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A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020 Derivatives such as options are complex financial instruments. The risk in option trading
leads to the demand of trading support systems for investors to control and hedge their
risk. The nonlinearity and non-stationarity of option dynamics are the main challenge
of option price forecasting. To address the problem, this study develops a multi-kernel
adaptive filters (MKAF) for online option trading. MKAF is an improved version of the
adaptive filter, which employs multiple kernels to enhance the richness of nonlinear
feature representation. The MKAF is a fully adaptive online algorithm. The strength
of MKAF is that the weights to the kernels are simultaneous optimally determined in
filter coefficient updates. We do not need to design the weights separately. Therefore,
MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce
the computation time in updating the filter, and prevent overadaptation, the number
of kernels is restricted by using coherence-based sparsification, which constructs a
set of dictionary and uses a coherence threshold to restrict the dictionary size. This
study compared the new method with traditional ones, we found the performance
improvement is significant and robust. Especially, the cumulated trading profits are
substantially increased Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting |
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