The ways financial analysts, traders, and other specialists use information and learn from each other are of fundamental importance to understanding how markets work and prices are set. This graduate-level textbook analyzes how markets aggregate information and examines the impacts of specific market arrangements--or microstructure--on the aggregation process and overall performance of financial markets. Xavier Vives bridges the gap between the two primary views of markets--informational efficiency and herding--and uses a coherent game-theoretic framework to bring together the latest results from the rational expectations and herding literatures. Vives emphasizes the consequences of market interaction and social learning for informational and economic efficiency. He looks closely at information aggregation mechanisms, progressing from simple to complex environments: from static to dynamic models; from competitive to strategic agents; and from simple market strategies such as noncontingent orders or quantities to complex ones like price contingent orders or demand schedules. Vives finds that contending theories like informational efficiency and herding build on the same principles of Bayesian decision making and that "irrational" agents are not needed to explain herding behavior, booms, and crashes. As this book shows, the microstructure of a market is the crucial factor in the informational efficiency of prices. Provides the most complete analysis of the ways markets aggregate information Bridges the gap between the rational expectations and herding literatures Includes exercises with solutions Serves both as a graduate textbook and a resource for researchers, including financial analysts

The models of portfolio selection and asset price dynamics in this volume seek to explain the market dynamics of asset prices. Presenting a range of analytical, empirical, and numerical techniques as well as several different modeling approaches, the authors depict the state of debate on the market selection hypothesis. By explicitly assuming the heterogeneity of investors, they present models that are descriptive and normative as well, making the volume useful for both finance theorists and financial practitioners. - Explains the market dynamics of asset prices, offering insights about asset management approaches - Assumes a heterogeneity of investors that yields descriptive and normative models of portfolio selections and asset pricing dynamics

The financial markets have become more and more important in modern society. The behavior of the financial markets, and its impacts on our society, relies crucially on the behavior of market participants, aka the investors of different types. Although descriptions of the financial markets on the macro level have caught great attentions of investors, regulators, and the ordinary people, how the market participants interact with each other in the financial market may provide deeper insights on how and why the financial markets behave. This book tries to supply as much research on the micro level of financial market behavior as possible to the readers. The author has been doing financial research, especially on the micro level, during the past two decades. The academic research on this broad area has undergone a rapid growth, with new results, methods, theories, and even paradigms, emerging and burgeoning almost every year. As a financial researcher in one of China’s top universities, the author has kept monitoring, digesting, and synthesizing the research articles in the area. This book is the outcome of this decades-long routine research work of the author. The book covers the fundamental economic theories of how different investors receive and interpret information. The empirical results of investors behavior are also discussed in depth. The book also shows the basic academic techniques of modeling the investors behavior.

"The process by which securities are traded is very different from the idealized picture of a frictionless and self-equilibrating market offered by the typical finance textbook. This book offers a more accurate and authoritative take on this process. The book starts from the assumption that not everyone is present at all times simultaneously on the market, and that participants have quite diverse information about the security's fundamentals. As a result, the order flow is a complex mix of information and noise, and a consensus price only emerges gradually over time as the trading process evolves and the participants interpret the actions of other traders. Thus, a security's actual transaction price may deviate from its fundamental value, as it would be assessed by a fully informed set of investors. The book takes these deviations seriously, and explains why and how they emerge in the trading process and are eventually eliminated. The authors draw on a vast body of theoretical insights and empirical findings on security price formation that have come to form a well-defined field within financial economics known as "market microstructure." Focusing on liquidity and price discovery, the book analyzes the tension between the two, pointing out that when price-relevant information reaches the market through trading pressure rather than through a public announcement, liquidity may suffer. It also confronts many striking phenomena in securities markets and uses the analytical tools and empirical methods of market microstructure to understand them. These include issues such as why liquidity changes over time and differs across securities, why large trades move prices up or down, and why these price changes are subsequently reversed, and why we observe temporary deviations from asset fair values"--

Recent years have seen the widespread application of Natural Computing algorithms (broadly defined in this context as computer algorithms whose design draws inspiration from phenomena in the natural world) for the purposes of financial modelling and optimisation. A related stream of work has also seen the application of learning mechanisms drawn from Natural Computing algorithms for the purposes of agent-based modelling in finance and economics. In this book we have collected a series of chapters which illustrate these two faces of Natural Computing. The first part of the book illustrates how algorithms inspired by the natural world can be used as problem solvers to uncover and optimise financial models. The second part of the book examines a number agent-based simulations of financial systems. This book follows on from Natural Computing in Computational Finance (Volume 100 in Springer’s Studies in Computational Intelligence series) which in turn arose from the success of EvoFIN 2007, the very first European Workshop on Evolutionary Computation in Finance & Economics held in Valencia, Spain in April 2007.