Multi-Horizon Prediction of Financial Technical Indicators via Ticker-Aware Deep Learning and Ensemble Optimization

We study the joint prediction of the continuous future values of two widely used technical indicators—the Moving Average Convergence Divergence (MACD) and the Stochastic Oscillator—across 15 simultaneous horizons (h = 1 to 15, i.e., 5 to 75 minutes ahead) on 15 heterogeneous assets (forex, cryptocurrencies, equities) using 5-minute intraday data from 2010–2025 (53.7M raw rows; 4.0M after quality filtering). Predicting indicator values directly, rather than binary signals or raw prices, is operationally motivated: systematic trading systems consume indicator values as inputs to signal generators, EMA-based indicators give smoother regression targets than raw returns, and bounded oscillators such as the Stochastic avoid the non-stationarity of raw prices. To our knowledge, this continuous multi-output multi-horizon formulation across heterogeneous assets has not been explicitly addressed in prior work, which mostly targets binary signals or single-asset, single-horizon settings. Our contributions are: (i) a comparison of 12 architectures, from ARIMA and Ridge to XGBoost, LightGBM, CatBoost, Bi-LSTM, GRU, TCN, and TFT; (ii) per-asset target normalization and a learned ticker embedding, associated with a 2.8× increase in MACD Pearson correlation over a global LSTM baseline (0.265 to 0.753); (iii) an ablation study across three axes; and (iv) walk-forward validation over three non-overlapping windows suggesting stable behavior of GRU v3 (std ≈ 0.004). The GRU+TFT ensemble reaches 64.3%/63.5% Directional Accuracy (val/test) on MACD, versus 0% for the persistence baseline, which has higher Pearson correlation (0.773 vs. 0.718) but never predicts a directional change. For the Stochastic Oscillator, DA reaches 69.9%/69.8% (val/test), consistent across all evaluated learned models. Accepted at the 7th ACM International Conference on AI in Finance (ICAIF '26), Milan, November 14–17, 2026.