Recently, cryptocurrencies have received substantial attention by investors given their innovative features, simplicity and transparency. We here analyse the increasingly popular Bitcoin and verify pertinence of the efficient market hypothesis. Recent research suggests that Bitcoin markets, while inefficient in their early days, transitioned into efficient markets recently. We challenge this claim by proposing simple trading strategies based on moving average filters, on classic time series models as well as on non-linear neural nets. Our findings suggest that trading performances of our designs are significantly positive; moreover, linear and non-linear approaches perform similarly except at singular time periods of the Bitcoin; finally, our results suggest that markets are becoming less rather than more efficient towards the sample end of the data.

Today, nearly all money exists in form of numbers in a computer, and finance can be considered as a special kind of IT application that represents the flow of money in form of cash flows between different participants. Thus, automated processing seems to be a natural choice and the financial sector should be expected to lead digitization and automation initiatives. It is all the more surprising that not only is this not the case but, on the contrary, the financial sector is lagging behind other sectors. In 2008, when Lehman Brothers went bankrupt at the height of the financial crisis, nobody-neither the big banks nor the regulatory authorities-had the structures and processes in place to systematically measure, even imprecisely, the systemic aspects of the risks inherent in the development of subprime lending, securitization, and risk transfer [1]. As a consequence, the top management did not have an adequate picture of these risks so that they could be denied during the build-up of the bubble and nobody was able to evaluate the implications of the failure of major financial institutions when the crisis eventually hit. The major shortcoming identified by the Basel Committee on Banking Supervision

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Real-time financial risk analytics is very challenging due to heterogeneous data sets within and across banks worldwide and highly volatile financial markets. Moreover, large financial organizations have hundreds of millions of financial contracts on their balance sheets. Since there is no standard for modelling financial data, current financial risk algorithms are typically inconsistent and non-scalable. In this paper, we present a novel implementation of a real-world use case for performing largescale financial risk analytics leveraging Big Data technology. Our performance evaluation demonstrates almost linear scalability.