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Essays On Machine Learning And Price Impact In Institutional Finance
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Book Synopsis Essays on Machine Learning and Price Impact in Institutional Finance by : Zihan Lin (Researcher in machine learning)
Download or read book Essays on Machine Learning and Price Impact in Institutional Finance written by Zihan Lin (Researcher in machine learning) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Essays on Conditional Asset Pricing and Machine Learning in Finance by : Stephen Owen
Download or read book Essays on Conditional Asset Pricing and Machine Learning in Finance written by Stephen Owen and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Essay on Big Data and Machine Learning in Finance by : Gunsu Son
Download or read book Essay on Big Data and Machine Learning in Finance written by Gunsu Son and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance by : El Bachir Boukherouaa
Download or read book Powering the Digital Economy: Opportunities and Risks of Artificial Intelligence in Finance written by El Bachir Boukherouaa and published by International Monetary Fund. This book was released on 2021-10-22 with total page 35 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Causality: The Impact of Financial Crises on Growth by : Mr.Andrew J Tiffin
Download or read book Machine Learning and Causality: The Impact of Financial Crises on Growth written by Mr.Andrew J Tiffin and published by International Monetary Fund. This book was released on 2019-11-01 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning in Asset Pricing by : Stefan Nagel
Download or read book Machine Learning in Asset Pricing written by Stefan Nagel and published by Princeton University Press. This book was released on 2021-05-11 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and Data Sciences for Financial Markets by : Agostino Capponi
Download or read book Machine Learning and Data Sciences for Financial Markets written by Agostino Capponi and published by Cambridge University Press. This book was released on 2023-04-30 with total page 743 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning for Finance by : Saurav Singla
Download or read book Machine Learning for Finance written by Saurav Singla and published by BPB Publications. This book was released on 2021-01-05 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Essays in Machine Learning in Finance by : Ye Ye
Download or read book Essays in Machine Learning in Finance written by Ye Ye and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Three Essays on the Application of Machine Learning for Risk Governance in Financial Institutions by : Abena Fosua Owusu
Download or read book Three Essays on the Application of Machine Learning for Risk Governance in Financial Institutions written by Abena Fosua Owusu and published by . This book was released on 2020 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence in Financial Services and Banking Industry by : Dr. V.V.L.N. Sastry
Download or read book Artificial Intelligence in Financial Services and Banking Industry written by Dr. V.V.L.N. Sastry and published by Idea Publishing. This book was released on 2020-03-20 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Machine Learning and AI in Finance by : German Creamer
Download or read book Machine Learning and AI in Finance written by German Creamer and published by Routledge. This book was released on 2021-04-05 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
Book Synopsis The Essentials of Machine Learning in Finance and Accounting by : Mohammad Zoynul Abedin
Download or read book The Essentials of Machine Learning in Finance and Accounting written by Mohammad Zoynul Abedin and published by Routledge. This book was released on 2021-06-20 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces machine learning in finance and illustrates how we can use computational tools in numerical finance in real-world context. These computational techniques are particularly useful in financial risk management, corporate bankruptcy prediction, stock price prediction, and portfolio management. The book also offers practical and managerial implications of financial and managerial decision support systems and how these systems capture vast amount of financial data. Business risk and uncertainty are two of the toughest challenges in the financial industry. This book will be a useful guide to the use of machine learning in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Book Synopsis Novel Financial Applications of Machine Learning and Deep Learning by : Mohammad Zoynul Abedin
Download or read book Novel Financial Applications of Machine Learning and Deep Learning written by Mohammad Zoynul Abedin and published by Springer Nature. This book was released on 2023-03-01 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state-of-the-art applications of machine learning in the finance domain with a focus on financial product modeling, which aims to advance the model performance and minimize risk and uncertainty. It provides both practical and managerial implications of financial and managerial decision support systems which capture a broad range of financial data traits. It also serves as a guide for the implementation of risk-adjusted financial product pricing systems, while adding a significant supplement to the financial literacy of the investigated study. The book covers advanced machine learning techniques, such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches, and their application to finance datasets. It also leverages real-world financial instances to practice business product modeling and data analysis. Software code, such as MATLAB, Python and/or R including datasets within a broad range of financial domain are included for more rigorous practice. The book primarily aims at providing graduate students and researchers with a roadmap for financial data analysis. It is also intended for a broad audience, including academics, professional financial analysts, and policy-makers who are involved in forecasting, modeling, trading, risk management, economics, credit risk, and portfolio management.
