This thesis contributes to the field of asset pricing by expanding our knowledge how different factors drive time variation in asset returns and risk premia. It is widely agreed upon in finance literature that macroeconomic and (geo-)political conditions impact asset returns. Yet, there is still relatively little-known how cross-asset risk premia vary with business cycle regimes. By analyzing macroeconomic sensitivities, the first study reveals that time-varying returns of certain alternative risk premia strategies are significantly related to economic conditions. The broad emergence and accessibility of so-called alternative data sources like machine-readable texts, give cause to take new approaches to approximate latent variables. The second study introduces an agnostic language model designed to generate domain- and period-specific vocabulary from raw news data to identify topic-related articles. Here, the framework is utilized to develop a point-in-time index that approximates changes in transition risk from climate-related news events. The index is applied to evaluate return sensitivity of publicly available green minus brown portfolio proxies. Based on investors climate objectives, different approaches to measure a firms environmental performance are considered for portfolio construction. The study shows that short-term transition risk tends to affect stock prices based on firms business activity but not emissions. In the third study another application of the language model is presented by constructing a real-time news index to measure country-specific, geopolitical risk. The approximation of a latent risk variable with media attention is in line with previous research on different unobservable risk measures utilizing news flow. However, avoiding even subtle look-ahead bias is essential for evaluating systematic investment propositions. The study documents that the proposed model can resemble the results of other more heavily curated methods