{"id":46992,"date":"2026-06-15T12:00:00","date_gmt":"2026-06-15T04:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=46992"},"modified":"2026-06-16T17:31:38","modified_gmt":"2026-06-16T09:31:38","slug":"factor-strategy-qfii-part-2","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/","title":{"rendered":"Factor Strategy \u2013 Integrating Broker Consensus to Enhance Foreign Concentration Strategies \u2013 QFII Part 2\u00a0\u00a0\u00a0"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-1024x576.png\" alt=\"\" class=\"wp-image-46993\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-1024x576.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-300x169.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-150x84.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-768x432.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii-1536x864.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-6a322794b91a4\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"ez-toc-cssicon\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a322794b91a4\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#From_Theory_to_Active_Execution_%E2%80%94_Translating_Foreign_Concentration_into_an_Actionable_Strategy\" >From Theory to Active Execution \u2014 Translating Foreign Concentration into an Actionable Strategy&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Backtesting_Framework_and_Parameter_Settings\" >Backtesting Framework and Parameter Settings&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Backtesting_Framework_and_Parameter_Settings-2\" >Backtesting Framework and Parameter Settings&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Strategy_Definitions_Pure_Concentration_vs_%E2%80%9CCross-Broker_Consensus%E2%80%9D_Optimization\" >Strategy Definitions: Pure Concentration vs. &#8220;Cross-Broker Consensus&#8221; Optimization&nbsp;&nbsp;&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Backtesting_Performance\" >Backtesting Performance&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Strategy_1_Pure_Concentration_Performance_Review\" >Strategy 1 (Pure Concentration) Performance Review<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Strategy_2_Disagreement_Fusion_Optimization_Impact\" >Strategy 2&nbsp;(Disagreement Fusion)&nbsp;Optimization Impact&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/#TEJ_Factor_Library_Comprehensive_Mapping_of_Quantitative_Signals_in_the_Taiwan_Market\" >TEJ Factor Library: Comprehensive Mapping of&nbsp;Quantitative Signals in the Taiwan Market&nbsp;<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"From_Theory_to_Active_Execution_%E2%80%94_Translating_Foreign_Concentration_into_an_Actionable_Strategy\"><\/span>From Theory to Active Execution \u2014 Translating Foreign Concentration into an Actionable Strategy&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Following our analysis in<strong>&nbsp;<a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/\" rel=\"noreferrer noopener nofollow\">Part 1:&nbsp;&nbsp;Factor Research&nbsp;\u2013 &nbsp;Tracking&nbsp;Smart Money Footprints via Foreign Institutional Concentration<\/a><\/strong>,&nbsp;we have systematically verified that the foreign institutional trading concentration factor (conc_qfii)&nbsp;possesses&nbsp;robust and cumulative return predictive power within the large-cap universe. This article takes a practical perspective to transform our empirical findings into fully executable trading strategies. Utilizing an event-driven&nbsp;backtesting&nbsp;engine, we evaluate real-world feasibility by strictly deducting transaction costs and enforcing realistic trading limitations.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong><em>\ud83d\udc49&nbsp;<a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/\" rel=\"noreferrer noopener nofollow\">If you have not yet read our baseline factor analysis&nbsp;regarding&nbsp;foreign&nbsp;institutional inflows and outflows, please refer to Part 1 first<\/a><\/em><\/strong><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Backtesting_Framework_and_Parameter_Settings\"><\/span>Backtesting Framework and Parameter Settings&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Based on the factor&#8217;s unique behavior, we transform it into actionable trading strategies. We embed explicit transaction costs, liquidity filters, and leverage caps into our event-driven