A study analyzed five exchange-traded funds, or ETFs, with identical 2684-session histories from January 4, 2016, to September 4, 2026. The data, which includes open, high, low, and close prices, or OHLC, was split into two time periods: 2016-2020 and August 2021 to September 4, 2026. The SPY ETF had a gross mean overnight return of 3.373656 basis points, with a geometric breakeven point of 1.587993 basis points per side. The study found that the gross compound return of 56.98655% became -11.04239% at 2 basis points per side and -90.82762% at 10 basis points per side. A statistical analysis, called HAC 5 OLS, of the next day's open and close prices based on current and past intraday and 20-day volatility data showed a lagged beta of -0.015328, t-value of -0.620745, and R-squared of 0.02267. This means that price-only predictors did not identify signed flows. The strongest counterexample was the IWM ETF with a low cost of 15.96% and the SPY ETF with a lagged return of 6.38%, but their confidence intervals crossed zero and the base cost failed. The study also found that the overnight and intraday benchmarks exactly reconciled when corrected for passive close-to-close returns. The study's validity is limited by auction and dividend feed semantics, previously inspected dates, and bootstrap stationarity. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, with additional information in statistics.json and diagnostics.json.
The performance of a merger exchange-traded fund, or ETF, is uncertain. The fund's returns were analyzed from January 2016 to December 2021 and then from January 2022 to August 2026. During the later period, the fund had a return of 20.68199%, compared to 15.04870% for another fund called MNA, 19.32620% for a fund called BIL, and 70.66035% for a fund called SPY. The merger fund's average annual return was 4.13930%, which is not much higher than the 3.88576% return of the BIL fund. To estimate the uncertainty of the fund's returns, 10,000 random samples were taken from 21-session blocks of data, which is a statistical method called bootstrapping. The results showed that the difference in returns between the merger fund and the BIL fund could be anywhere from -2.96955% to 3.59355% per year. A statistical model called ordinary least squares, or OLS, was used to analyze the relationship between the merger fund's returns and the returns of the SPY fund. The model showed that the merger fund's returns were not significantly related to the SPY fund's returns during the later period. The fund's performance was also analyzed in terms of its beta, which is a measure of its volatility, or risk, relative to the overall market. The results showed that the fund's beta was low, but this does not necessarily mean that it is not exposed to the risk of a market crash. The analysis also showed that the fund's returns were not significantly different from zero during the later period, which means that the evidence does not support the use of short-term trading strategies for this fund. The data used for the analysis came from a website called runway.adityaperswal.workers.dev, and the original motivation for the analysis came from a research paper published by the University of Chicago.
A study of seven exchange-traded funds (ETFs) from September 4, 2016, to September 4, 2026, found that defensive exposure lowers measured risk but does not establish alpha, which refers to a fund's ability to beat the market, or swing payoff. The study looked at the daily histories of these ETFs, with 2684 bars of data, to avoid backfilling, which means adding past data to a current dataset. As of September 4, 2023, the SPLV fund had a volatility of 11.2408% compared to the SPY fund's 14.9966%, with a beta, which measures a fund's risk relative to the market, of 0.35336 and a correlation, which measures how closely two funds move together, of 0.47142. The SPLV fund had a return of 26.6994% compared to the SPY fund's 111.5209%. Other funds, such as USMV, EFAV, and EEMV, also had lower volatility and returns compared to their respective counterparts. An ordinary least squares (OLS) regression analysis found that none of the funds had a positive intercept, which means they did not have a significant excess return. The study also found that the primary trading strategy had a mean return of -0.38446% per month, with a 3-month bootstrap confidence interval of -0.70870% to -0.03333%. The EEMV fund had a mean return of 0.02897% per month, with a confidence interval of -0.29972% to 0.35449%. The study concludes that defensive exposure supports lower ex-post risk exposure, but not faster profits or robust alpha. The results are limited by the market model, correlated funds, publication selection, and the inspected later window. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.aqr.com/-/media/AQR/Documents/Insights/White-Papers/Understanding-Defensive-Equity.pdf. All 8 regression rows and 27 test rows are saved.
