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Author: Henry Liu
Publisher:
ISBN: 9781707707881
Category :
Languages : en
Pages : 109
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Book Description
Financial markets are essentially time-series data-driven events consisting of valleys, peaks, and in-betweens of ups and downs. For more than a century, many pioneers had attempted to come up with various theoretical models to facilitate forecasting and timing market moves. For example, as early as in 1902, or 117 years ago, S. A. Nelson, a friend of Charles H. Dow, attempted to explain Dow's methods in his book titled The A B C of Stock Speculation, which became later known as "the Dow Theory." 20 years later in 1922, William Peter Hamilton carried on and wrote the book The Stock Market Barometer, which explained the Dow Theory in more detail. More recently in the last few decades, the advent of advanced computing technologies helped create numerous technical indicators, such as Relative Strength Index (RSI) by J. Welles Wilder (1978), Bollinger Bands (BB) by John Bollinger (2002), Moving Average Convergence Divergence (MACD) by Gerald Appel (2005), Stochastic Oscillator (SO) by George Lane (2007), to name a few. Those powerful theories and indicators have been heavily studied and well-known in the financial circle. However, they are empirical and lack quantitative verifications out of solid back-test results; or they might just be proprietary gauges locked in the computing facilities of those mega financial firms and thus not readily available to the general public. Based on the law of large numbers and ensemble machine learning, this text attempts to help explore to what extent we can actually forecast and time markets if it's impossible to do so precisely. For this purpose, the author developed a research-oriented, indicator-based system trading tool, named AlphaCovaria, to help demonstrate how to use various simplest, readily available technical indicators to forecast and time markets approximately while eliminating subjective speculations at the same time for potentially maximizing profits of trading with a formula-driven approach. This tool consists of three major programs named AlphaCurve, AlphaDriver, and BTDriver, respectively. The AlphaCurve charting tool provides intuitive, all-in-one, specially designed and constructed charts in color to help visualize how various forecasting and timing models work with the price movements of chosen securities and indicators. The AlphaDriver, a data-crunching tool, feeds AlphaCurve with security price movement data and various computed indicator stats by calling a commercial market data provider with specified timeframes of historic, intraday and real-time. The BTDriver is a back-test driver, which also aggregates profit profiles with a given look-back period, thus enabling the AlphaDriver to generate buy/sell signals on the fly dynamically and adaptively, rather than statically. The text is made concise and precise of about 100 pages only, with an Appendix illustrating how you can use your iPhone/iPad with Yahoo Finance Mobile App to facilitate your research and investing. Forecasting and timing markets are achievable if proper indicators, models and strategies are utilized with modern computing technologies.
Author: Henry Liu
Publisher:
ISBN: 9781707707881
Category :
Languages : en
Pages : 109
Get Book
Book Description
Financial markets are essentially time-series data-driven events consisting of valleys, peaks, and in-betweens of ups and downs. For more than a century, many pioneers had attempted to come up with various theoretical models to facilitate forecasting and timing market moves. For example, as early as in 1902, or 117 years ago, S. A. Nelson, a friend of Charles H. Dow, attempted to explain Dow's methods in his book titled The A B C of Stock Speculation, which became later known as "the Dow Theory." 20 years later in 1922, William Peter Hamilton carried on and wrote the book The Stock Market Barometer, which explained the Dow Theory in more detail. More recently in the last few decades, the advent of advanced computing technologies helped create numerous technical indicators, such as Relative Strength Index (RSI) by J. Welles Wilder (1978), Bollinger Bands (BB) by John Bollinger (2002), Moving Average Convergence Divergence (MACD) by Gerald Appel (2005), Stochastic Oscillator (SO) by George Lane (2007), to name a few. Those powerful theories and indicators have been heavily studied and well-known in the financial circle. However, they are empirical and lack quantitative verifications out of solid back-test results; or they might just be proprietary gauges locked in the computing facilities of those mega financial firms and thus not readily available to the general public. Based on the law of large numbers and ensemble machine learning, this text attempts to help explore to what extent we can actually forecast and time markets if it's impossible to do so precisely. For this purpose, the author developed a research-oriented, indicator-based system trading tool, named AlphaCovaria, to help demonstrate how to use various simplest, readily available technical indicators to forecast and time markets approximately while eliminating subjective speculations at the same time for potentially maximizing profits of trading with a formula-driven approach. This tool consists of three major programs named AlphaCurve, AlphaDriver, and BTDriver, respectively. The AlphaCurve charting tool provides intuitive, all-in-one, specially designed and constructed charts in color to help visualize how various forecasting and timing models work with the price movements of chosen securities and indicators. The AlphaDriver, a data-crunching tool, feeds AlphaCurve with security price movement data and various computed indicator stats by calling a commercial market data provider with specified timeframes of historic, intraday and real-time. The BTDriver is a back-test driver, which also aggregates profit profiles with a given look-back period, thus enabling the AlphaDriver to generate buy/sell signals on the fly dynamically and adaptively, rather than statically. The text is made concise and precise of about 100 pages only, with an Appendix illustrating how you can use your iPhone/iPad with Yahoo Finance Mobile App to facilitate your research and investing. Forecasting and timing markets are achievable if proper indicators, models and strategies are utilized with modern computing technologies.
