Forecasting the price of Bitcoin using neural networks

Abstract

The evolution of financial technology adds to the complexity of the global financial system and the underlying assets that store its value. This complexity manifests as an adverse market risk profile in assets where fintech can be considered an endogenous variable. A theoretical framework that may contribute toward an improved understanding of this relationship is established. In contrast to the adverse risk profile in these markets, however, the literature still suggests a value proposition in these fintech-endogenous markets. The suggested value proposition is investigated by means of an empirical literature review, and partial recreation of some key findings from previous literature. Subsequently, additional empirical findings are contributed through a comparative set of tests in a controlled environment, with some significant results, specifically in the case where an appropriate trading strategy is back-tested along with some neural network forecasting procedures. The implications for researchers and practitioners are emphasised by a re-contextualisation of how the findings could affect future research in forecasting- and trading methodologies as well as the status quo of portfolio management strategies that risk managers have at their disposal. They key contribution is that risk managers should be able to benefit from the erratic behaviour of fintech-endogenous markets in the form of non-negligible short-term abnormal profit, whilst not having to trade off the diversification properties consistent with the established literature. The junction of forecasting- and trading methodologies used here may result in a "best of both worlds" investment strategy where abnormal profits are possible in the short run, in a simultaneously well-hedged trading environment, which relies on (instead of mitigating) the erratic price-formation phenomena prevalent in fintech-endogenous markets.