Download or read book Essays on the Application of Machine Learning Techniques in the Empirical Asset Pricing Research written by Tizian Otto and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 156 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.
Download or read book Essays in Empirical Asset Pricing with Machine Learning written by Matthias Bûchner and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Empirical Asset Pricing Via Machine Learning written by Gerrit Liedtke and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Empirical Asset Pricing with Machine Learning written by Felix Kempf and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Empirical Asset Pricing with Machine Learning written by Matthias Büchner and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Empirical Asset Pricing Via Machine Learning written by Shihao Gu and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We synthesize the field of machine learning with the canonical problem of empirical asset pricing: measuring asset risk premia. In the familiar empirical setting of cross section and time series stock return prediction, we perform a comparative analysis of methods in the machine learning repertoire, including generalized linear models, dimension reduction, boosted regression trees, random forests, and neural networks. At the broadest level, we find that machine learning offers an improved description of expected return behavior relative to traditional forecasting methods. Our implementation establishes a new standard for accuracy in measuring risk premia summarized by an unprecedented out-of-sample return prediction R2. We identify the best performing methods (trees and neural nets) and trace their predictive gains to allowance of nonlinear predictor interactions that are missed by other methods. Lastly, we find that all methods agree on the same small set of dominant predictive signals that includes variations on momentum, liquidity, and volatility. Improved risk premia measurement through machine learning can simplify the investigation into economic mechanisms of asset pricing and justifies its growing role in innovative financial technologies.
Download or read book Essays in Asset Pricing and Machine Learning written by Jason Yue Zhu and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis we study two applications of machine learning to estimate models that explains asset prices by harnessing the vast quantity of asset and economic information while also capturing complex structure among sources of risk. First we show how to build a cross-section of asset returns, that is, a small set of basis or test assets that capture complex information contained in a given set of characteristics and span the Stochastic Discount Factor (SDF). We use decision trees to generalize the concept of conventional sorting and introduce a new approach to robustly recover the SDF, which endogenously yields optimal portfolio splits. These low-dimensional investment strategies are well diversified, easily interpretable, and reflect many characteristics at the same time. Empirically, we show that traditional cross-sections of portfolios and their combinations, especially deciles and long-short anomaly factors, present too low a hurdle for model evaluation and serve as the wrong building blocks for the SDF. Constructed from the same pricing signals, our cross-sections have significantly higher (up to a factor of three) out-of-sample Sharpe ratios and pricing errors relative to the leading reduced-form asset pricing models. In the second part of the thesis, I present deep neural networks to estimate an asset pricing model for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. The key innovations are to use the fundamental no-arbitrage condition as criterion function to construct the most informative test assets with an adversarial approach and to extract the states of the economy from many macroeconomic time series. Our asset pricing model outperforms out-of-sample all benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors and identifies the key factors that drive asset prices.
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.
Download or read book Essays in Machine Learning Applications for Asset Pricing written by Yavor Kovachev and published by . This book was released on 2021 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning in Empirical Asset Pricing written by Colm Kelly and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This note is part of Quality testing.
Download or read book Essays on the Application of Multiple Testing in Empirical Asset Pricing Research written by Viktoria-Sophie Wendt and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays on Empirical Asset Pricing and FinTech written by Amin Shams Moorkani and published by . This book was released on 2019 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies the determinants of return structure in the cross-section of cryptocurrencies as well as the time-series of market returns during the boom of 2017, the largest known price increase in the history of finance. The first chapter examines the cross-section of cryptocurrencies and hypothesizes and tests that, because of cryptocurrencies' unique features, common demand shocks should be the main driver of the covariance structure of cryptocurrency returns. I proxy for the degree of overlapping exposure to correlated demand shocks using a pairwise “connectivity” measure based on cryptocurrencies' trading locations. I find that this “connectivity” measure explains substantial covariation in the cross-section of cryptocurrency returns. Connected currencies exhibit substantial contemporaneous covariation. In addition, currencies connected to those that perform well outperform currencies connected to those that perform poorly by 71 basis points over the next day and 214 basis points over the next week. Evidence from new exchange listings and a quasi-natural experiment exploiting the shutdown of Chinese exchanges shows that the results cannot be explained by endogenous sorting of currencies into exchanges. Moreover, using machine learning techniques to analyze social media data, I find that the demand effects are 40 to 50% larger for currencies that rely more heavily on network externalities of user adoption. This amplified effect is consistent with the notion that demand for a cryptocurrency not only signals investment motives, but also can be perceived as user adoption that potentially affects the fundamental value of these assets. The second chapter examines the time-series of Bitcoin and the aggregate cryptocurrency returns during the boom of 2017. In particular, Bitcoin and other cryptocurrencies offer the promise of an anonymous, decentralized financial system free from banks and government intervention. Ironically, new large entities have gained centralized control over the vast majority of operations in the cryptocurrency world. One type of these large centralized entities is stable coin issuers who can act as a central bank in the crypto world by controlling the supply of money. This chapter examines the role of the largest stable coin, Tether, on Bitcoin and other cryptocurrency prices. Using algorithms to analyze blockchain data, we find that purchases with Tether are timed following market downturns and result in sizable increases in Bitcoin prices. The flow is attributable to one entity, clusters below round prices, induces asymmetric autocorrelations in Bitcoin, and suggests insufficient Tether reserves before month-ends. Rather than demand from cash investors, these patterns are most consistent with the supply-based hypothesis of unbacked digital money inflating cryptocurrency prices
Download or read book Machine Learning for Asset Management and Pricing written by Henry Schellhorn and published by SIAM. This book was released on 2024-03-26 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook covers the latest advances in machine learning methods for asset management and asset pricing. Recent research in deep learning applied to finance shows that some of the (usually confidential) techniques used by asset managers result in better investments than the more standard techniques. Cutting-edge material is integrated with mainstream finance theory and statistical methods to provide a coherent narrative. Coverage includes an original machine learning method for strategic asset allocation; the no-arbitrage theory applied to a wide portfolio of assets as well as other asset management methods, such as mean-variance, Bayesian methods, linear factor models, and strategic asset allocation; recent techniques such as neural networks and reinforcement learning, and more classical ones, including nonlinear and linear programming, principal component analysis, dynamic programming, and clustering. The authors use technical and nontechnical arguments to accommodate readers with different levels of mathematical preparation. The book is easy to read yet rigorous and contains a large number of exercises. Machine Learning for Asset Management and Pricing is intended for graduate students and researchers in finance, economics, financial engineering, and data science focusing on asset pricing and management. It will also be of interest to finance professionals and analysts interested in applying machine learning to investment strategies and asset management. This textbook is appropriate for courses on asset management, optimization with applications, portfolio theory, and asset pricing.
