StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices
This work addresses stock market prediction for financial analysts, but it is incremental as it builds on existing recurrent neural network frameworks.
The paper tackled stock price forecasting by comparing modern attention-based, convolutional, and recurrent models, finding that a vanilla LSTM consistently outperformed transformer-based methods in predictive accuracy and stability for buy/sell decisions.
Accurate forecasting of financial markets remains a long-standing challenge due to complex temporal and often latent dependencies, non-linear dynamics, and high volatility. Building on our earlier recurrent neural network framework, we present an enhanced StockBot architecture that systematically evaluates modern attention-based, convolutional, and recurrent time-series forecasting models within a unified experimental setting. While attention-based and transformer-inspired models offer increased modeling flexibility, extensive empirical evaluation reveals that a carefully constructed vanilla LSTM consistently achieves superior predictive accuracy and more stable buy/sell decision-making when trained under a common set of default hyperparameters. These results highlight the robustness and data efficiency of recurrent sequence models for financial time-series forecasting, particularly in the absence of extensive hyperparameter tuning or the availability of sufficient data when discretized to single-day intervals. Additionally, these results underscore the importance of architectural inductive bias in data-limited market prediction tasks.