Technical and Fundamental Features Analysis for Stock Market Prediction with Data Mining Methods

Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.Predicting stock prices is an essential objective in the financial world. Forecasting stock returns and their risk represents one of the most critical concerns of market decision makers. This thesis investigates the stock price forecasting with three approaches from the data mining concept and shows how different elements in the stock price can help to enhance the accuracy of our prediction. For this reason, the first and second approaches capture many fundamental indicators from the stocks and implement them as explanatory variables to do stock price classification and forecasting. In the third approach, technical features from the candlestick representation of the share prices are extracted and used to enhance the accuracy of the forecasting. In each approach, different tools and techniques from data mining and machine learning are employed to justify why the forecasting is working. Furthermore, since the idea is to evaluate the potential of features in the stock trend forecasting, therefore we diversify our experiments using both technical and fundamental features. Therefore, in the first approach, a three-stage methodology is developed while in the first step, a comprehensive investigation of all possible features which can be effective on stocks risk and return are identified. Then, in the next stage, risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, based on some filters and function-based clustering; and re-predicted the risk and return of stocks. In the second approach, instead of using single classifiers, a fusion model is proposed based on the use of multiple diverse base classifiers that operate on a common input and a meta-classifier that learns from base classifiers’ outputs to obtain a more precise stock return and risk predictions. A set of diversity methods, including Bagging, Boosting, and AdaBoost, is applied to create diversity in classifier combinations. Moreover, the number and procedure for selecting base classifiers for fusion schemes are determined using a methodology based on dataset clustering and candidate classifiers’ accuracy. Finally, in the third approach, a novel forecasting model for stock markets based on the wrapper ANFIS (Adaptive Neural Fuzzy Inference System) – ICA (Imperialist Competitive Algorithm) and technical analysis of Japanese Candlestick is presented. Two approaches of Raw-based and Signal-based are devised to extract the model’s input variables and buy and sell signals are considered as output variables. To illustrate the methodologies, for the first and second approaches, Tehran Stock Exchange (TSE) data for the period from 2002 to 2012 are applied, while for the third approach, we used General Motors and Dow Jones indexes.154 - Katedra financívyhově

Pemodelan Data Komoditas Pangan di Jawa Tengah Menggunakan ANFIS

Swasembada pangan merupakan cita-cita yng harus diwujudkan oleh setiap pemerintah daerah termasukprovinsi Jawa Tengah. Dengan terwujudnya swasembada pangan, maka kebutuhan pangan secara mandiri dan tidak tergantung pihak lain akan terpenuhi. Akan tetapi melihat kendala keterbatasan lahan dan berbagai faktor lainnya seperti perubahan iklim yang ekstrim, terbatasnya sarana prasarana dan kemampuan petani, swasembada pangansangat sulit dipenuhi.Agar cita-cita tersebut tidak salah arah, dipandang perlu untuk membuat model matematika yang akurat sebagai dasar perencanaan swasembada pangan di Jawa Tengah.Terkait dengan situasi tersebut, penelitian ini mengembangkan model prediksi untuk data produksi padiguna mendukung ketahanan dan keamanan pangan di Jawa Tengah. Model matematika yang dikembangkan adalahadaptive neuro fuzzy inference system (ANFIS)untuk data produksi padi di Jawa Tengah.Model ANFIS menggabungkan sistem fuzzy dan Neural Networks (NN). Sistem fuzzy merupakan aproksimator universal mampu mengklasifikasikan data yang mengandung ketidakpastian yang tinggi, sedangkan NN memiliki kemampuan pembelajaran yang baik terhadap data. Prosedur pembentukan arsitektur ANFIS didasarkan pada Lagrange Multiplier (LM)-test, yang meliputi pemilihan input, penentuan jumlah fungsi keanggotaan dan pembangkitan aturan (rules) fuzzy.Kajian empiris mengambil studi kasus databulananproduksi padi tahun 1990 sampai dengan tahun 2014 di Kabupaten Grobogan. Performaprediksi model ANFIS diukur berdasarkan nilai mean absolute percentage error (MAPE) dan root of mean squares error (RMSE). Kata kunci:ANFIS,LM-test, Pemodelan,Produksi pad

