{"id":47001,"date":"2026-06-15T12:00:00","date_gmt":"2026-06-15T04:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=47001"},"modified":"2026-06-16T11:50:26","modified_gmt":"2026-06-16T03:50:26","slug":"factor-research-qfii-part-1","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/","title":{"rendered":"Factor Research\u00a0\u2013 \u00a0Tracking\u00a0Smart Money Footprints via Foreign Institutional Concentration \u2013 QFII Part 1\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_Qfii1-1024x576.png\" alt=\"\" class=\"wp-image-47002\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1-1024x576.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1-300x169.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1-150x84.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1-768x432.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1-1536x864.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor_Qfii1.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-6a3227b0a46d8\" 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-6a3227b0a46d8\"  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-research-qfii-part-1\/#Foreign-Institutional_Inflows_and_Outflows_Deciphering_%E2%80%9CSmart_Money%E2%80%9D_Footprints_in_Taiwan_Blue-Chip_Stocks\" >Foreign-Institutional Inflows and Outflows: Deciphering &#8220;Smart Money&#8221; Footprints in Taiwan Blue-Chip Stocks&nbsp;&nbsp;&nbsp;&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-research-qfii-part-1\/#Foreign-Institutional_Trading_Concentration_The_Informed_Trading_Hypothesis_and_Critical_%E2%80%9CSize-Conditionality%E2%80%9D\" >Foreign-Institutional Trading Concentration: The Informed Trading Hypothesis and Critical &#8220;Size-Conditionality&#8221;&nbsp;&nbsp;&nbsp;&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Factor_Definition_and_Data_Sources\" >Factor Definition and Data Sources&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Data_Sources\" >Data Sources\u00a0<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Variable_Construction\" >Variable Construction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Descriptive_Statistics_Heavily_Right-Skewed\" >Descriptive Statistics: Heavily Right-Skewed<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Return_Performance_and_Risk_Factor_Testing_Fama-French_Alpha\" >Return Performance and Risk Factor Testing (Fama-French Alpha)<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Average_Expected_Return_and_Cumulative_Return_Analysis\" >Average Expected Return and Cumulative Return Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Risk_Factor_Regression_Fama-French_Alpha\" >Risk Factor Regression (Fama-French Alpha)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Information_Coefficient_IC_Analysis\" >Information Coefficient (IC) Analysis<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-qfii-part-1\/#Conclusion_Unlocking_Large-Cap_Chip_Secrets_%E2%80%94_How_to_Build_a_Real-World_Tactical_Strategy\" >Conclusion: Unlocking Large-Cap Chip Secrets \u2014 How to Build a Real-World Tactical Strategy?<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Foreign-Institutional_Inflows_and_Outflows_Deciphering_%E2%80%9CSmart_Money%E2%80%9D_Footprints_in_Taiwan_Blue-Chip_Stocks\"><\/span>Foreign-Institutional Inflows and Outflows: Deciphering &#8220;Smart Money&#8221; Footprints in Taiwan Blue-Chip Stocks&nbsp;&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In&nbsp;Taiwan&nbsp;stock market, Qualified Foreign Institutional Investors (QFIIs) serve as critical marginal capital for large-cap weight stocks. Taking Taiwan Semiconductor Manufacturing Company (TSMC, 2330.TW) as an example, statistics from the<strong>&nbsp;<a href=\"https:\/\/www.tejwin.com\/en\/news\/brokers-trading-taiwan\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/news\/brokers-trading-taiwan\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">TEJ Broker Trading Database<\/a><\/strong>&nbsp;show that from 2023 to May 2026, an average of&nbsp;<strong>57% of TSMC&#8217;s daily trading volume originated from foreign institutional brokers.<\/strong>&nbsp;This&nbsp;indicates&nbsp;that the asset&nbsp;allocation&nbsp;direction and trading dynamics of foreign capital exert a&nbsp;significant influence&nbsp;on the pricing of large-cap stocks.