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This chapter used empirical data from five developed markets and five emerging markets to perform an examination of anomalies using common financial economic approaches along with more innovative econometric models. Of the methodologies used to test for anomalies, the data-driven panel and quantile regressions were empirically found to be better suited over the traditionally common approaches to describe the non-linear, switching behavior of the anomalies. In the developed markets, the statistically significant small firms (size) had the highest average returns. In the developing markets, the lower price-to-earnings (P/E) ratios (value) had the highest average returns. In addition, the research found (1) a small country effect, (2) sales had a negative relationship with returns, and (3) a lower (higher) book-to-market (B/M) was associated with higher returns in the developed (developing) markets, indicating investors received a higher premium for growth (value) equities. The semi-strong form of the efficient market hypothesis was also found to be violated. The anomalies’ behavior varied between sorted portfolios, industries, and developed to emerging markets; though it was found to be consistent through time (not disrupted by bear or bull markets).

This chapter elaborates on the usefulness of embracing complexity theory, modeling outcomes rather than directionality, and modeling complex rather than simple outcomes in strategic management. Complexity theory includes the tenet that most antecedent conditions are neither sufficient nor necessary for the occurrence of a specific outcome. Identifying a firm by individual antecedents (i.e., noninnovative vs. highly innovative, small vs. large size in sales or number of employees, or serving local vs. international markets) provides shallow information in modeling specific outcomes (e.g., high sales growth or high profitability) – even if directional analyses (e.g., regression analysis, including structural equation modeling) indicate that the independent (main) effects of the individual antecedents relate to outcomes directionally – because firm (case) anomalies almost always occur to main effects. Examples: a number of highly innovative firms have low sales while others have high sales and a number of noninnovative firms have low sales while others have high sales. Breaking-away from the current dominant logic of directionality testing – null hypothesis significance testing (NHST) – to embrace somewhat precise outcome testing (SPOT) is necessary for extracting highly useful information about the causes of anomalies – associations opposite to expected and “statistically significant” main effects. The study of anomalies extends to identifying the occurrences of four-corner strategy outcomes: firms doing well in favorable circumstances, firms doing badly in favorable circumstances, firms doing well in unfavorable circumstances, and firms doing badly in unfavorable circumstances. Models of four-corner strategy outcomes advance strategic management beyond the current dominant logic of directional modeling of single outcomes.

Deviations from efficiency widely documented for the case of developed capital markets include the presence of seasonal patterns. These anomalies, fairly well known by investors, could possibly lead to obtaining extraordinary gains. Although markets from the developing and transitional economies have grown significantly during the last decades, research concerning their seasonal behavior is limited. This paper examines the day-of-the week and month-of-the year effect for the seven Latin American stock exchanges: Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela. Returns derived from the local nominal indexes, adjusted for inflation indexes, and dollar adjusted indexes are analyzed to identify the behavior of each exchange and draw some comparisons.

After the collapse of communist and socialist regimes at the beginning of 1990s, a number of Central and Eastern European (CEE) economies started their journey into capitalism by establishing private property and capital markets. As a result, a number of stock markets have since been established in the region. Since then, they have displayed considerable growth in size and degree of sophistication, and they have attracted the interest of academics for a number of reasons. First, these markets provide a possibility to re-examine existing asset-pricing models and pricing anomalies in the conditions of the evolving markets. Market efficiency of the CEE markets is tested in Ratkovicova (1999) and Gilmore and McManus (2001); a version of the CAPM is tested in Charemza and Majerowska (2000); Mateus (2004) explores the predictability of European emerging market returns within an unconditional asset-pricing framework while the January-pricing anomaly is studied in Henke (2003). Second, in the light of growing interdependencies between world equity markets due to enhanced capital movements, numerous studies have investigated the extent to which emerging European stock markets are integrated with global markets, and the extent to which they are subjects to global shocks (Gelos & Sahay, 2000; Gilmore & McManus, 2002; Scheicher, 2001). Among the CEE markets, those of the Vysegrad countries (Poland, Hungary and the Czech Republic) have attracted most of the attention of the academics due to their economies faster growth relative to their regional counterparts (Slovakia, Slovenia, Bulgaria, Croatia and Baltic countries), in addition to political stability and their (successfully realized) prospects of joining the European Union (EU).

