How accurate is LSTM stock prediction?
This module predicts the average trend of the next three days from day t and achieves 66.32% accuracy. Although they have proved the effectiveness of sentiment analysis by improving prediction performance, they have not utilized the strength of the LSTM model by passing input data of succeeding days.
The ML model which is based on LSTM achieved an accuracy of 99.71% in prediction. The feature vector of stock for the company contained 4 parameter values i.e. 'open', 'close', 'low', and 'high' with batch size as 50 for 100 epochs.
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In some recent studies, hybrid models (a combination of different ML models) are used to forecast stock prices. A hybrid model designed with the SVM and sentimental-based technique was proposed for Shanghai Stock Exchange prediction [25]. This hybrid model was able to achieve the accuracy of 89.93%.
In particular, the LSTM algorithm (Long Short- Term Memory) confirms the stability and efficiency in short-term stock price forecasting.
The proposed LSTM obtained better accuracy of 71.64% when compared with existing methods such as RNN that attained 65.67% and Artificial Neural Network (ANN) of 69.7%.
One drawback is that implementing LSTM networks on FPGA requires specialized hardware and software knowledge, which can be challenging for researchers . Furthermore, while LSTM models can achieve high accuracy, they may also be prone to overfitting if not properly trained and validated .
There is no way we can predict future of stock market. No Artificial Intelligence, no Machine learning, no human intelligence can predict market perfectly.
The stock market is known for being volatile, dynamic, and nonlinear. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors, such as politics, global economic conditions, unexpected events, a company's financial performance, and so on.
This work revealed that support vector machines (SVM), long short-term memory (LSTM), and artificial neural networks (ANN) are the most popular AI methods for stock market prediction.
Can ChatGPT 4 predict stocks?
ChatGPT is a comprehensive artificial intelligence language model that has been trained to engage in human-like conversations, generate texts, and provide users with answers to their questions. Moreover, it has recently been able to correctly predict stock market changes.
No, ChatGPT or any other artificial intelligence model, including ChatGPT-4, cannot predict the future with certainty. AI models like ChatGPT are trained on historical data and can generate responses based on patterns and information learned from that data.
However, recent advancements in deep learning algorithms have shown promising results in forecasting stock prices. This article provides a technical overview of how deep learning models, specifically recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are leveraged for stock price prediction.
In conclusion, AI can predict the stock market to some degree of accuracy, but it is not a magic bullet. AI algorithms can be affected by unexpected events and biased or incomplete data, and they should be used in conjunction with other factors and information when making investment decisions.
The right accuracy is around 85-90% but it depends on the data and the result in production. If you have a model with 95% accuracy in test and production, it should be kept.
3 RNN vs LSTM
RNNs are simpler and faster to train than LSTMs, as they have fewer parameters and computations. However, LSTMs can learn more complex and long-range patterns. RNNs have a limited memory capacity, while LSTMs can selectively remember or forget the relevant information.
It is widely used in weather forecasting, stock market predictions, and sales forecasting. LSTM networks are well-suited to this task because they can learn the temporal dependencies of the input sequence and use this learning to make predictions.
Finally, the LSTM model and the Informer model were integrated to form the LSTM-Informer model, and the experiment proves that the LSTM-Informer model has excellent performance in long-term power load forecasting.
Therefore, we can safely conclude that LSTM layers are still an invaluable component in a time series deep learning model. Moreover, they don't antagonize the Attention mechanism. Instead, they can still be combined with an Attention-based component to further improve the efficiency of a model.
To do so, LSTM leverages gating mechanisms to control the flow of information and gradients. This helps prevent the vanishing gradient problem and allows the network to learn and retain information over longer sequences. There are three gates included in LSTMs: the input gate, the forget gate, and the output gate.
Why is it so difficult to predict stock market?
Complexity — The stock market is an extremely complex system with countless variables that interact and influence prices. These include macroeconomic factors such as economic growth, interest rates, political events, natural disasters, consumer sentiment, corporate earnings, etc.
A University of Florida study found that AI model ChatGPT can predict stock market trends with up to 500% returns.
According to a new research paper, yes. Alejandro Lopez-Lira and Yuehua Tang, two finance professors at the University of Florida, put the chatbot to the test — and found that ChatGPT can often use news headlines to determine whether a stock price will go up or down.
ChatGPT can be used to gain an initial understanding of a company's fundamentals. By simply entering a company's ticker symbol or name, ChatGPT can provide insights into the company's business model and economic structure, giving traders a solid foundation for further research.
Add more lstm layers and increase no of epochs or batch size see the accuracy results. You can add regularizers and/or dropout to decrease the learning capacity of your model. may some adding more epochs also leads to overfitting the model ,due to this testing accuracy will be decreased.
