{"id":41712,"date":"2025-12-08T14:00:00","date_gmt":"2025-12-08T06:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=41712"},"modified":"2025-12-09T16:23:36","modified_gmt":"2025-12-09T08:23:36","slug":"factor-research-short-interest-ration-part-1","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/","title":{"rendered":"Factor Research \u2013 The SIR Short-Selling Factor: Extracting Negative Signals from Institutional Borrowing Activity \u2013 SIR Part 1"},"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-\u7cfb\u5217-1-2-1024x576.jpg\" alt=\"Factor Research \u2013 The SIR Short-Selling Factor: Extracting Negative Signals from Institutional Borrowing Activity \u2013 SIR Part 1\" class=\"wp-image-42042\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2-1024x576.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2-300x169.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2-150x84.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2-768x432.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2-1536x864.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1-2.jpg 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-69f0d28966b3f\" 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-69f0d28966b3f\"  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-short-interest-ration-part-1\/#The_Short-Side_Sentiment_Factor_Isolating_Smart_Money_Signals_in_Taiwans_Market\" >The Short-Side Sentiment Factor: Isolating Smart Money Signals in Taiwan\u2019s Market<\/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-short-interest-ration-part-1\/#Tracing_Informed_Traders_Through_SBL_Short_Selling_in_Taiwan\" >Tracing Informed Traders Through SBL Short Selling in Taiwan<\/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-short-interest-ration-part-1\/#Factor_Analysis\" >Factor Analysis<\/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-short-interest-ration-part-1\/#Construction_of_Short_Interest_Ratio_SIR\" >Construction of Short Interest Ratio, SIR<\/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-short-interest-ration-part-1\/#Descriptive_Statistics\" >Descriptive Statistics<\/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-short-interest-ration-part-1\/#Return_Analysis\" >Return Analysis<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/#Average_Return_Analysis\" >Average Return Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/#Cumulative_Return_Analysis\" >Cumulative Return Analysis<\/a><\/li><\/ul><\/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-short-interest-ration-part-1\/#Information_Coefficient_IC_Analysis\" >Information Coefficient (IC) Analysis<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/#Market-Wide_Information_Coefficient\" >Market-Wide Information Coefficient<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/#Size-Based_Heterogeneity_Analysis\" >Size-Based Heterogeneity Analysis<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-short-interest-ration-part-1\/#From_SBL-Based_Sentiment_Insights_to_Strategy_Construction\" >From SBL-Based Sentiment Insights to Strategy Construction<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Short-Side_Sentiment_Factor_Isolating_Smart_Money_Signals_in_Taiwans_Market\"><\/span>The Short-Side Sentiment Factor: Isolating Smart Money Signals in Taiwan\u2019s Market<a id=\"_msocom_1\"><\/a><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Short selling is often regarded as a channel through which \u201csmart money\u201d conveys negative private information. Yet in Taiwan\u2019s stock market, the information contained in short-selling activity is far from straightforward. This is largely due to the market\u2019s unique dual-track mechanism: <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>retail investors primarily short stocks through margin shorting, while institutional investors\u2014especially foreign institutions\u2014utilize securities borrowing and lending (SBL).<\/strong> <\/mark>The coexistence of these two structurally different channels introduces considerable noise into the signal.<\/p>\n\n\n\n<p>This study aims to isolate and highlight the component of <strong>short-selling behavior<\/strong> that truly reflects informed trading. Specifically, we focus on <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>SBL short sales, a segment dominated by institutional investors<\/strong>,<\/mark> and construct a factor that more cleanly captures the sentiment embedded in informed short-selling\u2014namely, the<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"> <strong>Short Interest Ratio (SIR)<\/strong><\/mark>. Using Taiwan as the empirical setting, we examine the factor\u2019s cross-sectional return predictability, explore its interaction with firm size, and ultimately evaluate its practical value in strengthening portfolio robustness through factor-based strategy backtesting.