The internal factors that influence the decision to change dividend growth rates include two competing models: the earnings and free cash flow models. As far as each of the components of each model is considered, the informative and efficient dividend payout decisions require that managers have to focus on the significant component(s) only. This study examines the cointegration, significance, and explanatory power of those components empirically. The expected outcomes serve two objectives. First, on an academic level, it is interesting to examine the extent to which payout practices meet the premises of the earnings and free cash flow models. The latter considers dividends and financing decisions as two faces of the same coin. Second, on a professional level, the outcomes help focus the management’s efforts on the activities that can be performed when considering a change in dividend growth rates.

This study uses data for the firms listed in two indexes: Dow Jones Industrial Average (DJIA30) and NASDAQ100. The data cover quarterly periods from 30 June 1989 to 31 March 2011. The methodology includes (a) cointegration analysis in order to test for model specification and (b) classical regression in order to examine the explanatory power of the components of earnings and free cash flow models.

The results conclude that: (a) Dividends growth rates are cointegrated with the two models significantly; (b) Dividend growth rates are significantly and positively associated with growth in sales and cost of goods sold only. Accordingly, these are the two activities that firms’ management need to focus on when considering a decision to change dividend growth rates, (c) The components of the earnings and free cash flow models explain very little of the variations in dividends growth rates. The results are to be considered a call for further research on the external (market-level) determinants that explain the variations in dividends growth rates. Forthcoming research must separate the effects of firm-level and market-level in order to reach clear judgments on the determinants of dividends growth rates.

This study contributes to the related literature in terms of offering updated robust empirical evidence that the decision to change dividend growth rate is discretionary to a large extent. That is, dividend decisions do not match the propositions of the earnings and free cash flow models entirely. In addition, the results offer solid evidence that financing trends in the period 1989–2011 showed heavy dependence on debt financing compared to other related studies that showed heavy dependence on equity financing during the previous period 1974–1984.

Today, firm performance must be measured not only on traditional metrics but also on those that reflect the changing imperatives and new metric knowledge. Thus, the focus of managers, investors, and researchers is shifting from rubrics like sales and profitability to growth as a more appropriate measure of firm performance. We aim to highlight the effects that growth of a firm can have on the level of its systematic risk. Using a sample of 203 firms across nine industries taken from the Indian manufacturing sector for a period of 17 years (1998–2014), we develop and test a panel vector autoregressive (VAR) model to analyze the causal relationship between growth aspects and systematic risk of firms. Results depict that a growth option available to firms increase their level of systematic risk and the risk decreases when firms start chasing this growth by increasing their assets in place. Sustainable growth rate, which depicts the growth potential of firms, plays an important role in reducing the level of systematic risk. The findings of this chapter are relevant to managers who think that growth is always beneficial.

Our chapter raises serious questions about the long-term efficiency of stock prices in relation to the realized returns of the underlying corporate real assets. In our large-scale calculations that cover horizons of 10, 20, 30, 40, and 50 years, returns on corporate real assets suffer a long-term decline, and have been below the yields of 10-year Treasury bonds since 1973. Real assets that received more external financing from capital markets and institutions actually report even lower realized long-term returns. The decline in realized returns cannot be attributed to declining risks as the volatilities of realized returns have been increasing over time. These surprising results may stimulate fresh debate on the roles and long-term performance of capital markets and institutions.

Several studies have observed that stocks tend to drop by an amount that is less than the dividend on the ex-dividend day, the so-called ex-dividend day anomaly. However, there still remains a lack of consensus for a single explanation of this anomaly. Different from other studies, this dissertation attempts to answer the primary research question: how can investors make trading profits from the ex-dividend day anomaly and how much can they earn? With this goal, I examine the economic motivations of equity investors through four main hypotheses identified in the anomaly's literature: the tax differential hypothesis, the short-term trading hypothesis, the tick size hypothesis, and the leverage hypothesis.

While the U.S. ex-dividend anomaly is well studied, I examine a long data window (1975–2010) of Thailand data. The unique structure of the Thai stock market allows me to assess all four main hypotheses proposed in the literature simultaneously. Although I extract the sample data from two data sources, I demonstrate that the combined data are consistently sampled. I further construct three trading strategies – “daily return,” “lag one daily return,” and “weekly return” – to alleviate the potential effect of irregular data observation.

