirabeni, K. E. Soderquist, P. Pisano

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Our purpose is to investigate how the relationship between employees and innovation management processes can drive organizations to successful innovations. This research is deeply related to a new way of thinking about human resources management practices. It’s not simply about improving the employees’ engagement, but rather about a different and more radical commitment: the employee can take on the role traditionally played by the customer, namely to become the first tester of an innovative product or service, the first user/customer and eventually the first investor in the innovation. This new perception of employees could create the basis of a novelty in the innovation process where innovation is taken to a next level when the problems with customer driven innovation on the one hand, and employees driven innovation on the other can be balanced. This research identifies an effective approach to innovation where the employees will participate throughout the whole innovation process, not only in the idea creation but also in the idea definition and development by giving feedback in parallel to that provided by customers and lead-users.

Keywords: Employee-Driven Innovation, engagement, human resource management, innovative companies.

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Authors: Muñoz-Sanchez, Victor Manuel, Perez-Flores, Antonio Manuel

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The main objective of this paper is to establish the relationship between models of state organization and the various types of collective identity expressed by the Spanish. The question of nationalism and identity ascription in Spain has always been a topic of special importance due to the presence in that country of territories where the population emits very different opinions of nationalist sentiment than the rest of Spain. The current situation of sovereignty challenge of Catalonia to the central government exemplifies the importance of the subject matter. In order to analyze this process of interrelation, we use a secondary data mining by applying the multiple correspondence analysis technique (MCA). As a main result a typology of four types of expression of collective identity based on models of State organization are shown, which are connected with the party position on this issue.

Keywords: Models of organization of the state, nationalism, collective identity, Spain, political parties.

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Authors: Soon-ok Myong, B.G. Nurzhanov

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This paper applies an anthropological approach to illuminate the dynamic cultural geography of Kazakhstani Korean ethnicity focusing on its turning point, the historic “Seoul Olympic Games in 1988." The Korean ethnic group was easily considered as a harmonious and homogeneous community by outsiders, but there existed deep-seated conflicts and hostilities within the ethnic group. The majority-s oppositional dichotomy of superiority and inferiority toward the minority was continuously reorganized and reinforced by difference in experience, memory and sentiment. However, such a chronic exclusive boundary was collapsed following the patriotism ignited by the Olympics held in their mother country. This paper explores the fluidity of subject by formation of the boundary in which constructed cultural differences are continuously essentialized and reproduced, and by dissolution of cultural barrier in certain contexts.

Keywords: Former Soviet Korean's Russianization, inferior/superior dichotomy, Seoul Olympic Games, subject's fluidity.

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Authors: Mohamed Moustafa Soliman, Mohamed Abd Elsalam

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Recent financial international scandals around the world have led to a number of investigations into the effectiveness of corporate governance practices and audit quality. Although evidence of corporate governance practices and audit quality exists from developed economies, very scanty studies have been conducted in Egypt where corporate governance is just evolving. Therefore, this study provides evidence on the effectiveness of corporate governance practices and audit quality from a developing country. The data for analysis are gathered from the top 50 most active companies in the Egyptian Stock Exchange, covering the three year period 2007-2009. Logistic regression was used in investigating the questions that were raised in the study. Findings from the study show that board independence; CEO duality and audit committees significantly have relationship with audit quality. The results also, indicate that institutional investor and managerial ownership have no significantly relationship with audit quality. Evidence also exist that size of the company; complexity and business leverage are important factors in audit quality for companies quoted on the Egypt Stock Exchange.

Keywords: Corporate governance, Boards of directors, corporate ownership, Audit Committees, Audit quality, and Egypt.

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Authors: Timothée Bligny, Clément Codron, Antoine Estruch, Nicolas Girodet, Clément Ginet

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Collateralized Debt Obligations are not as widely used nowadays as they were before 2007 Subprime crisis. Nonetheless there remains an enthralling challenge to optimize cash flows associated with synthetic CDOs. A Gaussian-based model is used here in which default correlation and unconditional probabilities of default are highlighted. Then numerous simulations are performed based on this model for different scenarios in order to evaluate the associated cash flows given a specific number of defaults at different periods of time. Cash flows are not solely calculated on a single bought or sold tranche but rather on a combination of bought and sold tranches. With some assumptions, the simplex algorithm gives a way to find the maximum cash flow according to correlation of defaults and maturities. The used Gaussian model is not realistic in crisis situations. Besides present system does not handle buying or selling a portion of a tranche but only the whole tranche. However the work provides the investor with relevant elements on how to know what and when to buy and sell.

Keywords: Synthetic Collateralized Debt Obligation (CDO), Credit Default Swap (CDS), Cash Flow Optimization, Probability of Default, Default Correlation, Strategies, Simulation, Simplex.

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Authors: Gemma García López

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The goal of this study is to analyze if search queries carried out in search engines such as Google, can offer emotional information about the user that performs them. Knowing the emotional state in which the Internet user is located can be a key to achieve the maximum personalization of content and the detection of worrying behaviors. For this, two studies were carried out using tools with advanced natural language processing techniques. The first study determines if a query can be classified as positive, negative or neutral, while the second study extracts emotional content from words and applies the categorical and dimensional models for the representation of emotions. In addition, we use search queries in Spanish and English to establish similarities and differences between two languages. The results revealed that text search queries performed by users on the Internet can be classified emotionally. This allows us to better understand the emotional state of the user at the time of the search, which could involve adapting the technology and personalizing the responses to different emotional states.