Author: Norbert Lloyd Enrick
Publisher:
ISBN:
Category : Marketing research
Languages : en
Pages : 232
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Book Description
Author: Roy D. Henriksson
Publisher:
ISBN: 9781332273102
Category : Mathematics
Languages : en
Pages : 48
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Book Description
Excerpt from On Market Timing and Investment, Performance Part II: Statistical Procedures for Evaluating Forecasting Skills I. Introduction In Part I, one of us developed a basic model of market timing forecasts where the forecaster predicts when stocks will outperform bonds and when bonds will outperform stocks but he does not predict the magnitude of the superior performance. In that analysis, it was shown that the pattern of returns from successful market timing has an isomorphic correspondence to the pattern of returns from following certain option investment strategies where the implicit prices paid for the options are less than their "fair" or market values. This isomorphic correspondence was used to derive an equilibrium theory of value for market timing forecasting skills. By analyzing how investors would use the market timer's forecast to modify their probability beliefs about stock returns, it has shown that the conditional probabilities of a correct forecast (conditional on the return on the market) provide both necessary and sufficient conditions for such forecasts to have a positive value. In the analysis presented here, we use the basic model of market timing derived in Part I to develop both parametric and nonparametric statistical procedures to test for superior forecasting skills. The evaluation of the performance of investment managers is a topic of considerable interest to both practitioners and academics. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
Author: Gregory Zuckerman
Publisher: Penguin
ISBN: 0735217998
Category : Business & Economics
Languages : en
Pages : 401
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Book Description
NEW YORK TIMES BESTSELLER Shortlisted for the Financial Times/McKinsey Business Book of the Year Award The unbelievable story of a secretive mathematician who pioneered the era of the algorithm--and made $23 billion doing it. Jim Simons is the greatest money maker in modern financial history. No other investor--Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros--can touch his record. Since 1988, Renaissance's signature Medallion fund has generated average annual returns of 66 percent. The firm has earned profits of more than $100 billion; Simons is worth twenty-three billion dollars. Drawing on unprecedented access to Simons and dozens of current and former employees, Zuckerman, a veteran Wall Street Journal investigative reporter, tells the gripping story of how a world-class mathematician and former code breaker mastered the market. Simons pioneered a data-driven, algorithmic approach that's sweeping the world. As Renaissance became a market force, its executives began influencing the world beyond finance. Simons became a major figure in scientific research, education, and liberal politics. Senior executive Robert Mercer is more responsible than anyone else for the Trump presidency, placing Steve Bannon in the campaign and funding Trump's victorious 2016 effort. Mercer also impacted the campaign behind Brexit. The Man Who Solved the Market is a portrait of a modern-day Midas who remade markets in his own image, but failed to anticipate how his success would impact his firm and his country. It's also a story of what Simons's revolution means for the rest of us.
Author: Henry Liu
Publisher:
ISBN:
Category :
Languages : en
Pages : 113
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Book Description
Note: This is the 2nd edition, in color, updated in April, 2021. Please check the cover for the subtitle of Second Edition before placing an order. If you prefer a cheaper black and white version, please expand "See all formats and editions" to find it. Financial markets are essentially time-series data driven events consisting of valleys, peaks, and in-betweens of ups and downs. For more than a century, many pioneers had attempted to come up with various theoretical models to facilitate forecasting and timing market moves. For example, as early as in 1902, or 119 years ago, S. A. Nelson, a friend of Charles H. Dow, attempted to explain Dow's methods in his book titled The A B C of Stock Speculation, which became later known as "the Dow Theory." 20 years later in 1922, William Peter Hamilton carried on and wrote the book The Stock Market Barometer, which explained the Dow Theory in more detail. More recently in the last few decades, the advent of advanced computing technologies helped create numerous technical indicators, such as Relative Strength Index (RSI) by J. Welles Wilder (1978), Moving Average Convergence Divergence (MACD) by Gerald Appel (2005), Stochastic Oscillator (SO) by George Lane (2007), and Bollinger Bands (BB) by John Bollinger (2002), etc. Those powerful theories and indicators have been heavily studied and well-known in the financial circle. However, they are empirical and lack quantitative verifications out of solid backtest results. This book helps fill these vacancies. This text attempts to help explore how one can forecast and time markets more quantitatively. For this purpose, the author developed a model-based system, named AlphaCovaria, to help demonstrate how to use various simplest, readily available technical indicators to forecast and time markets approximately while eliminating subjective speculations at the same time. Centered on various math models, the author's AlphaCovaria system has three main components: an AlphaCurve program for charting, a BTDriver program for running all backtests, and an AlphaCovaria driver for generating buy/sell signals based on symbol profiles learned through backtests. This kind of formula-driven approach is more promising for building more high-performance strategies. The text is made concise and precise of about 100 pages only, as a working method does not need to be wordy. Math models, data and charts can help explain more effectively and convincingly. Also, inspired by those classical models, the author came up with a new indicator named simple cascading indicator (sci), which beat all those classical models in most cases, based on the backtest results with 29 carefully selected symbols and past 15 years' price data. This 2nd edition of the book also shared my live trading experience using real money in my Fidelity and eTrade accounts with my AlphaCovaria system. Such data can be found nowhere else.
