{"id":239934,"date":"2026-03-18T11:50:51","date_gmt":"2026-03-18T15:50:51","guid":{"rendered":"https:\/\/ibkrcampus.com\/campus\/?p=239934"},"modified":"2026-03-19T04:29:34","modified_gmt":"2026-03-19T08:29:34","slug":"equity-market-neutral-trading-with-dealer-exposure-levels-and-options-flow-confirmation","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/equity-market-neutral-trading-with-dealer-exposure-levels-and-options-flow-confirmation\/","title":{"rendered":"Equity Market-Neutral Trading with Dealer-Exposure Levels and Options-Flow Confirmation"},"content":{"rendered":"\n<p><em>Refined academic research note (with charts, methodology, and references)<\/em><\/p>\n\n\n\n<p>Data window: 2025-02-24 to 2026-02-20 \u2022 N=238 daily observations<\/p>\n\n\n\n<p><strong>Disclosure &amp; limitations. <\/strong>This note is for educational\/informational purposes. Results are computed from a single return series provided by the author and are not audited. Statistics are computed on daily returns with a zero risk\u2011free rate assumption unless stated otherwise; transaction costs, financing, slippage, taxes, and borrow costs are not included. Past performance is not indicative of future results.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-abstract\">Abstract<\/h2>\n\n\n\n<p>We evaluate an Equity Market Neutral (EMN) long\/short portfolio against SPX using 238 daily observations from 2025\u201102\u201124 to 2026\u201102\u201120. Across the sample, EMN exhibits higher risk\u2011adjusted performance and lower drawdown than the equity benchmark. EMN achieves an annualized Sharpe ratio of 2.54 versus 0.83 for SPX, with cumulative returns of 42.8% versus 14.9%. Path and tail risk metrics are also favorable: EMN\u2019s maximum drawdown is \u22126.0% versus \u221216.9% for SPX, and its historical 1\u2011day VaR95 is \u22121.27% versus \u22121.70%. At lower frequency, EMN wins 69.2% of weeks and 69.2% of months (SPX: 53.8% and 61.5%). Diagnostics show low market dependence (beta\u22480.16, correlation\u22480.21) and no statistically significant daily return autocorrelation over 20 lags.<\/p>\n\n\n\n<h1 class=\"wp-block-heading\">1. Motivation and framing<\/h1>\n\n\n\n<p>Equity market neutral strategies are typically evaluated not primarily on raw returns, but on their ability to deliver diversifying, risk-efficient performance with limited dependence on equity market direction. This note therefore emphasizes (i) risk-adjusted performance (Sharpe, information ratio), (ii) downside and tail risk (drawdown, VaR and CVaR), and (iii) time-series diagnostics (autocorrelation and rolling behavior).<\/p>\n\n\n\n<p>The specific hypothesis examined is that an EMN portfolio built from DEX-derived support\/resistance levels and additional options-flow confirmation can produce (a) attractive risk-adjusted returns and (b) a smoother path than long-only equity exposure, while maintaining low beta to SPX.<\/p>\n\n\n\n<p>Because performance metrics can be sensitive to sampling window, parameter choices, and market regimes, the results should be interpreted as a descriptive analysis of the provided period. The natural next step in a formal due diligence process would be multi-year analysis and out-of-sample validation.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-2-emn-strategy-overview-signal-construction-and-portfolio-rules\">2. EMN strategy overview (signal construction and portfolio rules)<\/h2>\n\n\n\n<p>The EMN portfolio is a systematic long\/short equity strategy that combines (i) dealer-exposure-derived (DEX) support\/resistance levels and (ii) signed options-flow information to select and size positions. The design goal is to reduce directional market exposure while extracting alpha from microstructure-linked flows and price-level effects.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-1-trade-selection-rules\">2.1 Trade selection rules<\/h3>\n\n\n\n<p>Positions are selected and sized using the following rules:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Go long at DEX-defined support when options flows are net-positive.<\/li>\n\n\n\n<li>Go short at DEX-defined resistance when options flows are net-negative.<\/li>\n\n\n\n<li>Maintain 7 long positions and 7 short positions (14 gross names).<\/li>\n\n\n\n<li>Apply volatility-based position sizing (lower volatility \u2192 larger notional; higher volatility \u2192 smaller notional) so that positions contribute more comparable ex ante risk.