&nbsp;<\/p>\n\n\n\n<p>This&nbsp;article focuses on&nbsp;an analytical research&nbsp;of the&nbsp;<strong>Foreign-Institutional Trading Concentration (conc_qfii<\/strong><strong>)<\/strong>&nbsp;factor within the TEJ Factor Library. By calculating the ratio of a stock&#8217;s daily trading volume from foreign brokers to the total trading volume across all brokers, we measure the extent to which a&nbsp;stock&#8217;s&nbsp;trading activity is dominated by foreign institutional main players.&nbsp;<\/p>\n\n\n\n<p>Empirical results&nbsp;indicate&nbsp;that the&nbsp;<strong>conc_qfii<\/strong><strong>&nbsp;factor&nbsp;possesses&nbsp;strong predictive power for future expected returns<\/strong>&nbsp;in&nbsp;Taiwan&nbsp;stock market. Under&nbsp;<strong>specific size stratification<\/strong>&nbsp;and&nbsp;<strong>value-weighted&nbsp;<\/strong>frameworks, its cross-sectional stock-picking performance effectively outperforms the market benchmark (Formosa Return Index, IR0078). This article will systematically examine the theoretical foundation, cross-sectional distribution characteristics, and Information Coefficients (IC) of this factor. By clarifying the critical heterogeneity of foreign institutional chips, we&nbsp;lay&nbsp;a solid theoretical foundation for&nbsp;subsequent&nbsp;quantitative trading strategies.&nbsp;<\/p>\n\n\n\n<p class=\"has-background has-medium-font-size\" style=\"background-color:#ffe9ae\"><strong><em><a href=\"https:\/\/www.tejwin.com\/news\/factor-library-%e5%9b%a0%e5%ad%90%e8%b3%87%e6%96%99%e5%ba%ab\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em>\u27a1\ufe0f&nbsp;Explore the TEJ Factor Library deeper to unlock more&nbsp;quantitative research insights!&nbsp;<\/em><\/a>&nbsp;<\/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=\"Foreign-Institutional_Trading_Concentration_The_Informed_Trading_Hypothesis_and_Critical_%E2%80%9CSize-Conditionality%E2%80%9D\"><\/span>Foreign-Institutional Trading Concentration: The Informed Trading Hypothesis and Critical &#8220;Size-Conditionality&#8221;&nbsp;&nbsp;&nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The core logic behind the Foreign-Institutional Trading Concentration factor stems from the &#8220;<strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">information asymmetry<\/mark><\/strong>&#8221; and&nbsp;<strong>&#8220;<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">informed trading<\/mark>&#8220;<\/strong>&nbsp;hypotheses in financial markets: informed traders leave observable footprints in the cross-sectional distribution of trading volume when trading frequently, and trading volume variance rises as the share of informed trading increases (Lof&nbsp;&amp; Van Bommel, 2023).&nbsp;<\/p>\n\n\n\n<p>In Taiwan stock market, foreign institutions are typical informed traders and hold substantial amounts of capital (commonly referred to as &#8220;smart money&#8221;). Therefore,<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">&nbsp;<strong>the degree of foreign capital dominance in an individual stock&#8217;s trading can be viewed as the focus of institutional attention and&nbsp;allocation&nbsp;intensity<\/strong><\/mark>. Herd behavior (co-directional trading) among institutions accelerates price adjustments (Wermers, 1999),&nbsp;whereas&nbsp;retail investors are typically driven by attention biases to buy stocks, a behavioral bias from which institutional investors are&nbsp;relatively immune&nbsp;(Barber &amp; Odean, 2008). Consequently, focusing on foreign institutional volume rather than the entire market preserves a purer informed signal.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<p>However, this informed signal&nbsp;possesses&nbsp;a critical characteristic in the Taiwan market:&nbsp;<strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">Size-conditionality<\/mark><\/strong>. Due to the inherent trading structure of mega-cap stocks (e.g., TSMC), where foreign institutional brokers naturally account for a structurally higher proportion of volume, the performance of&nbsp;conc_qfii&nbsp;in predicting returns is completely reversed across different market capitalization scales: it exhibits<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">&nbsp;<strong>a positive relationship in large-cap stocks but a negative relationship in small-cap stocks<\/strong>.<\/mark> Therefore, the true value of this factor cannot be captured in a broad-market equal-weighted pool; it must be manifested within a framework of size stratification and value weighting.