SKU/Artículo: AMZ-B09P1VTHWT
Financial Time Series Forecasting using Neural Networks
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En stock
En stock
Peso con empaque:
0.24 kg
0.24 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Aim of this paper is to explore the application of Artificial Neural Networks (ANNs) for financial time series forecasting. In this paper, a Long Short-Term Memory network (LSTM), which is a special kind of recurrent neural network (RNN), was used for research purposes. LSTMs can deal with the vanishing gradient problem that plague regular RNNs and have the ability to build a long-term memory of previously seen data points. The research objective is to predict the daily percentage returns of the DAX for an observation period of 1.400 days from the 20th of May 2014 until the 21st of November 2019. To facilitate this, four different datasets including macroeconomic and consumer sentiment data were used. The best performance was achieved using basic index data together with macroeconomic factors, resulting in an overall mean directional accuracy (MDA) of 0.5275. The fully trained LSTM was then evaluated using statistical metrics. Compared to a simple naïve forecast, the LSTM model performs worse in every aspect, except for MDA. To test practicability of said model, a Buy & Sell trading strategy is derived and benchmarked against a simple Buy & Hold (B&H) strategy. The LSTM strategy generates higher returns compared to B&H or naïve strategies. Transaction cost destroy most of the profits, however, and before implementing such a trading strategy in the real world, several factors such as the “Lucky Event Issue” or persistence/self-destruction of profits need to be considered
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AR$19.424
49% OFF
AR$9.961
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