The output and productivity benefits of fintech collaboration : Scotland and Ireland

This paper is a thought piece on the impact of joint collaboration in fintech on output and productivity. It identifies a distinct fintech sector from both the Scottish and Irish financial and technology related industrial groupings and maps the largest employers amongst the fintech start ups. The multiplier effect of this subgrouping and the sectors productivity enhancing nature are used to forecast the job impact of such collaboration. Our analysis of the Scottish and Irish fintech sub-sector shows that joint collaboration in fintech will increase output and labour productivity, outpacing Scottish and Irish GDP growth and labour productivity. The key conclusion is that collaboration could be net positive for employment assuming no exogenous shocks caused by the new technology from other geographies

Essays on Islamic equity investing

Islamic finance is rapidly gaining momentum around the world. Interpretations of Shari’ah, or Islamic law, state that investments must be free from elements of riba (interest), gharar (uncertainty), maysir (speculation) and haram (unethical) business activities. Islamic equity investing, therefore, utilizes a set of business activity screens and accounting-based screens to exclude firms considered non-permissible under Shari’ah. Despite increased academic interest, there is still much uncertainty surrounding the financial implications of these investment principles. This Ph.D. thesis, comprised of three empirical essays, aims to contribute to this debate. The first essay offers a comprehensive examination of Islamic equity index performance. The findings show that Islamic equity indices have exhibited abnormal returns on a global and developed market level, primarily due to their exclusion of stocks in the financial services sector. The second essay attempts to study the determinants of Islamic investments’ financial performance, with a particular focus on the role of country-level factors. The third essay studies performance related issues associated with the accounting-based screening process. A significant proportion of these screens are documented to contribute positively to risk-adjusted performance, most notably in periods of financial market turmoil

CLASSIFICATION OF ILLEGALADVERTISEMENT : WORKING WITH IMBALANCED CLASSDISTRIBUTIONS USING MACHINE LEARNING

Interpreting human language entered a new era with the current prevalence of machine learning techniques. The field of natural language processing (NLP) concerns the interaction between human and computer languages. Machine learning is the scientific area involved with the design of algorithms that learn from past experience. These two areas within computer science – NLP and machine learning – enables complex ways of analyzing, and working with written language. The goal of this thesis is to implement a prototype that automatically find web-based advertisement related to illicit content. In such scenario, the source material comes mostly in theform of unstructured-, unlabeled-, raw text. In order to design a working algorithm in this context, a combination of machine learning and NLP techniques are used. Three machine learning algorithms were used in order to classify material by content: nearest neighbours algorithms, support vectormachines, and multilayer perceptron. Two dimensionality reduction techniques were used: principle component analysis and Latent Dirichlet allocation. It should be noted that the latter is more commonly used as method of explaining documents in terms of topic mixtures, rather than reducing dimensionality in a problem. NLP representation techniques such as the bag-of-word model andTF/IDF were used. These are essentially different variations of word embedding. The result is evaluated using metrics common ininformation retrieval: precision, recall, F1 measure, and confusionmatrices. We found that an important challenge in this context is class imbalance: content of interest is often overshadowed by data representing over-represented classes. Another important challengeis that there are multiple classes, and an accurate labelling of such ontology is often missing. In order to improve the accuracy, more annotated - historical - data is needed

