Summary of Gated Recurrent Neural Network with Tpe Bayesian Optimization For Enhancing Stock Index Prediction Accuracy, by Bivas Dinda
Gated recurrent neural network with TPE Bayesian optimization for enhancing stock index prediction accuracy
by Bivas Dinda
First submitted to arxiv on: 2 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE); Computational Finance (q-fin.CP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The recent advancements in deep learning architectures, neural networks, and access to financial data have revolutionized finance, leading to the development of advanced methods for predicting future stock prices. To address the complexity and volatility of the stock market, this study explores gated recurrent neural network (GRNN) algorithms such as LSTM, GRU, and hybrid models like GRU-LSTM, LSTM-GRU with Tree-structured Parzen Estimator (TPE) Bayesian optimization for hyperparameter optimization (TPE-GRNN). The goal is to improve the prediction accuracy of the next day’s closing price of the NIFTY 50 index using TPE-GRNN. A combination of eight influential factors from fundamental stock data, technical indicators, crude oil price, and macroeconomic data is used to train the models for capturing changes in the index with broader economic factors. The models’ performance is evaluated using R2, MAPE, and RMSE matrices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to predict the future prices of stocks. They use special computer programs called neural networks that get better as they learn from more data. They try different combinations of these programs to see which one works best. The goal is to make a program that can accurately predict the price of a specific stock market index, like the NIFTY 50 index in India. |
Keywords
» Artificial intelligence » Deep learning » Hyperparameter » Lstm » Neural network » Optimization
