5,225 research outputs found

    Does segmentation always improve model performance in credit scoring?

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    Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

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

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    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

    Regulatory reform : integrating paradigms

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    The Subprime crisis largely resulted from failures to internalize systemic risk evenly across financial intermediaries and recognize the implications of Knightian uncertainty and mood swings. A successful reform of prudential regulation will need to integrate more harmoniously the three paradigms of moral hazard, externalities, and uncertainty. This is a tall order because each paradigm leads to different and often inconsistent regulatory implications. Moreover, efforts to address the central problem under one paradigm can make the problems under the others worse. To avoid regulatory arbitrage and ensure that externalities are uniformly internalized, all prudentially regulated intermediaries should be subjected to the same capital adequacy requirements, and unregulated intermediaries should be financed only by regulated intermediaries. Reflecting the importance of uncertainty, the new regulatory architecture will also need to rely less on markets and more on"holistic"supervision, and incorporate countercyclical norms that can be adjusted in light of changing circumstances.Debt Markets,Banks&Banking Reform,Emerging Markets,Labor Policies,Financial Intermediation

    Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

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    This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system

    Agent-orientated auction mechanism and strategy design

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    Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games

    The emergence of the fintech industry in China: An evolutionary economic geography perspective

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    Over the last decade, the global economy has rapidly becoming digited. Digital technologies have transformed the economy and society, affecting all sectors of activity around the world. Among them, the financial sector is one of the most digitalized sectors, and the term ‘fintech’ is coined to describe the digitalization of the financial sector. Although the global fintech landscape is currently geographically concentrated in the United States and Europe, the pace of China’s fintech development has been dramatically accelerated. However, it is quite surprising that there is hardly any study that investigates fintech in China from a subnational scale. To fill this gap, this dissertation conducts a city-level analysis of the emergence of the fintech industry in China. Theoretically, I position this dissertation within the broad literature on evolutionary economic geography (EEG), which has emerged as one of the main paradigms in economic geography. This dissertation aims to provide a comprehensive understanding of the emergence of the new industry in regions. Conventional wisdom in EEG posits that new industry in regions tends to grow out of technologically related pre-existing industries. However, this conventional understanding is somewhat technology-centric. In response, this dissertation extends the scholarly work from technology-centric to embrace the role of the demand-side market and institutional logic in the emergence of the new industry in regions. It proposes that not only supply-side technology but also demand-side market and institutional logics matter for the emergence of the new industry in regions. Moreover, this dissertation ascribes the underlying logic of how technology, market, and institutional logics matter to the agentic processes of asset modification, particularly redeploying pre-existing assets and creating new assets. In other words, the emergence of the new industry in regions results from relevant regional actors’ purposeful actions in terms of modifying technological, market, and institutional assets. Methodologically, there is a dualism in evolutionary economic geography research between qualitative and quantitative work. To seek a methodology integration, this dissertation proposes the mixed-method that is composed of four concrete approaches, namely the triangulation approach, the embedded approach, the sequential exploratory approach, and the sequential explanatory approach. Among these concrete approaches, the embedded approach is utilized in empirical work. The embedded approach in this dissertation refers to the embedding of the qualitative case study (which deals with the ‘how’ questions) into quantitative research (which deals with the ‘whether and to what extent’ questions). Empirically, this dissertation first examines the emergence of fintech industries in China’s cities based on the quantitative regression analysis (mainly dealing with the ‘whether and to what extent’ questions) and then zooms in on the city of Shenzhen, which is the largest fintech hub in southern China, based on the qualitative case study (mainly dealing with the ‘how’ questions). The findings are as follows. (1) Based on a unique dataset from 2003 – 2019, this dissertation provides a city-level analysis of the fintech industry in China. The econometric results show that fintech industries tend to emerge in cities that have more fintech-related technologies, particularly in the fields of finance, e-commerce, data sciences, and security. This confirms the principle of technological relatedness. Moreover, it finds a positive relationship between the development of the fintech industry and the demand for fintech services. To the best of my knowledge, this is the first systematic evidence of the significant positive role of the demand-side market in the emergence of the new industry in regions. (2) In order to uncover the underlying processes (the question of ‘how’) that lead to the above significantly positive effect, this dissertation resorts to the qualitative case study. The case study shows that the rise of Shenzhen’s fintech industry mainly grows out of Shenzhen’s pre-existing internet and financial industry. By systematically comparing the processes that internet and financial industry diversify into the fintech industry, it finds that the emergence of the fintech industry in Shenzhen result from internet and financial firms’ purposeful actions in terms of redeploying their pre-existing technologies, market, and institutional logics, as well as creating the new ones that are necessary for fintech but are missing for the internet or financial firms. In other words, it is the processes of asset modification, particularly redeploying pre-existing assets and creating new assets, that give rise to the birth of the fintech industry, leading to the positive relationships found in the quantitative regression analysis

    Smart vending

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    The present study summarizes the evolution in vending business with the adoption and popularity of smart vending concept. Using the methods of descriptive, situational and content analysis are represented the features and benefits of smart vending machines as a form of out of store retailing. It’s spreading is the result of combining the processes of digital transformation, the transition to the circular economy model and the impact of the COVID-19 pandemic on business and society. The smart vending machine ensures a great consumer experience and commitment, but on the other hand it provides the vending operator with advanced features for precise at the site and even online management of the device, the sophisticated control of the inventories and the analysis of the clients, the possibilities of modern forms and technologies of payments and communication are diversified, the promotional strategy and the information provision of the consumers is improved
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