CLASSIFICATION OF ILLEGALADVERTISEMENT : WORKING WITH IMBALANCED CLASSDISTRIBUTIONS USING MACHINE LEARNING

Interpreting human language entered a new era with the current prevalence of machine learning techniques. The field of natural language processing (NLP) concerns the interaction between human and computer languages. Machine learning is the scientific area involved with the design of algorithms that learn from past experience. These two areas within computer science – NLP and machine learning – enables complex ways of analyzing, and working with written language. The goal of this thesis is to implement a prototype that automatically find web-based advertisement related to illicit content. In such scenario, the source material comes mostly in theform of unstructured-, unlabeled-, raw text. In order to design a working algorithm in this context, a combination of machine learning and NLP techniques are used. Three machine learning algorithms were used in order to classify material by content: nearest neighbours algorithms, support vectormachines, and multilayer perceptron. Two dimensionality reduction techniques were used: principle component analysis and Latent Dirichlet allocation. It should be noted that the latter is more commonly used as method of explaining documents in terms of topic mixtures, rather than reducing dimensionality in a problem. NLP representation techniques such as the bag-of-word model andTF/IDF were used. These are essentially different variations of word embedding. The result is evaluated using metrics common ininformation retrieval: precision, recall, F1 measure, and confusionmatrices. We found that an important challenge in this context is class imbalance: content of interest is often overshadowed by data representing over-represented classes. Another important challengeis that there are multiple classes, and an accurate labelling of such ontology is often missing. In order to improve the accuracy, more annotated - historical - data is needed

CLASSIFICATION OF ILLEGALADVERTISEMENT : WORKING WITH IMBALANCED CLASSDISTRIBUTIONS USING MACHINE LEARNING

Interpreting human language entered a new era with the current prevalence of machine learning techniques. The field of natural language processing (NLP) concerns the interaction between human and computer languages. Machine learning is the scientific area involved with the design of algorithms that learn from past experience. These two areas within computer science – NLP and machine learning – enables complex ways of analyzing, and working with written language. The goal of this thesis is to implement a prototype that automatically find web-based advertisement related to illicit content. In such scenario, the source material comes mostly in theform of unstructured-, unlabeled-, raw text. In order to design a working algorithm in this context, a combination of machine learning and NLP techniques are used. Three machine learning algorithms were used in order to classify material by content: nearest neighbours algorithms, support vectormachines, and multilayer perceptron. Two dimensionality reduction techniques were used: principle component analysis and Latent Dirichlet allocation. It should be noted that the latter is more commonly used as method of explaining documents in terms of topic mixtures, rather than reducing dimensionality in a problem. NLP representation techniques such as the bag-of-word model andTF/IDF were used. These are essentially different variations of word embedding. The result is evaluated using metrics common ininformation retrieval: precision, recall, F1 measure, and confusionmatrices. We found that an important challenge in this context is class imbalance: content of interest is often overshadowed by data representing over-represented classes. Another important challengeis that there are multiple classes, and an accurate labelling of such ontology is often missing. In order to improve the accuracy, more annotated - historical - data is needed

A review of Islamic mutual funds’ financial performance and investment style in Muslim and non-Muslim countries

Islamic finance has grown beyond its reputation of providing small-scale banking options and now provides investment and financing options for complex large-scale commercial transactions. Islamic investments are one area that has attracted the attention of investors due to its performance, especially during the economic downturn. The Shari’ah compliance nature of Islamic funds provides an opportunity for those Muslim investors to be part of the global investment sector who have previously been reluctant to invest in conventional mutual funds. The fact that the funds’ managers are prohibited from investing in activities such as weapons production, alcohol production and interest-bearing finance operations, makes Islamic mutual funds also attractive for those Non-Muslim investors who wish to invest ethically. Today there are hundreds of Islamic equity indices offered by Dow Jones, FTSE, MSCI and S&P. Despite the growing importance of Islamic funds, there have been limited studies exploring the performance of Islamic funds worldwide. Due to very limited data sets and not too rigorous analytical methods, these existent studies have neither investigated Islamic funds’ financial performance in noticeable detail nor analysed the investment style of more than six funds. For instance, relevant questions such as the financial performance of Islamic mutual funds’ beyond their investment styles or a difference in performance between funds from Muslim and non-Muslim countries have nearly not been investigated at all. Very recently, a study by Hoepner, Rammal and Rezec (2011) analysed the financial performance and investment style of 262 Islamic equity funds from 20 countries in five regions (Africa, Asia-Pacific, Europe, Gulf Cooperative Council-GCC, and North America). As comparison, previous studies did not even analyse 60 funds. Hoepner et al.’s study sampled a period of two decades and was therefore able to test the performance of the funds during economic booms as well as economic downturns. The findings of the study provide new insights into the performance of Islamic mutual funds in Muslim and Western markets and during financial crisis