Download or read book Essays on Empirical Asset Pricing written by Chishen Wei and published by . This book was released on 2011 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation contains two essays that use empirical techniques to shed light on open questions in the asset pricing literature. In the first essay, I investigate whether foreign institutional investors affect stock liquidity in domestic equity markets. The evidence indicates that stocks with higher foreign institutional ownership subsequently experience higher liquidity. However, it is difficult to interpret the causal relation of this finding because institutional investors self-select into more liquid stocks. To solve this problem, I exploit a provision in the 2003 US dividend tax cut which extends tax-relief to dividends from US tax-treaty countries but not to dividends from non-treaty countries. This natural experiment suggests a causal link between foreign institutional investors and liquidity. Consistent with the predictions of theoretical models, I find that liquidity improves due to foreign institutional investors increasing information competition. In the second essay, I introduce a new measure of difference of opinion using mutual fund portfolio weights to test prominent competing theories of the effect of heterogeneous beliefs on asset prices. The over-valuation theory (Miller (1977)) proposes that in the presence of short-sale constraints stock prices reflects only the view of optimistic investors which implies lower subsequent returns. Alternatively, neo-classical asset pricing models (Williams (1977), Merton (1987)) suggest that differences of opinions indicate high levels of information uncertainty or risk which implies higher expected returns. My initial result finds no support for the over-valuation theory. Instead, the measure used in this study finds that high differences of opinion stocks weakly outperform low differences of opinion stocks by 2.42% annually which is more consistent with the information uncertainty explanation.
Download or read book Machine Learning and Asset Pricing Models written by Rafael Amaral Porsani and published by . This book was released on 2018 with total page 151 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even though statistical-learning techniques have become increasingly popular in many scientific areas, few studies in the field of cross-sectional asset pricing have incorporated these in their essence. In the first chapter of this dissertation, we suggest a framework for testing the empirical performance of linear asset-pricing factor models, and for investigating anomalies, which employs an array of such techniques, bringing artificial intelligence and asset-pricing a step closer. The methodology utilized in our work combines a range of supervised learning algorithms with the model testing strategies of Avramov and Chordia (2006) and Brennan et al. (1998). Chapter 2 presents results generated by applying our framework to multiple asset pricing models. While simple in nature, the estimation procedure we use can have implications for risk management, the study of anomalies, the creation of optimal investment policies, and the general study of expected returns. Some of the concepts explored herein may take an added role in future studies which investigate these subjects, helping to reshape the way we think about asset prices and financial-market anomalies. The title of this dissertation is given after its first two chapters. Chapter 3 is titled "The Building Blocks of Employment: A Signal Processing Analysis". As argued by Hawking (2016), artificial intelligence and growing automation have decimated jobs in traditional manufacturing, and may engender further job destruction into the middle classes, promoting a widening of wealth inequality in their wake. In this chapter, we contribute to the general study of employment, a theme of critical importance today, by utilizing signal processing techniques to decompose into a myriad of building blocks the employment-to-population time series during the years 1975 to 2000 - a prolonged period where strong job gains were registered and recoveries from recessions were quick. An analysis of the main resulting components is presented. The components of employment produced by our signal-processing modeling approach are made available to researchers interested in better comprehending the employment rate during this period, and forces tied to job gains then. Chapter 4 is co-authored with Mahyar Kargar, and it is titled "The Evolution of Global Financial Integration: A Multivariate Analysis of Currencies and Equities". In this study, we rely on principal component regressions and canonical correlation analyses to show that not only currencies became more integrated with each other from the mid-nineties through the early years of the twenty-first century, but also different assets classes -- currencies and equities -- became more closely associated throughout the same period. Our framework suggests that a common set of latent factors was, during these years, ever more capable of explaining returns from disparate assets; although such buoyant trend in integration subsequently faced strong headwinds, no longer being present in recent times.
Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.