I find that the ex-dividend day anomaly exists in Thailand, is governed by the tax differential, and is driven by short-term trading activities. That is, investors trade heavily around the ex-dividend day to reap the benefits of the tax differential. I find mixed results for the predictions of the tick size hypothesis and results that are inconsistent with the predictions of the leverage hypothesis.

I conclude that, on the Stock Exchange of Thailand, juristic and foreign investors can profitably buy stocks cum-dividend and sell them ex-dividend while local investors should engage in short sale transactions. On average, investors who employ the daily return strategy have earned significant abnormal return up to 0.15% (45.66% annualized rate) and up to 0.17% (50.99% annualized rate) for the lag one daily return strategy. Investors can also make a trading profit by conducting the weekly return strategy and earn up to 0.59% (35.67% annualized rate), on average.

In the build-up of an investment decision, the existence of both active and passive investment vehicles triggers a puzzle for investors. Indeed the confrontation between active and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a bone of contention since the inception of these investment structures. Accordingly, the objective of this chapter is to distinctly underscore whether an investor should be concerned in choosing between active and diverse passive investment structures.

The survivorship bias-free dataset consists of 776 equity funds which are domiciled either in America or Europe, and are likewise exposed to the equity markets of the same regions. In addition to geographical segmentation, equity funds are also categorised by structure and management type, specifically actively managed mutual funds, index mutual funds and passive exchange traded funds (‘ETFs’). This classification leads to the analysis of monthly net asset values (‘NAV’) of 12 distinct equally weighted portfolios, with a time horizon ranging from January 2004 to December 2014. Accordingly, the risk-adjusted performance of the equally weighted equity funds’ portfolios is examined by the application of mainstream single-factor and multi-factor asset pricing models namely Capital Asset Pricing Model (Fama, 1968; Fama & Macbeth, 1973; Lintner, 1965; Mossin, 1966; Sharpe, 1964; Treynor, 1961), Fama French Three-Factor (1993) and Carhart Four-Factor (1997).

Solely examination of monthly NAVs for a 10-year horizon suggests that active management is equivalent to index replication in terms of risk-adjusted returns. This prompts investors to be neutral gross of fees, yet when considering all transaction costs it is a distinct story. The relatively heftier fees charged by active management, predominantly initial fees, appear to revoke any outperformance in excess of the market portfolio, ensuing in a Fool’s Errand Hypothesis. Moreover, both active and index mutual funds’ performance may indeed be lower if financial advisors or distributors of equity funds charge additional fees over and above the fund houses’ expense ratios, putting the latter investment vehicles at a significant handicap vis-à-vis passive low-cost ETFs. This chapter urges investors to concentrate on expense ratios and other transaction costs rather than solely past returns, by accessing the cheapest available vehicle for each investment objective. Put simply, the general investor should retreat from portfolio management and instead access the market portfolio using low-cost index replication structures via an execution-only approach.

The battle among actively managed and index replication equity funds in terms of risk-adjusted performance and alpha generation has been a grey area since the inception of mutual funds. The interest in the subject constantly lightens up as fresh instruments infiltrate financial markets. Indeed the mutual fund puzzle (Gruber, 1996) together with the enhanced growth of ETFs has again rejuvenated the active versus passive debate, making it worth a detailed analysis especially for the benefit of investors who confront a dilemma in choosing between the two management styles.

Stock selection models often use momentum and analysts’ expectation data. We find that earnings forecast revisions and direction of forecast revisions are more important than analysts’ forecasts in identifying mispriced securities. Investing with expectations data and momentum variables is consistent with maximizing the geometric mean and Sharpe ratio over the long run. Additional evidence is revealed that supports the use of multifactor models for portfolio construction and risk control. The anomalies literature can be applied in real-world portfolio construction in the U.S., international, and global equity markets during the 1998–2009 time period. Support exists for the use of tracking error at risk estimation procedures.

While perfection cannot be achieved in portfolio creation and modeling, the estimated model returns pass the Markowitz and Xu data mining corrections test and are statistically different from an average financial model that could have been used to select stocks and form portfolios. We found additional evidence to support the use of Arbitrage Pricing Theory (APT) and statistically-based and fundamentally-based multifactor models for portfolio construction and risk control. Markets are neither efficient nor grossly inefficient; statistically significant excess returns can be earned.