Prediction with Expert Advice for Trading and Hedging on the Foreign Exchange Market

In this thesis, we explore the application of prediction with expert advice algorithms for investing in the Foreign Exchange (FX) market.We introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data.We apply methods of prediction with expert advice to real-world foreign exchange trading data to find effective investment strategies. We build upon the framework of the long-short game, introduced by Vovk and Watkins (1998), and propose modifications aimed at improving the performance with respect to standard portfolio performance indicators.We apply the Weak Aggregating Algorithm (WAA) to find optimal risk management strategies for financial Market Makers (MMs), using hedging strategies as experts. We combine their hedging decisions to reduce portfolio risk and maximise profitability. We develop a variation of the WAA using discounting and evaluate the results on commonly traded FX currency pairs

Universal Codes from Switching Strategies

We discuss algorithms for combining sequential prediction strategies, a task which can be viewed as a natural generalisation of the concept of universal coding. We describe a graphical language based on Hidden Markov Models for defining prediction strategies, and we provide both existing and new models as examples. The models include efficient, parameterless models for switching between the input strategies over time, including a model for the case where switches tend to occur in clusters, and finally a new model for the scenario where the prediction strategies have a known relationship, and where jumps are typically between strongly related ones. This last model is relevant for coding time series data where parameter drift is expected. As theoretical ontributions we introduce an interpolation construction that is useful in the development and analysis of new algorithms, and we establish a new sophisticated lemma for analysing the individual sequence regret of parameterised models

Expert Aggregation for Financial Forecasting

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest over the last few years. One difficulty lies in the choice between several algorithms, as their estimation accuracy may be unstable through time. In this paper, we propose to apply an online aggregation-based forecasting model combining several machine learning techniques to build a portfolio which dynamically adapts itself to market conditions. We apply this aggregation technique to the construction of a long-short-portfolio of individual stocks ranked on their financial characteristics and we demonstrate how aggregation outperforms single algorithms both in terms of performances and of stability

The Nonprofit Marketplace: Bridging the Information Gap in Philanthropy

Argues for improving the supply of and demand for information and strengthening intermediaries and interactions to boost strategic grantmaking, effective nonprofit operations, and dialogue about transparency, organizational performance, and social impact