&nbsp;&nbsp;&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Definition_and_Data_Sources\"><\/span>Factor Definition and Data Sources&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Data_Sources\"><\/span>Data Sources\u00a0<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The trading data and multi-factor indicators used in this study are sourced from the&nbsp;TEJ&nbsp; Database:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Chip Data:<\/strong>&nbsp;TEJ Broker Trading Details&nbsp;(used to&nbsp;observe&nbsp;inflows\/outflows via foreign institutional broker channels).&nbsp;&nbsp;<\/li>\n\n\n\n<li><strong>Trading Data:<\/strong>&nbsp;Stock prices, market capitalization, industry classifications, and market multi-factors.&nbsp;&nbsp;<\/li>\n\n\n\n<li><strong>Benchmark Index:<\/strong>\u00a0<a href=\"https:\/\/www.twse.com.tw\/en\/indices\/indices\/series.html\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Formosa Return Index (IR0078)<\/a>.\u00a0<\/li>\n<\/ul>\n\n\n\n<p>The&nbsp;sample period&nbsp;spans from<strong>&nbsp;December 2020 to May 2026<\/strong>, and the research subjects cover all common stocks listed on the Taiwan Stock Exchange (TWSE) and the Taipei Exchange (TPEx).&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-buttons has-custom-font-size has-medium-font-size is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-a89b3969 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link has-midnight-gradient-background has-background wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/news\/brokers-trading-taiwan\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"><em>\u27a1\ufe0f&nbsp;TEJ broker &#8216;s trading details&nbsp;&nbsp;<\/em>&nbsp;<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Variable_Construction\"><\/span>Variable Construction<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>On each trading day, the buy amount\u00a0and sell amount from foreign brokers for stock i are summed up, divided by the total buy and sell amount of all brokers for that stock on the same day, and multiplied by 100:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"500\" height=\"133\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-931.png\" alt=\"\" class=\"wp-image-47008\" style=\"aspect-ratio:3.759806655512177;width:280px;height:auto\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-931.png 500w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-931-300x80.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-931-150x40.png 150w\" sizes=\"(max-width: 500px) 100vw, 500px\" \/><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>The factor value ranges between 0 and 100. Higher values indicate that the stock&#8217;s trading volume is heavily dominated by foreign brokers. Notably, <strong>this formula sums both buys and sells, making it a direction-neutral indicator<\/strong>. It measures the participation weight of foreign main players in the stock&#8217;s turnover rather than their net buying direction; thus, large-scale liquidations or selling pressure will equally drive up the concentration score.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Descriptive_Statistics_Heavily_Right-Skewed\"><\/span>Descriptive Statistics: Heavily Right-Skewed<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>On each trading day, stocks are sorted by their factor values from smallest to largest and divided into ten equal deciles (P1 lowest to P10 highest). As shown in Table 1, the cross-sectional distribution of the factor is <strong>heavily right-skewed<\/strong>. The average concentration of deciles P1 to P9 rises gently from 1.65% to 27.86%, with intra-group standard deviations strictly below 5%.<\/p>\n\n\n\n<p>However, for P10 (the highest decile), the mean abruptly jumps to 43.71%, with a standard deviation of 11.63%, and the maximum value touches 100%.<\/p>\n\n\n\n<p>Cross-sectional checks reveal that the market value weight of P10 is <strong>highly concentrated in a few mega-cap dragon stocks<\/strong> (the top three stocks alone account for over 50% of the total market capitalization weight in P10), re-emphasizing the profound importance of size stratification.