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Proceedings of the West Africa Built Environment Research (WABER) Conference 2021

FOREWORD: I would like to welcome each participant to the WABER 2021 Conference. Since its inception in 2009, the WABER Conference series has done a great deal to nurture and support researchers, initially in West Africa, also, in other parts of Africa and elsewhere. I would like to thank all delegates for your participation which enables us to keep this Conference going. The WABER Conference enjoys a positive international reputation and has continued to grow from strength to strength over the past 13 years. For this, I would like to thank our team, keynote speakers and participants over the years for every contribution you have made to the success of this Conference. This year's Conference has an excellent programme, line up of speakers and authors. I would like to thank and commend the authors of all 72 papers in this Conference proceedings. If the research paper writing process was compared to a marathon, the authors of the 72 papers in this publication would be adjudged as the ones who have endured and finished the race. We opened the call for papers for this Conference in December 2020 and over 100 abstracts were submitted by authors. However, it is one thing to propose to write a paper, and it is quite another thing to actually write the paper. Therefore, I would like to thank and congratulate all authors who succeeded in completing the process of getting published in this conference proceedings. It is befitting that we have an excellent range of interesting topics in the 72 papers to be discussed at this conference. We are honoured to welcome Professor Charles Egbu, Vice Chancellor of Leeds Trinity University, to give us a special opening address. In the three days of this conference, we will have various plenary presentations by experienced international academics and I would like to thank and welcome each of them below. Professor Albert Chan Richard Lorch Professor Taibat Lawanson Professor Dato’ Sri Ar Dr Asiah Abdul Rahim Professor George Ofori. In addition to these speakers, we have other interesting sessions on the programme including a special session for doctoral students and supervisors several other experienced speakers addressing various topics that should be of interest to many of us. I would like to thank all members of the organising team particularly Associate Professor Emmanuel Essah, Dr Yakubu Aminu Dodo and Dr Sam Moveh for their efforts which has helped to organise this Conference successfully. I would also like to thank all of our reviewers particularly Associate Professor Emmanuel Essah and Dr Haruna Moda for the considerable time and effort spent reviewing and checking all papers to ensure a high standard of quality. The WABER Conference Team always plays an excellent role in the success of our events and I would like to thank and appreciate the contributions of Florence, Sam Boakye, Victor Ayitey and his team, Kwesi Kwofie and Issah Abdul Rahman to the success of this Conference. I hope you enjoy our first hybrid conference and engage with our exciting speakers on the diverse topics that will be covered over the three days of this Conference

