A deep learning model for gas storage optimization

To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon. In this article, we utilize techniques inspired by reinforcement learning in order to optimize the operation plans of underground natural gas storage facilities. We provide a theoretical framework and assess the performance of the proposed method numerically in comparison to a state-of-the-art least-squares Monte-Carlo approach. Due to the inherent intricacy originating from the high-dimensional forward market as well as the numerous constraints and frictions, the optimization exercise can hardly be tackled by means of traditional techniques.

A machine learning-based price state prediction model for agricultural commodities using external factors

In recent times of noticeable climate change the consideration of external factors, such as weather and economic key figures, becomes even more crucial for a proper valuation of derivatives written on agricultural commodities. The occurrence of remarkable price changes as a result of severe changes in these factors motivates the introduction of different price states, each describing different dynamics of the price process. In order to include external factors we propose a two-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second, predictions of future price states are then obtained from short-term predictions of the external factors by means of either K-nearest neighbors or random forest classification. We apply our model to real corn futures data and generate price scenarios via a Monte Carlo simulation, which we compare to Sørensen (J Futures Mark 22(5):393–426, 2002). Thereby we obtain a better approximation of the real futures prices by the simulated futures prices regarding the error measures MAE, RMSE and MAPE. From a practical point of view, these simulations can be used to support the assessment of price risks in risk management systems or as decision support regarding trading strategies under different price states.

Beating the market? A mathematical puzzle for market efficiency

The efficient market hypothesis is highly discussed in economic literature. In its strongest form, it states that there are no price trends. When weakening the non-trending assumption to arbitrary short, small, and fully unknown trends, we mathematically prove for a specific class of control-based trading strategies positive expected gains. These strategies are model free, i.e., a trader neither has to think about predictable patterns nor has to estimate market parameters such as the trend’s sign like momentum traders have to do. That means, since the trader does not have to know any trend, even trends too small to find are enough to beat the market. Adjustments for risk and comparisons with buy-and-hold strategies do not satisfactorily solve the problem. In detail, we generalize results from the literature on control-based trading strategies to market settings without specific model assumptions, but with time-varying parameters in discrete and continuous time. We give closed-form formulae for the expected gain as well as the gain’s variance and generalize control-based trading rules to a setting where older information counts less. In addition, we perform an exemplary backtesting study taking transaction costs and bid-ask spreads into account and still observe—on average—positive gains.