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This contribution applies the cointegrated vector autoregressive (CVAR) model to analyze the long-run behavior and short-run dynamics of stock markets across five developed and three emerging economies. The main objective is to check whether liquidity conditions play an important role in stock market developments. As an innovation, liquidity conditions enter the analysis from three angles: in the form of a broad monetary aggregate, the interbank overnight rate and net capital flows, which represent the share of global liquidity that arrives in the respective country. A second aim is to understand whether central banks are able to influence the stock market.
'Animal spirits' is a term that describes the instincts and emotions driving human behaviour in economic settings. In recent years, this concept has been discussed in relation to the emerging field of narrative economics. When unscheduled events hit the stock market, from corporate scandals and technological breakthroughs to recessions and pandemics, relationships driving returns change in unforeseeable ways. To deal with uncertainty, investors engage in narratives which simplify the complexity of real-time, non-routine change. This book assesses the novelty-narrative hypothesis for the U.S. stock market by conducting a comprehensive investigation of unscheduled events using big data textual analysis of financial news. This important contribution to the field of narrative economics finds that major macro events and associated narratives spill over into the churning stream of corporate novelty and sub-narratives, spawning different forms of unforeseeable stock market instability.
This open access book addresses four standard business school subjects: microeconomics, macroeconomics, finance and information systems as they relate to trading, liquidity, and market structure. It provides a detailed examination of the impact of trading costs and other impediments of trading that the authors call rictions It also presents an interactive simulation model of equity market trading, TraderEx, that enables students to implement trading decisions in different market scenarios and structures. Addressing these topics shines a bright light on how a real-world financial market operates, and the simulation provides students with an experiential learning opportunity that is informative and fun. Each of the chapters is designed so that it can be used as a stand-alone module in an existing economics, finance, or information science course. Instructor resources such as discussion questions, Powerpoint slides and TraderEx exercises are available online.
Updated with a new chapter that draws on behavioral finance, the field that studies the psychology of investment decisions, the bestselling guide to investing evaluates the full range of financial opportunities.
This book rehabilitates beta as a definition of systemic risk by using particle physics to evaluate discrete components of financial risk. Much of the frustration with beta stems from the failure to disaggregate its discrete components; conventional beta is often treated as if it were "atomic" in the original Greek sense: uncut and indivisible. By analogy to the Standard Model of particle physics theory's three generations of matter and the three-way interaction of quarks, Chen divides beta as the fundamental unit of systemic financial risk into three matching pairs of "baryonic" components. The resulting econophysics of beta explains no fewer than three of the most significant anomalies and puzzles in mathematical finance. Moreover, the model's three-way analysis of systemic risk connects the mechanics of mathematical finance with phenomena usually attributed to behavioral influences on capital markets. Adding consideration of volatility and correlation, and of the distinct cash flow and discount rate components of systematic risk, harmonizes mathematical finance with labor markets, human capital, and macroeconomics.
'For amateurs and professionals alike wishing to deepen their understanding of the often mysterious and counter-intuitive fluctuations in asset prices, this book provides essential reading.' - Barry Riley, Financial Times 'Really required reading.' - Anthony Harris, Times According to mainstream economic theory, the prices of individual stocks respond rationally to unexpected news. However, real market movements appear to respond to news in more complex and sometimes perverse ways, overshooting or not reacting at all. Drawing on his hands-on experience, Professor Pepper puts forward a new theory based on the analysis of the supply of and demand for investible funds. He shows clearly that price movements are governed not by news but by the financial requirements of investors, requirements which therefore become a powerful forecasting tool.
Is the stock market responsive to macroeconomic news? This paper employs the daily returns of the Dow Jones Industrial Index, the S&P 500 index, the Russell 1000 index, and the Russell 2000 index to examine stock market reactions to a broad list of macroeconomic announcements, including money supply, inflation, employment, housing starts, and trade balances, etc. Several announcements concerning real economic activity that have received little attention in previous research are shown to have a significant impact on stock prices. The paper also presents preliminary evidence for the different reaction to macroeconomic news by small cap stocks and large cap stocks.
An analysis of the literature dealing with the demand for money
Professionals are constantly searching for competitive solutions to help determine current and future economic tendencies. Econometrics uses statistical methods and real-world data to predict and establish specific trends within business and finance. This analytical method sustains limitless potential, but the necessary research for professionals to understand and implement this approach is lacking. Applied Econometric Analysis: Emerging Research and Opportunities explores the theoretical and practical aspects of detailed econometric theories and applications within economics, political science, public policy, business, and finance. Featuring coverage on a broad range of topics such as cointegration, machine learning, and time series analysis, this book is ideally designed for economists, policymakers, financial analysts, marketers, researchers, academicians, and graduate students seeking research on the various techniques of econometric concepts.
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.