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A Deep Dive: Does Big Data Improve Maturity in the Developed Capital Markets?

机译:深入探讨:大数据是否可以改善发达资本市场的成熟度?

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Over this decade, the concept of big data has been applied to industries but the capital markets have been traditionally laggard to adoption. Within the financial services’ sector, Big Data has gained far more traction within retail banking and insurance due to the increasing desire of these financial institutions to profile and analyze their customers in a similar manner to early adopters of Big Data strategy such as Amazon, Baidu or Google. However, Big Data strategies have begun to make some impacts on few selected areas of the capital markets, including the social media sentiment analysis on the structured and unstructured data for trading, growth in volume, risk analytics, fraud prevention, market surveillance, predictability and forecasting of the equity prices; those are the early sign of the maturity of the capital markets. Technical and theoretical measures have evolved, but still these dimensions of the capital markets have been a mystery for the human beings till now. The Big Data in the form of structured, semi-structured and unstructured socio-economic and demographic information from social media and blogs from consumers has started indicating impacts on the capital markets which can lead to improving the real-time systems and transaction processing, and improving operational efficiency and maturity. The intent of this paper is threefold. First, it aims to bring the clear inference from the past researches to take a holistic analysis of the work done in the emerging area of Big Data and its implications on capital markets. Second, it’s to perform a deep analysis on how the influences of Big Data affect the assumptions in connection with Random Walk theory and Efficient Market Hypothesis. Third, it will provide a conclusive theoretical analysis of past research work by the scholars, which can establish the model to refine the nexus between investors’ sentiments and assets’ prices with advanced techniques in the Big Data. The paper has been divided into 4 broad sections. In the first section, the paper sets the introduction of connecting the dots and setting the context for the two different fields like Big Data and its influences on the capital markets. The second section explains the theoretical premises and frameworks needed for this research and does deep studies of the previous works in this area to establish conclusive references for the future study. The third section carries out the studies of emerging social media and technologies, analysis of the previous research works from the social media and the capital markets perspective. Finally, the fourth section concludes findings with recommendations.
机译:在过去的十年中,大数据的概念已应用于行业,但资本市场传统上一直落后于采用。在金融服务领域,大数据已在零售银行和保险领域获得更大的吸引力,因为这些金融机构越来越希望以与大数据战略的早期采用者类似的方式(例如亚马逊,百度)来描述和分析其客户或Google。但是,大数据战略已开始对资本市场的少数选定领域产生一些影响,包括针对交易的结构化和非结构化数据的社交媒体情绪分析,交易量增长,风险分析,预防欺诈,市场监控,可预测性和预测股票价格;这些是资本市场成熟的早期迹象。技术和理论方法已经发展,但到现在为止,资本市场的这些方面仍然是人类的一个谜。来自社交媒体和消费者博客的结构化,半结构化和非结构化社会经济和人口信息形式的大数据已经开始表明对资本市场的影响,可以导致改善实时系统和交易处理,以及提高运营效率和成熟度。本文的目的是三方面的。首先,它的目的是从过去的研究中得出清晰的推论,对新兴的大数据领域所做的工作及其对资本市场的影响进行整体分析。其次,结合随机游走理论和有效市场假说,对大数据的影响如何影响假设进行深入分析。第三,它将提供学者对过去研究工作的结论性理论分析,从而可以建立模型,以利用大数据中的先进技术完善投资者情绪与资产价格之间的联系。本文已分为4个主要部分。在第一部分中,论文介绍了连接点和设置两个不同领域(如大数据)及其对资本市场的影响的上下文。第二部分解释了这项研究所需的理论前提和框架,并对这一领域的先前工作进行了深入研究,以为今后的研究提供结论性参考。第三部分是对新兴社交媒体和技术的研究,从社交媒体和资本市场的角度分析了以前的研究成果。最后,第四部分总结研究结果并提出建议。

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