This study examines the determinants of the use of uncertain and weak modal words in 10-K filings and the effect of these words on analyst forecast attributes. I find that the use of uncertain and weak modal words in 10-K filings is positively (negatively) associated with firm size, volatility of business and operations (firm age and number of business segments). More importantly, after controlling for readability and management tone, I find that the use of uncertain and weak modal words in 10-K filings is associated with greater analyst following, lower forecast dispersion, greater forecast accuracy, and lower uncertainty in analysts’ overall and common information environment. The results of this study provide more insights into why management uses uncertain and weak modal words in 10-K filings and how these words in 10-K filings affect analysts’ behavior and their forecast outcomes.

Facility location, as an important topic in computer science and operations research, is concerned with placing facilities for "serving" demand points (each representing a customer) to minimize the (service) cost. In the real world, data is often associated with uncertainty because of measurement inaccuracy, sampling discrepancy, outdated data sources, resource limitation, etc. Hence, problems on uncertain data have attracted much attention. In this dissertation, we mainly study a classical facility location problem: the k- center problem and several of its variations, on uncertain points each of which has multiple locations that follow a probability density function (pdf). We develop efficient algorithms for solving these problems. Since these problems more or less have certain geometric flavor, computational geometry techniques are utilized to help develop the algorithms. In particular, we first study the k-center problem on uncertain points on a line, which is aimed to find k centers on the line to minimize the maximum expected distance from all uncertain points to their expected closest centers. We develop efficient algorithms for both the continuous case where the location of every uncertain point follows a continuous piecewise-uniform pdf and the discrete case where each uncertain point has multiple discrete locations each associated with a probability. The time complexities of our algorithms are nearly linear and match those for the same problem on deterministic points. Then, we consider the one-center problem (i.e., k= 1) on a tree, where each uncertain point has multiple locations in the tree and we want to compute a center in the tree to minimize the maximum expected distance from it to all uncertain points. We solve the problem in linear time by proposing a new algorithmic scheme, called the refined prune-and-search. Next, we consider the one-dimensional one-center problem of uncertain points with continuous pdfs, and the one-center problem in the plane under the rectilinear metric for uncertain points with discrete locations. We solve both problems in linear time, again by using the refined prune-and-search technique. In addition, we study the k-center problem on uncertain points in a tree. We present an efficient algorithm for the problem by proposing a new tree decomposition and developing several data structures. The tree decomposition and these data structures may be interesting in their own right. Finally, we consider the line-constrained k-center problem on deterministic points in the plane where the centers are required to be located on a given line. Several distance metrics including L1, L2, and L1 are considered. We also study the line-constrained k-median and k-means problems in the plane. These problems have been studied before. Based on geometric observations, we design new algorithms that improve the previous work. The algorithms and techniques we developed in this dissertation may and other applications as well, in particular, on solving other related problems on uncertain data.

