Forecasting the Market Dynamics of the Invesco QQQ Trust Using Interpretable Econometric and Deep Learning Approaches

Abstract

Background: Financial time-series forecasting requires models that balance predictive accuracy and interpretability, while the advantage of deep learning over classical econometric approaches remains uncertain for highly liquid financial instruments.

Objective: This study compares interpretable econometric and deep learning approaches to identify the most suitable model for forecasting the closing price and trading volume of the Invesco QQQ Trust.

Methods: The study used 1,258 daily observations from 2019 to 2024 obtained from Yahoo Finance. Closing price and trading volume were treated as separate forecasting targets. Separate ARIMA(7,1,0) models were applied to each target and compared with Vanilla LSTM, Bidirectional LSTM, and Stacked LSTM. Performance was evaluated using RMSE, MAE, and MAPE.

Results: ARIMA recorded the lowest errors for both targets, with MAPE values of 0.82% for closing price and 19.96% for trading volume. Vanilla LSTM was the best-performing deep learning architecture, while Bidirectional and Stacked LSTM produced larger errors. The higher volume error indicates that trading activity was more difficult to forecast than closing-price movements.

Conclusion: Within the examined QQQ data, period, and model configurations, ARIMA provided more accurate forecasts than the evaluated LSTM architectures. Greater model complexity did not necessarily improve predictive performance.