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><strong>Table 1: Descriptive Statistics of <\/strong><strong>conc_qfii Deciles<\/strong><\/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><td>Decile<\/td><td class=\"has-text-align-right\" data-align=\"right\">Minimum (Min)<\/td><td class=\"has-text-align-right\" data-align=\"right\">Maximum (Max)<\/td><td class=\"has-text-align-right\" data-align=\"right\">Mean<\/td><td class=\"has-text-align-right\" data-align=\"right\">Standard Deviation (Std)<\/td><td class=\"has-text-align-right\" data-align=\"right\">Observations (Count)<\/td><td class=\"has-text-align-right\" data-align=\"right\">Percentage (%)<\/td><\/tr><\/thead><tbody><tr><td>P1<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00<\/td><td class=\"has-text-align-right\" data-align=\"right\">6.83<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.65<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.10<\/td><td class=\"has-text-align-right\" data-align=\"right\">171,176<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.03<\/td><\/tr><tr><td>P2<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.36<\/td><td class=\"has-text-align-right\" data-align=\"right\">12.38<\/td><td class=\"has-text-align-right\" data-align=\"right\">4.79<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.64<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,622<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00<\/td><\/tr><tr><td>P3<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.08<\/td><td class=\"has-text-align-right\" data-align=\"right\">16.29<\/td><td class=\"has-text-align-right\" data-align=\"right\">7.89<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.03<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,537<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99<\/td><\/tr><tr><td>P4<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.00<\/td><td class=\"has-text-align-right\" data-align=\"right\">20.05<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.72<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.30<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,628<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00<\/td><\/tr><tr><td>P5<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.19<\/td><td class=\"has-text-align-right\" data-align=\"right\">24.07<\/td><td class=\"has-text-align-right\" data-align=\"right\">13.34<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.54<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,750<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00<\/td><\/tr><tr><td>P6<\/td><td class=\"has-text-align-right\" data-align=\"right\">4.74<\/td><td class=\"has-text-align-right\" data-align=\"right\">27.76<\/td><td class=\"has-text-align-right\" data-align=\"right\">15.95<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.81<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,406<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.98<\/td><\/tr><tr><td>P7<\/td><td class=\"has-text-align-right\" data-align=\"right\">6.69<\/td><td class=\"has-text-align-right\" data-align=\"right\">31.12<\/td><td class=\"has-text-align-right\" data-align=\"right\">18.82<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.11<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,531<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99<\/td><\/tr><tr><td>P8<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.17<\/td><td class=\"has-text-align-right\" data-align=\"right\">43.43<\/td><td class=\"has-text-align-right\" data-align=\"right\">22.37<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.54<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,637<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00<\/td><\/tr><tr><td>P9<\/td><td class=\"has-text-align-right\" data-align=\"right\">12.57<\/td><td class=\"has-text-align-right\" data-align=\"right\">61.03<\/td><td class=\"has-text-align-right\" data-align=\"right\">27.86<\/td><td class=\"has-text-align-right\" data-align=\"right\">4.53<\/td><td class=\"has-text-align-right\" data-align=\"right\">170,521<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99<\/td><\/tr><tr><td>P10<\/td><td class=\"has-text-align-right\" data-align=\"right\">16.63<\/td><td class=\"has-text-align-right\" data-align=\"right\">100.00<\/td><td class=\"has-text-align-right\" data-align=\"right\">43.71<\/td><td class=\"has-text-align-right\" data-align=\"right\">11.63<\/td><td class=\"has-text-align-right\" data-align=\"right\">171,062<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.02<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: Statistics are aggregated from all daily