Lorenzo Garlappi

Technological innovations are important for economic growth but they are also a source of various risks. This thesis is a collection of three self-contained essays in which I study how technology shocks create risk for firms and households, and affect stock prices. Overall, the thesis helps us better understand the role of labor in the transmission of these shocks to stock prices. The first essay examines the asset pricing implications of technological innovations that allow capital to displace labor: automation. I develop a theory in which firms with high share of displaceable labor are negatively exposed to such technology shocks due to competition that makes technology adoption appear profitable but in equilibrium erodes the expected profits. Empirically, I develop a firm-level measure of displaceable labor share, based on detailed job classifications from the O*NET database, and find that firms with high displaceable labor share have negative exposure to technology shocks. A long-short portfolio based on this new measure is highly correlated with macroeconomic measures of technology shocks. I further show that firms with negative exposure to these technology shocks earn a 4% per year return premium. At the firm level, I provide support for the hypothesis of costly automation following technology shocks. In the second essay, I study how investment shocks affect different types of labor. I construct panel data sets of geographical areas, manufacturing industries and individual workers to examine the effects of investment shocks at three different levels of observations. I utilize the cross-sectional variation in routine intensity of occupations across these three panel data sets. I show that investment shocks are an important source of job displacement and labor income risk. The third essay examines how a firm's capital intensity can affect the measurement of firm's exposure to investment shocks by a popular measure, the IMC portfolio. I show that this measure suggests a considerable premium for an exposure to investment shocks when applied in a sample of capital-intensive firms but almost no premium for the same exposure when applied in labor-intensive sample. I extend a model from previous literature by capital intensity to provide a possible explanation.

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Governments play an important role in financial markets around the world. This thesis studies theoretical mechanisms and empirical consequences of government actions in financial markets in order to better understand the organization of the financial sector and the inner working of governments. The first essay “Shadow Banks, Deposit Competition, and Monetary Policy” studies the transmission mechanism of monetary policy through the shadow banking system, a group of non-bank financial intermediaries conducting banking business in the economy. This essay shows empirically and theoretically that the shadow banking system partially offsets the impact of monetary policy on the traditional commercial banking system and may lead to unintended consequences in terms of the stability of the financial system. The second essay “Regulation and Market Liquidity” (co-authored with Professor Francesco Trebbi), explores whether the post-crisis financial regulations, including the Dodd-Frank Act and Basel III, have caused liquidity deterioration in the U.S. fixed income market. Against the popular claim that post-crisis regulations hurt liquidity, this essay finds no evidence of liquidity deterioration during periods of regulatory intervention. Instead, liquidity seems to have improved in this period. The third essay, “Factions in Nondemocracies: Theory and Evidence from the Chinese Communist Party” (co-authored with Professor Patrick Francois and Professor Francesco Trebbi), investigates theoretically and empirically the factional arrangements and dynamics within the Chinese Communist Party (CCP), the governing political party of the People's Republic of China. This essay documents a set of new empirical findings showing how factional politics affects the promotion of individual politicians within the CCP hierarchy. This essay proposes a theoretical model to rationalize these findings and conduct a set of counterfactual analyses of possible institutional changes within the CCP.

