Recurrent Neural Networks Approach to the Financial Forecast of Google Assets

By Luca Di Persio, O. Honchar. 2017

104 citations recorded by the source corpus.

— A huge quantity of learning tasks have to deal with sequential data, where either input or out-put data can have sequential nature. This is the case,e.g., of time series forecasting, speech recognition,video analysis, music generation, etc., since they all require algorithms able to model sequences. Duringrecent years, recurrent neural networks (RNNs) architectures have been successfully used in one as well as for multidimensional sequence learning tasks, quickly constituting the state of the art option for extracting patterns from temporal data. Concerning financial applications, one of from the most important examples of sequential data analysis problems is related to the forecasting the dynamic in time of structured financial products. To this end, we compare different RNNs architectures. In particular we consider the basic multi-layer RNN, long-short term memory (LSTM) and gated recurrent unit (GRU) performances on forecasting Google stock price movements. The latter will be done on different time horizons, mainly to explain associated hidden dynamics. In particular, we show that our approach allows to deal with long sequences, as in the case of LSTM. Moreover the obtained performances turn out to be of high level even on different time horizons. Indeed, we are able to obtain up to 72% of accuracy.

Subfields: Economics & Operations Research, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics & Operations Research

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Omer Berat Sezer

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, background): “The authors of [186] compared 3 different RNN models (basic RNN, LSTM, GRU) to predict the movement of Google stock price.”
• Transformers in Time-Series Analysis: A Tutorial (2022, methodology): “LSTMs and GRUs have been successfully used in many applications [14], [15], [16], [17], [18].”