observations within each decile over the sample period; concentration units are in %; data period: 2020\/12\u20132026\/05; Taiwan listed common stocks)<\/em><\/figcaption><\/figure>\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=\"Return_Performance_and_Risk_Factor_Testing_Fama-French_Alpha\"><\/span>Return Performance and Risk Factor Testing (Fama-French Alpha)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Average_Expected_Return_and_Cumulative_Return_Analysis\"><\/span>Average Expected Return and Cumulative Return Analysis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>To systematically evaluate the factor&#8217;s stock-picking efficacy, we analyze the daily average expected return over a future 21-day holding period. We cross-examine four distinct combinations: &#8220;Equal Weight vs. Value Weight&#8221; across &#8220;All Stocks vs. Large-Cap Stocks&#8221;.<\/p>\n\n\n\n<p>The empirical results in Table 2 unveil a critical phenomenon: <strong>if an &#8220;equal-weighted&#8221; scheme is applied, the long-short spread return (P10 &#8211; P1) is strictly negative for both all-stocks and large-cap stock universes<\/strong>, forming an inverted U-shaped profile (implying that higher concentration leads to lower returns). However, <strong>once we switch to &#8220;value weighting&#8221;, the long-short spread immediately flips to positive<\/strong>: +0.031% for all stocks and reaching a maximum of <strong>+0.040%<\/strong> for the large-cap value-weighted combination.<\/p>\n\n\n\n<p>Table 2: Average Daily Expected Return of conc_qfii Deciles \u2014 Four Portfolio Matrix (21D)<\/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><td>Decile<\/td><td class=\"has-text-align-right\" data-align=\"right\">Equal Weight &#8211; All Stocks<\/td><td class=\"has-text-align-right\" data-align=\"right\">Value Weight &#8211; All Stocks<\/td><td class=\"has-text-align-right\" data-align=\"right\">Equal Weight &#8211; Large Cap<\/td><td class=\"has-text-align-right\" data-align=\"right\">Value Weight &#8211; Large Cap<\/td><\/tr><\/thead><tbody><tr><td>P1<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.079%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.069%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.062%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><\/tr><tr><td>P2<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.062%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.064%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.073%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.083%<\/td><\/tr><tr><td>P3<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.059%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.055%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.075%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.097%<\/td><\/tr><tr><td>P4<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.073%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.092%<\/td><\/tr><tr><td>P5<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.066%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.073%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.068%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.085%<\/td><\/tr><tr><td>P6<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.069%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.085%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.086%<\/td><\/tr><tr><td>P7<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.069%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.088%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.086%<\/td><\/tr><tr><td>P8<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.063%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.082%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.057%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.080%<\/td><\/tr><tr><td>P9<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.059%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.082%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.056%<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.082%<\/td><\/tr><tr><td>P10 (Highest)<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.055%<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>0.100%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.056%<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>0.104%<\/strong><\/td><\/tr><tr><td>Long-Short Spread (P10 &#8211; P1)<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u22120.024%<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>+0.031%<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">\u22120.006%<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>+0.040%<\/strong><\/td><\/tr><tr><td>Monotonic \u03c1<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u22120.42<\/td><td class=\"has-text-align-right\" data-align=\"right\">+0.79<\/td><td class=\"has-text-align-right\" data-align=\"right\">\u22120.72<\/td><td class=\"has-text-align-right\" data-align=\"right\">+0.21<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: Monotonic p represents the Spearman rank correlation between the decile ranks P1 -&gt;P10 and their average returns; &gt; 0 &nbsp;indicates returns increase with concentration, &lt; 0 indicates a decrease; data period: 2020\/12\u20132026\/05; returns do not account for transaction costs<\/em><\/figcaption><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><em>Figure 1: Average Daily Expected Return of conc_qfii Deciles (Value-Weighted \u00b7 Large-Cap)<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"421\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-1024x421.png\" alt=\"\" class=\"wp-image-47049\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-1024x421.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-300x123.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-150x62.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-768x316.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939-1536x631.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-939.png 1861w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Note: Corresponding to the &#8220;Value Weight &#8211; Large Cap&#8221; column in Table 2; P10 clearly dominates, turning the long-short spread positive; returns do not account for transaction costs<\/em><\/figcaption><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>If we expand our view to the cumulative return across the entire sample period, the conclusion remains robust: under the framework of value weighting and large-cap constraints, the highest decile group (P10) demonstrates long-term, stable outperformance over the lowest decile group (P1) as well as the benchmark index.<\/p>\n\n\n\n<p><em>Figure 2: Cumulative Return of <\/em><em>conc_qfii <\/em><em>\uff0cValue-Weighted X Large-Cap: Highest Decile (P10), Lowest Decile (P1), and Benchmark Index (IR0078)<\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"367\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-1024x367.png\" alt=\"\" class=\"wp-image-47046\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-1024x367.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-300x108.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-150x54.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-768x275.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938-1536x551.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-938.png 1844w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\"><em>Note: Transaction costs are not considered<\/em><\/figcaption><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Risk_Factor_Regression_Fama-French_Alpha\"><\/span>Risk Factor Regression (Fama-French Alpha)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>To further verify whether the returns of the conc_qfii long-short portfolio are merely compensations for exposure to known systematic risk factors, we run time-series regressions of the long-short portfolios (P10 &#8211; P1) against CAPM, the Fama-French three-factor (FF3), and five-factor (FF5) models (applying Newey-West adjusted t-statistics).<\/p>\n\n\n\n<p><strong>Table 3: Risk Factor Regression Alpha Comparison for <\/strong><strong>conc_qfii Long-Short Portfolio (P10\u2212P1)<\/strong><\/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><td>Model<\/td><td>Equal Weight &#8211; All Stocks<\/td><td>Value Weight &#8211; All Stocks<\/td><td>Equal Weight &#8211; Large Cap<\/td><td>Value Weight &#8211; Large Cap<\/td><\/tr><\/thead><tbody><tr><td>CAPM<\/td><td>\u22120.978%*** (\u22125.01)<\/td><td>\u22120.703%** (\u22122.12)<\/td><td>\u22120.732%* (\u22121.95)<\/td><td>+0.775% (+1.24)<\/td><\/tr><tr><td>FF3<\/td><td>\u22120.880%*** (\u22124.59)<\/td><td>\u22120.295% (\u22121.40)<\/td><td>\u22120.640% (\u22121.35)<\/td><td><strong>+1.343%** (+2.03)<\/strong><\/td><\/tr><tr><td>FF5<\/td><td>\u22120.935%*** (\u22125.37)<\/td><td>\u22120.400% (\u22121.59)<\/td><td>\u22121.010%* (\u22121.94)<\/td><td>+0.886% (+1.40)<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: