StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices

• URL: http://arxiv.org/abs/2601.00197v1
• Date: Thu, 01 Jan 2026 04:09:51 GMT
• Title: StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices
• Authors: Shaswat Mohanty,
• Abstract summary: We present an enhanced StockBot architecture that systematically evaluates modern attention-based, convolutional, and recurrent time-series forecasting models.<n>A carefully constructed vanilla LSTM consistently achieves superior predictive accuracy and more stable buy/sell decision-making.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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