La valoración de empreses mediante la lógica borrosa

[spa] La complejidad de la toma de decisiones en el campo de la economía y las finanzas se ha incrementado en los últimos años. Como resultado, se está prestando cada vez más atención al desarrollo e implementación de modelos matemáticos que puedan dar respuesta a estos problemas. La investigación en el campo de la lógica borrosa ha sido un tema de creciente interés durante muchas décadas, ya que es un concepto fundamental y común en la ciencia. Desde 1965, cuando se publicó el título seminal "Fuzzy sets" (Zadeh, L. A. 1965), se produjo un cambio de la lógica binaria a la lógica multivalente. Este cambio permite dar paso a teorías relacionadas con la incertidumbre, a través de una metodología borrosa, para poder considerar todos los escenarios posibles en la toma de decisiones, teniendo en cuenta la objetividad y subjetividad de los parámetros a considerar. En general, el objetivo principal de esta tesis doctoral es identificar las características y oportunidades de negocio a través de un análisis de valoración de empresas, que permita una mejor interpretación del contexto incierto para la toma de decisiones. Es decir, la teoría de la decisión en la incertidumbre se desarrolla con la valoración de empresas. Se analiza la situación en la que se encuentra y se estudian las aportaciones que podemos hacer en este campo con los principales algoritmos de lógica difusa estudiados por autores como J. Gil Aluja, A. Kaufmann, R. Yager, entre otros, con especial énfasis en aquellos que han sido aplicados al ámbito empresarial y financiero. La valoración de empresas es un proceso fundamental y complejo en los sistemas económico-financieros. En un entorno que evoluciona hacia formas más complejas e inciertas, es necesario presentar nuevos modelos de valoración empresarial más dinámicos basados en técnicas de tratamiento y gestión de la incertidumbre y toma de decisiones, para eliminar la ambigüedad y la confusión en entornos inciertos. La primera aportación de este trabajo es el análisis del estado de la cuestión realizado a través de dos estudios bibliométricos que estudian las aportaciones de la comunidad científica a la lógica borrosa y la valoración empresarial. Destaca la importancia de los factores subjetivos a la hora de tomar decisiones en un entorno económico y financiero. La segunda contribución es el desarrollo de aplicaciones que muestren la toma de decisiones en la incertidumbre aplicada a los métodos de valoración de empresas. Este estudio nos permite desarrollar algoritmos genéricos y modelos matemáticos que se pueden aplicar a la realidad empresarial, para probar su utilidad. En este trabajo, se destacan el coeficiente de adecuación, el coeficiente de calificación, la distancia de Hamming, la teoría del clon, el modelo de preferencia subjetiva, el algoritmo húngaro, los operadores OWA, los intervalos y los expertones. La tercera contribución es un nuevo algoritmo que combina la matemática borrosa y la valoración de empresas, lo que contribuye al desarrollo de la teoría de la decisión en el ámbito empresarial. En concreto, se desarrolla un modelo de valoración de empresas mediante el descuento de flujos de caja y las matemáticas borrosas, mostrando su utilidad y la posibilidad de ser aplicado por la comunidad académica y profesional en el posterior análisis del valor de una empresa. El modelo propuesto sistematiza y ordena el uso de intervalos para establecer un valor de negocio mínimo y máximo para la empresa. Por lo tanto, hemos encontrado un intervalo de confianza del posible valor comercial. Finalmente, podríamos decir que a nivel general hay dos aportaciones importantes a destacar en esta tesis doctoral: la aplicabilidad y el desarrollo. Aplicamos algoritmos y modelos en los métodos de valoración de empresas y desarrollamos un nuevo algoritmo que contribuye al desarrollo de la teoría de la decisión

