A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it