x, 94 p. : ill. (some col.) / This paper examines how competition faced by firms affects asset risk and expected returns. Contrary to Hou and Robinson's (2006) findings, I find that cross-industry variation in competition, as measured by the concentration ratio, is not a robust determinant of unconditional expected stock returns. In contrast, within-industry competition, as measured by relative price markup, is positively related to expected stock returns. Moreover, this relation is not captured by commonly used models of expected returns. When using the Markov regime-switching model advocated by Perez-Quiros and Timmermann (2000), I test and find support for Aguerrevere's (2009) recent model of competition find risk dynamics. In particular, systematic risk is greater in more competitive industries during bad times and greater in more concentrated industries during good times. In addition, real investment by firms facing greater competition leads real investment by firms facing less competition, supporting Aguerrevere's notion that less competition results in higher growth options and hence higher risk in good times. / Committee in charge: Dr. Roberto Gutierrez, Chair; Dr. Roberto Gutierrez, Advisor; Dr. Diane Del Guercio, Inside Member; Dr. John Chalmers， Inside Member； Dr. Bruce Blonigen， Outside Member

The first paper revisits the link between interest rates and corporate bond credit spreads by applying Rigobon’s (2003) heteroskedasticity identification methodology. The second paper investigates the assumption that financial asset prices including stocks and bonds, reflect intrinsic value. The third paper decomposes the stock price into fundamental permanent, fundamental transitory, and non-fundamental shocks in order to explore the determinants of stock price fluctuations.

This dissertation consists of three essays, each examining distinct dimensions of cross-sectional variation in exchange rate changes and currency returns conditional on macroeconomic variables. Chapter 2: Protectionism, Bilateral Integration, and the Cross-Section of Ex-change Rate Returns in US Presidential Debates We study the impact of US presidential election TV debates on intraday exchange rates of 96 currencies from 1996 to 2016. Expectations about protectionist measures are the main transmission channel of debate outcomes. Currencies of countries with high levels of bilateral foreign trade with the US depreciate if the election probability of the protectionist candidate increases during the debate. We rationalize our results in a model where a debate victory of a protectionist candidate raises expectations about future tariffs and reduces future net exports to the US, resulting in relative depreciation of currencies with high bilateral trade integration. Chapter 3: Global Portfolio Network and Currency Risk Premia External portfolio investments of countries can explain cross-sectional variation in currency risk premia. Using bilateral portfolio holdings of 26 countries from 2001 to 2021, I construct a network centrality measure where a country is central if it is integrated with key countries that account for a large share in the supply of tradeable financial assets. I find that currency excess returns and interest rates decrease in network centrality. The network centralities are persistent over time and offer a country-specific economic source of risk that are able to explain robust differences in currency risk premia. Empirical asset pricing tests show that the derived risk factor is priced in a cross-section of currency portfolios. Further, negative global shocks cause currencies of central countries to appreciate, while currencies of peripheral countries depreciate. I discuss the findings with implications of a consumption-based capital asset pricing model where central countries have lower consumption growth in high marginal utility states, resulting in an appreciation of their currencies. Chapter 4: FX Dealer Constraints and External Imbalances We study the impact of FX dealer banks' financial health on the cross-sectional variation of exchange rates. Using individual balance sheet information of 39 dealers, we derive an intermediary constraints index that captures the risk-bearing capacity of intermediaries. A deterioration of the solvency of dealer banks impairs their risk-bearing capacity and increases their marginal value of wealth. We test the theoretical prediction of Gabaix and Maggiori (2015) that tightening financial constraints of intermediaries are associated with increasing currency risk premia in the cross-section of the riskiness of currencies, as measured by the net foreign assets of countries. We combine dealer-specific risks to macroeconomic fundamentals of a cross-section of currencies, i.e., the indebtedness to foreigners measured by countries' net foreign assets. We show that currency excess returns increase with a country's external imbalances when constraints are relaxed, but debtor currencies experience a depreciation when constraints tighten.

In recent years, machine learning models have gained traction in the field of empirical asset pricing for their risk premium prediction performance. In this thesis, we build upon the work of [1] by first evaluating models similar to their best performing model in a similar fashion, by using the same dataset and measures, and then expanding upon that. We explore the impact of different feature extraction techniques, ranging from simply removing added complex- ity to representation learning techniques such as incremental PCA and autoen- coders. Furthermore, we also introduce recurrent connections with LSTM and combine them with the earlier mentioned representation learning techniques. We significantly outperform [1] in terms of monthly out-of-sample R2, reach- ing a score of over 3%, by using a condensed version of the dataset, without interaction terms and dummy variables, with a feedforward neural network. However, across the board, all of our models fall short in terms of Sharpe ratio. Even though we find that LSTM works better than the benchmark, it does not outperform the feedforward network using the condensed dataset. We reason that this is because the features already contain a lot of temporal information, such as recent price trends. Overall, the autoencoder based models perform poorly. While the linear incremental PCA based models perform better than the nonlinear autoencoder based ones, they still perform worse than the bench- mark. / Under de senaste åren har maskininlärningsmodeller vunnit kredibilitet inom området empirisk tillgångsvärdering för deras förmåga att förutsäga riskpre- mier. I den här uppsatsen bygger vi på [1]s arbetet genom att först implemente- ra modeller som liknar deras bäst presterande modell och utvärdera dem på ett liknande sätt, genom att använda samma data och mått, och sedan bygga vida- re på det. Vi utforskar effekterna av olika variabelextraktionstekniker, allt från att helt enkelt ta bort extra komplexitet till representationsinlärningstekniker som inkrementell PCA och autoencoders. Vidare introducerar vi även LSTM och kombinerar dem med de tidigare nämnda representationsinlärningstekni- kerna. Min bästa modell presterar betydligt bättre än [1]s i termer av månatlig R2 för testdatan, och når ett resultat på över 3%, genom att använda en kompri- merad version av datan, utan interaktionstermer och dummyvariabler, med ett feedforward neuralt nätverk. Men överlag så brister alla mina modeller i ter- mer av Sharpe ratio. Även om LSTM fungerar bättre än riktvärdet, överträffar det inte feedforward-nätverket med den komprimerade datamängden. Vi re- sonerar att detta är på grund av inputvariablerna som redan innehåller en hel del information över tid, som de senaste pristrenderna. Sammantaget presterar de autoencoderbaserade modellerna dåligt. Även om de linjära inkrementell PCA-baserade modellerna presterar bättre än de olinjära autoencoderbaserade modellerna, presterar de fortfarande sämre än riktvärdet.