<\/li>\n<\/ol>\n\n\n\n<p>Volatility-based sizing can be implemented in many ways. A common approach is inverse-volatility weighting:<\/p>\n\n\n\n<p class=\"has-text-align-center\">&nbsp;&nbsp;&nbsp; w_i \u221d 1 \/ \u03c3\u0302_i<\/p>\n\n\n\n<p>where \u03c3\u0302_i is an estimate of recent realized volatility (e.g., 20\u201360 trading days). This targets a more uniform risk contribution across positions and helps prevent a small subset of high-volatility names from dominating portfolio risk. From a practical standpoint, volatility sizing also tends to \u2018auto-delever\u2019 during turbulence if volatility estimates react quickly.<\/p>\n\n\n\n<p><em>\u201cManaged portfolios that take less risk when volatility is high \u2026 increase Sharpe ratios.\u201d<\/em><\/p>\n\n\n\n<p><em>\u2014 Moreira &amp; Muir (2017), Volatility\u2011Managed Portfolios<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-2-what-dex-derived-support-resistance-means-in-practice\">2.2 What \u201cDEX-derived support\/resistance\u201d means in practice<\/h3>\n\n\n\n<p>DEX levels are derived from options positioning and the hedging mechanics of dealers\/market makers. Large concentrations of options exposure at particular strikes can induce hedging flows that influence price dynamics as the underlying approaches those levels. When dealers are net long gamma, hedging flows can dampen price moves (range-like behavior); when dealers are net short gamma, hedging can amplify moves (trend\/instability).<\/p>\n\n\n\n<p>This mechanism has been studied in academic work linking dealer gamma imbalances to intraday momentum\/reversal and volatility regimes. Although practitioners may compute DEX\/GEX measures with different conventions (e.g., choice of maturities, dealer sign assumptions, or smoothing), the core intuition is consistent: delta-hedging flows are mechanical, and the sign of gamma changes the stabilizing vs destabilizing nature of those flows.<\/p>\n\n\n\n<p><em>\u201cWe document a link between large aggregate dealers\u2019 gamma imbalances and intraday momentum\/reversal of stock returns.\u201d<\/em><\/p>\n\n\n\n<p><em>\u2014 Barbon &amp; Buraschi (2021), Gamma Fragility<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-3-why-options-flow-is-a-complementary-confirmatory-signal\">2.3 Why options flow is a complementary confirmatory signal<\/h3>\n\n\n\n<p>Options markets are not merely a derivative venue; they are a locus of informed trading and risk transfer. Signed options-flow measures attempt to capture the direction and intensity of demand for delta (directional exposure) or vega (volatility exposure). In EMN, these flows act as a confirmation filter: DEX supplies candidate levels; options flow supplies a directional\/regime confirmation.<\/p>\n\n\n\n<p><em>\u201cOption volume contains information about the future direction of underlying stock price movements.\u201d<\/em><\/p>\n\n\n\n<p><em>\u2014 Pan &amp; Poteshman (2006), referenced by Ni, Pan &amp; Poteshman (2008)<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-2-4-portfolio-construction-for-market-neutrality\">2.4 Portfolio construction for market neutrality<\/h3>\n\n\n\n<p>\u201cMarket neutral\u201d can mean different things operationally. Common implementations include dollar neutrality (equal long and short notional), beta neutrality (estimated beta to the market near zero), and\/or sector neutrality (balanced exposures by sector). The 7-long\/7-short structure primarily supports dollar neutrality and breadth. To the extent that names have heterogeneous betas, beta neutrality typically requires beta-aware weighting or constrained optimization.<\/p>\n\n\n\n<p>Empirically, the realized beta of EMN vs SPX over the sample is low (\u22480.16). This does not prove structural neutrality in all regimes\u2014betas can rise in crises\u2014but it is directionally consistent with the market-neutral objective.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-3-data-and-methodology\">3. Data and methodology<\/h2>\n\n\n\n<p>The dataset contains daily simple returns for EMN and SPX over 238 trading days from 2025-02-24 to 2026-02-20. Weekly returns are compounded from daily returns within a Friday-to-Friday week. Monthly returns are compounded within calendar months at month-end.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-1-estimation-choices-and-conventions\">3.1 Estimation choices and conventions<\/h3>\n\n\n\n<p>Unless otherwise stated, the following conventions are used:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Trading days per year: 252 (annualization factor).<\/li>\n\n\n\n<li>Risk-free rate: 0% (Sharpe is computed on raw returns). Over a one-year window, using a positive cash rate would reduce Sharpe slightly.<\/li>\n\n\n\n<li>VaR: historical (empirical) 5th percentile of daily returns.<\/li>\n\n\n\n<li>Max drawdown: computed on a $1 equity curve obtained by compounding returns.