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Essays On Conditional Asset Pricing And Machine Learning In Finance
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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 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 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.
Book Synopsis Essays in Asset Pricing and Machine Learning by : Jason Yue Zhu
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.
Book Synopsis Essays on Empirical Asset Pricing Via Machine Learning by : Gerrit Liedtke
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:
Book Synopsis Essays in Empirical Asset Pricing with Machine Learning by : Matthias Bûchner
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:
Book Synopsis Essays in Empirical Asset Pricing with Machine Learning by : Matthias Büchner
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:
Book Synopsis Essays in Empirical Asset Pricing with Machine Learning by : Felix Kempf
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:
Book Synopsis Essays in Machine Learning Applications for Asset Pricing by : Yavor Kovachev
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:
Book Synopsis Machine Learning for Asset Management and Pricing by : Henry Schellhorn
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.
Book Synopsis Essays on the Application of Machine Learning Techniques in the Empirical Asset Pricing Research by : Tizian Otto
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:
Book Synopsis Essays on Asset Pricing and Machine Learning by : Matteo Bagnara
Download or read book Essays on Asset Pricing and Machine Learning written by Matteo Bagnara and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays in Statistical Learning and Asset Pricing by : Guanhao Feng
Download or read book Essays in Statistical Learning and Asset Pricing written by Guanhao Feng and published by . This book was released on 2017 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: I have written three essays in the interdisciplinary area of statistical learning and asset pricing. The first essay focuses on Bayesian regularization on stock return predictability and its sensitivity analysis. The second essay studies a dynamic discrete model for intra-game odds to reveal the market expectation for the game outcomes. The last essay evaluates risk factor importance through taming the factor zoo in a high-dimensional setting.
Book Synopsis Essays on Empirical Asset Pricing and Behavioral Finance by : Ulrich Wessels
Download or read book Essays on Empirical Asset Pricing and Behavioral Finance written by Ulrich Wessels and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Asset Pricing by : Gabriel Ignacio Cuevas Rodriguez
Download or read book Essays on Asset Pricing written by Gabriel Ignacio Cuevas Rodriguez and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, I analyze firms' misallocation through the output distortions channel, using a production-based asset pricing model as a framework. In the model, alpha measures the firm's ability to choose technologies to adapt to exogenous shocks. I find in the cross-section of the test portfolios the estimated curvature parameter alpha is more than two times the original value obtained in Belo (2010). This implies misallocations reduce the firm's ability to respond to the different states of nature. I calibrate and solve the model in the special case of a single representative firm. I find that the impact of misallocation on firm value, production, capital, investment, and investment return is larger when firms' ability to adapt to exogenous shocks is reduced. This indicates that firms may be less agile to adapt across states of nature and provides more evidence of the detrimental effect of misallocations. In Chapter 2 (with Denis Mokanov and Danyu Zhang), we document several facts about equity analysts' earnings expectations: (1) consensus earnings expectations underreact to news unconditionally, (2) the degree of underreaction declines during high-volatility periods, and (3) the degree of underreaction declines over our sample. To account for these findings, we develop a simple model featuring time-varying inattention. We show that our model is able to account for the unconditional profitability of momentum, momentum crashes, and the diminishing profitability of momentum over our sample. We propose a trading strategy that mixes short-run and long-run momentum signals and show that the mixed momentum strategy outperforms the conventional momentum strategies. Finally, we use a machine learning algorithm to estimate the predictable component of earnings surprises and construct a portfolio that is long (short) on stocks with excessively pessimistic (optimistic) earnings expectations. The resultant trading strategy generates an annualized Sharpe ratio of about 1.16 and its returns are not explained by popular factor models.
