Liquidity has a potential impact on the investment strategies and financing strategies which can affect or be affected by the risk perspective. The thesis aims to establish linkage between liquidity and three risk-related issues in the finance literature. First, we inspect the impact of market liquidity on feedback trading. Our results suggest that the market liquidity should be included in the feedback traders’ demand function for shares in East Asian stock markets. We then analyse the listed US firms to test the impact financial flexibility on firm’s corporate social responsibility. We find a negative relationship between financial flexibility and CSR, which indicates that the two are substitutes to each other in hedging financing risk. Furthermore, we find the negative relationship between financial flexibility and CSR is affected by both CEO conservatism and the lifecycle stage of a firm. Finally, we investigate the impact of CEO inside debt compensation on the adjustment speed of cash holding of the listed US firms. We find that the CEOs with high inside debt compensation accelerate the adjustment of cash holding when the actual cash ratio is below target while decelerating the adjustment speed of cash holding when there is excess cash.

This thesis first investigates the asset pricing ability of a new risk factor, namely Risk-Neutral Skewness (estimated based on option data) in the global commodity futures market. Skewness trading behaviour in the option market is attributed to heterogeneous belief and selective hedging concern. The negative (positive) the Risk-Neutral Skewness is accompanied with excess trading on put (call) option contracts, which leads to underlings' over-pricing (under-pricing). Above results are robust to time-series and cross-sectional test and other alternatives. Secondly, a new functional mean change detection procedure is proposed via the Kolmogorov-Smirnov functional form. Simulations indicate decent testing power under the alternative. An empirical test procedure is deployed for crude oil and gold futures price term structure, showing real market data change. The multivariate forecasting regression analysis uncovers trading behaviours behind the real-world change occurrence. Lastly, the futures basis term structure is forecasted under the framework of the functional autoregressive predictive factor model with lag 1. By comparison, the new method outperforms other functional and non-functional methods, with maturities less than 10 months. The Model Confidence Set method statistically validate this result. A new variance minimization trading strategy is proposed and tested when the future futures basis is forecast and known.

This thesis is a collection of three contributions to sensitivity analysis of financial and insurance risk evaluations. Sensitivity analysis constitutes an important component of model building, interpretation and validation, particularly for models whose output is at the core of a risk management decision process. We study models comprising a (random) vector of input factors, an aggregation function mapping input factors to a random output, and a risk measure applied to the output. In most typical insurance and financial applications, the model's characteristic - a non-analytical and numerically expensive aggregation function evaluated on numerous input factors - renders most sensitivity analysis methodologies unfeasible. We develop sensitivity analysis procedures applicable specifically for the above model setting. First, we address the estimation of risk measures applied to the model output. The fundamental purpose of a risk measure is to distinguish between different risk profiles. However, strong assumptions on the risk measure's ability to distinguish risk severities lead to non robust estimators. We provide conditions when risk measures exhibit both, robustness and a consistent ranking of risks. Second, we develop a framework termed reverse sensitivity testing, that associates a critical increase in the risk measure to specific input factors. We provide analytical solutions of the stressed distribution of input factors that lead to the required increase in the outputs' risk measure. Third, we introduce a novel sensitivity measure, which quantifies the extent to which the model output is affected by a stress in an individual input factor. Compared to other sensitivity measures in the literature, the proposed measure incorporates the direct impact of the stressed input as well as indirect effects via other input factors that are dependent on the one being stressed. In this way the dependence between inputs is explicitly taken into account.

This doctoral thesis investigates the incentives that affect the decisions of firms to undertake R&D investment and examining the impact of financial constraints on the levels of R&D expenditure of AIM-listed firms in the UK. The thesis comprises six chapters. The first chapter provides an introduction to the research, followed by an overview of the Alternative Investment Market in Chapter 2. Chapter 3 investigates the incentives that influence a firm’s decision to carry out R&D investment. The key empirical findings from a dynamic logistic regression suggest that large sized firms are better at generating innovative activities, that young firms tend to be more likely to innovate, that competitive markets are better at stimulating innovative activities, and that corporate income tax rates have a positive impact on this probability. Chapter 4 explores the impact of financing constraints on the levels of R&D expenditure. Using a system GMM estimator, the empirical findings suggest that working capital buffers R&D levels from transitory financial shocks, thus avoiding the high adjustments costs associated with any change in levels of R&D investment. Chapter 5 investigates the impact of the proceeds from the disposal of fixed assets on R&D expenditure. In contrast to prior literature, the main findings of this chapter suggest that there is a negative association between R&D expenditure and the cash raised from voluntary asset sales, indicating severe binding financing constraints. Practical implementations, promising ideas for future research, and the main findings of this research are summarized in the concluding chapter of the thesis.