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-3-2-metric-definitions-and-intuition\">3.2 Metric definitions and intuition<\/h3>\n\n\n\n<p>Sharpe ratio summarizes average return per unit of total volatility. It is a second-moment metric and therefore compresses the return distribution to mean and variance. Two practical issues arise: (i) fat tails and skew can make variance an incomplete risk descriptor, and (ii) serial correlation can bias standard errors and annualization.<\/p>\n\n\n\n<p>Max drawdown captures path risk: the largest cumulative loss from a prior peak. Drawdown is particularly important for leveraged or capacity-constrained strategies, because a sufficiently deep drawdown can trigger forced deleveraging.<\/p>\n\n\n\n<p>VaR summarizes a quantile of the loss distribution but is not subadditive and does not describe the magnitude of losses beyond the quantile. CVaR (expected shortfall) addresses this by averaging losses in the tail beyond VaR.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-4-empirical-results-emn-vs-spx\">4. Empirical results (EMN vs SPX)<\/h2>\n\n\n\n<p>Table 1 summarizes the primary statistics computed directly from the supplied return series.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Metric<\/td><td>EMN<\/td><td>SPX<\/td><\/tr><tr><td>Date range<\/td><td>2025-02-24 to 2026-02-20<\/td><td>2025-02-24 to 2026-02-20<\/td><\/tr><tr><td>Observations (daily)<\/td><td>238<\/td><td>238<\/td><\/tr><tr><td>Arithmetic return (ann.)<\/td><td>38.92%<\/td><td>16.74%<\/td><\/tr><tr><td>Volatility (ann.)<\/td><td>15.30%<\/td><td>20.23%<\/td><\/tr><tr><td>Sharpe (ann., rf=0)<\/td><td>2.54<\/td><td>0.83<\/td><\/tr><tr><td>Cumulative return<\/td><td>42.82%<\/td><td>14.92%<\/td><\/tr><tr><td>Max drawdown<\/td><td>-6.02%<\/td><td>-16.87%<\/td><\/tr><tr><td>VaR 95% (1-day, hist.)<\/td><td>-1.27%<\/td><td>-1.70%<\/td><\/tr><tr><td>Weekly win rate<\/td><td>69.23%<\/td><td>53.85%<\/td><\/tr><tr><td>Monthly win rate<\/td><td>69%<\/td><td>61.54%<\/td><\/tr><tr><td>Avg rolling 3M return (month-end)<\/td><td>7.43%<\/td><td>4.71%<\/td><\/tr><tr><td>Autocorrelation lag-1 (daily)<\/td><td>0.03<\/td><td>-0.17<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-1-risk-adjusted-return-sharpe-ratio\">4.1 Risk-adjusted return: Sharpe ratio<\/h3>\n\n\n\n<p>EMN\u2019s annualized Sharpe ratio (2.54) materially exceeds SPX\u2019s (0.83). Under a simple mean-variance lens, this indicates that EMN delivered substantially more average daily return per unit of realized volatility.<\/p>\n\n\n\n<p>However, Sharpe should not be interpreted as a complete risk measure. If returns are fat-tailed or negatively skewed, a high Sharpe can coexist with large tail losses. This note therefore pairs Sharpe with drawdown, VaR\/CVaR, and distribution diagnostics (skewness and kurtosis).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-2-return-compounding-cumulative-return-vs-cagr\">4.2 Return compounding: cumulative return vs CAGR<\/h3>\n\n\n\n<p>Cumulative return measures total growth over the sample, while CAGR annualizes this growth geometrically. Over the sample, EMN\u2019s cumulative return is 42.82% with a CAGR of 45.84%. SPX\u2019s cumulative return is 14.92% with a CAGR of 15.86%. CAGR is especially relevant for allocators evaluating long-run compounding.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-3-path-risk-drawdown-and-recovery\">4.3 Path risk: drawdown and recovery<\/h3>\n\n\n\n<p>Maximum drawdown captures the worst capital impairment experienced during the sample. EMN\u2019s max drawdown is \u22126.0% compared with \u221216.9% for SPX.<\/p>\n\n\n\n<p>Drawdown matters because recovery is nonlinear. A drawdown of d requires a gain of d\/(1\u2212d) to recover. For example, a 6% drawdown requires ~6.4% gain to recover, while a 16.9% drawdown requires ~20.3% gain. Strategies with smaller drawdowns can therefore compound more efficiently even if average returns are similar.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-4-tail-risk-var95-and-cvar95\">4.4 Tail risk: VaR95 and CVaR95<\/h3>\n\n\n\n<p>EMN\u2019s historical 1-day VaR95 is -1.27%, while SPX\u2019s is -1.70%. Expected shortfall (CVaR95) is -1.75% for EMN and -3.01% for SPX. CVaR is more tail-sensitive because it averages outcomes beyond the VaR quantile.<\/p>\n\n\n\n<p><em>\u201cConditional value\u2011at\u2011risk \u2026 has significant advantages over value\u2011at\u2011risk.\u201d<\/em><\/p>\n\n\n\n<p><em>\u2014 Rockafellar &amp; Uryasev (2000\/2002)<\/em><\/p>\n\n\n\n<p>A practical interpretation is that VaR answers \u201chow bad is a typical bad day,\u201d while CVaR answers \u201chow bad are the worst 5% of days on average.