Forecasting high-frequency futures returns using online langevin dynamics

Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency. © 2011 IEEE.

Trial by market: the Brightstar incubation experiment

Established firms accumulate a significant body of knowledge, expertise and capabilities that are often secondary to their central revenue generating activities. How do they leverage this expertise in non-core technology into future value creation opportunities? In this paper we examine an attempt by the telecommunications firm BT to create value from the accumulated knowledge within its laboratories by setting up an incubator. While conceived by the board as a mechanism for leveraging the value of non-core technology into the workplace, corporate support for the incubator was withdrawn after only three years and prompted the incubator to partner with a venture capital firm, NVP, in the spin-out of ventures. Through analysis of this single case we observe how entering into such a relationship reduces the transaction costs of accessing complementary resources, capabilities and competences, while simultaneously reducing a number of the risks associated with venturing for both parties. Partnering with the venture capitalist allows the established firm to get its intellectual property into the market, for it to be tested by the market and further developed. © 2010 Inderscience Enterprises Ltd.

The roles of energy and material efficiency in meeting steel industry CO2 targets.

Identifying strategies for reducing greenhouse gas emissions from steel production requires a comprehensive model of the sector but previous work has either failed to consider the whole supply chain or considered only a subset of possible abatement options. In this work, a global mass flow analysis is combined with process emissions intensities to allow forecasts of future steel sector emissions under all abatement options. Scenario analysis shows that global capacity for primary steel production is already near to a peak and that if sectoral emissions are to be reduced by 50% by 2050, the last required blast furnace will be built by 2020. Emissions reduction targets cannot be met by energy and emissions efficiency alone, but deploying material efficiency provides sufficient extra abatement potential.