Stats

A study of 60,000 paths for the EEM stock used a few assumptions: the current price is 67.84, the stop price is 65.93, and the target price is 75.11. The study also assumed that the log returns of the stock over time are not completely random, but have some pattern. Additionally, it used historical data to determine the regime, or market condition, instead of estimating it in real-time. The study used 10,000 paths per cell, 60 sessions, and blocks of 5, 20, and 60 days. The regimes used were from September 13, 2021, to December 31, 2024, with 831 innovations, and from January 1, 2025, to September 11, 2026, with 424 innovations. The data came from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=EEM&timeframe=1Day&feed=sip&adjustment=all. The results showed that before 2025, the stop price was hit first 71.57% to 73.38% of the time, and the target price was hit first 9.43% to 13.93% of the time. After 2025, the stop price was hit first 47.01% to 48.78% of the time, and the target price was hit first 35.76% to 41.61% of the time. The mean profit and loss per share were also calculated, with the mean profit ranging from -0.3335 to 2.2584 per share, and the 5th percentile loss ranging from -3.1506 to -4.1537 per share. The study noted that the historical regime had a 13.10% target-first rate for L20, compared to 39.63% for the recent regime, and that the mean profit per share was -0.4363 for the historical regime and 2.2584 for the recent regime. The study also noted that both 5th percentile losses exceeded the nominal loss of 1.91. The study assumed a fixed size of one share and did not use the Kelly criterion to determine the optimal bet size. A news scan found the headline '60,000 block-resampled EEM paths: regime choice dominates fixed-barrier odds'.

A study examined the uncertainty of the target magnitude of the EEM investment. It made three assumptions: target sampling uncertainty is different from future path uncertainty, preserving local dependence is more informative than independent and identically distributed rolling-window resampling, and stationarity and circular endpoints remain assumptions. Using a numpy seed of 2026091304, the study performed 3000 resamples for block sizes of 20, 60, and 120, and sampled 1255 signed 60-session gains per draw from the source https://runway.adityaperswal.workers.dev/data/stock-bars. The bootstrap results for the 5th, 50th, and 95th percentiles of the 90th percentile were: for block size 20, 8.731818, 10.621883, and 14.212705; for block size 60, 8.007592, 10.610411, and 14.325737; and for block size 120, 7.439117, 10.610411, and 14.425957. The range for block size 60 mapped the entry point from 67.84 to approximately 73.272350 to 77.558580, but the mandated entry point of 75.11 remained unchanged. These results are illustrative sampling bands under chosen dependence assumptions and do not guarantee 90% coverage or provide price forecasts, stop odds, or Kelly evidence. A counterexample of structural change in country or sector composition could make resampled old blocks unrepresentative, and the wraparound method connects unrelated endpoints. Larger blocks increased left uncertainty, and additional slicing did not resolve future regime uncertainty. No trades were simulated, and no strategy returns, drawdowns, win rate, or out-of-sample results were claimed. A specific mandate prohibited retesting the accepted concept, and fixed equal-slot sizing prevailed.

The trading strategy is based on certain premises. First, the empirical IID bootstrap is a scenario model that is not based on live odds. Second, the actual strategy of ratchets and 15-minute enforcement is not included. Third, the size of the investment stays the same. Using a numpy seed of 20260913, the strategy was tested with 10,000 paths and 60 sessions, using the 1,255 latest adjusted daily returns from a specific website. The starting price was 67.84. The results showed that the close-only barriers were 65.93 and 75.11. The stop-first percentage was 64.25%, the target-first percentage was 24.11%, and the neither percentage was 11.64%. The terminal hold percentage was between 13.18% and 19.52%. A close-only counterexample showed that the intraday stop and recovery were missed. The IID model misses certain factors such as clustered volatility, raw distributions, gaps, fills, costs, and ranking. These percentages cannot be used to calculate the Kelly criterion or override accepted rules. A binary payoff reference of +7.27 and -1.91 gives a breakeven percentage of 20.81% before costs and timeouts. The empirical fund odds are not available because there are no closed trades. The strongest bear implication is that the long-run premium does not guarantee profits in this 60-session path. The strategy preserves one share and does not include target inflation.

This is a simulation of the EEM fund. It uses daily empirical resampling to show the difference between the endpoint and path, assuming that each day's results are independent, which is a simplification. Using a fixed starting point, 10000 paths were simulated over 60 sessions, with daily returns sampled from the last 1255 days. The simulation started at 67.84, with barriers set at 65.93 and 75.11, checked at the end of each day. The simulation stopped when it first fell to 63.83, or reached a target of 24.21, or neither, with the following results: 13.49 percent of paths ended at or below -5 percent, and 19.24 percent ended at or above 95 percent. The simulation did not account for several factors, including serial dependence, live ranking, and fees. The results are model frequencies, not estimated real win odds or trade backtest results. One limitation of the simulation is that it does not account for intraday stops followed by recovery. No investment decisions should be based on this simulation, which preserves fixed equal slots and a one-share implementation.

A study was done on the EEM stock to see how it reacts to big price jumps. The study used 20,000 fake paths, each with 60 overnight price jumps, to see what could happen. It found that 42.745% of these paths had at least one price jump below a certain level, which is 2.815448% below the current entry point. The median worst price jump was 2.556054%, and the worst 5% of price jumps were 5.568038% or worse. This study assumes that the stock is held for all 60 nights and does not take into account things like stop-loss orders, target prices, or dividends. It also does not try to predict what will happen in the future. The study found that a 1.91% stop loss should not be considered guaranteed. The allocation of one share remains the same. This study does not change or add to the current understanding of the stock's momentum.

A news scan found the text describing a risk analysis of the EEM investment. The analysis used historical data from a source that adjusts for certain changes, and it used a specific method called numpy with a seed of 20260912 to create 20,000 different paths of 60 daily returns. The starting point was 67.84, with a stop at 65.93472 and a target at 75.111066. The analysis found that 56.865% of the time, the stop was hit first, 38.82% of the time the target was hit first, and 4.315% of the time neither was hit. The average return was 2.749172%, with 5% of the paths returning -2.808491% or less, and 95% of the paths returning 10.717963% or less. This analysis is simplified and does not take into account certain factors, such as costs and overnight drops, so it should not be used to make actual investment decisions. The analysis assumes a prior trend in equity and a fixed investment size of 1 share. It also notes that a sudden overnight drop or a crash could affect the results. The conclusion is that this analysis is only an illustration of potential outcomes and should not be used to adjust investment targets or sizes.