La valoración de empreses mediante la lógica borrosa

Programa de Doctorat en Empresa[spa] La complejidad de la toma de decisiones en el campo de la economía y las finanzas se ha incrementado en los últimos años. Como resultado, se está prestando cada vez más atención al desarrollo e implementación de modelos matemáticos que puedan dar respuesta a estos problemas. La investigación en el campo de la lógica borrosa ha sido un tema de creciente interés durante muchas décadas, ya que es un concepto fundamental y común en la ciencia. Desde 1965, cuando se publicó el título seminal "Fuzzy sets" (Zadeh, L. A. 1965), se produjo un cambio de la lógica binaria a la lógica multivalente. Este cambio permite dar paso a teorías relacionadas con la incertidumbre, a través de una metodología borrosa, para poder considerar todos los escenarios posibles en la toma de decisiones, teniendo en cuenta la objetividad y subjetividad de los parámetros a considerar. En general, el objetivo principal de esta tesis doctoral es identificar las características y oportunidades de negocio a través de un análisis de valoración de empresas, que permita una mejor interpretación del contexto incierto para la toma de decisiones. Es decir, la teoría de la decisión en la incertidumbre se desarrolla con la valoración de empresas. Se analiza la situación en la que se encuentra y se estudian las aportaciones que podemos hacer en este campo con los principales algoritmos de lógica difusa estudiados por autores como J. Gil Aluja, A. Kaufmann, R. Yager, entre otros, con especial énfasis en aquellos que han sido aplicados al ámbito empresarial y financiero. La valoración de empresas es un proceso fundamental y complejo en los sistemas económico-financieros. En un entorno que evoluciona hacia formas más complejas e inciertas, es necesario presentar nuevos modelos de valoración empresarial más dinámicos basados en técnicas de tratamiento y gestión de la incertidumbre y toma de decisiones, para eliminar la ambigüedad y la confusión en entornos inciertos. La primera aportación de este trabajo es el análisis del estado de la cuestión realizado a través de dos estudios bibliométricos que estudian las aportaciones de la comunidad científica a la lógica borrosa y la valoración empresarial. Destaca la importancia de los factores subjetivos a la hora de tomar decisiones en un entorno económico y financiero. La segunda contribución es el desarrollo de aplicaciones que muestren la toma de decisiones en la incertidumbre aplicada a los métodos de valoración de empresas. Este estudio nos permite desarrollar algoritmos genéricos y modelos matemáticos que se pueden aplicar a la realidad empresarial, para probar su utilidad. En este trabajo, se destacan el coeficiente de adecuación, el coeficiente de calificación, la distancia de Hamming, la teoría del clon, el modelo de preferencia subjetiva, el algoritmo húngaro, los operadores OWA, los intervalos y los expertones. La tercera contribución es un nuevo algoritmo que combina la matemática borrosa y la valoración de empresas, lo que contribuye al desarrollo de la teoría de la decisión en el ámbito empresarial. En concreto, se desarrolla un modelo de valoración de empresas mediante el descuento de flujos de caja y las matemáticas borrosas, mostrando su utilidad y la posibilidad de ser aplicado por la comunidad académica y profesional en el posterior análisis del valor de una empresa. El modelo propuesto sistematiza y ordena el uso de intervalos para establecer un valor de negocio mínimo y máximo para la empresa. Por lo tanto, hemos encontrado un intervalo de confianza del posible valor comercial. Finalmente, podríamos decir que a nivel general hay dos aportaciones importantes a destacar en esta tesis doctoral: la aplicabilidad y el desarrollo. Aplicamos algoritmos y modelos en los métodos de valoración de empresas y desarrollamos un nuevo algoritmo que contribuye al desarrollo de la teoría de la decisión

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

Accounting regulation in Nigeria : institutionalisation, accounting quality effects and capital market effects

This study examines three different aspects of accounting regulation in Nigeria. The first empirical chapter (chapter 2) examines the process of the institutionalisation of IFRS in Nigeria and its outcome. Using data from documents, interviews and survey, the chapter finds that IFRS is substantively adopted by Nigerian listed firms, as they use it for internal reporting. Furthermore, the institutionalisation process involves three levels of social order (i.e., Social, political and economic level; organisational field; and organisational level) at which different agents reinforce one another to ensure that institutionalisation of IFRS in Nigeria is substantive. The second empirical chapter examines whether accounting regulation in the form of IFRS adoption and/or enforcement of accounting standards lead(s) to higher accounting quality. The effects of these two regulatory mechanisms were assessed on three dimensions of accounting quality using fixed-effect regressions for earnings management, binary logistic regression for timely loss recognition, and a system dynamic panel model for earnings persistence on a sample of non-financial companies listed on the Nigerian Stock Exchange. The chapter finds that IFRS adoption significantly increases earnings management and reduces earnings persistence, while institutional reform, through the setting up of the Financial Reporting Council of Nigeria (FRCN) to enforce and monitor compliance with accounting standards, reduces earnings management. The third empirical chapter examines the effect of accounting regulation in the form of IFRS adoption and enforcement on market liquidity in Nigeria. The chapter adopts a longitudinal research design and analyses hand-collected panel data sets from semi-structured archives. Three proxies of market liquidity (i.e., bid-ask spread, zero returns, and volume) were adopted for the study. Firm-quarter observations of 1,416, 1,417 and 1,418 were analysed using a random-effect model for bid-ask-spread and a fixed-effect regression for both zero returns and volume, respectively. The chapter finds that both IFRS adoption and enforcement significantly improve the Nigerian stock market liquidity.School of Social Sciences PhD scholarshi