Author: Roy Henriksson
Publisher: Sagwan Press
ISBN: 9781377037677
Category : History
Languages : en
Pages : 50
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Book Description
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
Author: Henry H Liu
Publisher: Createspace Independent Publishing Platform
ISBN: 9781986487528
Category :
Languages : en
Pages : 484
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Book Description
Machine learning is a newly-reinvigorated field. It promises to foster many technological advances that may improve the quality of our life significantly, from the use of latest, popular, high-gear gadgets such as smart phones, home devices, TVs, game consoles and even self-driving cars, and so on, to even more fun social and shopping experiences. Of course, for all of us in the circles of high education, academic research and various industrial fields, it offers more challenges and more opportunities. Whether you are a CS student taking a machine learning class or targeting a machine learning degree, or a scientist or an engineer entering the field of machine learning, this text helps you get up to speed with machine learning quickly and systematically. By adopting a quantitative approach, you will be able to grasp many of the machine learning core concepts, algorithms, models, methodologies, strategies and best practices within a minimal amount of time. Throughout the text, you will be provided with proper textual explanations and graphical exhibitions, augmented not only with relevant mathematics for its rigor, conciseness, and necessity but also with high quality examples. The text encourages you to take a hands-on approach while grasping all rigorous, necessary mathematical underpinnings behind various machine learning models. Specifically, this text helps you: *Understand what problems machine learning can help solve *Understand various machine learning models, with the strengths and limitations of each model *Understand how various major machine learning algorithms work behind the scene so that you would be able to optimize, tune, and size various models more effectively and efficiently *Understand a few state-of-the-art neural network architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders (AEs), and so on The author's goal is that after you are done with this text, you should be able to start embarking on various serious machine learning projects immediately, either using conventional machine learning models or state-of-the-art deep neural network models.
Author: Robert Kissell
Publisher: Amacom Books
ISBN: 9780814407240
Category : Business & Economics
Languages : en
Pages : 382
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Book Description
"The decisions that investment professionals and fund managers make have a direct impact on investor return. Unfortunately, the best implementation methodologies are not widely disseminated throughout the professional community, compromising the best interests of funds, their managers, and ultimately the individual investor. But now there is a strategy that lets professionals make better decisions. This valuable reference answers crucial questions such as: * How do I compare strategies? * Should I trade aggressively or passively? * How do I estimate trading costs, ""slice"" an order, and measure performance? and dozens more. Optimal Trading Strategies is the first book to give professionals the methodology and framework they need to make educated implementation decisions based on the objectives and goals of the funds they manage and the clients they serve."
Author: Stanley S. C. Huang
Publisher: Windsor Books/Probus
ISBN: 9780930233167
Category : Business & Economics
Languages : en
Pages : 0
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Book Description
Presents a complete master plan for forecasting major bull and bear markets. A one stop source for profitable stock market timing tools.
Author: Jon Danielsson
Publisher: John Wiley & Sons
ISBN: 1119977118
Category : Business & Economics
Languages : en
Pages : 307
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Book Description
Financial Risk Forecasting is a complete introduction to practical quantitative risk management, with a focus on market risk. Derived from the authors teaching notes and years spent training practitioners in risk management techniques, it brings together the three key disciplines of finance, statistics and modeling (programming), to provide a thorough grounding in risk management techniques. Written by renowned risk expert Jon Danielsson, the book begins with an introduction to financial markets and market prices, volatility clusters, fat tails and nonlinear dependence. It then goes on to present volatility forecasting with both univatiate and multivatiate methods, discussing the various methods used by industry, with a special focus on the GARCH family of models. The evaluation of the quality of forecasts is discussed in detail. Next, the main concepts in risk and models to forecast risk are discussed, especially volatility, value-at-risk and expected shortfall. The focus is both on risk in basic assets such as stocks and foreign exchange, but also calculations of risk in bonds and options, with analytical methods such as delta-normal VaR and duration-normal VaR and Monte Carlo simulation. The book then moves on to the evaluation of risk models with methods like backtesting, followed by a discussion on stress testing. The book concludes by focussing on the forecasting of risk in very large and uncommon events with extreme value theory and considering the underlying assumptions behind almost every risk model in practical use – that risk is exogenous – and what happens when those assumptions are violated. Every method presented brings together theoretical discussion and derivation of key equations and a discussion of issues in practical implementation. Each method is implemented in both MATLAB and R, two of the most commonly used mathematical programming languages for risk forecasting with which the reader can implement the models illustrated in the book. The book includes four appendices. The first introduces basic concepts in statistics and financial time series referred to throughout the book. The second and third introduce R and MATLAB, providing a discussion of the basic implementation of the software packages. And the final looks at the concept of maximum likelihood, especially issues in implementation and testing. The book is accompanied by a website - www.financialriskforecasting.com – which features downloadable code as used in the book.