<\/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\"><em><strong>\u27a1\ufe0f <\/strong><\/em> <a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\" target=\"_blank\" rel=\"noreferrer noopener\"><em><strong>Dive into the Factor Library for deeper research insights!<\/strong><\/em><\/a><\/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=\"Tracing_Informed_Traders_Through_SBL_Short_Selling_in_Taiwan\"><\/span>Tracing Informed Traders Through SBL Short Selling in Taiwan<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Taiwan\u2019s short-selling framework is shaped by two distinct mechanisms: margin short selling, predominantly used by retail investors, and SBL short selling, a channel for institutional investors, including foreign institutions. These two systems represent fundamentally different trader profiles, leading to very different informational interpretations behind their short-selling activities.<\/p>\n\n\n\n<p>Local academic research provides strong evidence supporting this distinction. Studies by Lee et al. (2017) and Ting et al. (2018) reach highly consistent conclusions: retail-driven margin short selling behaves more like noise trading, and high margin short balances often precede positive future returns due to subsequent price reversals. In contrast, short selling conducted through the institutional SBL channel reliably predicts negative future returns, indicating that it is institutional short sellers\u2014not retail traders\u2014who act as informed participants in the market.<\/p>\n\n\n\n<p>Building on this foundation, our core hypothesis is straightforward:<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"> <strong>in Taiwan\u2019s market, only institutional short-selling activity captures the true negative information implied by smart money<\/strong>.<\/mark> Accordingly, to construct a factor that accurately reflects the expectations of informed traders, we focus exclusively on <strong>SBL short-sale balances<\/strong> as the basis for measuring short-selling sentiment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Analysis\"><\/span>Factor Analysis<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>This section outlines the construction of the Short Interest Ratio (SIR) factor and the portfolio-sorting framework used to evaluate its predictive ability. We adopt portfolio sorting as the primary empirical approach and assess whether SIR delivers stable and economically meaningful cross-sectional return predictability\u2014abstracting from transaction costs at this stage. Our analysis proceeds through three perspectives:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Descriptive Statistics<\/strong><\/li>\n\n\n\n<li><strong>Return Analysis<\/strong><\/li>\n\n\n\n<li><strong>Information Coefficient (IC) Analysis<\/strong><\/li>\n<\/ol>\n\n\n\n<p>Together, these components allow us to examine the SIR factor from both distributional and performance-based angles, setting the foundation for later strategy construction and backtesting.<\/p>\n\n\n\n<p>This study uses data obtained entirely from <strong>Taiwan Economic Journal (TEJ)<\/strong> database, include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Factor Indicators<\/strong>: Short Interest Ratio \u2013 Short Sale Balance Amount of SBL (SIR_Sbl_BA), hereafter referred to as <strong>SIR<\/strong>, and 52-Week High Momentum (MOM52WH)<\/li>\n\n\n\n<li><strong>Price &amp; Trading Data<\/strong>: price, returns, market value<\/li>\n\n\n\n<li><strong>Sample Period:<\/strong> Jan 2013 \u2013 Jul 2025.<\/li>\n\n\n\n<li><strong>Scope:<\/strong> All common stocks listed on the Taiwan Stock Exchange (TWSE) and Taipei Exchange (TPEx).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Construction_of_Short_Interest_Ratio_SIR\"><\/span>Construction of Short Interest Ratio, SIR<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Following the findings of prior Taiwan-focused research, we construct the Short Interest Ratio (SIR) by concentrating exclusively on <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"><strong>SBL short-sale balances<\/strong>,<\/mark> which are dominated by institutional investors. This choice is intentional: unlike retail-driven margin shorting\u2014which frequently reflects noise trading\u2014SBL activity captures the behavior of informed traders and therefore conveys more meaningful negative sentiment.<\/p>\n\n\n\n<p>Consistent with international literature such as Boehmer et al. (2008), we define the <strong>Short Interest Ratio (SIR)<\/strong> as :<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"606\" height=\"61\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u87a2\u5e55\u64f7\u53d6\u756b\u9762-2025-12-03-113230.png\" alt=\"\" class=\"wp-image-41714\" style=\"width:695px;height:auto\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u87a2\u5e55\u64f7\u53d6\u756b\u9762-2025-12-03-113230.png 606w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u87a2\u5e55\u64f7\u53d6\u756b\u9762-2025-12-03-113230-300x30.