Book Synopsis Essays on Empirical Asset Pricing by : Xiang Zhang
Download or read book Essays on Empirical Asset Pricing written by Xiang Zhang and published by . This book was released on 2013 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis consists of three essays on empirical asset pricing around three themes: evaluating linear factor asset pricing models by comparing their misspecified measures, understanding the long-run risk on consumption-leisure to investigate their pricing performances on cross-sectional returns, and evaluating conditional asset pricing models by using the methodology of dynamic cross-sectional regressions. The first chapter is ̀̀Comparing Asset Pricing Models: What does the Hansen-Jagannathan Distance Tell Us?''. It compares the relative performance of some important linear asset pricing models based on the Hansen-Jagannathan (HJ) distance using data over a long sample period from 1952-2011 based on U.S. market. The main results are as follows: first, among return-based linear models, the Fama-French (1993) five-factor model performs best in terms of the normalized pricing errors, compared with the other candidates. On the other hand, the macro-factor model of Chen, Roll, and Ross (1986) five-factor is not able to explain industry portfolios: its performance is even worse than that of the classical CAPM. Second, the Yogo (2006) non-durable and durable consumption model is the least misspecified, among consumption-based asset pricing models, in capturing the spread in industry and size portfolios. Third, the Lettau and Ludvigson (2002) scaled consumption-based CAPM (C-CAPM) model obtains the smallest normalized pricing errors pricing gross and excess returns on size portfolios, respectively, while Santos and Veronesi (2006) scaled C-CAPM model does better in explain the return spread on portfolios of U.S. government bonds. The second chapter (̀̀Leisure, Consumption and Long Run Risk: An Empirical Evaluation'') uses a long-run risk model with non-separable leisure and consumption, and studies its ability to price equity returns on a variety of portfolios of U.S. stocks using data from 1948-2011. It builds on early work by Eichenbaum et al. (1988) that explores the empirical properties of intertemporal asset pricing models where the representative agent has utility over consumption and leisure. Here we use the framework in Uhlig (2007) that allows for a stochastic discount factor with news about long-run growth in consumption and leisure. To evaluate our long-run model, we assess its performance relative to standard asset pricing models in explaining the cross-section of returns across size, industry and value-growth portfolios. We find that the long-run consumption-leisure model cannot be rejected by the J-statistic and it does better than the standard C-CAPM, the Yogo durable consumption and Fama-French three-factor models. We also rank the normalized pricing errors using the HJ distance: our model has a smaller HJ distance than other candidate models. Our paper is the first, as far as we are aware, to use leisure data with adjusted working hours as a measure of leisure i.e., defined as the difference between a fixed time endowment and the observable hours spent on working, home production, schooling, communication, and personal care (Yang (2010)). The third essay: ̀̀Empirical Evaluation of Conditional Asset Pricing Models: An Economic Perspective'' uses dynamic Fama-MacBeth cross-sectional regressions and tests the performance of several important conditional asset pricing models when allowing for time-varying price of risk. It compares the performance of conditional asset pricing models, in terms of their ability to explain the cross-section of returns across momentum, industry, value-growth and government bond portfolios. We use the new methodology introduced by Adrian et al. (2012). Our main results are as follows: first we find that the Lettau and Ludvigson (2001) conditional model does better than other models in explaining the cross-section of momentum and value-growth portfolios. Second we find that the Piazessi et al. (2007) consumption model does better than others in pricing the cross-section of industry portfolios. Finally, we find that in the case of the cross-section of risk premia on U.S. government bond portfolios the conditional model in Santos and Veronesi (2006) outperforms other candidate models. Overall, however, the Lettau and Ludvigson (2001) model does better than other candidate models. Our main contributions here is using a recently developed method of dynamic Fama-MacBeth regressions to evaluate the performance of leading conditional CAPM (C-CAPM) models in a common set of test assets over the time period from 1951-2012.
Book Synopsis Essays in Empirical Asset Pricing and International Finance by : Zilong Niu
Download or read book Essays in Empirical Asset Pricing and International Finance written by Zilong Niu and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Two Essays on Asset Pricing and Asset Choice by : James Eric Gunderson
Download or read book Two Essays on Asset Pricing and Asset Choice written by James Eric Gunderson and published by . This book was released on 2004 with total page 336 pages. Available in PDF, EPUB and Kindle. Book excerpt:
