Cost Function and Model Combination for VaR-Based Asset Allocation Using Neural Networks  

Oleh:

Chapados, N. ; Bengio, Y.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 4 (2001), page 890-906.
Topik: asset allocation; cost function; model combination; VaR - based; asset allocation; neural networks

Ketersediaan

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

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Isi artikel

We introduce an asset - allocation framework based on the active control of the value - at - risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behaviour, in conjunction with a traditional mean - variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparameters during training. We show that committees using both paradigms are significantly outperforming the benchmark market performance.

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