Stellar Price Surges 15% Amid Ripple SEC News and Bullish ML Forecasts

Stellar (XLM) has seen a notable surge, rising more than 15% in the past 24 hours on August 8, as it broke out of a descending channel pattern. The upward movement coincided with a broader wave of optimism in the cryptocurrency market, fueled by a major development in the ongoing Ripple vs. U.S. Securities and Exchange Commission (SEC) legal battle. On August 7, both parties filed a joint dismissal of appeals, triggering significant price gains across several digital assets [1].

To assess potential future price movement, Finbold applied a machine learning-based algorithm using multiple large language models (LLMs) to generate an average price target for XLM as of August 31, 2025. The model incorporated momentum-based indicators such as the moving average convergence/divergence (MACD), relative strength index (RSI), stochastic oscillators, and 50-day moving averages (MA) to improve forecasting accuracy. Based on the results, the average projected price for Stellar is $0.487, which represents a potential 17.86% increase from its current price of $0.413 [1].

Individual model outputs varied, with Claude 3.5 Sonnet projecting the highest price of $0.51—implying a 23.82% upside—while Grok 3 predicted $0.480 and ChatGPT-4o forecast $0.470, suggesting gains of 16.08% and 13.67%, respectively [1]. All models, however, were bullish, albeit to different degrees.

The recent price rally was not only driven by the Ripple-SEC news but also by a 260% increase in trading volume, which now stands at $1.02 billion—well above the $290 million weekly average. The surge reflects heightened investor interest and growing confidence in XLM’s market potential.

Further optimism is building around Stellar’s upcoming Protocol 23 mainnet upgrade, scheduled for September 3. The update is expected to introduce smart contracts and enhanced tokenization capabilities, including parallel transaction processing, which could attract more decentralized finance (DeFi) and real-world asset (RWA) platforms. Given that RWA transaction volume on the Stellar network rose 199% last month, the upgrade is likely to fuel further bullish speculation [1].

The use of machine learning in financial forecasting is gaining traction, with academic research exploring hybrid models that integrate Bayesian classification and regression techniques to improve accuracy [2]. Although distinct from Finbold’s approach, these methodologies highlight the growing importance of advanced analytics in understanding market dynamics.

While such models can provide valuable insights, analysts emphasize that they should not be the sole basis for investment decisions. Predictive outputs are probabilistic estimates and subject to change based on evolving market conditions, regulatory developments, and technological advancements. Investors are advised to consider multiple sources, including fundamental and macroeconomic indicators, before making any trading decisions [1].

Machine learning is increasingly applied beyond finance. A recent paper in Frontiers in Physics discussed its use in identifying market state transitions and predicting potential crashes [4], while another in IOPscience explored its application in photometric redshift estimation for astrophysics [5]. These studies illustrate the broad applicability of machine learning across data-rich disciplines.

In conclusion, the Finbold prediction of $0.487 for Stellar on August 31, 2025, is a forward-looking estimate derived from algorithmic analysis. It should be treated as a potential scenario rather than a guaranteed outcome. As with all digital assets, XLM remains subject to high volatility and unpredictable external factors. Investors are urged to approach such forecasts with a critical mindset and integrate them into a broader, well-researched investment strategy.

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Source:

[1] Finbold – Machine learning algorithm predicts Stellar price on August 31, 2025 (https://finbold.com/machine-learning-algorithm-predicts-stellar-price-on-august-31-2025/)

[2] MDPI – Hybrid Bayesian Model for Financial Time Series Prediction (https://www.mdpi.com/2073-8994/17/8/1261)

[4] Frontiers in Physics – Machine Learning for Early Warning of Market Crashes (https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1647667/full)

[5] IOPscience – Photometric Redshift Estimation Using Machine Learning (https://iopscience.iop.org/article/10.3847/1538-4365/ade999)