In the first essay of this dissertation, I provide evidence on the usefulness of machine learning techniques to predict a firm's future earnings and its implied cost of capital. These techniques have the potential to offer significant marginal explanatory power over prior approaches which rely on either linear models or analyst forecasts. I adopt a deep neural network approach that incorporates lagged and contemporaneous accounting variables to predict future earnings. My evidence demonstrates that this forecasting approach offers significant explanatory power that can improve on analyst forecasts. In addition, the deep learning approach outperforms linear models and displays substantially less bias than the human analyst forecasts. When I turn to the implied cost of capital from my earnings forecast model, I find that my deep-learning-derived estimates significantly outperform popular linear-model-based estimates. I argue that these findings have interesting implications for a variety of questions in finance and accounting.In the second study composing this dissertation, I turn from machines to people and consider reputational spillovers from buy-side analysts to sell-side analysts after a financial misconduct event. I detect evidence of negative reputational spillovers in the form of diminished market reliance on recommendations by sell-side analysts after buy-side analysts from the same brokerage firm are associated with financial misconduct. This penalty is significantly related to the buy-side analyst's gender, suggesting that market participants condition their expectations regarding analyst behavior on analyst-specific characteristics.
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