engine, while&nbsp;<strong>simultaneously&nbsp;utilizing&nbsp;a point-in-time architecture to&nbsp;completely eliminate&nbsp;look-ahead biase<\/strong>s. The detailed&nbsp;backtesting&nbsp;parameter configurations are outlined below:&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Backtesting_Framework_and_Parameter_Settings-2\"><\/span><strong>Backtesting Framework and Parameter Settings&nbsp;<\/strong><a id=\"_msocom_1\"><\/a><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stock&nbsp;Pool<\/strong>\uff1aAll&nbsp;common stocks listed on the Taiwan Stock Exchange&nbsp;(TWSE)&nbsp;and Taipei Exchange&nbsp;(TPEx)&nbsp;<\/li>\n\n\n\n<li><strong>Period<\/strong>: December 2020\u2014May 2026.&nbsp;September 30, 2024, is&nbsp;designated&nbsp;as the strict Out-of-Sample (OOS) cut-off point&nbsp;<\/li>\n\n\n\n<li><strong>Rebalancing<\/strong>:&nbsp;&nbsp;Quarterly (Every 3 months)&nbsp;to&nbsp;minimize turnover&nbsp;and mitigate&nbsp;transaction frictions&nbsp;<\/li>\n\n\n\n<li><strong>Initial capital<\/strong>: NT$10 million&nbsp;<\/li>\n\n\n\n<li><strong>Transaction Costs<\/strong>\uff1a&nbsp;<\/li>\n\n\n\n<li>Buy: 0.1425%&nbsp;commission&nbsp;<\/li>\n\n\n\n<li>Sell: 0.1425%&nbsp;commission + 0.3% securities transaction tax&nbsp;<\/li>\n\n\n\n<li><strong>Slippage Assumption<\/strong>\uff1a1&nbsp;tick per transaction&nbsp;<\/li>\n\n\n\n<li><strong>Leverage Constraint<\/strong>\uff1a0.9&nbsp;(portfolio market value cannot exceed 90% of net asset value)&nbsp;<\/li>\n\n\n\n<li><strong>Liquidity Overlays<\/strong>: Stocks that are locked at the limit price for the entire day&nbsp;(Limited Whole Day)&nbsp;or classified as Disposition Securities are automatically filtered out.&nbsp;<\/li>\n\n\n\n<li><strong>Benchmark Index<\/strong>: Formosa Return Index&nbsp;(IR0078).&nbsp;<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Strategy_Definitions_Pure_Concentration_vs_%E2%80%9CCross-Broker_Consensus%E2%80%9D_Optimization\"><\/span><strong>Strategy Definitions: Pure Concentration vs. &#8220;Cross-Broker Consensus&#8221; Optimization&nbsp;&nbsp;&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>We construct two strategy variations for empirical comparison. Both strategies follow the exact same core routine (Industry Neutralization&nbsp;\u2192&nbsp;Market Cap Top 30% Large-Caps&nbsp;\u2192&nbsp;Select Top 50 Stocks&nbsp;\u2192&nbsp;Value-Weighted). They differ solely in the mathematical formulation of their final ranking scores.&nbsp;&nbsp;<\/p>\n\n\n\n<p>The process of&nbsp;<strong>industry neutralization<\/strong>&nbsp;(subtracting the daily industry mean from an individual stock&#8217;s factor score)&nbsp;is vital to eliminate structural sector biases.&nbsp;Without industry neutralization, concentration rankings would heavily over-concentrate in a handful of industries with naturally higher foreign broker coverage&nbsp;(such as semiconductors); neutralization diversifies the portfolio&#8217;s sector risk exposures, which ultimately boosts risk-adjusted returns&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>Strategy 1&nbsp;(Pure Concentration Strategy): Single-Factor Driven&nbsp;<br><\/strong><\/mark>Stocks are sorted directly by their industry-neutralized foreign institutional&nbsp;concentration&nbsp;cross-sectional z-scores, and the top 50 stocks are selected.&nbsp;Ranking Score = z&nbsp;(conc_qfii)&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>Strategy 2 (Composite Fusion Strategy): Incorporating Broker Channel&nbsp;Disagreement&nbsp;<br><\/strong><\/mark>Strategy 2 incorporates the&nbsp;<strong>Broker Channel&nbsp;Disagreement (Disagree)<\/strong>&nbsp;factor to perform multi-factor fusion optimization.&nbsp;<br>The Broker Channel&nbsp;Disagreement factor partitions the entire Taiwan brokerage landscape into three institutional channels:&nbsp;<strong>Foreign Brokers (F)<\/strong>,&nbsp;<strong>Private Domestic Brokers (P)<\/strong>, and&nbsp;<strong>Government-Affiliated Brokers (G)<\/strong>. We first compute the net buy-sell ratio for each channel (daily net buy-sell amount \u00f7 individual stock daily total volume). After&nbsp;winsorizing&nbsp;the ratios at the 1%\/99% thresholds, we extract their cross-sectional z-scores. The&nbsp;<em>Disagree<\/em>&nbsp;metric is defined as the cross-sectional standard deviation across these three channels:&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><math xmlns=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" display=\"block\"><msub class=\"SCXW146102208 BCX0\"><mrow class=\"SCXW146102208 BCX0\"><mtext class=\"SCXW146102208 BCX0\">Disagree<\/mtext><\/mrow><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">i<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">t<\/mi><\/mrow><\/msub><mo class=\"SCXW146102208 BCX0\">=<\/mo><msqrt class=\"SCXW146102208 BCX0\"><mrow class=\"SCXW146102208 BCX0\"><mfrac class=\"SCXW146102208 BCX0\"><mn class=\"SCXW146102208 BCX0\">1<\/mn><mn class=\"SCXW146102208 BCX0\">3<\/mn><\/mfrac><munderover class=\"SCXW146102208 BCX0\"><mo stretchy=\"false\" class=\"SCXW146102208 BCX0\">\u2211<\/mo><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">g<\/mi><mo class=\"SCXW146102208 BCX0\">\u2208<\/mo><mo fence=\"false\" class=\"SCXW146102208 BCX0\">{<\/mo><mi class=\"SCXW146102208 BCX0\">F<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">P<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">G<\/mi><mo fence=\"false\" class=\"SCXW146102208 BCX0\">}<\/mo><\/mrow><mo class=\"SCXW146102208 BCX0\">\u200b<\/mo><\/munderover><mrow class=\"SCXW146102208 BCX0\"><mo fence=\"false\" class=\"SCXW146102208 BCX0\">(<\/mo><\/mrow><msub class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">z<\/mi><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">g<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">i<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">t<\/mi><\/mrow><\/msub><mo class=\"SCXW146102208 BCX0\">\u2212<\/mo><msub class=\"SCXW146102208 BCX0\"><mrow class=\"SCXW146102208 BCX0\"><mover accent=\"true\" class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">z<\/mi><mo class=\"SCXW146102208 BCX0\">\u203e<\/mo><\/mover><\/mrow><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">i<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">t<\/mi><\/mrow><\/msub><msup class=\"SCXW146102208 BCX0\"><mo fence=\"false\" class=\"SCXW146102208 BCX0\">)<\/mo><mn class=\"SCXW146102208 BCX0\">2<\/mn><\/msup><\/mrow><\/msqrt><mo class=\"SCXW146102208 BCX0\">,<\/mo><mo class=\"SCXW146102208 BCX0\">\u2003<\/mo><msub class=\"SCXW146102208 BCX0\"><mrow class=\"SCXW146102208 BCX0\"><mover accent=\"true\" class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">z<\/mi><mo class=\"SCXW146102208 BCX0\">\u203e<\/mo><\/mover><\/mrow><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">i<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">t<\/mi><\/mrow><\/msub><mo class=\"SCXW146102208 BCX0\">=<\/mo><mfrac class=\"SCXW146102208 BCX0\"><mn class=\"SCXW146102208 BCX0\">1<\/mn><mn class=\"SCXW146102208 BCX0\">3<\/mn><\/mfrac><munderover class=\"SCXW146102208 BCX0\"><mo stretchy=\"false\" class=\"SCXW146102208 BCX0\">\u2211<\/mo><mi class=\"SCXW146102208 BCX0\">g<\/mi><mo class=\"SCXW146102208 BCX0\">\u200b<\/mo><\/munderover><mrow class=\"SCXW146102208 BCX0\"><msub class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">z<\/mi><mrow class=\"SCXW146102208 BCX0\"><mi class=\"SCXW146102208 BCX0\">g<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">i<\/mi><mo class=\"SCXW146102208 BCX0\">,<\/mo><mi class=\"SCXW146102208 BCX0\">t<\/mi><\/mrow><\/msub><\/mrow><\/math><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>High&nbsp;Disagreement:<\/strong>&nbsp;Indicates that the three broker channels are pulling in opposite directions (e.g., foreign brokers are buying aggressively, private domestic brokers are selling heavily, and government banks remain idle). Even if the foreign concentration is high, the directional signal is diluted due to intense confrontation.&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Low&nbsp;Disagreement:<\/strong>&nbsp;Indicates that the net trading directions of all three major broker channels are highly aligned&nbsp;(synchronized buying or synchronized selling). This implies that the capital flows captured by foreign institutions are backed by a broader &#8220;cross-channel market consensus,&#8221; resulting in a cleaner and more reliable chip signal&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Consequently, Strategy 2 defines its composite ranking score as:&nbsp;<\/p>\n\n\n\n<p>Ranking Score = z (conc_qfii) &#8211; 0.5 x z(Disagree)<\/p>\n\n\n\n<p>The negative sign inside the formula penalizes high&nbsp;disagreement. Strategy 2 aims to identify stocks that not only exhibit intense foreign concentration but also display low&nbsp;disagreement (cross-channel consensus), explicitly steering clear of&nbsp;stocks&nbsp;prone to institutional tug-of-wars.