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The economy’s heavy dependence on fossil energy links oil prices to real economic activities, inflation, and financial markets. This dissertation studies the extent to which fluctuations in oil prices are related to inflation and the prices and expected returns of Treasury bonds.Chapter 2 shows that the correlation between U.S. core inflation and oil price changes exhibits a time-varying pattern since the 1970s. The significant resurgence of the positive correlation after the 2007 financial crisis is puzzling, given the subdued macroeconomic impact of oil price shocks since the mid-1980s. A two-sector DSGE model illustrates that the relation between the price of oil and core inflation depends on the type of shocks embedded in oil price changes. Oil supply shocks cause the price of oil and core inflation to co-move, whereas the aggregate demand shocks driven by economic growth lead to opposing changes in the price of oil and core inflation. The economic mechanisms uncovered in the model and historical geopolitical events together provide a consistent and logical explanation of the time-varying correlations observed in the data.Chapter 3 examines the economic impact of oil prices on Treasury bond returns. I find novel evidence that growth rates of crude oil prices can explain contemporaneous excess returns on nominal U.S. Treasury bonds and inflation swaps, and also predict expected future excess returns on inflation swaps. Empirical results suggest that the impact of oil prices on nominal bonds is through the impact on expected inflation. I then build a two-sector New Keynesian model to study theoretical interactions between the economic drivers of oil prices, expected inflation, and bond yields. The model shows that oil supply and demand shocks have opposite impacts on bond yields and expected inflation. The conventional wisdom that high oil prices lead to high expected inflation and nominal yields is true only if high oil prices are driven by a negative shock to the supply of oil. In contrast, when oil prices are driven by a positive shock to productivity growth, high oil prices can lead to low expected inflation and nominal yields.

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In this thesis, I present three essays on the interaction of two dynamic aspects of mergers and acquisitions. First, merger activity follows waves within industries over time. Second, acquirers' announcement returns are on average small and decline within merger waves. In the first essay, I develop a model of merger waves to study the interplay between merger timing and market anticipation of deal announcements. I show that the pattern of small and declining announcement returns for acquirers in merger waves is consistent with the notion that the market learns over time and is thus able to better anticipate deal announcements. This explanation contrasts with existing theories which attribute the declining pattern in announcement returns to a decline in deal quality. The model delivers several predictions about time-series and cross-section aspects of acquirers' stock returns during merger wave episodes. In the second essay, I test a set of the model's predictions. As a testing laboratory, I use four industries that underwent merger deregulations in the 1990s. Consistent with existing theories, high quality deals tend to be announced early in a merger wave. However, I show that this pattern in deal quality does not explain the declining pattern in acquirers' announcement effects. Consistent with the model's predictions, I find that what matters for this pattern is the unexpected portion of deal timing. I also find evidence of contagion effects on acquirers' peers that is consistent with the information channel in the model. In the third essay, I study the drivers of merger waves by examining the allocation of equity proceeds raised at times of high merger activity. My results indicate that firms do not systematically increase debt repayment or equity payout with equity proceeds raised in high merger years. This pattern does not conform with the view that managers believe the stock is overvalued at the time of the equity issue. Instead, the observed pattern of proceeds allocation is consistent with the existence of time-varying adverse selection and investment lags. The evidence supports the idea that these frictions are important elements behind the dynamics of merger and acquisition activity.

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In the first chapter of this thesis, I propose a nonlinear filtering method to estimate latent processes based on the Taylor series approximations. The filter extends conventional methods such as the extended Kalman filter or the unscented Kalman filter and provides a tractable way to estimate filters of any order. I apply the filter to different models and demonstrate that this method is a good approach for the estimation of unobservable states as well as for parameter inference. I also find that filters with Taylor approximations can be as accurate as conventional Monte Carlo filters and computationally more efficient. Through this chapter I show that filters with Taylor approximations are a good approach for a number of problems in finance and economics that involve nonlinear dynamic modeling. In the second chapter, I investigate the recently documented, large time-series variation in the empirical market Sharpe ratio. I revisit the empirical evidence and ask whether estimates of Sharpe ratio volatility may be biased due to the limitations of the standard ordinary least squares (OLS) methods used in estimation. Based on simulated data from a standard calibration of the long-run risks model, I find that OLS methods used in prior literature produce Sharpe ratio volatility five times larger than its true variability. The difference arises due to measurement error. To address this issue, I propose the use of filtering techniques that account for the Sharpe ratio's time variation. I find that these techniques produce Sharpe ratio volatility estimates of less than 15% on a quarterly basis, which match more closely the predictions of standard asset pricing models.

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