کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
963920 1479117 2014 34 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects
ترجمه فارسی عنوان
ترکیبی از شبکه های عصبی تصادفی و ژنتیکی در اثرات اهرمی متغیرهای تجاری و هیبریدی
موضوعات مرتبط
علوم انسانی و اجتماعی اقتصاد، اقتصادسنجی و امور مالی اقتصاد و اقتصادسنجی
چکیده انگلیسی


• We apply six models in a forecasting and trading exercise.
• The utility of five forecast combination techniques is explored.
• A hybrid leverage factor based is introduced.
• A fitness function for NN in trading application is introduced.

The motivation of this paper is 3-fold. Firstly, we apply a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and a Psi-Sigma Network (PSN) architecture in a forecasting and trading exercise on the EUR/USD, EUR/GBP and EUR/CHF exchange rates and explore the utility of Kalman Filter, Genetic Programming (GP) and Support Vector Regression (SVR) algorithms as forecasting combination techniques. Secondly, we introduce a hybrid leverage factor based on volatility forecasts and market shocks and study if its application improves the trading performance of our models. Thirdly, we introduce a specialized loss function for Neural Networks (NNs) in financial applications. In terms of our results, the PSN from the individual forecasts and the SVR from our forecast combination techniques outperform their benchmarks in statistical accuracy and trading efficiency. We also note that our trading strategy is successful, as it increased the trading performance of most of our models, while our NNs loss function seems promising.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of International Financial Markets, Institutions and Money - Volume 30, May 2014, Pages 21–54
نویسندگان
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