The paper introduces PRICE, a systematic framework for adapting Large Language Models to short‑term Bitcoin price forecasting. PRICE combines parameter‑efficient fine‑tuning with LoRA, recursive multi‑step inference, integer‑rounded numerical representation, Context‑Task‑Format prompting, and exact zero‑temperature decoding, all built on a 4‑bit quantized LLaMA‑3 8B model. Ablation studies and comparative evaluations show that each component improves accuracy and reliability, enabling PRICE to achieve the lowest forecasting errors among eight transformer‑based and time‑series foundation models.
arXiv:2607. 23370v1 Announce Type: new Abstract: Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance.
MoFE is a deep learning framework that combines Fourier Neural Operators with a Mixture-of-Experts architecture to forecast cryptocurrency prices. It models volatility as a mix of multi-frequency components—including fundamental growth, mining costs, halving events, and market sentiment—using adaptive FNO and convolutional experts. Experiments on Bitcoin data from 2020 to 2025 show MoFE outperforms existing models in short‑term horizons, reducing phase‑lag errors and improving directional accuracy and information coefficient, which translates into higher Sharpe ratios in simulated trading.
DualCast is a dual‑path language model that forecasts financial time‑series by combining a fast numerical forecaster with an optional text‑conditioned revision mechanism. The fast path trains only new financial‑token embeddings and output heads on a frozen Qwen3‑8B backbone, while the slow path uses a LoRA adapter to incorporate news and refine predictions. In zero‑shot tests across equities and energy prices at multiple time resolutions, the slow path achieves the lowest mean absolute percentage error in most settings, especially for longer horizons, and news ablations show additional gains in many markets.
The paper investigates how the choice of loss function versus the choice of forecasting model affects cryptocurrency volatility predictions. By comparing seven loss functions and five models, and aligning forecast levels before evaluation, the study finds that after level adjustment model choice dominates performance differences, while loss-induced variations largely disappear. The work clarifies that apparent loss effects in raw comparisons are largely due to forecast level differences rather than intrinsic model performance.
arXiv:2606. 00060v1 Announce Type: cross Abstract: This paper investigates whether machine learning forecasts of hourly BTC-USDT returns can be converted into economically meaningful trading performance after transaction costs.
CryptoL is a unified framework for forecasting cryptocurrency prices that tackles extreme scale differences, non‑stationary dynamics, and inter‑dependencies among OHLC variables. It normalizes forecasting error in context‑normalized coordinates within the RevIN pipeline, uses channel‑dependent affine transformations to preserve candle‑order relations, and adds scale‑adaptive stabilization and a soft feasibility loss to enforce OHLC inequalities. Experiments on diverse crypto assets show that CryptoL improves accuracy, training stability, and the frequency of financially valid predictions compared to baseline methods.
arXiv:2608. 12259v1 Announce Type: new Abstract: Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost.
arXiv:2502. 18834v3 Announce Type: replace-cross Abstract: Financial time series (FinTS) record the behavior of human-brain-augmented decision-making, capturing valuable historical information that can be leveraged for profitable investment strategies.
arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.
VertiFuseX is a hybrid LSTM architecture that fuses multi‑scale temporal representations at the penultimate layer, stacking features from LSTM, Bi‑LSTM, and St‑LSTM branches and a parallel DNN stream. On 15 years of global equity index data, it reduces MAPE by 30‑54% and improves MAE and RMSE by over 40% compared to LSTM baselines, outperforming seven state‑of‑the‑art models across 33 metric‑dataset comparisons. The model is lightweight (675k parameters, 2.6 MB footprint) with 1.5 ms/sample inference latency and demonstrates robust, interpretable forecasting with reduced drawdowns in algorithmic trading simulations.
UQ-LOB is a lightweight, encoder‑agnostic module that adds uncertainty quantification to any pretrained limit order book (LOB) encoder. It offers two variants: UQ‑regression, which outputs a calibrated Gaussian over future tick displacement, and UQ‑classification, which outputs a categorical distribution over down/up/stationary. On 5.2 billion LOB events across seven cryptocurrency assets, UQ‑regression achieves near‑nominal 68 % interval coverage, and selecting the top 10 % most confident predictions boosts directional macro F1 by 0.11–0.15 for regression and 0.05–0.11 for classification, reaching F1 scores of 0.88 (down) and 0.83 (up) at a 5‑second horizon.
arXiv:2606. 24950v1 Announce Type: new Abstract: Financial decision-making is contextual: forecasting prices, valuing companies, and assessing event exposure weigh price history, accounting fundamentals, macroeconomic regime, and contemporaneous text.