Stock Analysis in the Twenty-First Century and Beyond For years, financial analysts have struggled with the fact that practically all the financial measures used to analyze corporate performance lack predictive power when it comes to forecasting the market performance of the company’s stock. Numerous academic studies have documented and reported this lack of predictability. Correlation coefficients close to zero have been reported for the relationship between stock market performance and such critical financial measures as earnings growth, sales growth, price/earnings ratio, return on equity, intrinsic value (models based on discounted cash flow or dividends), and many more. It is this disconnect between traditional financial measures and the performance of stocks in the marketplace that has led to the now-famous efficient market hypothesis, the cornerstone of modern portfolio theory. To accept the idea that the future performance of stocks is unpredictable is to say that nothing a company does will affect the future performance of its stock in the market, and that is absurd. It would be more accurate to say that everything a company does will affect the future performance of its stock in the market. The problem with this statement is that it makes the forecasting of future stock performance so complex that it removes it from the realm of human solution. Confident in the belief that something other than chance and irrational investors determine future stock prices, several research groups around the world have started exploring the use of intelligent computer programs (programs that self-organize based on environmental feedback). Early results are very promising and have provided a glimpse of the economic forces described by Adam Smith as the invisible hand that guides economic activity. Stock Analysis in the Twenty-First Century and Beyond describes the stock analysis problem and explores one of the more successful efforts to harness the new intelligent computer technology. Many people mistakenly classify Artificially Intelligent (AI) computer systems as a form of quantitative analysis. There are two distinct differences between advanced AI systems and traditional quantitative analysis. They are (1) who makes up the selection rules and weighting and (2) what information is used to discriminate between good- and poor-performing securities. In most quantitative systems, even in an advanced expert system form, humans make up the investment rules and mathematically derive the weightings associated with the rules. Computer systems that depend on outside human intelligence to program their actions are not inherently intelligent. In advanced AI systems, the computer makes up its own rules and weightings. The computer learns from examples of good- and poor-performing stocks and determines its own ways for discriminating between them. The procedures that are derived by the computer are often so complex that they defy human understanding. In addition to making up its own rules, advanced AI systems look at corporate financial data differently. Just like in the human brain, where information is not stored in the brain cells but rather in the connections and relationships between cells, so too is corporate performance information stored in the relationships between financial numbers. Assessing the performance of companies is not so much in the numbers as it is in the connections between the numbers. Financial analysts recognized this early on and have used first-order relational information in the form of financial ratios for many years (price/book, debt/equity, current assets / current liabilities, price/earnings, etc.). Now with advanced AI systems, we are finally able to look at and evaluate high-order interrelationships in financial data that have been far too complex to analyze with less sophisticated systems. These then are the fundamental differences between what has been used in the past and what will be used in the future. Cdr. Thomas E. Berghage

For years, financial analysts have struggled with the fact that practically all the financial measures used to analyze corporate performance lack predictive power when it comes to forecasting the market performance of the company's stock. Numerous academic studies have documented and reported this lack of predictability. Correlation coefficients close to zero have been reported for the relationship between stock market performance and such critical financial measures as earnings growth, sales growth, price/earnings ratio, return on equity, intrinsic value (models based on discounted cash flow or dividends), and many more. It is this disconnect between traditional financial measures and the performance of stocks in the marketplace that has led to the now-famous efficient market hypothesis, the cornerstone of modern portfolio theory. To accept the idea that the future performance of stocks is unpredictable is to say that nothing a company does will affect the future performance of its stock in the market, and that is absurd. It would be more accurate to say that everything a company does will affect the future performance of its stock in the market. The problem with this statement is that it makes the forecasting of future stock performance so complex that it removes it from the realm of human solution.

What are the grand dynamics that drive the accumulation and distribution of capital? Questions about the long-term evolution of inequality, the concentration of wealth, and the prospects for economic growth lie at the heart of political economy. But satisfactory answers have been hard to find for lack of adequate data and clear guiding theories. In this work the author analyzes a unique collection of data from twenty countries, ranging as far back as the eighteenth century, to uncover key economic and social patterns. His findings transform debate and set the agenda for the next generation of thought about wealth and inequality. He shows that modern economic growth and the diffusion of knowledge have allowed us to avoid inequalities on the apocalyptic scale predicted by Karl Marx. But we have not modified the deep structures of capital and inequality as much as we thought in the optimistic decades following World War II. The main driver of inequality--the tendency of returns on capital to exceed the rate of economic growth--today threatens to generate extreme inequalities that stir discontent and undermine democratic values if political action is not taken. But economic trends are not acts of God. Political action has curbed dangerous inequalities in the past, the author says, and may do so again. This original work reorients our understanding of economic history and confronts us with sobering lessons for today.

Händler und erfahrene Privatanleger kommen immer mehr zu dem Schluß, daß die traditionellen Prognosemethoden der fundamentalen oder technischen Analyse offenbar zu widersprüchlichen Ergebnissen kommen. Bei den fundamentalen Analysten geht der Markt eher schwach, bei den technischen Analysten geht er fester. Die Folge von solch gegensätzlichen Signalen ist, daß viele Händler und Anleger sich auf eine Kombination von wissenschaftlicher Theorie und Anlagepsychologie stützen. Bill Williams zeigt hier neue Perspektiven der Marktbeobachtung auf. Er kombiniert Elemente traditioneller technischer Chartmethoden mit Chaostheorie und Psychologie. Das Ergebnis ist ein komplexes, fünfdimensionales Handelsprogramm mit ausführlichen Erläuterungen und Beispielen zu Bereichen wie Fraktalanalyse, Oszillatoren und psychologischen Aspekten. (10/98)

Market Analysis for the New Millennium foreshadows sweeping changes in market analysis for the twenty-first century and beyond. Featuring ground-breaking essays by 12 authors, this book builds a compelling case and shows you the evidence that will change the way you view financial markets and market analysis.

How institutions and individuals can address complex social, financial, and environmental problems on a systemic level—and invest in a more secure future. Investment today has evolved from the basic, conventional approach of the past. Investors have come to recognize the importance of sustainable investment and are more frequently considering environmental and social factors in their decisions. Yet the complexity of the times forces us to recognize and transition to a third stage of investment practice: system-level investing. In this paradigm-shifting book, William Burckart and Steve Lydenberg show how system-level investors support and enhance the health and stability of the social, financial, and environmental systems on which they depend for long-term returns. They preserve and strengthen these fundamental systems while still generating competitive or otherwise acceptable performance. This book is for those investors who believe in that transition. They may be institutions, large or small, concerned about the long-term stability of the environment and society. They may be individual investors who want their children and grandchildren to inherit a just and sustainable world. Whoever they may be, Burckart and Lydenberg show them the what, why, and how of system-level investment in this book: what it means to manage system-level risks and rewards, why it is imperative to do so now, and how to integrate this new way of thinking into their current practice. “Burckart and Lydenberg are the Wayne Gretzkys of investing: Showing us not where investing is, but where it’s going.” —Jon Lukomnik, Managing Partner, Sinclair Capital; Senior Fellow, High Meadows Institute