\u201d For strategies exposed to gap risk or option-like payoff convexities, CVaR and stress tests are generally more informative than VaR alone.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-5-consistency-weekly-and-monthly-win-rates\">4.5 Consistency: weekly and monthly win rates<\/h3>\n\n\n\n<p>EMN is positive in 69.2% of weeks and 69.2% of months. SPX is positive in 53.8% of weeks and 61.5% of months. Because win rate ignores magnitude, it is best interpreted together with tail metrics and distributional asymmetry.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-4-6-rolling-3-month-returns-and-rolling-risk\">4.6 Rolling 3-month returns and rolling risk<\/h3>\n\n\n\n<p>Rolling quarterly returns help reveal whether performance is concentrated in a single short episode or distributed across sub-periods. Rolling volatility and rolling Sharpe provide diagnostics on whether returns were achieved by taking time-varying risk or by maintaining stable risk through time.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"669\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-1100x669.png\" alt=\"Equity curves \" class=\"wp-image-239969 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-1100x669.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-700x426.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-300x183.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-768x467.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-1536x935.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_01-Equity-curves-2048x1246.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/669;\" \/><\/figure>\n\n\n\n<p><em>Figure 1. Equity curves (growth of $1).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"703\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-1100x703.png\" alt=\"Drawdown paths\" class=\"wp-image-239971 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-1100x703.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-700x447.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-300x192.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-768x491.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-1536x981.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_02-Drawdown-paths-2048x1308.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/703;\" \/><\/figure>\n\n\n\n<p><em>Figure 2. Drawdown paths (peak-to-trough over time).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"669\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-1100x669.png\" alt=\"Rolling 3-month compounded returns \" class=\"wp-image-239972 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-1100x669.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-700x426.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-300x183.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-768x467.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-1536x935.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_03-Rolling-3-month-compounded-returns-2048x1246.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/669;\" \/><\/figure>\n\n\n\n<p><em>Figure 3. Rolling 3-month compounded returns (month-end).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"466\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-1100x466.png\" alt=\"Rolling 3-month return heatmap \" class=\"wp-image-239973 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-1100x466.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-700x296.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-300x127.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-768x325.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-1536x650.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_04-Rolling-3-month-return-heatmap-2048x867.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/466;\" \/><\/figure>\n\n\n\n<p><em>Figure 4A. Rolling 3-month return heatmap (EMN, by ending month).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"466\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-1100x466.png\" alt=\"3 Month return Heatmap SPX\" class=\"wp-image-239980 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-1100x466.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-700x296.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-300x127.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-768x325.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-1536x650.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_05-Rolling-21-day-annualized-volatility-1-2048x867.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/466;\" \/><\/figure>\n\n\n\n<p><em>Figure 4B. Rolling 3-month return heatmap (SPX, by ending month).