In this dissertation, new model reference adaptive control architectures are presented with stability, performance, and robustness considerations, to address challenges related to the verification of adaptive control systems. The challenges associated with the transient performance of adaptive control systems is first addressed using two new approaches that improve the transient performance. Specifically, the first approach is predicated on a novel controller architecture, which involves added terms in the update law entitled artificial basis functions. These terms are constructed through a gradient optimization procedure to minimize the system error between an uncertain dynamical system and a given reference model during the learning phase of an adaptive controller. The second approach is an extension of the first one and minimizes the effect of the system uncertainties more directly in the transient phase. In addition, this approach uses a varying gain to enforce performance bounds on the system error and is further generalized to adaptive control laws with nonlinear reference models. Another challenge in adaptive control systems is to achieve system stability and a prescribed level performance in the presence of actuator dynamics. It is well-known that if the actuator dynamics do not have sufficiently high bandwidth, their presence cannot be practically neglected in the design since they limit the achievable stability of adaptive control laws. Another major contribution of this dissertation is to address this challenge. In particular, first a linear matrix inequalities-based hedging approach is proposed, where this approach modifies the ideal reference model dynamics to allow for correct adaptation that is not affected by the presence of actuator dynamics. The stability limits of this approach are computed using linear matrix inequalities revealing the fundamental stability interplay between the parameters of the actuator dynamics and the allowable system uncertainties. In addition, these computations are used to provide a depiction of the feasible region of the actuator parameters such that the robustness to variation in the parameters is addressed. Furthermore, the convergence properties of the modified reference model to the ideal reference model are analyzed. Generalizations and applications of the proposed approach are then provided. Finally, to improve upon this linear matrix inequalities-based hedging approach a new adaptive control architecture using expanded reference models is proposed. It is shown that the expanded reference model trajectories more closely follow the trajectories of the ideal reference model as compared to the hedging approach and through the augmentation of a command governor architecture, asymptotic convergence to the ideal reference model can be guaranteed. To provide additional robustness against possible uncertainties in the actuator bandwidths an estimation of the actuator bandwidths is incorporated. Lastly, the challenge presented by the unknown physical interconnection of large-scale modular systems is addressed. First a decentralized adaptive architecture is proposed in an active-passive modular framework. Specifically, this architecture is based on a set-theoretic model reference adaptive control approach that allows for command following of the active module in the presence of module-level system uncertainties and unknown physical interconnections between both active and passive modules. The key feature of this framework allows the system error trajectories of the active modules to be contained within apriori, user-defined compact sets, thereby enforcing strict performance guarantees. This architecture is then extended such that performance guarantees are enforced on not only the actuated portion (active module) of the interconnected dynamics but also the unactuated portion (passive module). For each proposed adaptive control architecture, a system theoretic approach is included to analyze the closed-loop stability properties using tools from Lyapunov stability, linear matrix inequalities, and matrix mathematics. Finally, illustrative numerical examples are included to elucidate the proposed approaches.

Observers are used in the monitoring and control of dynamical systems to deduce the values of unmeasured states. Designing an observer requires having an accurate model of the plant — if the model parameters are characterized imprecisely, the observer may not provide reliable estimates. An interval observer, which comprises an upper and lower observer, bounds the plant's states from above and below, given the range of values of the imprecisely characterized parameters, i.e., it defines an interval in which the plant's states must lie at any given instant. We propose a linear programming-based method of interval observer design for two cases: 1) only the initial conditions of the plant are uncertain; 2) the dynamical parameters are also uncertain. In the former, we optimize the transient performance of the interval observers, in the sense that the volume enclosed by the interval is minimized. In the latter, we optimize the steady state performance of the interval observers, in the sense that the norm of the width of the interval is minimized at steady state. Interval observers are typically designed to characterize the widest interval that bounds the states. This thesis proposes an interval observer design method that utilizes additional, but still-incomplete information, that enables the designer to identify tighter bounds on the uncertain parameters under certain operating conditions. The number of bounds that can be refined defines a class of systems. The definition of this class is independent of the specific parameters whose bounds are refined. Applying robust optimization techniques, under a cardinality constrained model of uncertainty, we design a single observer for an entire class of systems. These observers guarantee a minimum level of performance with respect to the aforementioned metrics, as we optimize the worst-case performance over a given class of systems. The robust formulation allows the designer to tune the level of uncertainty in the model. If many of the uncertain parameter bounds can be refined, the nominal performance of the observer can be improved, however, if few or none of the parameter bounds can be refined, the nominal performance of the observer can be designed to be more conservative.