&nbsp;<\/p>\n\n\n\n<p>Table&nbsp;1: Factor Strategy Configurations&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-regular\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#ffe9ae\"><thead><tr><th>Strategy Name&nbsp;<\/th><th>Strategy Type&nbsp;<\/th><th>Ranking Score Formulation&nbsp;<\/th><\/tr><\/thead><tbody><tr><td>Strategy 1&nbsp;(Pure Concentration)&nbsp;<\/td><td>Single Factor&nbsp;<\/td><td>z(conc_qfii)&nbsp;<\/td><\/tr><tr><td>Strategy 2 (Concentration +&nbsp;Disagreement Fusion)&nbsp;<\/td><td>Multi-Factor Fusion&nbsp;<\/td><td>z(conc_qfii) \u2212 0.5 \u00d7 z(Disagree)&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: Both strategies implement the identical stock-picking routine and differ only in score metrics;&nbsp;z(\uff0d) denotes the cross-sectional z-score after industry neutralization)<\/em>&nbsp;<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Backtesting_Performance\"><\/span><strong>Backtesting Performance&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To protect our research from historical overfitting traps, we establish a demanding triad of performance hurdle rates: the Full-Sample Sharpe Ratio must exceed 1.256, the Out-of-Sample&nbsp;(OOS)&nbsp;Sharpe Ratio must exceed 1.668, and the annualized Alpha must be strictly greater than 0. These thresholds are set using the actual performance of the benchmark index&nbsp;(IR0078)&nbsp;over identical timeframes.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p>Table 2&nbsp;and Figure 3 present the net performance metrics after accounting for all real-world transaction taxes, broker commissions, and slippage frictions:&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong>Table 2:&nbsp;Backtesting&nbsp;Performance Metrics for&nbsp;conc_qfii&nbsp;Top 50 Large-Cap Value-Weighted Portfolio (Net of Costs)<\/strong>&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#ffe9ae\"><thead><tr><th>Performance Metric&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\">Strategy 1&nbsp;<br>(Pure Concentration)&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\">Strategy 2&nbsp;<br>(Concentration +&nbsp;Disagreement)&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\">Benchmark Index&nbsp;(IR0078)&nbsp;<\/th><\/tr><\/thead><tbody><tr><td>Annualized Return&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">27.65%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">30.12%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">25.77%&nbsp;<\/td><\/tr><tr><td>Cumulative Return&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">260.67%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">298.82%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">233.51%&nbsp;<\/td><\/tr><tr><td>Annualized Volatility&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">21.59%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">21.33%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">19.83%&nbsp;<\/td><\/tr><tr><td>Sharpe Ratio (Full Sample)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.239&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>1.342<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.256&nbsp;<\/td><\/tr><tr><td>Sortino Ratio&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.868&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.022&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.799&nbsp;<\/td><\/tr><tr><td>Max&nbsp;drawdown&nbsp;(MDD)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u221231.03%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u221226.51%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u221228.60%&nbsp;<\/td><\/tr><tr><td>Annualized Alpha&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">+1.71%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>+3.88%<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u2014&nbsp;<\/td><\/tr><tr><td>Beta&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.006&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.996&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u2014&nbsp;<\/td><\/tr><tr><td>Out-of-Sample Sharpe (OOS Sharpe)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>1.969<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>2.000<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.668&nbsp;<\/td><\/tr><tr><td>Daily