First, we document the co-existence of the time series momentum and of the term structure factors in the global commodity futures market. We demonstrate that the strategies based on the joint time series momentum and term structure trading signal outperform time series momentum only strategies and term structure only strategies. Second, we propose a Multivariate Volatility Regulated Kelly strategy, which imposes extra variance penalization compared to the Kelly criterion. We furthermore demonstrate the superiority of our method in relatively low correlated portfolios, relative to the fractional Kelly and full Kelly strategies. The simulation results and Chinese commodity future empirical results strongly support our method. Third, we combine the shrinkage theory and CUSUM change point detection in order to improve the covariance estimators. The change point embedded covariance estimator can pe1jorm better than any shrinking covariance estimators in the portfolio management. We empirically test different shrinkage estimators based portfolios in global futures markets.

This thesis consists of three empirical essays on certain aspects of the behaviour of the stock market. The first study measures the impact of political reform on stock market volatility in Southeast Asian countries using a GARCH-family of model. We find that these major political changes have positive impact on the stability of the stock market. The second study employs an Autoregressive Distributed Lag model and Toda-Yamamoto (1995) Granger causality test to assess the interaction between Thailand's stock market and macroeconomic variables. We find long-run and short-run interactions exists between the stock market index and macro variables. The third study provides another look at the volatility of the stock exchange through variance decomposition. With a short-length dataset from Thailand, we find that discount rate news and cash flow news are equally important.

Since the activities of market participants can be influenced by financial outcomes, providing accurate forecasts of these financial outcomes can help participants to reduce the risk of adjusting to any market change in the future. Predictions of financial outcomes have usually been obtained by conventional statistical models based on researchers' knowledge. With the development of data collection and storage, an extensive set of explanatory variables will be extracted from big data capturing more economic theories and then applied to predictive methods, which can increase the difficulty of model interpretation and produce biased estimation. This may further reduce predictive ability. To overcome these problems, variable selection techniques are frequently employed to simplify model selection and produce more accurate forecasts. In this PhD thesis, we aim to combine variable selection approaches with traditional reduced-form models to define and forecast the financial outcomes in question (market implied ratings, Initial Public Offering (IPO) decisions and the failure of companies). This provides benefits for market participants in detecting potential investment opportunities and reducing credit risk. Making accurate predictions of corporate credit ratings is a crucial issue for both investors and rating agencies, since firms' credit ratings are associated with financial flexibility and debt or equity issuance. In Chapter 2, we attempt to determine market-implied credit ratings in relation to financial ratios, market-driven factors and macroeconomic indicators. We conclude that applying variable selection techniques, the least absolute shrinkage and selection operator (LASSO) and its extension (Elastic net) can improve predictive power. Moreover, the predictive ability of LASSO-selected models is clearly better than that of the benchmark ordered probit model in all out-of-sample predictions. Finally, fewer predictors can be selected into LASSO models controlled by BIC-type tuning parameter to produce more accurate out-of-sample prediction than its counterpart AIC-type selector. Next, the LASSO technique is further applied to binary event prediction. A bank's decision to go public by issuing an Initial Public Offering (IPO) is the binary object in Chapter 3, which transforms the operations and capital structure of a bank. Much of the empirical investigation in this area focuses on the determinants of the IPO decision, applying accounting ratios and other publicly available information in non-linear models. We mark a break with this literature by offering methodological extensions as well as an extensive and updated US dataset to predict bank IPOs. Combining the least absolute shrinkage and selection operator (LASSO) with a Cox proportional hazard, we uncover value in several financial factors as well as market-driven and macroeconomic variables, in predicting a bank's decision to go public. Importantly, we document a significant improvement in the model's predictive ability compared to standard frameworks used in the literature. Finally, we show that the sensitivity of a bank's IPO to financial characteristics is higher during periods of global financial crisis than in calmer times. Moving to another line of variable selection techniques, Bayesian Model Averaging (BMA) is combined with reduced-form models in Chapter 4. The failure of companies is closely related to the health of the whole economy, since the beginning of the most recent global crisis was the bankruptcy of Lehman Brothers. In this chapter, we forecast the failure of UK private firms incorporating with financial ratios and macroeconomic variables. Since two important financial crises and firm heterogeneities are covered in our dataset, the predictive powers of candidate models in different periods and cross-sections are validated. We first detect that applying BMA to the discrete hazard models can improve the predictive performance in different sub-periods. However, comparing the results with classified models, it should be noted that the Naive Bayes (NB) classifier provides slightly higher predictive accuracy than BMA models of discrete hazard models. Moreover, the predictive performance of the discrete hazard model and its BMA version are more sensitive to adding time or industry dummy variables than other competing models. Considering financial crisis or firm heterogeneity can influence the predictive power of each candidate model in the out-of-sample prediction of failure.

