Forex prediction engine: framework, modelling techniques and implementations

Having accurate prediction in foreign exchange (Forex) market is useful because it provides intelligent information for investment strategy. This paper studies extracted repeating patterns of historical Forex time series, so to predict future trend direction by matching the forming trend with a repeating pattern. In the proposed Forex prediction engine, global pattern movements over a period of time are extracted using a linear regression line (LRL) enhanced technique, and then further segmented into what we called up and down curves. Subsequently, the artificial neural network (ANN) is applied to classify or group the uptrend and downtrend patterns. Finally, the dynamic time warping (DTW) is used through brute force to identify a trend pattern similar to the current trend at least for the beginning part. The remaining part of the matched pattern can provide predictive clues about next day trend movement. The experimental results generated on the dataset of AUD–USD and EUR–USD currencies between 2012 and 2013 demonstrate reliable accuracy performance of 72%

Intelligent agents in electronic markets for information goods: customization, preference revelation and pricing

Electronic commerce has enabled the use of intelligent agent technologies that can evaluate buyers, customize products, and price in real-time. Our model of an electronic market with customizable products analyzes the pricing, profitability and welfare implications of agent-based technologies that price dynamically based on product preference information revealed by consumers. We find that in making the trade-off between better prices and better customization, consumers invariably choose less-than-ideal products. Furthermore, this trade-off has a higher impact on buyers on the higher end of the market and causes a transfer of consumer surplus towards buyers with a lower willingness to pay. As buyers adjust their product choices in response to better demand agent technologies, seller revenues decrease since the gains from better buyer information are dominated by the lowering of the total value created from the transactions. We study the strategic and welfare implications of these findings, and discuss managerial and technology development guidelines.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

21P. Customer-centric Model for Performance Management in Banking Industry Using Soft System Methodology

This research uses soft system methodology in exploring a real world problem in managing the performance of banks’ branches. In the first step, a rich picture is drawn based on the semi-structured interviews with experienced personnel and managers of Iranian commercial banks. Extracting a rich picture about the problem situation and roots of the problem, and based on literature review and well-known theories including resource-based view of the firm and service-profit chain, the paper proposes a conceptual model for customer-centric performance management system (PMS). The proposed model suggests an integration of customer relationship management system and PMS using customer lifetime value metric in managing the bank’s performance. The paper also discusses the benefits of this metric. In practice, the model has a potential to provide more strategic use of information system (IS) by increasing the use of managerial knowledge and strategy making being extracted from IS

A geographic knowledge discovery approach to property valuation

This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely data driven and does not require previous knowledge of the area applied. To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm. For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the investigation of relationships between spatial entities beyond first degree connectivity. A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality

New Entrepreneurship in Urban Diasporas in our Modern World

ABSTRACT: Entrepreneurship among migrants – often called new, migrant or ethnic entrepreneurship – has over the past years become a significant component of the urban economy in many developed countries. Migrant entrepreneurship has a considerable welfare enhancing impact on the city, notably a contribution to innovation and growth, creation of new jobs for less favoured population groups, advancement of benefits from cultural diversity, and reinforcement of economic opportunities related to international connectivity. The present paper aims to investigate the backgrounds of migrant entrepreneurship in large Dutch cities, in particular, the critical success factors of business performance of these entrepreneurs in relation to their ethnic background, their levels of skill, and other specific and general contextual factors. To address the drivers of break-out strategies for new markets, a sample of second-generation Moroccan entrepreneurs is extensively interviewed to extract detailed information at a micro business level. The wealth of qualitative information on both input factors and output (performance) achievements is next systematically coded in a qualitative survey table which is converted into a format that is suitable for application of a rough set analysis. This is an artificial intelligence technique that is able to extract and identify the set of combinations of different drivers that altogether make up for a final outcome. The results show that longer stay in the host country, male gender, family network support and education of the entrepreneurs concerned are critical variables for the business performance of these urban diaspora entrepreneurs. KEYWORDS: Migrant entrepreneurship, break-out strategies, change agents, second-generation migrant entrepreneurs, urban economy, innovation, cultural diversity, international connectivity, business performance, rough set analysis, urban diaspora entreprene