Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
240
result(s) for
"Finance Decision making Data processing."
Sort by:
Global algorithmic capital markets : high frequency trading, dark pools, and regulatory challenges
Global capital markets have undergone fundamental transformations in recent years and, as a result, have become extraordinarily complex and opaque. Trading space is no longer measured in minutes or seconds but in time units beyond human perception: milliseconds, microseconds, and even nanoseconds. Technological advances have thus scaled up imperceptible and previously irrelevant time differences into operationally manageable and enormously profitable business opportunities for those with the proper high-tech trading tools. These tools include the fastest private communication and trading lines, the most powerful computers and sophisticated algorithms capable of speedily analysing incoming news and trading data and determining optimal trading strategies in microseconds, as well as the possession of gigantic collections of historic and real-time market data. 0Fragmented capital markets are also becoming a rapidly growing reality in Europe and Asia, and are an established feature of U.S. trading. This raises urgent market governance issues that have largely been overlooked. Global Algorithmic Capital Markets seeks to understand how recent market transformations are affecting core public policy objectives such as investor protection and reduction of systemic risk, as well as fairness, efficiency, and transparency. 0The operation and health of capital markets affect all of us and have profound implications for equality and justice in society. This unique set of chapters by leading scholars, industry insiders, and regulators discusses ways to strengthen market governance for the benefit of society at whole.
Book
Big data science in finance
Explains the mathematics, theory, and methods of Big Data as applied to finance and investingData science has fundamentally changed Wall Street—applied mathematics and software code are increasingly driving finance and investment-decision tools. Big Data Science in Finance examines the mathematics, theory, and practical use of the revolutionary techniques that are transforming the industry. Designed for mathematically-advanced students and discerning financial practitioners alike, this energizing book presents new, cutting-edge content based on world-class research taught in the leading Financial Mathematics and Engineering programs in the world. Marco Avellaneda, a leader in quantitative finance, and quantitative methodology author Irene Aldridge help readers harness the power of Big Data.Comprehensive in scope, this book offers in-depth instruction on how to separate signal from noise, how to deal with missing data values, and how to utilize Big Data techniques in decision-making. Key topics include data clustering, data storage optimization, Big Data dynamics, Monte Carlo methods and their applications in Big Data analysis, and more. This valuable book:Provides a complete account of Big Data that includes proofs, step-by-step applications, and code samplesExplains the difference between Principal Component Analysis (PCA) and Singular Value Decomposition (SVD)Covers vital topics in the field in a clear, straightforward mannerCompares, contrasts, and discusses Big Data and Small DataIncludes Cornell University-tested educational materials such as lesson plans, end-of-chapter questions, and downloadable lecture slidesBig Data Science in Finance: Mathematics and Applications is an important, up-to-date resource for students in economics, econometrics, finance, applied mathematics, industrial engineering, and business courses, and for investment managers, quantitative traders, risk and portfolio managers, and other financial practitioners.
eBook
Uncertainty in big data analytics: survey, opportunities, and challenges
Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/or predicting future courses of action with high precision and advanced decision-making strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain.
Journal Article
Blockchain meets machine learning: a survey
Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.
Journal Article
Over a decade of social opinion mining: a systematic review
Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.
Journal Article
Conflict translates environmental and social risk into business costs
Sustainability science has grown as a field of inquiry, but has said little about the role of large-scale private sector actors in socio-ecological systems change. However, the shaping of global trends and transitions depends greatly on the private sector and its development impact. Market-based and command-and-control policy instruments have, along with corporate citizenship, been the predominant means for bringing sustainable development priorities into private sector decision-making. This research identifies conflict as a further means through which environmental and social risks are translated into business costs and decision making. Through in-depth interviews with finance, legal, and sustainability professionals in the extractive industries, and empirical case analysis of 50 projects worldwide, this research reports on the financial value at stake when conflict erupts with local communities. Over the past decade, high commodity prices have fueled the expansion of mining and hydrocarbon extraction. These developments profoundly transform environments, communities, and economies, and frequently generate social conflict. Our analysis shows that mining and hydrocarbon companies fail to factor in the full scale of the costs of conflict. For example, as a result of conflict, a major, world-class mining project with capital expenditure of between US$3 and US$5 billion was reported to suffer roughly US$20 million per week of delayed production in net present value terms. Clear analysis of the costs of conflict provides sustainability professionals with a strengthened basis to influence corporate decision making, particularly when linked to corporate values. Perverse outcomes of overemphasizing a cost analysis are also discussed.
