Simple Beats Sophisticated: LSTMs Still Rule Stock Forecasting

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Author: Denis Avetisyan

New research challenges the prevailing trend toward transformer-based models, demonstrating that standard LSTMs consistently deliver superior stock price predictions.

A comparative analysis of forecasting methodologies demonstrates that while both autoregressive and teacher-forced price prediction models inform portfolio valuation, the resulting trajectories-whether driven by predicted values or ground truth-reveal distinct performance characteristics in a one-day-ahead simulation for MSFT stock.
A comparative analysis of forecasting methodologies demonstrates that while both autoregressive and teacher-forced price prediction models inform portfolio valuation, the resulting trajectories-whether driven by predicted values or ground truth-reveal distinct performance characteristics in a one-day-ahead simulation for MSFT stock.

Vanilla LSTMs outperform transformer architectures in financial time series forecasting, particularly when data is limited, highlighting the benefits of architectural simplicity and stability.

Despite advances in deep learning, accurately forecasting volatile financial markets remains a persistent challenge. This is addressed in ‘StockBot 2.0: Vanilla LSTMs Outperform Transformer-based Forecasting for Stock Prices’, which systematically compares modern time-series forecasting models for stock price prediction. Surprisingly, the research demonstrates that a standard Long Short-Term Memory (LSTM) network consistently outperforms more complex transformer-based architectures, particularly when data is limited. Does this suggest that architectural simplicity and inductive bias are crucial for robust financial time-series modeling, and what implications does this have for future research in this domain?

Decoding Market Signals: The Foundation of Time Series Forecasting

Stock prediction, despite its allure, presents a formidable challenge primarily addressed through the principles of time series forecasting. This statistical technique analyzes a sequence of data points indexed in time order – in this case, historical stock prices – to extrapolate future values. The underlying premise rests on the assumption that past patterns, trends, and seasonality contain information relevant to future behavior. Rather than attempting to pinpoint specific events that cause price fluctuations, time series forecasting focuses on identifying and modeling the inherent structures within the data itself. Sophisticated algorithms, ranging from simple moving averages to complex autoregressive integrated moving average (ARIMA) models and even machine learning techniques, are employed to decompose these time series into meaningful components and generate probabilistic predictions. The success of these forecasts isn’t about predicting the unpredictable, but rather about quantifying the probability of different outcomes based on the observed historical record.

The pursuit of stock prediction isn’t merely an academic exercise; the precision of forecasting directly correlates to prospective financial returns. Even marginal improvements in predictive accuracy can translate into substantial gains when applied to large-scale investment strategies. Consequently, the development and implementation of robust forecasting methodologies are paramount. These aren’t simply statistical refinements, but rather a critical need for techniques that can reliably identify trends, mitigate risk, and capitalize on market fluctuations. The demand for sophisticated algorithms – from autoregressive integrated moving average (ARIMA) models to complex machine learning approaches – stems directly from the financial implications of even slight forecasting errors, emphasizing the crucial link between methodological rigor and profitable outcomes.

The pursuit of stock prediction fundamentally begins with the acquisition of dependable historical data, and platforms like Yahoo Finance serve as a readily available and crucial resource for this purpose. This data, encompassing past stock prices, trading volumes, and other relevant financial indicators, forms the bedrock upon which time series forecasting models are built. Without a comprehensive and accurate record of past performance, any attempt to predict future market behavior would be severely compromised. The integrity of this initial data directly influences the reliability of subsequent analyses and forecasts, meaning that careful consideration must be given to data sourcing and validation before embarking on any predictive modeling endeavor. Access to this information isn’t merely a preliminary step, but the foundational element enabling quantitative analysis and informed investment strategies.

One-day-ahead forecasting of AAPL stock demonstrates that both autoregressive and teacher-forced price predictions can drive portfolio value, though their performance characteristics differ.
One-day-ahead forecasting of AAPL stock demonstrates that both autoregressive and teacher-forced price predictions can drive portfolio value, though their performance characteristics differ.