A news scan found the following analysis: there are three main points to consider. Firstly, before analyzing price returns, certain dates, rules, costs, and controls need to be established, as noted in note 213. Secondly, in the SPY stock, 82 training monthly events have a prior 5-day correlation of -0.09716 and a slope of -0.06601, compared to 44 later events with a correlation of +0.06206 and a slope of +0.04214. Similarly, the EFA stock has a correlation of -0.04610 and the EEM stock has a correlation of -0.05156, which are relatively small and not significant estimates. Thirdly, the SPY stock's range of later 19 trades has a mean of -0.186537%, with a 95% confidence interval of [-0.96363, 0.57048] from 10,000 circular 3-calendar-month blocks, including inactive months, with a seed of 906. The T+1 range of 12 trades has a return of -8.791%, with a mean of -0.75713%, while the prior T+2 has a return of +5.63693%, with a mean of +0.79162%. However, there is no causal attribution to settlement change because the market regime also changed. The EFA stock's range has a return of +0.62029% over 19 trades, and the EEM stock's range has a return of -4.60343% over 21 trades, but neither has a positive lower confidence bound. The conclusion is that there is no reliable positive edge, and the primary range quarter Kelly clips 0. The full hold positive SPY 1.22081% annual 0.3313% is not robust, and the frictionless adjusted buy hold 109.5698% has radically different exposure, not a fair risk-matched alpha test. All three market observations are correlated, and not 57 independent SPY trades. A counterexample is that larger signed-flow shocks may exhibit stronger reversal than the negative-return proxy. The initial event-block bootstrap was replaced with registered calendar-month blocks before the final recording, and the saved stats.json contains the final numbers. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and the primary paper https://aaltodoc.aalto.fi/server/api/core/bitstreams/0f01d3c2-3b16-44f9-9135-3f03cdd29334/content.
A news scan found the headline about sector reversal uncertainty and passive exposure. The analysis is based on three premises. Premise 1 involves 549 later trades, 184 rotation decisions, and 44 entry months, with a note that treating 549 as independent exaggerates information. A NumPy analysis with 10,000 circular 3-month block resamples, using a seed of 2026090504, retains concurrent trade clusters, with a mean 95% interval of -0.1050% to 0.3364% and a range of -0.2139% to 0.0512%. These are conditional historical intervals, not posterior probabilities. Premise 2 compares equal nine ETF buy-hold later, with a return of 72.671% and a drawdown of 16.389%, to a fixed return of 19.897% and a drawdown of 18.425%. The daily OLS beta is 1.0168, and the annual arithmetic intercept is -9.903%. The range beta is 0.4178, and the intercept is -10.540%, which is descriptive but does not claim causal alpha or significance. The training basket return is 85.453% versus a fixed return of -21.436%, with a range of -16.849%. Premise 3 notes that even the range zero-cost mean CI is -0.0178% to 0.2502%, and the 2-bp one-way range is -0.0561% to 0.2102%, which spans zero. The fixed low-cost mean is positive, but execution and benchmark remain constraints. The conclusion is that there is no robust deployable edge for the range, and the fixed positive headline is not selection alpha. A strong counterargument is that smaller actual costs or turnover netting could improve fixed returns, and repeated forced exit and rebuy overstates optimized turnover. Netting is untested, and not all reversal fails. The current surviving ETF selection, shared research dates, stationarity, and corporate action assumptions limit soundness. Three own forced liquidations provide 0 natural exits, which is not a conditional edge. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars, with a query of trades fundquant. Full statistics, symbol, year rows, and seed are in the code.
A news scan found a report about a trading strategy. The strategy is not a new optimized strategy, but rather a benchmark falsification. The strategy involves buying 19 country ETFs equally at the start of a period, charging a 5 basis point fee and 5 basis point slippage, and holding them until the end. The daily adjusted closes are marked with no terminal sale. From 2017 to 2022, the strategy resulted in a 31.9001% return, with a 39.1696% drawdown. From 2023 to September 4, the strategy resulted in a 101.8003% return, with a 14.0588% drawdown. A monthly rotation strategy resulted in a 113.2853% return, but with higher risk, including a 19.2653% drawdown and a daily OLS beta of 1.23544. The strategy was compared to the SPY, which had a 3.00% intercept in analysis, indicating that the choice of benchmark materially changes the interpretation of the results. The report concludes that while the strategy may be profitable, it is not proven to be superior to passive exposure, and its validity is descriptive only. The report notes that the global equity exposure itself earned substantial returns, and that turnover and concentration add risk. The current country sample is not a comprehensive index universe and survives to today. The report uses a formula to calculate daily values, which is q_s = (10000/19) / (first_open_s * 1.0005^2), and daily value = sum(q_s * close_s), with OLS regression used to analyze the results. The source of the data is https://runway.adityaperswal.workers.dev/data/stock-bars and artifacts/benchmark.json.
