Predicting Google’s Stock Price with LSTM Model
| Autor: | Tianlei Zhu, Yuexin Liao, Zheng Tao |
|---|---|
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Proceedings of Business and Economic Studies. 5:82-87 |
| ISSN: | 2209-265X 2209-2641 |
| DOI: | 10.26689/pbes.v5i5.4361 |
| Popis: | Stock market has a profound impact on the market economy, Hence, the prediction of future movement of stocks is of great significance to investors. Therefore, an efficient prediction system can solve this problem to a great extent. In this paper, we used the stock price of Google Inc. as a prediction object, selected 3810 adjusted closing prices, and used long short-term memory (LSTM) method to predict the future price trend of the stock. We built a three-layer LSTM model and divided the entire data into a test set and a training set according to the ratio of 8 to 2. The final results show that while the LSTM model can predict the stock trend of Google Inc. very well, it cannot predict the specific price accurately. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|