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In this chapter, we argue that under- and over-reaction are both parts of the price dynamics caused by investor's naïve judgmental extrapolation. We propose to use the Holt–Winters model, a parsimonious model with two parameters, to represent investor's conservatism (anchoring) and representativeness (trending). The complexity of earning information, which is broken down into a drift, a transitory shock, and an autocorrelated permanent shock, add further volatility to the price. We explain the price dynamics caused by the interplay of the earning model and investor's naïve belief. It is further argued that empirical “underreaction” and “overreaction” differ from true under- and overreaction. The simulated results with the proposed model confirm with empirical findings on under- and overreaction.

Introduction: According to the effective market hypothesis, investors act rationally when making an investment decision. The hypothesis assumes that investors invest in a way that maximizes their returns, taking into account the new information received. If the information released on the market is interpreted in the same way by all investors, no investor would be able to earn above the market. This hypothesis is valid in case of efficient markets. In the event that investors show irrational behavior to the information released on the market, the markets move away from efficiency. Overreaction behavior is one of the non-rational behaviors of investors. Overreaction behavior involves investors overreacting by misinterpreting the new information released to the market. According to De Bondt and Thaler’s (1985), overreaction hypothesis in the event that investors overreact to the news coming to the market, after a period the false evaluation, the price of the security is corrected with the reversal movement, without the need of any positive or negative information. Aim: The purpose of this study is to examine investors’ overreaction behavior in mergers and acquisitions. For this purpose, overreaction behavior was analyzed for companies whose stocks are traded on the Borsa Istanbul, which were involved in mergers or acquisitions. Method: In the study, companies that made mergers and acquisitions for the period 2007–2017 were determined, and abnormal returns and cumulative abnormal returns were calculated by using monthly closing price data of these companies. Moreover, whether investors overreact to the merger and acquisition decision is examined separately for one-, three- and five-year periods. Findings: As a result of the research, it has been observed that there is a reverse return for one-, three-, and five-year periods. However, it has been determined that the overreaction hypothesis is valid for only one year.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

This paper gives a selective review on some recent developments of nonparametric methods in both continuous and discrete time finance, particularly in the areas of nonparametric estimation and testing of diffusion processes, nonparametric testing of parametric diffusion models, nonparametric pricing of derivatives, nonparametric estimation and hypothesis testing for nonlinear pricing kernel, and nonparametric predictability of asset returns. For each financial context, the paper discusses the suitable statistical concepts, models, and modeling procedures, as well as some of their applications to financial data. Their relative strengths and weaknesses are discussed. Much theoretical and empirical research is needed in this area, and more importantly, the paper points to several aspects that deserve further investigation.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Analyzing how the post-Soviet transition interacts with the crisis of market finance exhibits a new “greed-based economic system” in the making. Asset grabbing is at its core and hinders capital accumulation. All the various privatization schemes have triggered off asset grabbing, asset stripping, and asset tunneling. A global contagion of such behavior has spread the power and cohesion of managers/shareholders (oligarchs) worldwide. Financial asset grabbing is less straightforward, though much widespread, and operates in financial markets through new financial products, securitization, firms buying their own shares, hedge funds, stock price manipulation, short selling, and the distribution of stock options.Shadow banking, and more generally a global informal economy, results from grabbing strategies in financial markets that breach the formal rules of capitalism. In alleviating and circumventing the rules, the oligarchy paves the way for economic malpractices and crime, calling capitalist laws into question.In such context, systemic greed underlies unconstrained maximization of relative wealth, for which asset grabbing is a rational means, in a winner-take-all economy. At the present stage of our research, a greed-based economy cannot yet be theoretically defined as a transition either to a new phase of capitalism or to another different system.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

We highlight two mechanisms of limited attention for expert information intermediaries, i.e., analysts, and the effects of such limited attention on the market price discovery process. We approach analysts' limited attention from the perspective of day-to-day arrival of information and processing of tasks. We examine the attention-limiting role of competing tasks (number of earnings announcements and forecasts for portfolio firms) and distracting events (number of earnings announcements for non-portfolio firms) in analysts' forecast accuracy and the effects of such, on the subsequent price discovery process. Our results show that competing tasks worsen analysts' forecast accuracy, and competing task induced limited attention delays the market price adjustment process. On the other hand, distracting events can improve analysts' forecast accuracy and accelerate market price adjustments when such events relate to analysts' portfolio firms through industry memberships.