A country momentum dependence audit was conducted. The audit analyzed 130 completed monthly trades and 133 range trades from September 4, 2023, to 2026. The trades were grouped by entry month, resulting in 44 and 45 clusters. Using NumPy with a seed of 90502, 10,000 independent month-cluster resamples were generated, producing mean-return 95% intervals of 0.21096% to 3.50982% for monthly trades and -0.56978% to 0.72463% for range trades. A stronger serial-dependence sensitivity test was also conducted with a seed of 90503, using stationary blocks that averaged three entry months, and 10,000 samples, resulting in mean-return 95% intervals of 0.35802% to 3.46067% for monthly trades and -0.51449% to 0.60859% for range trades. The positive fractions of 0.9938 and 0.6264 are bootstrap frequencies, not current win probabilities. Daily ordinary least squares analysis against the SPY ticker showed a monthly beta of 0.93818 and an annual arithmetic intercept of 3.00129%, with an R2 of 0.38703, while the range beta was 0.06893 and the annual arithmetic intercept was -0.53141%, with an R2 of 0.01884. The correlation between EWY and EWT was 0.76154, between EWY and EWP was 0.47974, and between EWT and EWP was 0.52041. The monthly annual returns were 14.83% for 2023, -8.26% for 2024, 40.23% for 2025, and 44.37% for 2026 to date, while the range returns were 3.25%, -2.08%, 6.00%, and -4.38% for the same periods. The conclusion is to distinguish between positive monthly historical evidence and failed range implementation, and not to reject all momentum or use monthly payoff to size range exits. The soundness of the audit is bounded by current surviving wrappers, one unusually strong recent chip cycle, historical rather than prospective holdout, and monthly dependence beyond resampling assumptions. The data source is https://runway.adityaperswal.workers.dev/data/stock-bars, and the scripts used are artifacts/stats.py and monthblock.py, with backtests 70-77.
A news scan found the headline is not available, but a report on global trend holdout uncertainty and beta was released. The report discusses defense without demonstrated alpha. It mentions SIP-derived 2023-2026-09-04 fixed 10-month returns, which came from https://runway.adityaperswal.workers.dev/data/stock-bars. The report states that 18 closed trades had an average return of +2.3818%. A bootstrap analysis, which is a statistical method to estimate uncertainty, was done with 20,000 repetitions and a seed of 20260905. The mean return was between -1.1377% and 6.3696% with 95% confidence. About 10.375% of the draws were nonpositive. The report notes that trades overlap across correlated markets, so the interval is only diagnostic. If the best trade, which had a return of +24.5168%, is removed, the mean return falls to +1.0797%. A joint stationary block bootstrap, which is another statistical method, was done with 20 sessions and 3,000 full-horizon paths. The annual arithmetic strategy mean return was between 1.8463% and 13.6657%. The active versus benchmark return was between -9.7113% and -0.7710%. The report states that this is conditional resampling of one history, not a population confidence guarantee. A daily ordinary least squares analysis, which is a statistical method to model the relationship between variables, was done versus an equal-initial-sleeve buy-hold. The beta, which measures volatility, was 0.6631, and the annualized intercept was -0.7287%. The R2, which measures the goodness of fit, was 0.7887. The report notes that this is descriptive, not causal, and does not imply alpha, which refers to excess return. The daily correlation between SPY and EFA was 0.7598, and between SPY and IEF was 0.0528 in the test period, versus 0.1931 in 2022. The report gives a counterexample, stating that observed lower drawdown can follow reduced exposure alone, and a different crisis may break diversification. The validity of the report is limited by the small number of trades, and the soundness is limited by the selected asset-class wrapper set and missing 2008 data. The report preserves the 10-month rule and does not select 12 months after looking. The policy allocation is 0.
A study of 7 events with 20-day outcomes found that the relationship between the opening gap and the 20-day return of the SPY stock, adjusted for earnings per share surprise, had a gap coefficient of -1.45 and a surprise coefficient of +0.0083, with an R-squared value of 49.7%. The correlation between the gap and return was -0.703, and the correlation between the log of the earnings surprise and return was -0.155. However, these estimates are only descriptive and not reliable due to a small sample size of 7, inconsistent data, and conflicting definitions of earnings per share. For example, DELL's stock continued to rise by 28.0% over 20 days after a 13.1% gap in February, while HPE's stock reversed and fell by 28.1% after a 33.7% gap. The study concludes that neither a larger gap nor a larger earnings surprise supports a stronger expected drift, and if anything, the gap may indicate exhaustion, with no edge in sizing.