Stock price forecasting has long been a challenging task due to the volatile nature of the stock market. To address these limitations, this paper introduces MASTER (MArkert-Guided Stock TransformER), a novel model that leverages market information for automatic feature selection and effectively models both intra-stock and inter-stock information aggregation. The authors conduct an ablation study to evaluate different combinations of parameters and find that most settings consistently outperform the baselines. Additionally, the influence of temperature β in the gating mechanism is studied. A smaller β results in stronger feature selection, while a larger β reduces the gating effect. The performance of MASTER is evaluated on the CSI300 dataset, with similar results observed on the CSI800 dataset. The paper also presents visualizations of attention maps to demonstrate how MASTER captures momentary and cross-time stock correlations. Cross-time correlation analysis reveals that correlation patterns are asymmetric and change slowly as the lookback window slides to forecast on different dates. In conclusion, existing methods often overlook time-specific details of stock sequences and fail to adequately capture momentary and cross-time stock correlations. MASTER addresses these issues by incorporating market-guided feature selection and effectively modeling intra-stock and inter-stock information aggregation. Experimental results demonstrate its superiority over previous works, while visualizations provide valuable insights into realistic stock correlation patterns. Overall, this refined summary provides a comprehensive overview of MASTER's contributions in addressing key challenges in stock price forecasting and highlights its superior performance compared to existing methods through experimental evaluations and visualizations of captured stock correlations.
Stock price forecasting is difficult because the stock market changes a lot.
- Stock price forecasting means trying to predict how much the price of stocks will go up or down in the future.
- Volatile means that something changes a lot and is not stable.
- The paper talks about a model called MASTER that uses information from the stock market to help make predictions.
- Model means a way of doing something or solving a problem.
- Ablation study means testing different settings of MASTER to see which ones work best.
- Baselines are other methods or models that are used for comparison.
- Influence means how something affects or changes something else.
- Temperature β is studied to see how it affects feature selection and gating effect in MASTER.
- Gating mechanism refers to how certain features are selected or chosen by the model.
- Performance evaluation means checking how well MASTER works on real data from the stock market.
- Dataset refers to a collection of data, in this case, information about stocks in CSI300 and CSI800 markets.
- Visualizations mean pictures or graphs that show information in an easy-to-understand way.
- Attention maps show how MASTER looks at different parts of the data when making predictions.
MASTER: Market-Guided Stock TransformER for Stock Price Forecasting
Stock price forecasting has long been a challenging task due to the volatile nature of the stock market. To address these limitations, this paper introduces MASTER (MArkert-Guided Stock TransformER), a novel model that leverages market information for automatic feature selection and effectively models both intra-stock and inter-stock information aggregation.
Ablation Study and Parameter Evaluation
The authors conduct an ablation study to evaluate different combinations of parameters and find that most settings consistently outperform the baselines. Additionally, the influence of temperature β in the gating mechanism is studied. A smaller β results in stronger feature selection, while a larger β reduces the gating effect.
Evaluation on CSI300/CSI800 Datasets
The performance of MASTER is evaluated on the CSI300 dataset, with similar results observed on the CSI800 dataset. The paper also presents visualizations of attention maps to demonstrate how MASTER captures momentary and cross-time stock correlations. Cross-time correlation analysis reveals that correlation patterns are asymmetric and change slowly as the lookback window slides to forecast on different dates.
Conclusion
In conclusion, existing methods often overlook time-specific details of stock sequences and fail to adequately capture momentary and cross-time stock correlations. MASTER addresses these issues by incorporating market-guided feature selection and effectively modeling intra-stock and inter-stock information aggregation. Experimental results demonstrate its superiority over previous works, while visualizations provide valuable insights into realistic stock correlation patterns. Overall, this refined summary provides a comprehensive overview of MASTER's contributions in addressing key challenges in stock price forecasting and highlights its superior performance compared to existing methods through experimental evaluations and visualizations of captured stock correlations
Stock price forecasting has remained an extremely challenging problem for many decades due to the high volatility of the stock market. Recent efforts have been devoted to modeling complex stock correlations toward joint stock price forecasting. Existing works share a common neural architecture that learns temporal patterns from individual stock series and then mixes up temporal representations to establish stock correlations. However, they only consider time-aligned stock correlations stemming from all the input stock features, which suffer from two limitations. First, stock correlations often occur momentarily and in a cross-time manner. Second, the feature effectiveness is dynamic with market variation, which affects both the stock sequential patterns and their correlations. To address the limitations, this paper introduces MASTER, a MArkert-Guided Stock TransformER, which models the momentary and cross-time stock correlation and leverages market information for automatic feature selection. MASTER elegantly tackles the complex stock correlation by alternatively engaging in intra-stock and inter-stock information aggregation. Experiments show the superiority of MASTER compared with previous works and visualize the captured realistic stock correlation to provide valuable insights.
