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Author: Markus Faessler Publisher: ISBN: Category : Languages : en Pages :
Book Description
Due to various contradicting empirical studies, this thesis aims to provide new complementary perspectives on the relation and causation of real economic growth, stock market development and stock market participation rate. This was done by applying an unsupervised machine-learning algorithm, an agglomerative hierarchical clustering method. The motivation to use a clustering method was to simplify complex data structures in order "to represent the data in a way which will suggest fruitful hypotheses (Jardine, 1970, p.117)." Once the theoretical background with regard to stock market development, economic growth, equity premium puzzle and methodology was laid out, the findings were presented. The empirical results implied a breakdown of the OECD countries into four clusters which were compared with their respective mean values. The implications of these results may allow for an analysis as to the direction of causation on a cluster-base. Furthermore, as this thesis includes stock market participation rate in the analysis, it suggests an analysis of potentially underlying premises which may influence both stock market participation rate and the relation and causation of economic growth and financial markets development akin.
Author: Markus Faessler Publisher: ISBN: Category : Languages : en Pages :
Book Description
Due to various contradicting empirical studies, this thesis aims to provide new complementary perspectives on the relation and causation of real economic growth, stock market development and stock market participation rate. This was done by applying an unsupervised machine-learning algorithm, an agglomerative hierarchical clustering method. The motivation to use a clustering method was to simplify complex data structures in order "to represent the data in a way which will suggest fruitful hypotheses (Jardine, 1970, p.117)." Once the theoretical background with regard to stock market development, economic growth, equity premium puzzle and methodology was laid out, the findings were presented. The empirical results implied a breakdown of the OECD countries into four clusters which were compared with their respective mean values. The implications of these results may allow for an analysis as to the direction of causation on a cluster-base. Furthermore, as this thesis includes stock market participation rate in the analysis, it suggests an analysis of potentially underlying premises which may influence both stock market participation rate and the relation and causation of economic growth and financial markets development akin.
Author: Mr. Anil Ari Publisher: International Monetary Fund ISBN: Category : Business & Economics Languages : en Pages : 32
Book Description
The qualitative and granular nature of most structural indicators and the variety in data sources poses difficulties for consistent cross-country assessments and empirical analysis. We overcome these issues by using a machine learning approach (the partial least squares method) to combine a broad set of cross-country structural indicators into a small number of synthetic scores which correspond to key structural areas, and which are suitable for consistent quantitative comparisons across countries and time. With this newly constructed dataset of synthetic structural scores in 126 countries between 2000-2019, we establish stylized facts about structural gaps and reforms, and analyze the impact of reforms targeting different structural areas on economic growth. Our findings suggest that structural reforms in the area of product, labor and financial markets as well as the legal system have a significant impact on economic growth in a 5-year horizon, with one standard deviation improvement in one of these reform areas raising cumulative 5-year growth by 2 to 6 percent. We also find synergies between different structural areas, in particular between product and labor market reforms.
Author: Karen Khachikyan Publisher: ISBN: 9783961168958 Category : Languages : en Pages : 112
Book Description
The goal of this Master thesis is to examine the advantages and disadvantages of advanced Machine Learning algorithms compared to traditional econometric methods. More specifically, the predictive performance, interpretability and possibilities for casual inference of various tree-based-methods will be compared to the well-established linear regression models. For this purpose, the Stock Market Participation puzzle, which was originally examined by van Rooij, Lusardi, and Alessie (2007) using OLS and IVGMM regressions, will be used for the empirical part of the thesis. The performance of each model is determined by the ROC curve and the according AUC value. Moreover, measures for variable significance are exploited like Feature importance and Permutation Feature importance, which prove the substantial role of financial literacy and income for investing. Albeit Decision Tree and Random Forest models show similar results to the linear models even after optimization, the optimized XGBoost model appears to excel in the majority of cases. This is confirmed by the Diebold-Mariano test and cross-validation.