BACKGROUND: When it comes to corporate decision making, the traditional rational model suggests that deliberative analysis yields good results. Thus, when contemplating strategic moves, executives are “required” to conduct deliberative analyses. As today’s business environment is becoming increasingly complex and fast-paced, however, executives often face the dilemma of having to make carefully considered strategic decisions on the one hand and not having enough time on the other hand. Intuition offers an efficient solution in this situation. PURPOSE: The purpose of this study is to investigate how corporate executives employ both rationality and intuition in making strategic decisions under uncertain, complex and time-pressured circumstances. RESEARCH METHOD: We conducted three face-to-face interviews with executives from three companies in Sweden. Each interview lasted around one hour.    RESULTS: Drawing on previous psychological and managerial research, we argue that rationality and intuition are better viewed as being complementary rather than separate. Findings from the study suggest that intuition could serve as an effective and efficient means for managers to make strategic decisions; and that intuition indeed plays a role in strategic decision making under complex, uncertain and time limited contexts.

This thesis explores numerical methods for solving nonlinear partial differential equations (PDEs) that arise in option pricing problems. The goal is to develop or identify robust and efficient techniques that converge to the financially relevant solution for both one and two factor problems. To illustrate the underlying concepts, two nonlinear models are examined in detail: uncertain volatility and passport options. For any nonlinear model, implicit timestepping techniques lead to a set of discrete nonlinear equations which must be solved at each timestep. Several iterative methods for solving these equations are tested. In the cases of uncertain volatility and passport options, it is shown that the frozen coefficient method outperforms two different Newton-type methods. Further, it is proven that the frozen coefficient method is guaranteed to converge for a wide class of one factor problems. A major issue when solving nonlinear PDEs is the possibility of multiple solutions. In a financial context, convergence to the viscosity solution is desired. Conditions under which the one factor uncertain volatility equations are guaranteed to converge to the viscosity solution are derived. Unfortunately, the techniques used do not apply to passport options, primarily because a positive coefficient discretization is shown to not always be achievable. For both uncertain volatility and passport options, much work has already been done for one factor problems. In this thesis, extensions are made for two factor problems. The importance of treating derivative estimates consistently between the discretization and an optimization procedure is discussed. For option pricing problems in general, non-smooth data can cause convergence difficulties for classical timestepping techniques. In particular, quadratic convergence may not be achieved. Techniques for restoring quadratic convergence for linear problems are examined. Via numerical examples, these techniques are also shown to improve the stability of the nonlinear uncertain volatility and passport option problems. Finally, two applications are briefly explored. The first application involves static hedging to reduce the bid-ask spread implied by uncertain volatility pricing. While static hedging has been carried out previously for one factor models, examples for two factor models are provided. The second application uses passport option theory to examine trader compensation strategies. By changing the payoff, it is shown how the expected distribution of trading account balances can be modified to reflect trader or bank preferences.

This dissertation contains three applications of time series in finance and macroeconomics. The first essay compares the cumulative returns for stocks and bonds at investment horizons from one to ten years by using a test for spatial dominance. Spatial dominance is a variation of stochastic dominance for nonstationary variables. The results suggest that for investment horizons of one year, bonds spatially dominate stocks. In contrast, for investment horizons longer than five years, stocks spatially dominate bonds. This result is consistent with the advice given by practitioners to long term investors of allocating a higher proportion of stocks in their portfolio decisions. The second essay presents a method that allows testing of whether or not an asset stochastically dominates the other when the time horizon is uncertain. In this setup, the expected utility depends on the distribution of the value of the asset as well as the distribution of the time horizon, which together form the weighted spatial distribution. The testing procedure is based on the Kolmogorov Smirnov distance between the empirical weighted spatial distributions. An empirical application is presented assuming that the event of exit time follows an independent Poisson process with constant intensity. The last essay applies a dynamic factor model to generate out-of-sample forecasts for the inflation rate in Mexico. Factor models are useful to summarize the information contained in large datasets. We evaluate the role of using a wide range of macroeconomic variables to forecast inflation, with particular interest on the importance of using the consumer price index disaggregated data. The data set contains 54 macroeconomic series and 243 consumer price subcomponents from 1988 to 2008. The results indicate that factor models outperform the benchmark autoregressive model at horizons of one, two, four and six quarters. It is also found that using disaggregated price data improves forecasting performance.