alpha is the monthly regression intercept in %; transaction costs are not considered; numbers in parentheses indicate Newey-West adjusted t-statistics; significance levels: *** p&lt;0.01, p&lt;0.05, * p&lt;0.1; data period: 2020\/12\u20132026\/05)<\/em><\/figcaption><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>&nbsp;The regression findings (Table 3) mirror the return profiles precisely:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Negative Bias Under Equal Weighting:<\/strong> The long-short Alpha for &#8220;Equal Weight &#8211; All Stocks&#8221; is intensely and significantly negative across all models (FF5 alpha = -0.935%). This reflects that under an equal-weighted blueprint, the right-tailed factor is dragged into reverse by a few mega-caps and volatile small-cap stocks.<\/li>\n\n\n\n<li><strong>Premium in Value-Weighted Large-Caps:<\/strong> Conversely, only the &#8220;Value Weight &#8211; Large Cap&#8221; configuration yields strictly positive Alphas across all three models, with the FF3 model capturing statistical significance at the 5% level (alpha = +1.343%, t = 2.03).<\/li>\n\n\n\n<li><strong>Elimination of Size and Value Camouflage:<\/strong> Crucially, after controlling for Size and Value factors (moving from CAPM to FF3), the long-short alpha increases rather than shrinks (from +0.775% to +1.343%). If this premium were merely an implicit proxy for size or value risk, controlling for them should absorb and diminish the intercept. Its expansion successfully dispels doubts of &#8220;size or value factor camouflage,&#8221; demonstrating that foreign concentration provides standalone predictive information.<\/li>\n<\/ul>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Information_Coefficient_IC_Analysis\"><\/span>Information Coefficient (IC) Analysis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The Information Coefficient (IC) employs Spearman&#8217;s Rank Correlation to calculate the sorting consistency between individual stock factor values and future returns. Operating independently of intra-group weights or extreme outliers, it stands as the cleanest tool for diagnosing raw factor direction.<\/p>\n\n\n\n<p>The IC summary in Table 4 shows that all holding periods possess significantly positive mean ICs, with a 21-day win rate touching 60%. Yet, this unearths a classic quantitative paradox: &#8220;<strong>If the broad-market IC mean is significantly positive, why is the equal-weighted long-short spread return negative?&#8221;<\/strong><\/p>\n\n\n\n<p>The culprit lies in <strong>the weight distortions of extreme deciles combined with small-cap noise interference<\/strong>. While Rank IC establishes the cross-sectional reality that &#8220;higher concentration dictates a higher subsequent return rank,&#8221; the equal-weighted dollar return is heavily skewed and distorted by idiosyncratic small-cap behaviors.<\/p>\n\n\n\n<p>Once the asset universe is narrowed down to the top 30% of large-cap stocks by market capitalization, the 21-day mean IC doubles from 0.0186 to 0.0403, the risk-adjusted IC (Information Ratio) elevates to 0.3864, and the 21-day win rate reaches 64.48%. This serves as definitive proof that <strong>the predictive muscle of this factor is highly concentrated within large-cap equities<\/strong>, while its performance in the total market is simply diluted by opposing signals from small-cap stocks.<\/p>\n\n\n\n<p>Table 4: Statistical Summary of conc_qfii Information Coefficients (IC) \u2014 All Stocks vs. Large-Cap Stocks<\/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><td>&nbsp;<\/td><td colspan=\"4\">All Stocks (1D)<\/td><td colspan=\"4\">Large-Cap \uff08 the top 30% stocks by market capitalization \uff09<\/td><\/tr><tr><td>Metric<\/td><td>1D<\/td><td>&nbsp;5D<\/td><td>10D<\/td><td>21D<\/td><td>1D<\/td><td>5D<\/td><td>&nbsp;10D<\/td><td>21D<\/td><\/tr><\/thead><tbody><tr><td>IC Mean<\/td><td>0.0187<\/td><td>0.0216<\/td><td>0.0201<\/td><td>0.0186<\/td><td>0.0212<\/td><td>0.0312<\/td><td>0.0371<\/td><td><strong>0.0403<\/strong><\/td><\/tr><tr><td>IC Std<\/td><td>0.0896<\/td><td>0.0844<\/td><td>0.0779<\/td><td>0.0744<\/td><td>0.1267<\/td><td>0.1124<\/td><td>0.1066<\/td><td>0.1043<\/td><\/tr><tr><td>Risk Adjusted