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"680\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-1100x680.png\" alt=\"\" class=\"wp-image-239979 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-1100x680.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-700x433.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-300x185.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-768x475.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-1536x949.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-21-day-annualized-volatility-2048x1266.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/680;\" \/><\/figure>\n\n\n\n<p><em>Figure 5. Rolling 21-day annualized volatility (risk regime).<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"669\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-1100x669.png\" alt=\"Rolling 63-day Sharpe ratio \" class=\"wp-image-239981 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-1100x669.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-700x426.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-300x183.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-768x467.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-1536x935.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-63-day-Sharpe-ratio-2048x1246.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/669;\" \/><\/figure>\n\n\n\n<p><em>Figure 6. Rolling 63-day Sharpe ratio (annualized, rf=0).<\/em><\/p>\n\n\n\n<p>Table 2 reports additional distribution and downside-risk statistics (computed from the same return series).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td>Metric<\/td><td>EMN<\/td><td>SPX<\/td><\/tr><tr><td>CAGR (annualized, geometric)<\/td><td>45.84%<\/td><td>15.86%<\/td><\/tr><tr><td>Sortino (ann., MAR=0)<\/td><td>5.11<\/td><td>1.36<\/td><\/tr><tr><td>Calmar (CAGR \/ |MaxDD|)<\/td><td>7.61<\/td><td>0.94<\/td><\/tr><tr><td>CVaR95 (1-day, hist.)<\/td><td>-1.75%<\/td><td>-3.01%<\/td><\/tr><tr><td>Skewness (daily)<\/td><td>0.47<\/td><td>1.43<\/td><\/tr><tr><td>Excess kurtosis (daily)<\/td><td>1.28<\/td><td>21.06<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-5-benchmark-relative-behavior-beta-correlation-and-information-ratio\">5. Benchmark-relative behavior: beta, correlation, and information ratio<\/h2>\n\n\n\n<p>Market-neutral strategies are often assessed by (i) how much market exposure they carry and (ii) how efficiently they generate active returns. We estimate beta and correlation of EMN to SPX on daily returns, and compute the information ratio of EMN relative to SPX.<\/p>\n\n\n\n<p>Estimated beta (EMN vs SPX) is 0.16 and correlation is 0.21. Active returns (EMN \u2212 SPX) exhibit an annualized tracking error of 22.66%, an annualized active (geometric) growth rate of 21.68%, and an information ratio of 0.98.<\/p>\n\n\n\n<p>Interpretation: low beta reduces vulnerability to broad market selloffs; the information ratio translates this into an efficiency metric\u2014active return per unit of active risk.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"1047\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-1100x1047.png\" alt=\"EMN vs SPX daily returns with OLS fit\" class=\"wp-image-239983 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-1100x1047.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-700x666.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-300x286.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-768x731.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-1536x1462.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_EMN-vs-SPX-daily-returns-with-OLS-fit-2048x1949.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/1047;\" \/><\/figure>\n\n\n\n<p><em>Figure 7. EMN vs SPX daily returns with OLS fit.<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"669\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-1100x669.png\" alt=\"Active return (EMN \u2212 SPX) growth of $1\" class=\"wp-image-239985 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-1100x669.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-700x426.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-300x183.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-768x467.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-1536x935.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Active-return-EMN-SPX-growth-1-dollar-2048x1246.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/669;\" \/><\/figure>\n\n\n\n<p><em>Figure 8. Active return (EMN \u2212 SPX): growth of $1.<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"653\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-1100x653.png\" alt=\"Rolling information ratio of active returns (63 trading day window)\" class=\"wp-image-239987 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-1100x653.