Financial Volatility Trading Using Recurrent Neural Networks  

Oleh:

Tino, P. ; Dorffner, G. ; Schittenkopf, C.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 4 (2001), page 865-874.
Topik: FINANCIAL; financial volatility; trading; neural network

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We simulate daily trading of straddles on financial indexes. The straddles are traded based on predictions of daily volatility differences in the indexes. The main predictive models studied are recurrent neural nets (RNN). Such applications have often been studied in isolation. However, 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 noisy data, or behave like finite - memory sources with shallow memory ; they hardly beat classical fixed - order Markov models. To overcome data non stationarity, 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. 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 to trade volatility. Experimental results show that while GARCH models cannot generate any significantly positive profit, by careful use of recurrent networks or Markov models, the market makers can generate a statistically significant excess profit, but then there is no reason to prefer RNN 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 non stationarity in the data.

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