An Adaptive Multimodal Learning Model for Financial Market Price Prediction
Why this work is in the frame
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Bibliographic record
Abstract
Investors’ trading behavior is influenced by a multimode of information sources such as technical analysis, news dissemination, and sentiment, which results in the non-stationary behavior of financial time series. With advancements in deep learning, studies considering temporal relationships in each data mode and applying heterogeneous data fusion techniques for market prediction are increasing. While net price change prediction is helpful for investors, most previous deep learning models only predict the up/down trend of price as the non-stationary behavior of price time series influences the regression performance. In this work, we present an adaptive model for price regression, which learns interdependencies between the distribution of multimode data and the amount of price change around an average price for snapshots of systems. We use news content, the mood in specialized newsgroups, and technical indicators for data representation. Different news topics, also known as modalities, can be absorbed by investors with different diffusion speeds; hence we use a concept-based news representation method that reflects news topics in a news vector. Also, our model considers the positive/negative mood in specialized newsgroups and technical indicators. To capture complex temporal characteristics in the distribution of economic concepts in the news sequence, we use a recurrent convolutional neural network and other recurrent layers to perceive changes in technical indicators and mood in specialized newsgroups. In the fusion layer, our model learns to normalize data points based on their estimated distribution and the importance weight of each data mode to handle multimodality challenges. To overcome the non-stationary behavior of price, we let the network learn how to drift the predicted values around the average price of that packet. Our experiments demonstrate a significant 40.11% error reduction compared to the baselines. We also discuss the adaptability, and price prediction capability of our proposed approach.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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