This dissertation explores how investors make decisions about their investments over time. The researchers use a model that considers how much attention investors pay to the stock market, showing that investors don't trade consistently but rather in bursts of activity. They also use a method called Boosted Regression Trees to predict stock returns and volatility, finding that more information leads to better predictions. Additionally, they find that the relationship between risk and return in the stock market is not always straightforward, with a positive trade-off at low and medium volatility levels but an inverted relationship at high volatility levels. This new understanding helps explain why there is no consensus on the risk-return trade-off in the stock market.