Book Synopsis Artificial Intelligence in Asset Management by : Söhnke M. Bartram
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Book Synopsis Essays in Technological Innovation & Financial Economics by : Abhimanyu Mukerji
Download or read book Essays in Technological Innovation & Financial Economics written by Abhimanyu Mukerji and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the effects of technological innovation, particularly recent developments in machine learning and artificial intelligence (ML/AI), on firm growth, productivity, investment and competitiveness. It has two parts. The first chapter of my dissertation takes a broad view to ask a more fundamental question: do these technologies add value, and how can we quantify this? Academic literature is divided into two broad schools of thought. The first is that ML/AI represent general purpose technologies comparable to electricity or the steam engine, citing the extensive and expanding applications as supporting evidence. The second suggests that the utility of ML/AI is, in reality, more limited, and that the technological landscape is still evaluating added value while in the inflationary phases of a hype cycle. The major challenge associated with this literature is in measuring timing and intensity: what firms use ML/AI, and how extensively is it applied in business functions? The bulk of research in this field has focused on job postings data, which requires subjective feature construction by the researcher. Moreover, jobs data does not provide a precise time series of adoption and utilization intensity. My paper improves upon these approaches by developing a novel methodology based on cutting edge techniques from natural language processing. I adopt deep learning and topic modeling frameworks for unsupervised textual analysis to generate measures superior to more traditional scaled frequency-based approaches. I show that ML/AI utilization is associated with enhanced predictive capabilities and reduced cash flow volatility, with significantly more accurate earnings forecasts by firms. Firms using ML/AI show higher capital and labor productivity, as well as higher sales growth, profitability and market returns. My work helps shed light on the impact of ML/AI in a corporate setting, building on similar work focusing more granularly on labor markets. I show that the evidence is supportive of the general purpose technology hypothesis, and that the widespread adoption of ML/AI is correlated with positive outcomes across a range of industries and markets. Moreover, I show a substitution effect, with firms cutting back on employment and increasing investment in technological innovation. In the second chapter, I work towards understanding the effects of these new technologies on smaller firms. In particular, I study the role of democratized access to ML/AI technologies in encouraging productivity and innovation. Technological innovation has historically been a major driver of economic growth, with Schumpeterian creative destruction and subsequent resource reallocation supporting higher levels of equilibrium output. In recent decades, there has been evidence that suggests that these economic mechanisms may not be working well: increased barriers to entry, reduced business dynamism, asymmetric contributions to technological innovation, a widening gap between small and large firms, and reduced productivity growth. This has led to decreased industry competitiveness and new firm market entry, with risks of predatory pricing, reduced wage growth and consumer surplus, and diminished incentives to innovate. Larger firms have seen greatly increased R&D investment and growth in digital capital holdings, which has fueled high research productivity, product diversification and technological complements. I emphasize the role of open-source ML/AI technologies in reducing this disparity and leveling the playing field for smaller firms: specifically, I study the unexpected public release of TensorFlow. The open-source release of TensorFlow rep- resents an exogenous shock to the cost of ML/AI related digital capital: firms are able to enjoy the benefits of these technologies without prohibitive investments in high skill human capital and technological infrastructure. This natural experiment provides a unique setting to study the effect of open-source technology in supporting small firm growth. My main findings are consistent with the hypothesis that digital capital accumulation positively impacts firm growth. I show that small, TensorFlow user firms have higher ex-post sales growth, market returns, and profitability. These firms are also more likely to innovate, and the evidence is suggestive that a larger share of user firms is associated with subsequent declines in a range of industry concentration measures. My findings support the reasoning that digital capital encourages ML/AI utilization which allows for greater unstructured task automation leading to increased labor productivity. Firms are also able to better forecast demand and reduce volatility of uncertain future cash flows. My research emphasizes asymmetric gains from technological innovation as a driver of productivity slowdown and reduced wage growth. I show that open-source technologies supporting infrastructure may help enhance competition and the scope for future proprietary innovation. Finally, I relate ML/AI capital formation to a broader literature discussing the efficacy and applications of these new technologies, and their effects on labor markets and productivity growth.
Book Synopsis Three Essays on Machine Learning in Empirical Finance by : Jinhua Wang
Download or read book Three Essays on Machine Learning in Empirical Finance written by Jinhua Wang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
