Essays On Machine Learning And Price Impact In Institutional Finance
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Essays On Machine Learning And Price Impact In Institutional Finance
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Author : Zihan Lin (Researcher in machine learning)
language : en
Publisher:
Release Date : 2022
Essays On Machine Learning And Price Impact In Institutional Finance written by Zihan Lin (Researcher in machine learning) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Institutional investors play crucial roles in financial markets. First, they delegate investment for individual investors. We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, and the returns of predictive long-short portfolios are higher following a period of high sentiment. Second, institutional investors provide liquidity to investor demand. We hypothesize and provide evidence that prices are more inelastic when demand is less diversifiable. We decompose order-flow imbalances into components with varying degrees of diversifiability and estimate their price impacts. Our findings are consistent with weaker liquidity provision at less diversifiable levels.
Essay On Big Data And Machine Learning In Finance
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Author : Gunsu Son
language : en
Publisher:
Release Date : 2023
Essay On Big Data And Machine Learning In Finance written by Gunsu Son and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Despite structural differences between the options and stock markets, few studies have discussed the behavior and impact of high-frequency traders (HFTs) in the options market. Options exchanges identify high-frequency/algorithmic traders as Professional Customers (PCs). In this study, we use granular data that identifies trades by customers, PCs, and Market Makers (MMs). We find that PCs mainly trade as a counterparty to customers, similar to MMs. However, the liquidity provision by PCs leads to order flow toxicity: PCs use a "cream skimming" strategy that imposes adverse selection costs on MMs. PCs mainly trade with uninformed customers, most likely leveraging their speed and algorithmic advantage. PCs provide less liquidity when the market and stock volatility are high. Customer call option trades made with PCs have one-tenth of price impact and no return or volatility predictability, while there is significant price impact in addition to return and volatility predictability when executed against MMs during the next 30 minutes. Our finding on HFTs' non-arbitrage channel of order flow toxicity is new and suggests that the role of HFTs should be better understood in the context of the options market structure.
Essays On Conditional Asset Pricing And Machine Learning In Finance
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Author : Stephen Owen
language : en
Publisher:
Release Date : 2021
Essays On Conditional Asset Pricing And Machine Learning In Finance written by Stephen Owen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
In recent years there has been wide-scale access to improved statistical estimation techniques and the implementation of such techniques in financial economics. In this dissertation, I provide two brief overviews of the evolution of linear factor models in asset pricing and machine learning in finance. I then provide four research essays that implement machine learning in financial economic research settings. The first essay revisits tests of the conditional Capital Asset Pricing Model in an international context using multivariate generalized autoregressive conditional heteroskedasticity techniques. The second essay studies the use of hierarchical clustering in mean-variance optimal portfolio management. The third essay proposes a novel paragraph embedding technique that leverages the question-and-answer structure of earnings announcement calls to model the similarity between documents. The fourth and final essay studies the impact that dodgy managers have on idiosyncratic security performance.
Machine Learning And Data Sciences For Financial Markets
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Author : Agostino Capponi
language : en
Publisher: Cambridge University Press
Release Date : 2023-04-30
Machine Learning And Data Sciences For Financial Markets written by Agostino Capponi and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-30 with Mathematics categories.
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Machine Learning In Asset Pricing
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Author : Stefan Nagel
language : en
Publisher: Princeton University Press
Release Date : 2021-05-11
Machine Learning In Asset Pricing written by Stefan Nagel and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-11 with Business & Economics categories.
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
Machine Learning For Finance
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Author : Saurav Singla
language : en
Publisher: BPB Publications
Release Date : 2021-01-05
Machine Learning For Finance written by Saurav Singla and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-05 with Computers categories.