Journal Article
Financial text analysis and credit risk assessment using a GPT-4 and improved BERT fusion model
This study aims to improve the identification of potential credit risks in unstructured financial texts. It addresses the core problem of financial text analysis and credit risk assessment by proposing a hybrid model that combines the generative semantic understanding of Generative Pre-trained Transformer-4 (GPT-4) with the enhanced feature extraction of Bidirectional Encoder Representations from Transformers (BERT). To overcome the limitations of traditional methods—such as weak contextual reasoning in long texts, insufficient recognition of industry-specific terminology, and implicit credit risk expressions—the model incorporates a financial dictionary enhancement module and a named entity recognition (NER) component. GPT-4 is leveraged for prompt-based generation to extract latent risk information from complex texts, including annual reports. A dual-model semantic fusion mechanism with attention weighting constructs a multi-level risk assessment system that integrates contextual understanding, industry adaptability, and interpretability. Experiments on multiple publicly available financial datasets and real-world annual reports demonstrate the model’s effectiveness. Results show that the proposed approach outperforms representative baseline models in accuracy, adaptability, and interpretability. This work carries both theoretical and practical significance for research at the intersection of financial technology and natural language processing.
Journal Article
The future of fintech
This article describes the growing field of financial technology (fintech) and the different financial paradigms and technologies that support it. Fintech is primarily a disintermediation force where disruptive technologies are the drivers. This framework discusses 10 primary areas in fintech comprising a taxonomy, which categorizes research in the field and also proposes a pedagogical structure. Pitfalls of fintech are also analyzed. Overall, the great strides made in computing technology, mathematics, statistics, psychology, econometrics, linguistics, cryptography, big data, and computer interfaces have combined to create an explosion of fintechs.
Journal Article
Genetic Variation in Financial Decision-Making
Individuals differ in how they construct their investment portfolios, yet empirical models of portfolio risk typically account only for a small portion of the cross-sectional variance. This paper asks whether genetic variation can explain some of these individual differences. Following a major pension reform Swedish adults had to form a portfolio from a large menu of funds. We match data on these investment decisions with the Swedish Twin Registry and find that approximately 25% of individual variation in portfolio risk is due to genetic variation. We also find that these results extend to several other aspects of financial decision-making.
Journal Article
EEG-Based Emotion Classification in Financial Trading Using Deep Learning: Effects of Risk Control Measures
Day traders in the financial markets are under constant pressure to make rapid decisions and limit capital losses in response to fluctuating market prices. As such, their emotional state can greatly influence their decision-making, leading to suboptimal outcomes in volatile market conditions. Despite the use of risk control measures such as stop loss and limit orders, it is unclear if these strategies have a substantial impact on the emotional state of traders. In this paper, we aim to determine if the use of limit orders and stop loss has a significant impact on the emotional state of traders compared to when these risk control measures are not applied. The paper provides a technical framework for valence-arousal classification in financial trading using EEG data and deep learning algorithms. We conducted two experiments: the first experiment employed predetermined stop loss and limit orders to lock in profit and risk objectives, while the second experiment did not employ limit orders or stop losses. We also proposed a novel hybrid neural architecture that integrates a Conditional Random Field with a CNN-BiLSTM model and employs Bayesian Optimization to systematically determine the optimal hyperparameters. The best model in the framework obtained classification accuracies of 85.65% and 85.05% in the two experiments, outperforming previous studies. Results indicate that the emotions associated with Low Valence and High Arousal, such as fear and worry, were more prevalent in the second experiment. The emotions associated with High Valence and High Arousal, such as hope, were more prevalent in the first experiment employing limit orders and stop loss. In contrast, High Valence and Low Arousal (calmness) emotions were most prominent in the control group which did not engage in trading activities. Our results demonstrate the efficacy of our proposed framework for emotion classification in financial trading and aid in the risk-related decision-making abilities of day traders. Further, we present the limitations of the current work and directions for future research.
Journal Article