Stock Market Embedding and Prediction: A Deep Learning Method
| Autor: | Yuzhou Chen, Junji Wu, Hui Bu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Feature engineering
0209 industrial biotechnology Artificial neural network business.industry Computer science Deep learning Feature extraction Financial market 02 engineering and technology Machine learning computer.software_genre Data modeling 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Stock market Artificial intelligence business computer MACD |
| Zdroj: | 2018 15th International Conference on Service Systems and Service Management (ICSSSM). |
| DOI: | 10.1109/icsssm.2018.8464968 |
| Popis: | It has always been a challenging issue for people to understand the stock market and make reasonable predictions. For a very long period, investors are trying to manually extract useful features from the financial market which generates an enormous volume of data every single day. People create a lot of technical indicators like MACD, TR, MFI to describe momentum, volume and volatility signals of the financial time series. However, the limitation is evident due to the efficiency of manually feature engineering. With the rapidly growing volume of data, deep neural network shows excellent performance in many research areas like natural language processing, voice recognition, image identification. It provides a new view to dig out potentially useful information automatically. In this paper, we present a novel end-to-end training using an embedding method to automatically extract features and get a summary representation of the daily market. Moreover, we apply the Long Short-Term Memory (LSTM) with attention mechanism to predict daily return ratio of HS300 index. The features extracted by the embedding layer show greater predictive power than manually defined technical signals by 92.42% lower MSE. Moreover, the use of attention mechanism also provides an average enhance of 55.68% in MSE. Our study shows that deep neuron network structure has a strong potential for better understanding market complex behaviors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|