Understand the essentials of Machine Learning and its impact in financial sector KEY FEATURESÊ _Explore the spectrum of machine learning and its usage. _Understand the NLP and Computer Vision and their use cases. _Understand the Neural Network, CNN, RNN and their applications. _ÊUnderstand the Reinforcement Learning and their applications. _Learn the rising application of Machine Learning in the Finance sector. Ê_Exposure to data mining, data visualization and data analytics. DESCRIPTION The fields of machining adapting, profound learning, and computerized reasoning are quickly extending and are probably going to keep on doing as such for a long time to come. There are many main impetuses for this, as quickly caught in this review. Now and again, the advancement has been emotional, opening new ways to deal with long-standing innovation challenges, for example, progresses in PC vision and picture investigation.Ê Ê The book demonstrates how to solve some of the most common issues in the financial industry.Ê The book addresses real-life problems faced by practitioners on a daily basis. The book explains how machine learning works on structured data, text, and images. You will cover the exploration of Nave Bayes, Normal Distribution, Clustering with Gaussian process, advanced neural network, sequence modeling, and reinforcement learning. Later chapters will discuss machine learning use cases in the finance sector and the implications of deep learning. The book ends with traditional machine learning algorithms. Ê Machine Learning has become very important in the finance industry, which is mostly used for better risk management and risk analysis. Better analysis leads to better decisions which lead to an increase in profit for financial institutions. Machine Learning to empower fintech to make massive profits by optimizing processes, maximizing efficiency, and increasing profitability. WHAT WILL YOU LEARN _ Ê Ê Ê You will grasp the most relevant techniques of Machine Learning for everyday use. _ Ê Ê Ê You will be confident in building and implementing ML algorithms. _ Ê Ê Ê Familiarize the adoption of Machine Learning for your business need. _ Ê Ê Ê Discover more advanced concepts applied in banking and other sectors today. _ Ê Ê Ê Build mastery skillset in designing smart AI applications including NLP, Computer Vision and Deep Learning. WHO THIS BOOK IS FORÊ Data Scientist, Machine Learning Engineers and Individuals who want to adopt machine learning in the financial domain. Practitioners are working in banks, asset management, hedge funds or working the first time in the finance domain. Individuals who want to learn about applications of machine learning in finance or individuals entering the fintech domain. TABLE OF CONTENTS 1.Introduction 2.Naive Bayes, Normal Distribution and Automatic Clustering Processes 3.Machine Learning for Data Structuring 4.Parsing Data Using NLP 5.Computer Vision 6.Neural Network, GBM and Gradient Descent 7.Sequence Modeling 8.Reinforcement Learning For Financial Markets 9.Finance Use Cases 10.Impact of Machine Learning on Fintech 11.Machine Learning in Finance 12.eKYC and Anti-Fraud Policy 13.Uses of Data Mining and Data Visualization 14.Advantages and Disadvantages of Machine Learning 15.Applications of Machine Learning in Other Industries 16.Ethical considerations in Artificial Intelligence 17.Artificial Intelligence in Banking 18.Common Machine Learning Algorithms 19.Frequently Asked Questions
Powering The Digital Economy Opportunities And Risks Of Artificial Intelligence In Finance
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Author : El Bachir Boukherouaa
language : en
Publisher: International Monetary Fund
Release Date : 2021-10-22
Powering The Digital Economy Opportunities And Risks Of Artificial Intelligence In Finance written by El Bachir Boukherouaa and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-22 with Business & Economics categories.
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Machine Learning And Causality The Impact Of Financial Crises On Growth
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Author : Mr.Andrew J Tiffin
language : en
Publisher: International Monetary Fund
Release Date : 2019-11-01
Machine Learning And Causality The Impact Of Financial Crises On Growth written by Mr.Andrew J Tiffin and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Computers categories.
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
Essays In Machine Learning In Finance
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Author : Ye Ye
language : en
Publisher:
Release Date : 2022
Essays In Machine Learning In Finance written by Ye Ye and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
The bond market is one of the largest financial markets, with $52.9 trillion of debt outstanding for the US market as of 2021. The implied interest rate for borrowing at different horizons is the fundamental object for this market. However, a complete set of interest is not observed and must be estimated from the noisy market data. In two papers, we develop machine learning methods to precisely estimate the term structure of interest rates and to understand and manage interest-rate related risks. In the first paper, we introduce a robust, flexible and easy-to-implement method for estimating the yield curve from Treasury securities. This method is non-parametric and optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness reward. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks, which positions our method as the new standard for yield curve estimation. In the second paper, we develop a sparse factor model for bond returns, that unifies non- parametric term structure estimation with cross-sectional factor modeling. Building on the modeling framework of the first paper, we estimate an optimal set of sparse basis functions, which maps into a cross-sectional conditional factor model. Our estimated factors are investable portfolios of traded assets, that replicate the full term structure and are sufficient to hedge against interest rate changes. In an extensive empirical study on U.S. Treasury securities, we show that the term structure of excess returns is well explained by four factors. We introduce a new measure for the time-varying complexity of bond markets based on the exposure to higher-order factors.
Artificial Intelligence In Financial Services And Banking Industry
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Author : Dr. V.V.L.N. Sastry
language : en
Publisher: Idea Publishing
Release Date : 2020-03-20
Artificial Intelligence In Financial Services And Banking Industry written by Dr. V.V.L.N. Sastry and has been published by Idea Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-20 with Business & Economics categories.
In the last couple of years, the finance and banking sectors have increasingly deployed and implemented Artificial Intelligence (AI) technologies. AI and machine learning are being rapidly adopted for a range of applications for front-end and back end processes to both business and financial management operations. Thus, it is quite significant to consider the financial stability repercussions of such uses. Since AI is relatively new, the data on the usage is largely unavailable, any analysis may be necessarily considered Preliminary1 . Some of the current and potential use cases of AI and machine learning in the finance sector include the following. Institutions use AI and machine learning methods to optimize scarce capital, back-test models, and analyze the market impact of trading large positions. Financial institutions and vendors use AI and machine learning techniques to evaluate credit quality for market and price insurance contracts, and to automate client interaction. Brokers, hedge funds, and other firms are using AI and machine learning to find pointers for higher (and uncorrelated) returns to optimize trading execution. Private and public sector institutions use these technologies for data quality assessment, surveillance, regulatory compliance, and fraud detection. This book seeks to map the use of AI in current state of affairs in the banking and financial sector. By doing so, it explores: The present uses of AI in banking and finance and its narrative across the globe.