In this thesis we examine optimal execution models that take into account both market microstructure impact and informational costs. Informational footprint is related to order flow and is represented by the trader's influence on the expected order flow process, while microstructure influence is captured by instantaneous price impact. Indeed, a key piece of information missing from many execution models in the literature is the temporal summary of recent order flow which is known to have an impact on the behavior of liquidity providers. Instead, execution and limit order book models often consider only the limited information summarized by a snapshot of the limit order book. Excluded then, are the important mesoscopic trends in market order flow as well as the informational impact made when an executed order perturbs the expected order flow process.

In the following chapters, we propose several continuous-time stochastic control problems that balance between microstructure and informational costs. Incorporating the trade imbalance leads to the consideration of the current market state and specifically whether one's orders lean with or against the prevailing order flow. Several objective functions are treated, as we account for both symmetric and asymetric execution costs that arise when trading under differing market conditions. We then initiate statistical analysis on Nasdaq limit order book data to investigate the links between market order flow, price impact and liquidity at the mesoscopic timescale. We find that temporal measures of order flow play a key role in the price formation process and show how these features can be incorporated into an execution model for which closed-form solutions can be obtained.