Preparing the Data Stream: From Raw Values to Model Inputs

The Adjusted Closing Price is utilized as the dependent variable in predictive modeling due to its comprehensive reflection of a security’s value, accounting for corporate actions like dividends, stock splits, and rights offerings. Unlike the standard Closing Price, the Adjusted Closing Price provides a consistent historical record, enabling accurate calculations of returns and minimizing distortion when analyzing long-term price trends. This normalization is critical for time series analysis, as it ensures that price movements represent genuine market fluctuations rather than artificial changes caused by corporate restructuring. Consequently, the Adjusted Closing Price serves as the foundational data point for generating predictive signals and evaluating model performance.

Z-score scaling, also known as standardization, is implemented to normalize the Adjusted Closing Price data before model training. This technique transforms the data by subtracting the mean and dividing by the standard deviation, resulting in a distribution with a mean of 0 and a standard deviation of 1. Applying Z-score scaling improves model stability by preventing features with larger scales from dominating the learning process. Critically, standardization mitigates the risk of data leakage; while other scaling methods like Min-Max scaling can introduce information from the future into past data points, Z-score scaling relies solely on historical data for each individual data point’s transformation, preserving the temporal order and preventing unrealistic predictions. The formula for Z-score scaling is z = (x - \mu) / \sigma, where x is the original data point, μ is the mean of the time series, and σ is the standard deviation.

The sliding window approach addresses the incompatibility between time series data and standard supervised learning algorithms by converting the continuous data stream into discrete, labeled samples. This is achieved by creating sequences of data points – the “window” – which serve as input features X, and the subsequent value in the time series as the target variable y. The window slides forward one step at a time, generating multiple input-output pairs. For example, a window size of 60 would use 60 consecutive Adjusted Closing Prices to predict the price at time t+1. This process effectively restructures the time series into a set of training examples suitable for algorithms designed to learn from labeled data, enabling the prediction of future values based on historical patterns.

Ten-day-ahead forecasting of AAPL demonstrates that both autoregressive and teacher-forced price predictions can drive portfolio value, though their performance differs as visualized in the resulting value curves.
Ten-day-ahead forecasting of AAPL demonstrates that both autoregressive and teacher-forced price predictions can drive portfolio value, though their performance differs as visualized in the resulting value curves.

Harnessing Temporal Dependencies: Transformer Architectures

Transformer architectures have emerged as a dominant approach to sequence modeling due to their reliance on the attention mechanism. This mechanism allows the model to assess the relevance of each element within an input sequence when processing information, effectively bypassing the limitations of recurrent neural networks in handling long-range dependencies. Unlike traditional sequential models, Transformers process the entire input sequence in parallel, which enables significant computational efficiency and facilitates the capture of contextual relationships across extended sequences. This parallel processing capability, combined with the attention mechanism’s ability to selectively focus on pertinent parts of the input, has led to state-of-the-art performance in diverse applications including natural language processing, machine translation, and time series analysis.

The core functionality of the Transformer architecture lies in its ability to dynamically assess the relevance of each element within an input sequence during prediction. Unlike recurrent models that process data sequentially, Transformers utilize the attention mechanism to compute a weighted sum of all input elements, where the weights reflect the importance of each element to the current prediction. This allows the model to directly capture dependencies between distant elements in the sequence – termed “long-range dependencies” – without being limited by the vanishing gradient problem inherent in recurrent architectures. The attention weights are determined by a learned function that considers the relationships between all pairs of elements, effectively allowing the model to focus on the most pertinent information for each prediction step.

Our research, conducted within the StockBot framework for automated trading, indicates that a standard Long Short-Term Memory (LSTM) network consistently surpasses the performance of both attention-based Transformer architectures and convolutional models in stock price forecasting. Specifically, the LSTM achieved competitive predictive accuracy, as measured by standard financial metrics, while simultaneously exhibiting more stable behavior in downstream trading simulations. This suggests that, despite the widespread adoption of Transformers in sequence modeling, simpler recurrent neural networks can offer a robust and effective solution for time-series forecasting tasks within the context of financial markets, potentially due to their inherent stability and reduced computational complexity.

Comparing autoregressive and teacher-forced forecasting methods on MSFT stock over a ten-day horizon demonstrates that while both predict price trends, teacher-forced forecasting yields a significantly higher portfolio value.
Comparing autoregressive and teacher-forced forecasting methods on MSFT stock over a ten-day horizon demonstrates that while both predict price trends, teacher-forced forecasting yields a significantly higher portfolio value.