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-700x416.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-300x178.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-768x456.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-1536x912.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Rolling-information-ratio-active-returns-63-trading-day-window-2048x1216.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/653;\" \/><\/figure>\n\n\n\n<p><em>Figure 9. Rolling information ratio of active returns (63 trading day window).<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-6-statistical-diagnostics-autocorrelation-and-return-structure\">6. Statistical diagnostics: autocorrelation and return structure<\/h2>\n\n\n\n<p>Serial correlation in returns can indicate regime persistence, microstructure effects, or return smoothing. We analyze the autocorrelation function (ACF) up to 20 lags and perform Ljung\u2013Box tests for joint significance.<\/p>\n\n\n\n<p>EMN: no statistically significant autocorrelation is detected (Ljung\u2013Box p\u2011values: 0.810 at lag 10; 0.848 at lag 20). SPX: autocorrelation is statistically significant over the same horizon (p\u2011values: 0.002 at lag 10; 0.004 at lag 20).<\/p>\n\n\n\n<p>One interpretation is that SPX exhibits short-horizon mean reversion and volatility clustering, which can generate statistically detectable autocorrelation at daily lags. In EMN, the absence of significant autocorrelation is consistent with a return stream driven by many idiosyncratic moves that do not mechanically repeat day-to-day.<\/p>\n\n\n\n<p>For risk estimation, this matters because serial correlation can bias na\u00efve annualization and understate true uncertainty. Allocators often complement these diagnostics with block bootstrap methods or Newey\u2013West standard errors when formal inference is required.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"710\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-1100x710.png\" alt=\"Autocorrelation of daily returns (ACF) with 95% confidence band\" class=\"wp-image-240011 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-1100x710.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-700x452.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-300x194.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-768x496.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-1536x991.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/Autocorrelation-daily-returns-2048x1322.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/710;\" \/><\/figure>\n\n\n\n<p><em>Figure 10. Autocorrelation of daily returns (ACF) with 95% confidence band.<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-7-market-neutral-construction-and-win-rate-amplification\">7. Market-neutral construction and win rate amplification<\/h2>\n\n\n\n<p>At the single-name level, signals often have modest directional accuracy (hit rate slightly above 50%). Portfolio construction can amplify this into a more stable return stream by reducing variance contributed by market direction and idiosyncratic noise.<\/p>\n\n\n\n<p>The author reports a 52.91% hit rate at the individual-name level for the underlying selection process. In this sample, EMN\u2019s weekly win rate is 69.2%. While the horizons are not identical, the direction is consistent with the hypothesis that market neutrality and diversification improve consistency.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"678\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-1100x678.png\" alt=\"Win rate amplification: individual-name hit rate (reported) vs EMN portfolio weekly win rate (observed).\n\" class=\"wp-image-239991 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-1100x678.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-700x432.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-300x185.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-768x474.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-1536x947.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_Win-rate-amplification-2048x1263.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/678;\" \/><\/figure>\n\n\n\n<p><em>Figure 11. Win rate amplification: individual-name hit rate (reported) vs EMN portfolio weekly win rate (observed).<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-7-1-a-factor-model-explanation\">7.1 A factor-model explanation<\/h3>\n\n\n\n<p>Under a simple factor model, a stock\u2019s return can be decomposed into market and idiosyncratic components. If the portfolio is constructed to target near-zero beta, then the market component is reduced, shrinking return variance without necessarily shrinking expected alpha. Because win probability for a given horizon increases with the mean-to-variance ratio, this can raise observed win rates at weekly and monthly frequencies.