The dissertation addresses direct adaptive control frameworks for Lyapunov stabilization of the MIMO nonlinear uncertain systems for both uncertain discrete-time and continuous-time systems. For system theory, the development of continuous-time theory always comes along with its discrete-time counterpart. However, for direct adaptive control frameworks we find relative few Lyapunov-based results published, which is mainly due to difficulty to find feasible Lyapunov candidates and to prove negative definiteness of the Lyapunov difference. Furthermore, digital computer is widely used in all fields. Most of time, we have to deal with the direct source of discrete-time signals, even the discrete-time signals arise from continuous-time settings as results of measurement or data collection process. These motivate our study in this field. For discrete-time systems, we have investigated the results with trajectory dependent hypothesis, where the Lyapunov candidate function V combines the information from the current state k and one step ahead k-1 along the track x(k), for k≥0. The proposed frameworks guarantee partial stability of the closed-loop systems, such that the feedback gains stabilize the closed-loop system without the knowledge of the system parameters. In addition, our results show that the adaptive feedback laws can be characterized by Kronecker calculus. Later, we release this trajectory dependent hypothesis for normal discrete-time nonlinear systems. At the same time, the continuous-time cases are also studied when system with matched disturbances, where the disturbances can be characterized by known continuous function matrix and unknown parameters. Here, the trajectory dependent Lyapunov candidates (tdLC), so long as the time step |t(k)-t(k-1) | ≤ £_ and the corresponding track |x(k)-x(k-1)| ≤ £` are sufficiently small, only exist in discrete-time case. In addition, we have extended the above control designs to systems with exogenous disturbances and £d2 disturbances. Finally, we develop a robust direct adaptive control framework for linear uncertain MIMO systems under the variance of unknow system matrix from given stable solution is bounded, that is |A-Ac| ¡Ý |B Kg| ≤ |£GA|. In general, through Lyapunov-based design we can obtain the global solutions and direct adaptive control design can simultaneously achieve parameter estimation and closed-loop stability.

Abstract Based upon the Nordhaus' model, we can analyze the political business cycle (PBC) of parliamentarian system. This is our point in this paper. Adding an uncertain factor in the Nordhaus' assumptions, we can get unemployment rate of optimal control path by using the dynamic optimal control theory. Comparing these two results, the model of political business cycle of parliamentarian system has higher elective frequency and lower amplitude in the unemployment rate of optimal control path. From the social welfare point of view, which one is better is hard to say? The social welfare is decided by voters' preferences when voters face these two type of PBC. Keywords: Political business cycle (PBC). Parliamentarian system. The optimal

This dissertation presents novel adaptive control laws in both state feedback and output feedback forms. In the setting of state feedback adaptive control K-modification provides a tunable stiffness term that results in a frequency dependent filtering effect, smoother transient responses, and time delay robustness in an adaptive system. K-modification is combined with the recently developed Kalman filter (KF) based adaptive control and derivative-free (DF) adaptive control. K-modification and its combinations with KF adaptive control and DF adaptive control preserve the advantages of each of these methods and can also be combined with other modification methods such as - and e-modification. An adaptive output feedback control law based on a state observer is also developed. The main idea behind this approach is to apply a parameter dependent Riccati equation to output feedback adaptive control. The adaptive output feedback approach assumes that a state observer is employed in the nominal controller design. The observer design is modified and employed in the adaptive part of the design in place of a reference model. This is combined with a novel adaptive weight update law. The weight update law ensures that estimated states follow both the reference model states and the true states so that both state estimation errors and state tracking errors are bounded. Although the formulation is in the setting of model following adaptive control, the realization of the adaptive controller uses the observer of the nominal controller in place of the reference model to generate an error signal. Thus the only components that are added by the adaptive controller are the realizations of the basis functions and the weight adaptation law. The realization is even less complex than that of implementing a model reference adaptive controller in the case of state feedback. The design procedure of output feedback adaptive control is illustrated with two examples: a simple wingrock dynamics model and a more complex aeroelastic aircraft transport model.