Predicting Stock Returns Using Past Returns PDF Download

Author: Lisa Marie Klein
Publisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 152

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Author: Wesley R. Gray
Publisher: John Wiley & Sons
ISBN: 111907150X
Category : Business & Economics
Languages : en
Pages : 230

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Book Description
DIY Financial Advisor: A Simple Solution to Build and Protect Your Wealth DIY Financial Advisor is a synopsis of our research findings developed while serving as a consultant and asset manager for family offices. By way of background, a family office is a company, or group of people, who manage the wealth a family has gained over generations. The term 'family office' has an element of cachet, and even mystique, because it is usually associated with the mega-wealthy. However, practically speaking, virtually any family that manages its investments—independent of the size of the investment pool—could be considered a family office. The difference is mainly semantic. DIY Financial Advisor outlines a step-by-step process through which investors can take control of their hard-earned wealth and manage their own family office. Our research indicates that what matters in investing are minimizing psychology traps and managing fees and taxes. These simple concepts apply to all families, not just the ultra-wealthy. But can—or should—we be managing our own wealth? Our natural inclination is to succumb to the challenge of portfolio management and let an 'expert' deal with the problem. For a variety of reasons we discuss in this book, we should resist the gut reaction to hire experts. We suggest that investors maintain direct control, or at least a thorough understanding, of how their hard-earned wealth is managed. Our book is meant to be an educational journey that slowly builds confidence in one's own ability to manage a portfolio. We end our book with a potential solution that could be applicable to a wide-variety of investors, from the ultra-high net worth to middle class individuals, all of whom are focused on similar goals of preserving and growing their capital over time. DIY Financial Advisor is a unique resource. This book is the only comprehensive guide to implementing simple quantitative models that can beat the experts. And it comes at the perfect time, as the investment industry is undergoing a significant shift due in part to the use of automated investment strategies that do not require a financial advisor's involvement. DIY Financial Advisor is an essential text that guides you in making your money work for you—not for someone else!

Author: Michael Nofer
Publisher: Springer
ISBN: 3658095083
Category : Computers
Languages : en
Pages : 140

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Book Description
Michael Nofer examines whether and to what extent Social Media can be used to predict stock returns. Market-relevant information is available on various platforms on the Internet, which largely consist of user generated content. For instance, emotions can be extracted in order to identify the investors' risk appetite and in turn the willingness to invest in stocks. Discussion forums also provide an opportunity to identify opinions on certain companies. Taking Social Media platforms as examples, the author examines the forecasting quality of user generated content on the Internet.

Author: Graham Elliott
Publisher: Elsevier
ISBN: 0444627405
Category : Business & Economics
Languages : en
Pages : 667

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Book Description
The highly prized ability to make financial plans with some certainty about the future comes from the core fields of economics. In recent years the availability of more data, analytical tools of greater precision, and ex post studies of business decisions have increased demand for information about economic forecasting. Volumes 2A and 2B, which follows Nobel laureate Clive Granger's Volume 1 (2006), concentrate on two major subjects. Volume 2A covers innovations in methodologies, specifically macroforecasting and forecasting financial variables. Volume 2B investigates commercial applications, with sections on forecasters' objectives and methodologies. Experts provide surveys of a large range of literature scattered across applied and theoretical statistics journals as well as econometrics and empirical economics journals. The Handbook of Economic Forecasting Volumes 2A and 2B provide a unique compilation of chapters giving a coherent overview of forecasting theory and applications in one place and with up-to-date accounts of all major conceptual issues. Focuses on innovation in economic forecasting via industry applications Presents coherent summaries of subjects in economic forecasting that stretch from methodologies to applications Makes details about economic forecasting accessible to scholars in fields outside economics

Author: Murat Aydogdu
Publisher:
ISBN:
Category :
Languages : en
Pages : 21

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Book Description
A single hidden layer neural network can be trained to predict whether a stock will be in the top, middle, or bottom third of sample stocks based on its return over the next month based on return, trading volume, and volatility measures available at the end of this month. In my preliminary work using S&P 500 stocks, the network has limited success in predicting which stocks are likely to go up but the prediction strength is not strong enough to help build profitable portfolios. While neural networks have pushed artificial intelligence forward in many fields, and while the investment industry has been shifting more towards quantitative prediction using neural networks and other machine learning models, their place in empirical finance research has been limited. My work aims to contribute to this growing literature.

Author: David G. McMillan
Publisher:
ISBN:
Category :
Languages : en
Pages : 30

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Book Description
This paper considers whether the log dividend yield provides forecast power for stock returns. While this is an oft-researched topic there is no consensus answer and yet it remains crucial in our understanding of asset pricing. Using a five-year rolling window we compare forecasts from the dividend yield model to those from the historical mean model across forecast magnitude, sign and investment metrics. Results show that in each case the dividend yield model provides superior forecasts. While the difference in, for example, RMSE and the success ratio is small, results support improved market timing and a higher Sharpe ratio using the dividend yield model. In explaining these results, we note that recursive forecasts do not perform as well and thus it is the nature of time-variation within the forecast parameter that is important. We also argue that such time-variation is linked to economic performance. Overall, these results support stock returns forecasting but stresses the importance of time-variation in the forecast model to ensure forecast power.

Author: Roy Henriksson
Publisher:
ISBN: 9781021216878
Category : Business & Economics
Languages : en
Pages : 0

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Author: Rajendra Akerkar
Publisher: Jones & Bartlett Publishers
ISBN: 1449662706
Category : Computers
Languages : en
Pages : 375

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Book Description
A knowledge-based system (KBS) is a system that uses artificial intelligence techniques in problem-solving processes to support human decision-making, learning, and action. Ideal for advanced-undergraduate and graduate students, as well as business professionals, this text is designed to help users develop an appreciation of KBS and their architecture and understand a broad variety of knowledge-based techniques for decision support and planning. It assumes basic computer science skills and a math background that includes set theory, relations, elementary probability, and introductory concepts of artificial intelligence. Each of the 12 chapters is designed to be modular, providing instructors with the flexibility to model the book to their own course needs. Exercises are incorporated throughout the text to highlight certain aspects of the material presented and to simulate thought and discussion. A comprehensive text and resource, Knowledge-Based Systems provides access to the most current information in KBS and new artificial intelligences, as well as neural networks, fuzzy logic, genetic algorithms, and soft systems.

Author: Venkat Eleswarapu
Publisher:
ISBN:
Category :
Languages : en
Pages : 40

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Book Description
It is now well documented that returns on the aggregate stock market are predictable and negatively autocorrelated over longer investment horizons. In this paper, we investigate the predictability of the aggregate stock market returns using past returns of glamour and value stocks. We find the relationships between returns of glamour and value stocks with future stock market returns are quite different. In particular, we find that annual excess returns on the stock market index are negatively related to the returns of glamour stocks in the previous 36-month period. In contrast, the past returns of value stocks do not have any explanatory power in predicting aggregate stock market excess returns. Furthermore, stock market returns, which are purged of the effects of the glamour stocks, do not have any reliable predictive power in explaining the future stock market returns. In contrast, the glamour stocks have a predictive power even after controlling for the information in the past market returns. Our evidence of the unique predictive ability of glamour stocks seems to be inconsistent with the time-varying market risk-premium explanation for the predictability of the aggregate stock market returns.

Author: Emmanuel Jurczenko
Publisher: John Wiley & Sons
ISBN: 1786305445
Category : Business & Economics
Languages : en
Pages : 460

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Book Description
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.