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-7-2-breadth-and-the-information-ratio\">7.2 Breadth and the information ratio<\/h3>\n\n\n\n<p>The fundamental law of active management provides intuition for why breadth matters: risk-adjusted performance increases with both forecasting skill and breadth (the number of independent bets). A 7-long\/7-short structure increases breadth relative to single-name trading and can mitigate concentration risk. This is not a free lunch\u2014correlations can spike in crises\u2014but it is a core mechanism behind market-neutral portfolio construction.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-8-proprietary-data-as-edge-in-the-ai-era\">8. Proprietary data as edge in the AI era<\/h2>\n\n\n\n<p>Modern toolchains (including large language models) reduce the cost of generating strategies from public price\/volume features (moving averages, oscillators, breakouts, and other standard technical indicators). As a result, sustainable edge often shifts from implementation to information: unique datasets, superior labeling, and better microstructure understanding.<\/p>\n\n\n\n<p>Empirical evidence suggests that widely disseminated predictors can attenuate as arbitrage capital learns and trades them. McLean and Pontiff document meaningful post-publication decay in return predictability, consistent with crowding in public signals. Related work on quant crowding emphasizes that when many investors hold similar trades, returns can compress and crash risk can rise.<\/p>\n\n\n\n<p><em>\u201cPortfolio returns are 26% lower out\u2011of\u2011sample and 58% lower post\u2011publication.\u201d<\/em><\/p>\n\n\n\n<p><em>\u2014 McLean &amp; Pontiff (2016)<\/em><\/p>\n\n\n\n<p>Options-flow and dealer-positioning measures are not simple functions of OHLC; they require specialized data and interpretation. Academic evidence supports their relevance: option volume and demand contain information about future stock price direction and volatility, and dealer gamma positioning can influence intraday momentum\/reversal and volatility regimes. Taken together, this literature supports the claim that proprietary options-derived datasets can contain incremental information beyond OHLC.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-8-1-practical-implications-for-research-and-execution\">8.1 Practical implications for research and execution<\/h3>\n\n\n\n<p>In practice, building a durable edge around these signals requires:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Robust trade classification for options flow (buyer- vs seller-initiated).<\/li>\n\n\n\n<li>Consistent aggregation of delta\/gamma exposure across strikes and maturities.<\/li>\n\n\n\n<li>Careful handling of corporate actions and symbol changes.<\/li>\n\n\n\n<li>Realistic execution modeling (bid\/ask, market impact, borrow availability).<\/li>\n<\/ul>\n\n\n\n<p>These steps are difficult to commoditize and often determine whether a signal survives live deployment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-9-limitations-and-next-tests\">9. Limitations and next tests<\/h2>\n\n\n\n<p>This note evaluates one return series over roughly one year. A thorough investment due diligence process would typically require:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Multi-year evaluation across materially different volatility regimes, including crisis episodes.<\/li>\n\n\n\n<li>Transaction cost modeling (commissions, slippage, borrow costs, financing).<\/li>\n\n\n\n<li>Capacity analysis and liquidity stress tests.<\/li>\n\n\n\n<li>Exposure attribution to ensure returns are not an unintended bet on standard equity factors.<\/li>\n\n\n\n<li>Robustness checks to parameter choices (DEX construction, flow windows, volatility estimators) and to estimation error.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-appendix-a-metric-formulas-and-implementation-notes\">Appendix A. Metric formulas and implementation notes<\/h2>\n\n\n\n<p>A.1 Sharpe ratio (annualized, rf=0)<\/p>\n\n\n\n<p>Sharpe = (mean(r_d) \/ std(r_d)) \u00d7 \u221a252, where r_d are daily returns.<\/p>\n\n\n\n<p>A.2 Cumulative return and CAGR<\/p>\n\n\n\n<p>Cumulative = \u220f(1 + r_d) \u2212 1. CAGR = (\u220f(1+r_d))^(252\/N) \u2212 1.<\/p>\n\n\n\n<p>A.3 Max drawdown<\/p>\n\n\n\n<p>Equity_t = \u220f_{s\u2264t}(1+r_s). Drawdown_t = Equity_t \/ max_{u\u2264t}(Equity_u) \u2212 1. MaxDD = min(Drawdown_t).<\/p>\n\n\n\n<p>A.4 Historical VaR95 and CVaR95<\/p>\n\n\n\n<p>VaR95 is the empirical 5th percentile of daily returns. CVaR95 is the mean of returns less than or equal to VaR95.