Financial Volatility Trading Using Recurrent Neural Networks

We simulate daily trading of straddles on the financial indexes DAX and FTSE 100. The straddles are traded based on predictions of daily volatility differences in the underlying indexes. The main predictive models studied in this paper are recurrent neural networks (RNNs). In the past, applications of RNNs in the financial domain were often studied in isolation. We argue against such a practice by showing that, due to the special character of daily financial time-series, it is difficult to make full use of RNN representational power. Recurrent networks either tend to overestimate the noisy data, or behave like finite-memory sources with a relatively shallow memory. In fact, they can hardly beat (rather simple) classical fixed-order Markov models. To overcome the inherent nonstationarity in the data, we use a special technique that combines 'sophisticated' models fitted on a larger data set, with a fixed set of simple-minded symbolic predictors using only recent inputs, thereby avoiding older (and potentially misleading) data. Finally, we compare our predictors with the GARCH family of econometric models designed to capture time-dependent volatility structure in financial returns. GARCH models have been used in the past to trade volatility. Experimental results show that while GARCH models are not able to generate any significantly positive profit, by careful use of recurrent networks or Markov models, the market makers can generate a statistically significant excess profit. However, on this type of problems, there is no reason to prefer RNNs over much more simple and straightforward Markov models. We argue that any report containing RNN results on financial tasks should be accompanied by results achieved by simple finite-memory sources combined with simple techniques to fight nonstationarity in the data. Index Terms--Financial indexes, Markov models, options, prediction suffix trees, recurrent neural networks, straddle, volatility.