The current thesis is a collection of essays on costly signaling, smoothing (partial adjustment), and risk shifting through various pay outs by bank holding firms. The thesis is based on three chapters, or sections, which are through econometric investigations on the above mentioned topics. The major findings of the investigations are, one, a detailed firm level information content analysis of costly signaling by banks via different pay out methods, two, that partial adjustment or smoothing via pay outs can also be perceived as costly signals which is based on the information content of allied measures like bank specific speed of adjustments, and half-life periods, three, that rather than dividend pay outs share repurchases play relatively significant role in risk shifting exhibited by banking firms. Chapter 1 is devoted to the analysis of different types of dividend and other pay out signaling under information asymmetry (between the outsider shareholders of banks and the insider managers), and impact of various bank specific variables on the levels of pay outs/ signaling, thus revealing the information content of such signaling. Both panel data analysis and vector auto regression analysis have been conducted to achieve these findings. Another finding in this section is a comparative analysis between share repurchases and dividend pay outs by bank holding firms. Chapter2 is devoted to the investigation of bank specific partial adjustments of dividends, a modified partial adjustment model is used which is capable of investigating bank specific speeds of adjustments and half-life periods which may vary over periods. Such a model is an improvement over basic smoothing models in the standard literature which have mainly investigated the industry average speed of adjustment, and hence less efficient in investigating the bank specific information content of such measures. Chapter 3 provides analysis based on a system of equations model on, one, whether risk shifting has been exhibited by the bank holding firms for a comprehensive period between 1990-2015, and two, which are the specific pay out channels through which such risk shifting or wealth transfers have taken place.

Thesis (M.B.A.)--Boston University / PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / 2031-01-01

This thesis analyzes models of financial markets that incorporate the possibility of arbitrage opportunities. The first part demonstrates how explicit formulas for optimal trading strategies in terms of minimal required initial capital can be derived in order to replicate a given terminal wealth in a continuous-time Markovian context. Towards this end, only the existence of a square-integrable market price of risk (rather than the existence of an equivalent local martingale measure) is assumed. A new measure under which the dynamics of the stock price processes simplify is constructed. It is shown that delta hedging does not depend on the "no free lunch with vanishing risk" assumption. However, in the presence of arbitrage opportunities, finding an optimal strategy is directly linked to the non-uniqueness of the partial differential equation corresponding to the Black-Scholes equation. In order to apply these analytic tools, sufficient conditions are derived for the necessary differentiability of expectations indexed over the initial market configuration. The phenomenon of "bubbles," which has been a popular topic in the recent academic literature, appears as a special case of the setting in the first part of this thesis. Several examples at the end of the first part illustrate the techniques contained therein. In the second part, a more general point of view is taken. The stock price processes, which again allow for the possibility of arbitrage, are no longer assumed to be Markovian, but rather only It^o processes. We then prove the Second Fundamental Theorem of Asset Pricing for these markets: A market is complete, meaning that any bounded contingent claim is replicable, if and only if the stochastic discount factor is unique. Conditions under which a contingent claim can be perfectly replicated in an incomplete market are established. Then, precise conditions under which relative arbitrage and strong relative arbitrage with respect to a given trading strategy exist are explicated. In addition, it is shown that if the market is quasi-complete, meaning that any bounded contingent claim measurable with respect to the stock price filtration is replicable, relative arbitrage implies strong relative arbitrage. It is further demonstrated that markets are quasi-complete, subject to the condition that the drift and diffusion coefficients are measurable with respect to the stock price filtration.