Validating Predictive Power: Simulating a Trading Strategy

StockBot offers a robust environment for assessing the practical value of predictive models within the dynamic financial landscape. Rather than relying solely on static error metrics, the framework simulates a complete trading strategy, converting forecasted values into actionable buy and sell signals. This approach allows for a more nuanced evaluation; a model achieving high accuracy on historical data may still falter when deployed in a simulated market environment due to transaction costs or market volatility. By mimicking real-world trading conditions, StockBot identifies models capable of consistently generating profitable strategies, offering a more holistic understanding of forecasting performance beyond simple error reduction. The simulation accounts for key considerations such as trade execution and associated costs, providing a realistic benchmark for comparing different forecasting techniques and optimizing model parameters for actual trading applications.

StockBot employs two distinct forecasting methodologies to assess predictive model performance: Autoregressive Forecasting and Teacher-Forcing Forecasting. Autoregressive forecasting leverages the model’s own past predictions as inputs for future predictions, creating a closed-loop system that mirrors real-world trading conditions where future data is unknown. Conversely, Teacher-Forcing Forecasting utilizes actual historical data as inputs during the prediction process, providing the model with ‘ground truth’ at each step. This allows for a benchmark evaluation of the model’s inherent learning capacity, independent of potential compounding errors from previous predictions. By comparing results generated through both methods, StockBot provides a nuanced understanding of a model’s robustness and its ability to generalize beyond the training dataset, offering valuable insights into its practical viability for live trading scenarios.

The performance of forecasting models within StockBot is rigorously assessed using Root Mean Squared Error (RMSE), a standard metric for quantifying the difference between predicted and actual values. Results demonstrate that Long Short-Term Memory (LSTM) networks consistently achieve the lowest, or very nearly the lowest, RMSE scores across both autoregressive and teacher-forcing deployment strategies. This superior performance extends to longer-term forecasting, with LSTM exhibiting the lowest RMSE in ten-day-ahead predictions, as detailed in Table 3. These findings suggest LSTM’s capacity to effectively capture temporal dependencies within stock data, leading to more accurate forecasts and potentially improved trading strategy outcomes.

The research presented underscores a principle often lost in the pursuit of novelty: elegance through simplicity. While transformer architectures have gained prominence, this study reveals that, in the context of stock price prediction, a well-configured LSTM can achieve superior performance, particularly when data is constrained. This resonates with a core tenet of robust system design-avoiding unnecessary complexity. As Edsger W. Dijkstra aptly stated, “Simplicity is prerequisite for reliability.” The findings suggest that focusing on a stable, understandable architecture-like the LSTM-can yield more reliable results than chasing the latest, more intricate models, especially within the volatile realm of financial forecasting. The inherent stability of the LSTM allows it to navigate the noisy data more effectively than the transformer, demonstrating that architectural choices profoundly influence behavioral outcomes.

Beyond the Horizon

The persistent outperformance of a comparatively simple LSTM architecture, as demonstrated by this work, invites a re-evaluation of prevailing trends in financial time series forecasting. The field has, for a time, been captivated by the promise of attention mechanisms and increasingly complex transformer networks. However, these models demand substantial data to realize their potential-a resource often scarce in financial markets. The results suggest that elegant solutions, rooted in established principles of recurrent neural networks, may offer greater robustness and stability when facing limited data regimes.

Future investigation should not focus solely on architectural novelty, but on a deeper understanding of why simpler models thrive in these conditions. Is it a matter of regularization, preventing overfitting to noise? Or does the sequential nature of LSTM better capture the inherent temporal dependencies within stock prices? Documentation captures structure, but behavior emerges through interaction-and it is the interaction between model complexity and data availability that dictates success.

A fruitful avenue for research lies in hybrid approaches-combining the strengths of LSTMs with targeted attention mechanisms, or exploring methods to efficiently transfer knowledge from data-rich to data-poor environments. The goal should not be to build ever-more-complex systems, but to engineer solutions that are both performant and interpretable-systems that reflect a fundamental understanding of the underlying dynamics, rather than merely memorizing patterns.

Original article: https://arxiv.org/pdf/2601.00197.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-05 06:27