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This dissertation investigates the effects of frictions that emerge from financial markets on business-cycle fluctuations. The purpose of Chapter 1 is to situate my work in the literature and to stress its contributions. In Chapter 2, I reassess the role of financial frictions in amplifying the impacts of productivity shocks using a framework in which a fraction of firms are borrowing-constrained and land is a collateral asset. A first finding is that amplification effects are much lower when land is supplied elastically. However, financial shocks that affect the maximum allowable ratio of loans to collateral have greater effects on output. Another result pertains to the role of the elasticity of substitution between land and capital in responses to financial shocks: lower values generate greater output responses. While Chapter 2's environment is set up to be in a closed-economy, the last two chapters involve two-country settings. Chapter 3 still intersects with Chapter 2 on some dimensions, in particular, land dynamics and financial frictions that feature borrowing-constrained firms. The borrowing mechanism brings about a distortion in labour markets that interacts with a class of preferences that are non-separable between consumption and leisure. Technology shocks contribute to explain international co-movements, whereas financial shocks allow the model to replicate the lack of international risk sharing that is characterized by the quantity anomaly and the Backus-Smith puzzle. In Chapter 4, I apply Chari, Kehoe and McGrattan’s (2007) business cycle accounting method to a two-country, two-good real business cycle model. Using their approach, I measure the same closed-economy time-varying wedges and I introduce an international wedge that accounts for discrepancies between the growth in real exchange rates and in the stochastic discount factors ratio. In fact, the effects of financial frictions embedded in Chapter 3's framework can be retrieved from a combination of labour and investment wedges. The volatility of the international wedge corresponds to a metric of bilateral risk sharing. An important finding is that, from a non-separable preferences specification of the baseline model, the investment wedge partly accounts for the Backus-Smith puzzle. This suggests that distortions in national capital markets are important to consider for international risk sharing. / Thesis (Ph.D, Economics) -- Queen's University, 2013-04-29 22:56:23.03

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The decision to enter a foreign market has long-term implications for the investing firm, as has its choice of entry mode. The hierarchical model of market entry modes proposes that entry modes can be categorised as equity-based or non-equity based, and further categorised by type as joint ventures and wholly owned subsidiaries, exports and contractual agreements. The hierarchical model of market entry modes proposes that there are factors that influence the entry mode at the level of equity versus non-equity but not within the type of equity or non-equity.

This study examines the price reaction to the announcements of joint venture and strategic alliance formation, the main determinants of the partnering firm's choices to enter a specific joint venture and a specific strategic alliance, and the impact of such alliance formation on partnering firms' valuation. The analysis of the price reaction at the announcement of alliance formation indicates that market can distinguish between value creating and non-value creating alliances. I also provide evidence supporting the argument that alliance formation is not a random process. A firm's choice of entering an alliance designed as diversifying or non-diversifying strategy is a result of a complex interaction of external factors and internal needs. Finally, using the change in excess value from the year prior to the year following the alliance formation, I document that alliance formation negatively impacts the valuation of the single segment partnering firms relative to their industry peers, and has no impact on the valuation of multiple segment firms. Thus, single segment firms entering alliances are facing the trade-off between the longterm benefit provided by the alliance and the immediate costs affecting the activity developed in the house.

A thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in the Faculty of Science, School of Computer Science and Applied Mathematics University of the Witwatersrand. October 2016. / Automated algorithmic trade execution is a central problem in modern financial markets, however finding and navigating optimal trajectories in this system is a non-trivial task. Many authors have developed exact analytical solutions by making simplifying assumptions regarding governing dynamics, however for practical feasibility and robustness, a more dynamic approach is needed to capture the spatial and temporal system complexity and adapt as intraday regimes change. This thesis aims to consolidate four key ideas: 1) the financial market as a complex adaptive system, where purposeful agents with varying system visibility collectively and simultaneously create and perceive their environment as they interact with it; 2) spin glass models as a tractable formalism to model phenomena in this complex system; 3) the multivariate Hawkes process as a candidate governing process for limit order book events; and 4) reinforcement learning as a framework for online, adaptive learning. Combined with the data and computational challenges of developing an efficient, machine-scale trading algorithm, we present a feasible scheme which systematically encodes these ideas. We first determine the efficacy of the proposed learning framework, under the conjecture of approximate Markovian dynamics in the equity market. We find that a simple lookup table Q-learning algorithm, with discrete state attributes and discrete actions, is able to improve post-trade implementation shortfall by adapting a typical static arrival-price volume trajectory with respect to prevailing market microstructure features streaming from the limit order book. To enumerate a scale-specific state space whilst avoiding the curse of dimensionality, we propose a novel approach to detect the intraday temporal financial market state at each decision point in the Q-learning algorithm, inspired by the complex adaptive system paradigm. A physical analogy to the ferromagnetic Potts model at thermal equilibrium is used to develop a high-speed maximum likelihood clustering algorithm, appropriate for measuring critical or near-critical temporal states in the financial system. State features are studied to extract time-scale-specific state signature vectors, which serve as low-dimensional state descriptors and enable online state detection. To assess the impact of agent interactions on the system, a multivariate Hawkes process is used to measure the resiliency of the limit order book with respect to liquidity-demand events of varying size. By studying the branching ratios associated with key quote replenishment intensities following trades, we ensure that the limit order book is expected to be resilient with respect to the maximum permissible trade executed by the agent. Finally we present a feasible scheme for unsupervised state discovery, state detection and online learning for high-frequency quantitative trading agents faced with a multifeatured, asynchronous market data feed. We provide a technique for enumerating the state space at the scale at which the agent interacts with the system, incorporating the effects of a live trading agent on limit order book dynamics into the market data feed, and hence the perceived state evolution. / LG2017

Thesis advisor: Frank Gollop / This dissertation analyzes advertising strategies in specialized markets like the prescription drugs, travel, health insurance and real-estate markets where the consumers' purchasing decisions are influenced by experts (e.g., doctors, travel agents, employers and real-estate agents). / Thesis (PhD) — Boston College, 2006. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

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