Applying GMDH-Type Neural Network and Genetic Algorithm for Stock Price Prediction of Iranian Cement Sector

The cement industry is one of the most important and profitable industries in Iran and great content of financial resources are investing in this sector yearly. In this paper a GMDH-type neural network and genetic algorithm is developed for stock price prediction of cement sector. For stocks price prediction by GMDH type-neural network, we are using earnings per share (EPS), Prediction Earnings Per Share (PEPS), Dividend per share (DPS), Price-earnings ratio (P/E), Earnings-price ratio (E/P) as input data and stock price as output data. For this work, data of ten cement companies is gathering from Tehran stock exchange (TSE) in decennial range (1999-2008). GMDH type neural network is designed by 80% of the experimental data. For testing the appropriateness of the modeling, reminder of primary data were entered into the GMDH network. The results are very encouraging and congruent with the experimental result

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Performance Evaluation for the Sustainable Supply Chain Management

Supply chain SC activities transform natural resources, raw materials, and components into various finished products that are delivered to end customers. A high efficient SC would bring great benefits to an enterprise such as integrated resources, reduced logistics costs, improved logistics efficiency, and high quality of overall level of services. In contrast, an inefficient SC will bring additional transaction costs, information management costs, and resource waste, reduce the production capacity of all enterprises on the chain, and unsatisfactory customer relationships. So the evaluation of a SC is important for an enterprise to survive in a competitive market in a globalized business environment. Therefore, it is important to research the various methods, performance indicator systems, and technology for evaluating, monitoring, predicting, and optimizing the performance of a SC. A typical procedure of the performance evaluation (PE) of a SC is to use the established evaluation performance indicators, employ an analytical method, follow a given procedure, to carry out quantitatively or qualitatively comparative analysis to provide the objective and accurate evaluation of a SC performance in a selected operation period. Various research works have been carried out in proposing the performance indicator systems and methods for SC performance evaluations. But there are no widely accepted indicator systems that can be applied in practical SC performance evaluations due to the fact that the indicators in different systems have been defined without a common understanding of the meanings and the relationships between them, and they are nonlinear and very complicated

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Study on stock trading and portfolio optimization using genetic network programming

制度:新 ; 報告番号:甲3002号 ; 学位の種類:博士(工学) ; 授与年月日: 2010/3/15 ; 早大学位記番号:新525

Forecasting USAF JP-8 Fuel Needs

Oil is still one of the strategic energy resources for both the U.S. and the USAF today. Accurate oil prediction is important for the U.S. in order to improve the national strategy and the related budget concerns. Today, the U.S. is roughly importing 58% of its petroleum products. Moreover, in Fiscal Year (FY) 2007 the USAF total energy costs exceeded $6.9 billion. Aviation fuel accounted for approximately 81% of the total AF energy costs. Fluctuations in oil prices have huge impacts on the USAF’s JP-8 budgetary calculations. In order to handle this problem, the need for accurate forecasts arises. In this study, we forecast the USAF’s JP-8 consumption and costs for the next five year period. The study shows that JP-8 consumption figures will go on to follow the recent trend via Holt’s Linear Method. Also, the study shows that good short-term predictions can be obtained with more simple and easy-to-implement methods, versus complex ones. When we consider long-term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified forecast accuracy measures. Our results indicate that the USAF’s JP-8 cost for each of the next 5 years will be somewhere between 6.3 and 7.5 billion dollars

Applied Analysis and Synthesis of Complex Systems: Proceedings of the IIASA-Kyoto University Joint Seminar, June 28-29, 2004

This two-day seminar aimed at introducing the new development of the COE by Kyoto University to IIASA and discussing general modeling methodologies for complex systems consisting of many elements, mostly via nonlinear, large-scale interactions. We aimed at clarifying fundamental principles in complex phenomena as well as utilizing and synthesizing the knowledge derived out of them. The 21st Century COE (Center of Excellence) Program is an initiative by the Japanese Ministry of Education, Culture, Science and Technology (MEXT) to support universities establishing discipline-specific international centers for education and research, and to enhance the universities to be the world's apex of excellence with international competitiveness in the specific research areas. Our program of "Research and Education on Complex Functional Mechanical Systems" is successfully selected to be awarded the fund for carrying out new research and education as Centers of Excellence in the field of mechanical engineering in 2003 (five-year project), and is expected to lead Japanese research and education, and endeavor to be the top in the world. The program covers general backgrounds in diverse fields as well as a more in-depth grasp of specific branches such as complex system modeling and analysis of the problems including: nonlinear dynamics, micro-mesoscopic physics, turbulent transport phenomena, atmosphere-ocean systems, robots, human-system interactions, and behaviors of nano-composites and biomaterials. Fundamentals of those complex functional mechanical systems are macroscopic phenomena of complex systems consisting of microscopic elements, mostly via nonlinear, large-scale interactions, which typically present collective behavior such as self-organization, pattern formation, etc. Such phenomena can be observed or created in every aspect of modern technologies. Especially, we are focusing upon; turbulent transport phenomena in climate modeling, dynamical and chaotic behaviors in control systems and human-machine systems, and behaviors of mechanical materials with complex structures. As a partial attainment of this program, IIASA and Kyoto University have exchanged Consortia Agreement at the beginning of the program in 2003, and this seminar was held to introduce the outline of the COE program of Kyoto University to IIASA researchers and to deepen the shared understandings on novel complex system modeling and analysis, including novel climate modeling and carbonic cycle management, through joint academic activities by mechanical engineers and system engineers. In this seminar, we invited a distinguished researcher in Europe as a keynote speaker and our works attained so far in the project were be presented by the core members of the project as well as by the other contributing members who participated in the project. All IIASA research staff and participants of YSSP (Young Scientist Summer Program) were cordially invited to attend this seminar to discuss general modeling methodologies for complex systems