Average Turnover&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.74%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.95%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u2014&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: Benchmark is the Formosa Return Index IR0078; full data period: 2020\/12\u20132026\/05; Out-of-Sample&nbsp;(OOS)&nbsp;cut-off point: 2024-09-30<\/em>&nbsp;<\/figcaption><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong><em>Figure 3: Cumulative Return Equity Curves of&nbsp;conc_qfii&nbsp;Top 50 Large-Cap Portfolio Variations vs. Benchmark Index (IR0078)<\/em>&nbsp;<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"500\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-1024x500.png\" alt=\"\" class=\"wp-image-46995\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-1024x500.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-300x146.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-150x73.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-768x375.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-1536x750.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factor_Qfii_3-2048x1000.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Dissecting Strategy Performance :<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Strategy_1_Pure_Concentration_Performance_Review\"><\/span>Strategy 1 (Pure Concentration) Performance Review<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Relying solely on foreign institutional concentration rankings, Strategy 1 successfully satisfies two out of three criteria: first, its risk-adjusted performance during the Out-of-Sample (OOS) phase is outstanding, recording an OOS Sharpe Ratio of 1.969, beating the baseline hurdle of 1.668; second, it delivers a positive annualized Alpha of +1.71%. In terms of raw returns, its annualized return of 27.65% outperforms the benchmark&#8217;s 25.77%.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Strategy_2_Disagreement_Fusion_Optimization_Impact\"><\/span>Strategy 2&nbsp;(Disagreement Fusion)&nbsp;Optimization Impact&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>When we overlay the broker&nbsp;disagreement factor onto Strategy 1 to filter for &#8220;cross-broker consensus,&#8221; Strategy 2 achieves a clean triumph across all three hurdles:&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The Full-Sample Sharpe Ratio expands to 1.342, outperforming the index baseline hurdle of 1.256.&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The annualized Alpha expands to +3.88%, doubling the performance of Strategy 1 and confirming a high stock-picking edge.&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>By filtering out assets caught in aggressive cross-selling, Strategy 2 compresses the Maximum&nbsp;Drawdown&nbsp;to -26.51%.&nbsp;&nbsp;&nbsp;<\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>During the OOS phase, Strategy 2&nbsp;maintains&nbsp;an excellent Sharpe Ratio of 2.000, validating that the fusion of chip concentration and cross-channel market consensus provides a powerful enhancement effect.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>In summary, the empirical&nbsp;backtests&nbsp;perfectly&nbsp;validate&nbsp;our baseline factor analytics: the strategy must be built upon Large-Cap Stocks and Value Weighting, and it continues to comfortably outperform the benchmark index even after factoring in all transaction costs and liquidity limitations.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Combining the empirical evidence from this two-part factor series, we draw two key conclusions for chip-based&nbsp;quantitative investing in Taiwan:&nbsp;<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">The Allocation Blueprint Dictates Survival (Size-Conditionality Execution)<\/mark><\/strong>: The foreign institutional trading concentration factor&nbsp;(conc_qfii)&nbsp;possesses a strong size-conditionality. Applying a broad-market equal-weighted implementation introduces small-cap short-squeeze noise, which distorts and neutralizes the factor&#8217;s alpha. Our backtesting results demonstrate that&nbsp;<strong>only by anchoring the strategy within the &#8220;top 30% large-cap universe&#8221; and deploying a &#8220;value-weighted&#8221; allocation matrix<\/strong>&nbsp;can a portfolio absorb real-world transaction costs and reliably beat the market benchmark.