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What is Thesis about?

This master thesis explores the construction of deep neural networks to enhance multi-asset cross-sectional momentum (CSMOM) and time series momentum (TSMOM) strategies, challenging the efficient market hypothesis. It presents transformer-based models such as the CSMOM Transformer Ranker and TSMOM Encoder-Decoder Transformer, which are empirically tested against traditional benchmarks using a dataset of 50 continuous futures contracts from 2000 to 2022. The findings indicate mixed performance of the propose

Author

Vicky Gupta

Language

EN