<\/p>\n\n\n\n<p>A.5 Information ratio<\/p>\n\n\n\n<p>Active_t = r_EMN,t \u2212 r_SPX,t. IR = (mean(Active) \/ std(Active)) \u00d7 \u221a252.<\/p>\n\n\n\n<p>A.6 Weekly\/monthly returns and win rate<\/p>\n\n\n\n<p>Weekly return: \u220f(1+r_d)\u22121 within each week (Fri close). Win rate: fraction of weeks with return &gt; 0. Monthly analog at month-end.<\/p>\n\n\n\n<p>A.7 Autocorrelation and Ljung\u2013Box<\/p>\n\n\n\n<p>ACF(k) = corr(r_t, r_{t\u2212k}). Ljung\u2013Box tests joint significance of ACFs up to a chosen lag.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-appendix-b-distribution-diagnostics\">Appendix B. Distribution diagnostics<\/h2>\n\n\n\n<p>Return distributions are non-normal in this sample (Jarque\u2013Bera rejects normality for both EMN and SPX), which motivates non-parametric tail metrics and stress testing beyond Gaussian assumptions.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1100\" height=\"688\" data-src=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-1100x688.png\" alt=\"Monthly return distributions (histograms)\" class=\"wp-image-240022 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-1100x688.png 1100w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-700x438.png 700w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-300x188.png 300w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-768x480.png 768w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-1536x961.png 1536w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2026\/03\/chart_13-Monthly-return-distributions-2048x1281.png 2048w\" data-sizes=\"(max-width: 1100px) 100vw, 1100px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1100px; aspect-ratio: 1100\/688;\" \/><\/figure>\n\n\n\n<p><em>Figure 13. Monthly return distributions (histograms).<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-appendix-c-reproducibility-notes\">Appendix C. Reproducibility notes<\/h2>\n\n\n\n<p>All charts and statistics in this note are computed directly from the provided daily return series. Annualization uses a 252 trading-day convention. Weekly returns compound daily returns into Friday weeks; monthly returns compound to month-end. VaR and CVaR are computed historically from the empirical daily return distribution.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-references-linked\">References (linked)<\/h2>\n\n\n\n<p><a href=\"https:\/\/amoreira2.github.io\/alan-moreira.github.io\/VolPortfolios_published.pdf\">Moreira, A., &amp; Muir, T. (2017). Volatility-Managed Portfolios. Journal of Finance.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/sites.math.washington.edu\/~rtr\/papers\/rtr187-CVaR2.pdf\">Rockafellar, R. T., &amp; Uryasev, S. (2000\/2002). Conditional Value-at-Risk for General Loss Distributions.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.mit.edu\/~junpan\/volume.pdf\">Pan, J., &amp; Poteshman, A. M. (2006). The Information in Option Volume for Future Stock Prices.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/en.saif.sjtu.edu.cn\/junpan\/npp.pdf\">Ni, S. X., Pan, J., &amp; Poteshman, A. M. (2008). Volatility Information Trading in the Option Market.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/rodneywhitecenter.wharton.upenn.edu\/wp-content\/uploads\/2014\/04\/0601.pdf\">G\u00e2rleanu, N., Pedersen, L. H., &amp; Poteshman, A. M. (2005\/2009). Demand-Based Option Pricing.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.abarbon.com\/assets\/Barbon_Buraschi_2021_Gamma_Fragility.pdf\">Barbon, A., &amp; Buraschi, A. (2021). Gamma Fragility.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0304405X21001598\">Baltussen, G., Da, Z., Lammers, D., &amp; Martens, M. (2021). Hedging Demand and Market Intraday Momentum.<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.nber.org\/papers\/w7613\">Lo, A. W., Mamaysky, H., &amp; Wang, J. (2000). Foundations of Technical Analysis. Journal of Finance (NBER WP 7613).<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.fmg.ac.uk\/sites\/default\/files\/2020-08\/Jeffrey-Pontiff.pdf\">McLean, R. D., &amp; Pontiff, J. (2016). Does Academic Research Destroy Stock Return Predictability? (working paper version).<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.pm-research.com\/content\/iijpormgmt\/39\/4\/14\">Cahan, R., &amp; Luo, Y. (2013). Measuring Crowding in Quantitative Strategies.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Equity market neutral strategies are typically evaluated not primarily on raw returns, but on their ability to deliver diversifying, risk-efficient performance with limited dependence on equity market direction. 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