Author: David Dümig Publisher: GRIN Verlag ISBN: 3346106551 Category : Business & Economics Languages : en Pages : 97
Book Description
Academic Paper from the year 2019 in the subject Business economics - Investment and Finance, , language: English, abstract: We conduct a comparative analysis of methods in the machine learning repertoire, including penalized linear models, generalized linear models, boosted regression trees, random forests, and neural networks, that investors can deploy to forecast the cross-section of stock returns. Gaining more widespread use in economics, machine learning algorithms have demonstrated the ability to reveal complex, nonlinear patterns that are difficult or largely impossible to detect with conventional statistical methods and are often more robust to the effects of multi-collinearity among predictors. We provide new evidence that machine learning techniques can improve the economic value of cross-sectional return forecasts. The implications of machine learning for quantitative finance are becoming both increasingly apparent and controversial. There is a growing discussion over whether machine learning tools can and should be applied to predict stock returns with greater precision. Broadly speaking, models that can be used to explain the returns of individual stocks draw on stock and firm characteristics, such as the market price of financial instruments and companies' accounting data. These characteristics can also be used to predict expected returns out-of-sample.
Author: Isaiah Hull Publisher: ISBN: 9781484263747 Category : Languages : en Pages : 0
Book Description
Machine learning has taken time to move into the space of academic economics. This is because empirical research in economics is concentrated on the identification of causal relationships in parsimonious statistical models; whereas machine learning is oriented towards prediction and is generally uninterested in either causality or parsimony. That leaves a gap for students, academics, and professionals who lack a standard reference on machine learning for economics and finance. This book focuses on economic and financial problems with an empirical dimension, where machine learning methods may offer something of value. This includes coverage of a variety of discriminative deep learning models (DNNs, CNNs, LSTMs, and DQNs), generative machine learning models (GANs and VAEs), and tree-based models. It also covers the intersection of empirical methods in economics and machine learning, including regression analysis, natural language processing, and dimensionality reduction. TensorFlow offers a toolset that can be used to define and solve any graph-based model, including those commonly used in economics. This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. This simplifies otherwise complicated concepts, enabling the reader to solve workhorse theoretical models in economics and finance using TensorFlow. You will: • Define, train, and evaluate machine learning models in TensorFlow 2 • Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems • Solve theoretical models in economics.
Author: Shigeyuki Hamori Publisher: MDPI ISBN: 3039362240 Category : Business & Economics Languages : en Pages : 230
Book Description
Artificial intelligence (AI) is regarded as the science and technology for producing an intelligent machine, particularly, an intelligent computer program. Machine learning is an approach to realizing AI comprising a collection of statistical algorithms, of which deep learning is one such example. Due to the rapid development of computer technology, AI has been actively explored for a variety of academic and practical purposes in the context of financial markets. This book focuses on the broad topic of “AI and Financial Markets”, and includes novel research associated with this topic. The book includes contributions on the application of machine learning, agent-based artificial market simulation, and other related skills to the analysis of various aspects of financial markets.
Author: Sara Zervos Publisher: ISBN: Category : Languages : en Pages : 32
Book Description
Is there a strong empirical association between stock market development and long-term economic growth? Cross-country regressions suggest that there is a positive and robust association.Levine and Zervos empirically evaluate the relationship between stock market development and long-term growth. The data suggest that stock market development is positively associated with economic growth. Moreover, instrumental variables procedures indicate a strong connection between the predetermined component of stock market development and economic growth in the long run.While cross-country regressions imply a strong link between stock market development and economic growth, the results should be viewed as suggestive partial correlations that stimulate additional research rather than as conclusive findings. Much work remains to be done to shed light on the relationship between stock market development and economic growth. Careful case studies might help identify causal relationships and further research could be done on the time-series property of such relationships.Research should also be done to identify policies that facilitate the development of sound securities markets.This paper - a product of the Finance and Private Sector Development Division, Policy Research Department - is part of a larger effort in the department to study the relationship between financial systems and economic growth. The study was funded by the Bank's Research Support Budget under the research project Stock Market Development and Financial Intermediary Growth (RPO 679-53).
Author: Yves Hilpisch Publisher: "O'Reilly Media, Inc." ISBN: 1492055387 Category : Business & Economics Languages : en Pages : 478
Book Description
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
Author: Justin Grimmer Publisher: Princeton University Press ISBN: 0691207550 Category : Computers Languages : en Pages : 360
Book Description
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry
Author: Markus Faessler
Author: Markus Faessler
Author: Mr. Anil Ari
Author: Karen Khachikyan
Author: David Dümig
Author: Isaiah Hull
Author: Shigeyuki Hamori
Author: Sara Zervos
Author: Yves Hilpisch
Author: 
Author: Justin Grimmer