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">Multi-Factor Fusion&nbsp;(Concentration + Consensus)&nbsp;is a Crucial&nbsp;Quantitative Tool<\/mark><\/strong>: While following pure foreign concentration&nbsp;(Strategy 1)&nbsp;offers steady index-enhancement features, integrating the Broker Channel&nbsp;Disagreement factor&nbsp;(Strategy 2)&nbsp;to select large-caps backed by cross-broker consensus provides substantial improvements. It simultaneously elevates returns&nbsp;(annualized Alpha of +3.88%)&nbsp;and mitigates&nbsp;downside risk&nbsp;(MDD contained to -26.51%). This multi-factor approach&nbsp;represents&nbsp;an actionable, highly robust&nbsp;quantitative strategy suitable for institutional large-cap asset allocation.&nbsp;<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"TEJ_Factor_Library_Comprehensive_Mapping_of_Quantitative_Signals_in_the_Taiwan_Market\"><\/span><strong>TEJ Factor Library: Comprehensive Mapping of&nbsp;Quantitative Signals in the Taiwan Market&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The foreign institutional trading concentration factor&nbsp;(conc_qfii)&nbsp;represents one component of chip and factor research. Built upon high-quality, long-horizon historical data with strict Point-in-Time (completely free of look-ahead bias) characteristics, the TEJ Factor Library offers a comprehensive&nbsp;quantitative framework encompassing&nbsp;<strong>Sentiment, Ownership &amp; Chip-Flows, Momentum, Value, Quality, and Growth factors<\/strong>.&nbsp;&nbsp;<\/p>\n\n\n\n<p>For&nbsp;quantitative researchers, portfolio managers, and institutional investors, structurally clean data with fully transparent computational logic forms the bedrock of hypothesis testing and strategy alpha generation. Whether you aim to deploy multi-factor models to optimize asset models or extract institutional smart money signals across the Taiwan market, the TEJ Factor Library serves as a reliable&nbsp;quantitative asset.&nbsp;&nbsp;<\/p>\n\n\n\n<p><strong><em>\u27a1\ufe0f Discover the TEJ Factor Library&nbsp;immediately&nbsp;to elevate your&nbsp;quantitative investment strategies to the next horizon!<\/em><\/strong>&nbsp;<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong><em>\u27a1\ufe0f <a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\" target=\"_blank\" rel=\"noreferrer noopener\">Discover the TEJ Factor Library to elevate your quantitative investment strategies to the next horizon!<\/a><\/em><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-tej wp-block-embed-tej\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"K7fBGEx80F\"><a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/\">Factor Research \u2013 The SIR Short-Selling Factor: Extracting Negative Signals from Institutional Borrowing Activity \u2013 SIR Part 1<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Factor Research \u2013 The SIR Short-Selling Factor: Extracting Negative Signals from Institutional Borrowing Activity \u2013 SIR Part 1&#8221; &#8212; TEJ\" src=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/embed\/#?secret=OZoMVCjfDJ#?secret=K7fBGEx80F\" data-secret=\"K7fBGEx80F\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Boost your quantitative strategy with QFII concentration &#038; broker consensus! Discover how the conc_qfii fusion strategy delivers a 30.12% annualized return in the Taiwan large-cap market. <\/p>\n","protected":false},"featured_media":46993,"template":"","tags":[3183,2926,3540,3657,2962,3676],"insight-category":[3656],"class_list":["post-46992","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-chip-analysis-2","tag-factor-investing","tag-factor-library","tag-institutional-investors","tag-market-data","tag-qfii","insight-category-factor-investing"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/46992","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/insight"}],"version-history":[{"count":7,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/46992\/revisions"}],"predecessor-version":[{"id":47173,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/46992\/revisions\/47173"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/46993"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=46992"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=46992"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=46992"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}