XPL Plummets 387.93% in 24 Hours Amid Market Downturn

On OCT 14 2025, XPL dropped by 387.93% within 24 hours to reach $0.449, XPL dropped by 4009.4% within 7 days, dropped by 5612.4% within 1 month, and dropped by 6508.26% within 1 year.

The sharp decline in XPL over the last 24 hours marked the latest in a series of severe corrections that have persisted across multiple timeframes. The drop was particularly pronounced compared to recent performance, reflecting a broader shift in investor sentiment and liquidity conditions. The 1-month and 1-year declines highlight a long-term bearish trend, with the stock having lost nearly 99% of its value over the year. Analysts project that the decline has been driven by structural shifts in the market, as well as the lack of any substantial catalysts for a rebound.

Technical indicators over the past several weeks have shown increasingly bearish signals, with RSI and MACD trending lower across all measured timeframes. These patterns are consistent with prolonged periods of selling pressure and limited short-covering activity. The absence of a significant bounce-back in volume during the drop has further signaled a lack of buyer interest, deepening concerns over the stock’s near-term trajectory.

Backtest Hypothesis

Given the recent performance of XPL and the behavior of its technical indicators, a backtesting strategy can be constructed to evaluate how the stock has historically reacted to similar market events. The hypothesis is that large single-day declines, particularly those exceeding 10%, could serve as potential triggers for a broader sell-off, with the subsequent performance varying based on the timing and strength of the initial event.

To run an event-driven back-test, specific parameters must be defined. First, the ticker of interest would be "XPL." The event would be defined as a single-day drop in the closing price of 10% or more. The evaluation horizon would then span multiple timeframes—such as 1 day, 5 days, and 20 days—allowing for a comprehensive view of post-event performance. Additional rules, such as maximum holding periods or stop-loss triggers, could be implemented to refine the strategy and simulate real-world constraints.