IC<\/td><td>0.2089<\/td><td>0.2557<\/td><td>0.2577<\/td><td>0.2496<\/td><td>0.1671<\/td><td>0.2773<\/td><td>0.3477<\/td><td><strong>0.3864<\/strong><\/td><\/tr><tr><td>IC &gt; 0 (%)<\/td><td>57.29<\/td><td>59.04<\/td><td>60.15<\/td><td>60.06<\/td><td>55.90<\/td><td>60.89<\/td><td>63.75<\/td><td><strong>64.48<\/strong><\/td><\/tr><tr><td>IC &gt; 0.03 (%)<\/td><td>44.10<\/td><td>45.30<\/td><td>44.83<\/td><td>44.28<\/td><td>47.60<\/td><td>50.09<\/td><td>52.95<\/td><td>54.52<\/td><\/tr><tr><td>IC &gt; 0.05 (%)<\/td><td>35.06<\/td><td>36.72<\/td><td>35.52<\/td><td>33.76<\/td><td>40.31<\/td><td>44.10<\/td><td>45.48<\/td><td>47.32<\/td><\/tr><tr><td>IC t-value<\/td><td>6.88<\/td><td>8.42<\/td><td>8.48<\/td><td>8.22<\/td><td>5.50<\/td><td>9.13<\/td><td>11.45<\/td><td><strong>12.72<\/strong><\/td><\/tr><tr><td>IC p-value<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><td>&lt;0.001***<\/td><\/tr><tr><td>IC Skewness<\/td><td>0.1287<\/td><td>0.1068<\/td><td>0.0239<\/td><td>0.0040<\/td><td>0.0082<\/td><td>0.0658<\/td><td>\u22120.0995<\/td><td>\u22120.1253<\/td><\/tr><tr><td>IC Kurtosis<\/td><td>0.1940<\/td><td>\u22120.1189<\/td><td>0.0045<\/td><td>\u22120.1251<\/td><td>\u22120.1741<\/td><td>\u22120.0297<\/td><td>\u22120.1138<\/td><td>\u22120.1442<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Note: IC is the daily cross-sectional Spearman rank correlation between factor value and expected return; Risk-Adjusted IC = IC mean \/ IC std; sample period: 2020\/12\u20132026\/05; significance levels: *** p&lt;0.01, p&lt;0.05, * p&lt;0.1)<\/em><\/figcaption><\/figure>\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_Unlocking_Large-Cap_Chip_Secrets_%E2%80%94_How_to_Build_a_Real-World_Tactical_Strategy\"><\/span>Conclusion: Unlocking Large-Cap Chip Secrets \u2014 How to Build a Real-World Tactical Strategy?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Through a systematic portfolio sorting methodology and rigorous IC diagnostic testing, we have successfully verified that <strong>Foreign-Institutional Trading Concentration (conc_qfii) is an exceptional Alpha source for tracking the &#8220;smart money&#8221; within Taiwan&#8217;s large-cap space<\/strong>. However, this factor is not a silver bullet to be blindly followed; its stark<strong> &#8220;size-conditionality&#8221;<\/strong> mapping proves that it functions as a strong positive predictor among large caps but reverses into a negative one among small caps. This emphasizes that only by adopting a dedicated <strong>&#8220;large-cap focus&#8221; combined with a &#8220;value-weighted&#8221; allocation blueprint<\/strong> can the true profitable power of this factor be entirely unleashed.<\/p>\n\n\n\n<p>Now that we have rigorously established the factor&#8217;s stock-picking efficacy in theory and cross-sectional data, the next logical step is to step onto the high-friction battlefield of active execution. In Part 2 (Strategy and Empirical Backtesting), we will unveil the complete process of translating these insights into actionable trading strategies and dissect their performance against the real world!<\/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><em>\u27a1\ufe0f&nbsp;<\/em><\/em><\/strong><a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/\" target=\"_blank\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-qfii-part-2\/\" rel=\"noreferrer noopener nofollow\"><em>Unlock Part 2: Strategy &amp; Backtesting<\/em><\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Track QFII &#8216;smart money&#8217; footprints in Taiwan large-cap stocks! Learn how the Foreign-Institutional Trading Concentration (conc_qfii) factor predicts returns. <\/p>\n","protected":false},"featured_media":47002,"template":"","tags":[3183,2926,3540,3657,2962,3676],"insight-category":[3656],"class_list":["post-47001","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\/47001","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":17,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/47001\/revisions"}],"predecessor-version":[{"id":47051,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/47001\/revisions\/47051"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/47002"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=47001"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=47001"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=47001"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}