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Financial signaling theory has hypothesized that signals are backed up by improved cash flow. The relationship between signals and cash flow, however, has never been empirically investigated. The purpose of this paper is to conduct such an investigation by examining the relationship between cash flow and dividend signal, the most frequently employed financial signal. In particular, this study examines if there are false dividend signals that are not supported by better cash flow and how the market reacts to them. A two-stage empirical dividend signalling model is developed. Unexpected dividend increases of non-financial and non-regulated firms from 1982 through 1986 are investigated. Event study methodology and the Analysis of Variance comparison are used in the testing. The empirical results indicate that while the majority of signaling events are supported by improved cash flow, some dividend signals, especially strong ones, are not. The market is partly deceived by false signals at the dividend announcement. When the new cash flow information arrives, however, the market reacts negatively and quickly to the falsely signaling firms.

Executive replacements have historically created fluctuations in the market value of a company and precipitated inappropriate investor reaction. However, the direction and statistical significance of relationships between executive turnover, market value, financial risk, and investor reaction among a census of highly performing firms was previously unexplored. The purpose of this study was to determine the extent of the relationship between CEO turnover and indicators of company performance. Theoretical foundation for this study was the efficient markets hypothesis. Hypotheses tests were designed to support an ex-post facto research methodology for pre-post comparison of volatility of financial metrics, which are indicators of market value (market value added), investor reaction (Tobin's q), risk (beta), executive performance (economic value added and return on assets), and turnover frequency given CEO succession. Statistically significant differences in firm risk emerged from comparisons of highly performing firms exemplified in the foundational leadership text Good to Great. Approximately 45 % of firms sampled did not experience volatility of financial metrics, which supported the presence of a leadership legacy, or strategic management behavior which minimized financial risk. Contrary to prior studies, financial metrics sampled within an interval immediately surrounding the succession event were less indicative of significant financial risk as compared to metrics sampled over the entire tenure of executives. Implications for positive social change include reducing investor risk in selection of equity holdings; capital fairly directed to entities results in benefits for society including job creation, economic stimulus, safer retirement accounts, and corporate sustainability.

High Frequency Trading (HFTs) have dramatically changed the way markets operate through supplanting traditional market makers. Numerous studies and pundits have postulated a link between HFTs and market sell-off severity. Developed by Easley and O’Hara, the Volume Synchronized Probability of Informed Trading (VPIN) is a metric that uses volume imbalances to determine the probability of informed trading. This study finds that a time-based variation of VPIN can be useful in predicting market sell-offs as it has a positive relationship with forward semivariance and a negative relationship with forward returns under different market conditions.

Volatility is inherently unobservable, and thus the selection of models and their definition is crucial in financial research. This dissertation attempts to check the role of investor sentiment and forecast Value-at-Risk (VaR) of the stock market using both parametric and nonparametric approaches. In the first essay, based on daily return data of three stock indices and four individual stocks from January 1988 to December 2006, the role of day-of-the-week, as well as investor sentiment, is examined using two approaches: linear regression to test investor sentiment effect on stock returns and Logit regression to test the investor sentiment effect on market direction. The results indicate that there is a significant positive role of investor sentiment in the market. However, the outcome also shows that the role of the day-of-the-week effect varies among stocks Based on the results presented in the first essay, in the second paper investor sentiment effect was included in both mean and conditional variance equations of GARCH models. By comparing augmented GARCH models considering investor sentiment effect with traditional GARCH models, the result demonstrated that aug-mented GARCH models are signifiantly better than traditional GARCH models where AIC, BIC, log-likelihood, and out-of-sample VaR forecasting were employed. The research indicates that a significant role of investor sentiment in forecasting conditional mean and conditional volatility and the accuracy of GARCH models is improved by accounting for investor sentiment effect. Compared with the first and second essays employing a parametric method to analyze the stock market, the third paper adopts a nonparametric approach to estimate the conditional probability distribution of asset returns. It is evident that the exact conditional mean and conditional variance is inherently unobservable for time series. In practice, conditional variance is often achieved from different parametric models, such as GARCH, EGARCH, IGARCH, etc., by assuming di®erent distributions such as normal, student's t, or skewed t. Therefore, the accuracy of forecasting strongly depends on the distribution assumption. The nonparametric method avoids the need for a distribution assumption by using a neural network to estimate the potentially nonlinear relationship between VaR and returns. Our results show that the neural network approach outperforms traditional GARCH models. (96 pages)