Keywords: Emotion classification, text search queries, emotional analysis, sentiment analysis in text, natural language processing.

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Authors: Syed Kamarul Bakri Syed Ahmad Bokharey, Kalaikumar Vallyutham, Narayanan Sambu Potty, Nabilah Abu Bakar

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Infrastructure investments are important in developing countries, it will not only help to foster the economic growth of a nation, but it will also act as a platform in which new forms of partnership and collaboration can be developed mainly in East Asian countries. Since the last two decades, many infrastructure projects had been completed through build-operate-transfer (BOT) type of procurement. The developments of BOT have attracted participation of local and foreign private sector investor to secure funding and to deliver projects on time, within the budget and to the required specifications. Private sectors are preferred by the government in East Asia to participate in BOT projects due to lack of public funding. The finding has resulted that the private sector or promoter of the BOT projects is exposed to multiple risks which have been discussed in this paper. Effective risk management methods and good managerial skills are required in ensuring the success of the project. The review indicated that mitigation measures should be employed by the promoter throughout the concession period and support from the host government is also required in ensuring the success of the BOT project.

Keywords: BOT project, risks management, concessionaire, consortium.

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Authors: Michelle Zeyuan Ma, Hermann Heilmeier

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Recently, meat production ecosystem impacts initiated many hot discussions and researchers, and it is a difficult implementation to reduce such impacts due to the demand of meat products. It calls for better management and control of ecosystem impacts from every aspects of meat production. This article analyzes the ecosystem impacts of meat production based on meat products life cycle. The analysis shows that considerable ecosystem impacts are caused by different meat production steps: initial establishment phase, animal raising, slaughterhouse processing, meat consumption, and wastes management. Based on this analysis, the impacts are summarized as: leading factor for biodiversity loss; water waste, land use waste and land degradation; greenhouse gases emissions; pollution to air, water, and soil; related major diseases. The article also provides a discussion on a solution-sustainable food system, which could help in reducing ecosystem impacts. The analysis method is based on the life cycle level, it provides a concept of the whole meat industry ecosystem impacts, and the analysis result could be useful to manage or control meat production ecosystem impacts from investor, producer and consumer sides.

Keywords: Eutrophication, life cycle based analysis, sustainable food, waste management.

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: F. Guspi, E. García

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This exploratory study gives an overview of the evolution of the main financial and performance indicators of the Academic Spin-Off’s and High Growth Academic Spin-Off’s in year 3 and year 6 after its creation in the region of Catalonia in Spain. The study compares and evaluates results of these different measures of performance and the degree of success of these companies for each University. We found that the average Catalonian Academic Spin-Off is small and have not achieved the sustainability stage at year 6. On the contrary, a small group of High Growth Academic Spin-Off’s exhibits robust performance with high profits in year 6. Our results support the need to increase selectivity and support for these companies especially near year 3, because are the ones that will bring wealth and employment. University role as an investor has rigid norms and habits that impede an efficient economic return from their ASO investment. Universities with high performance on sales and employment in year 3 not always could sustain this growth in year 6 because their ASO’s are not profitable. On the contrary, profitable ASO exhibit superior performance in all measurement indicators in year 6. We advocate the need of a balanced growth (with profits) as a way to obtain subsequent continuous growth.

Keywords: Academic Spin-Off (ASO), University Entrepreneurship, Entrepreneurial University, high growth, New Technology Based Companies (NTBC), University Spin-Off.

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Authors: Peng Yew Wong

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House prices in the Australian capital cities were at record levels subsequent to Global Financial Crisis (GFC) 2008 and many believed that foreign investors, especially the Chinese investors, were the main reason for the Australian capital cities’ house prices escalation. This research conducted an Australian cross border semi-structured interviews in Shanghai, China to uncover historical evidence and emerging trend supporting the existence of a significant relationship between overseas investors and residential housing markets performance in Australia subsequent to the GFC 2008. Some unique investment strategies of private investors from China which emphasised on non-capitalist factors such as early education were identified, alongside with some insights on the significant China government policies that have incentivised the cross border investments from China. It is believed that this understanding will assist policy makers to effectively manage the overheated Australian residential property market without compromising the steady flow of FREI.

Keywords: Australian housing market, residential property, foreign real estate investment, education, China investor.

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Authors: Eko Budi Santoso, Kazia Laturette, Stanislaus Adnanto Mastan

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This study aims to examine the association between disclosure of social responsibility and tax aggressiveness in developing countries, namely Thailand. This is due to the increasing trend of disclosure of social responsibility in developing countries, even though this disclosure of information is still voluntary. On the other hand, developing countries have low taxation rate and investor protection infrastructures that allow the disclosure of social responsibility to be used opportunistically as a tool to fool the attainment of interests. This study also examines the role of assurance on the association between corporate social responsibility disclosure and tax aggressiveness. The assurance aims to provide confidence that the disclosure of social responsibility by the company is valid. This research builds an index to measure the disclosure of social responsibility based on the rules issued by the innovative Global Reporting. The results of the study are based on a sample of publicly traded companies in Thailand, which showed a positive association between disclosure of corporate social responsibility and tax aggressiveness, but it was further discovered that these results were mitigated by the existence of assurance against disclosure of corporate social responsibility. The results of this study indicate that the disclosure of corporate social responsibility can show that the company cares about the issue of social responsibility but does not automatically make the company as one that holds ethical values ​​in its business practices.

Keywords: Corporate Social Responsibility disclosure, tax aggressiveness, sustainability assurance, business ethics.

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