Application of Machine Learning With News Sentiment in Stock Trading Strategies
Bibliographic record
Abstract
This study empirically tested the feasibility of machine learning in trading strategies using technical indicators and news information as the feature variables for machine learning. Six indicators were adopted in this study, including moving average (MA), moving average convergence/divergence (MACD), relative strength index (RSI), stochastic oscillator (KD), and on-balance volume (OBV), and news sentiment ratio (SR) developed in this study via text mining. Selected machine learning models, including support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), recurrent neural network (RNN), and long short-term memory (LSTM), were also employed for investigation. This study backtested the daily historical data of the constituent stocks in the Taiwan Top 50 ETF from January 1, 2003, to December 31, 2018, using three categories of trading strategies along with conventional and countertrend operations. The following conclusions were drawn after analyzing the performance of these trading strategies via various means: 1. Technical indicators such as MA, MACD, and RSI performed poorly in most cases. 2. Specific parameters were of relative importance to several technical indicators, including MA, MACD, RSI, and OBV. 3. OBV was a technical indicator with a positive impact on trading strategies. 4. The machine learning-based XGBoost models were able to outperform trading strategies with technical indicators under specific scenarios. 5. SR, the news sentiment ratio developed in this study, could not significantly improve the performance of machine learning models. The empirical results of this study suggest that these machine-learning models are capable of analyzing long-term stock price movements to some extent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".