This thesis presents a novel approach to optimize execution strategies in the Foreign Exchange Spot Market, focusing on the application of data-driven methodologies and stochastic modeling.
It begins by proposing a new measure to evaluate the limit order book volume imbalance, which considers multiple price levels and their weighted impact on market predictions.
Then the research employs a combination of convolutional neural networks and long short-term memory models to analyze the limit order book data and its imbalances, i.e. the previously introduced limit order book volume imbalance and the order flow imbalance, which takes into consideration their time evolution. This methodology allows for the exploration of price movements and the identification of optimal execution strategies by predicting future mid-price movements, which are embedded in the drift term of the stochastic model. The findings indicate that the developed models outperform traditional strategies by adapting more effectively to market dynamics.
Further, this work develops a backtesting environment that simulates market conditions to empirically validate the effectiveness of the proposed strategies against historical data. The results demonstrate a superior performance in terms of profitability and cost-efficiency compared to traditional models that do not utilize the drift term from predictive analytics in their stochastic model of the mid-price.
Future work may extend these methodologies to other financial markets and explore prediction models that include the influence of multiple assets.