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Author: Long Hai Vo
Publisher:
ISBN:
Category : Capital market
Languages : en
Pages : 31
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Book Description
Author: Long Hai Vo
Publisher:
ISBN:
Category : Capital market
Languages : en
Pages : 31
Get Book Here
Book Description
Author: Long Vo
Publisher:
ISBN:
Category :
Languages : en
Pages : 33
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Book Description
An immediate consequence of the Efficient Market Hypothesis (EMH) is the absence of auto-correlation of the return series of the financial prices and the exclusion of excess profitability made by any (active) trading strategy. However, the precondition for the validity of EMH, which assumes that all market participants can promptly receive and rationally react to the relevant information affecting the prices, might be (approximately) true for a long time horizon, but not for a short time horizon. By examining local long-range dependence (measured by the rolling Rescaled Range estimates of the Hurst index) of an empirical example, the local market inefficiency is inferred, and excess profitability of a simple trend-following trading strategy is observed. Moreover, the significant positive cross-correlation between the local Hurst index estimates and the returns of the trend-following trading strategies implies the potential for constructing a more profitable trading system by incorporating the former into the latter.
Author: Andrew W. Lo
Publisher: Princeton University Press
ISBN: 1400829097
Category : Business & Economics
Languages : en
Pages : 449
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Book Description
For over half a century, financial experts have regarded the movements of markets as a random walk--unpredictable meanderings akin to a drunkard's unsteady gait--and this hypothesis has become a cornerstone of modern financial economics and many investment strategies. Here Andrew W. Lo and A. Craig MacKinlay put the Random Walk Hypothesis to the test. In this volume, which elegantly integrates their most important articles, Lo and MacKinlay find that markets are not completely random after all, and that predictable components do exist in recent stock and bond returns. Their book provides a state-of-the-art account of the techniques for detecting predictabilities and evaluating their statistical and economic significance, and offers a tantalizing glimpse into the financial technologies of the future. The articles track the exciting course of Lo and MacKinlay's research on the predictability of stock prices from their early work on rejecting random walks in short-horizon returns to their analysis of long-term memory in stock market prices. A particular highlight is their now-famous inquiry into the pitfalls of "data-snooping biases" that have arisen from the widespread use of the same historical databases for discovering anomalies and developing seemingly profitable investment strategies. This book invites scholars to reconsider the Random Walk Hypothesis, and, by carefully documenting the presence of predictable components in the stock market, also directs investment professionals toward superior long-term investment returns through disciplined active investment management.
Author: Guglielmo Maria Caporale
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Renuka Sharma
Publisher: John Wiley & Sons
ISBN: 1394214316
Category : Computers
Languages : en
Pages : 358
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Book Description
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The book: details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average; explains the rapid expansion of quantum computing technologies in financial systems; provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions; explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers. Audience The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Author: Allan Timmermann
Publisher:
ISBN:
Category : Information theory in economics
Languages : en
Pages : 32
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Book Description
Author: Gilles Teyssière
Publisher: Springer Science & Business Media
ISBN: 3540346252
Category : Business & Economics
Languages : en
Pages : 394
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Book Description
Assembles three different strands of long memory analysis: statistical literature on the properties of, and tests for, LRD processes; mathematical literature on the stochastic processes involved; and models from economic theory providing plausible micro foundations for the occurrence of long memory in economics.
Author: Wing-Keung Wong
Publisher: Mdpi AG
ISBN: 9783036530802
Category : Business & Economics
Languages : en
Pages : 232
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Book Description
The Efficient Market Hypothesis believes that it is impossible for an investor to outperform the market because all available information is already built into stock prices. However, some anomalies could persist in stock markets while some other anomalies could appear, disappear and re-appear again without any warning. A Special Issue on "Efficiency and Anomalies in Stock Markets" will be devoted to advancements in the theoretical development of market efficiency and anomaly in the Stock Market, as well as applications in Stock Market efficiency and anomalies.
Author: Hersh Shefrin
Publisher:
ISBN: 9780195161212
Category : Business & Economics
Languages : en
Pages : 410
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Book Description
Even the best Wall Street investors make mistakes. No matter how savvy or experienced, all financial practitioners eventually let bias, overconfidence, and emotion cloud their judgement and misguide their actions. Yet most financial decision-making models fail to factor in these fundamentals of human nature. In Beyond Greed and Fear, the most authoritative guide to what really influences the decision-making process, Hersh Shefrin uses the latest psychological research to help us understand the human behavior that guides stock selection, financial services, and corporate financial strategy. Shefrin argues that financial practitioners must acknowledge and understand behavioral finance--the application of psychology to financial behavior--in order to avoid many of the investment pitfalls caused by human error. Through colorful, often humorous real-world examples, Shefrin points out the common but costly mistakes that money managers, security analysts, financial planners, investment bankers, and corporate leaders make, so that readers gain valuable insights into their own financial decisions and those of their employees, asset managers, and advisors. According to Shefrin, the financial community ignores the psychology of investing at its own peril. Beyond Greed and Fear illuminates behavioral finance for today's investor. It will help practitioners to recognize--and avoid--bias and errors in their decisions, and to modify and improve their overall investment strategies.
Author: Srikanta Patnaik
Publisher: Springer Nature
ISBN: 9811552436
Category : Technology & Engineering
Languages : en
Pages : 853
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Book Description
This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.