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u87a2\u5e55\u64f7\u53d6\u756b\u9762-2025-12-03-113230-150x15.png 150w\" sizes=\"(max-width: 606px) 100vw, 606px\" \/><\/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=\"Descriptive_Statistics\"><\/span>Descriptive Statistics<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>To understand how the SIR factor is distributed across Taiwan\u2019s equity market, we begin with a detailed descriptive analysis. A notable characteristic of the dataset is the large number of stocks with <strong>zero SBL short-sale activity (SIR = 0)<\/strong>. Using a conventional equal-sized portfolio sort would mix these zero observations with marginally positive values, obscuring the informative variation in short-selling intensity.<\/p>\n\n\n\n<p>To address this, we employ a modified sorting procedure. On each trading day, stocks are grouped as follows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Portfolio 1 (P1):<\/strong> All stocks with <strong>SIR = 0<\/strong>, representing securities with no observable short-selling pressure.<\/li>\n\n\n\n<li><strong>Portfolios 2 to 5 (P2\u2013P5):<\/strong> All remaining stocks with <strong>SIR &gt; 0<\/strong>, ranked from low to high and split evenly into four portfolios.<\/li>\n<\/ul>\n\n\n\n<p>This approach yields five portfolios in total. P1 represents the \u201cno-shorting\u201d group, while P2 through P5 capture progressively higher levels of short-selling pressure.<\/p>\n\n\n\n<p>Table 1 summarizes the descriptive statistics for each portfolio over the full sample period. One standout finding is that P1 accounts for 25.56% of all observations, implying that roughly one-quarter of listed companies are not subject to institutional short-selling at any given time.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><th class=\"has-text-align-center\" data-align=\"center\">Portfolio<\/th><th class=\"has-text-align-right\" data-align=\"right\">Min<\/th><th class=\"has-text-align-right\" data-align=\"right\">Max<\/th><th class=\"has-text-align-right\" data-align=\"right\">Mean<\/th><th class=\"has-text-align-right\" data-align=\"right\">Std<\/th><th class=\"has-text-align-right\" data-align=\"right\">Count<\/th><th class=\"has-text-align-right\" data-align=\"right\">%<\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>1<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00000<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00000<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00000<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00000<\/td><td class=\"has-text-align-right\" data-align=\"right\">1302663<\/td><td class=\"has-text-align-right\" data-align=\"right\">25.56%<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>2<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00001<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00143<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00060<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00042<\/td><td class=\"has-text-align-right\" data-align=\"right\">950185<\/td><td class=\"has-text-align-right\" data-align=\"right\">18.65%<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>3<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00144<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00447<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00276<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00086<\/td><td class=\"has-text-align-right\" data-align=\"right\">946740<\/td><td class=\"has-text-align-right\" data-align=\"right\">18.58%<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>4<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00448<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01239<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00774<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00226<\/td><td class=\"has-text-align-right\" data-align=\"right\">948538<\/td><td class=\"has-text-align-right\" data-align=\"right\">18.61%<\/td><\/tr><tr><td class=\"has-text-align-center\" data-align=\"center\"><strong>5<\/strong><\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01240<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.10665<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02706<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01516<\/td><td class=\"has-text-align-right\" data-align=\"right\">947694<\/td><td class=\"has-text-align-right\" data-align=\"right\">18.60%<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><em>Data Period\uff1a Jan 2013 \u2013 Jul 2025 <\/em>&nbsp;&nbsp;&nbsp;<\/p>\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=\"Return_Analysis\"><\/span>Return Analysis<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This section examines whether the Short Interest Ratio (SIR) contains predictive power for future stock returns. Our analysis focuses on two elements:<br>&nbsp;(1) the return spread between the extreme SIR portfolios\u2014P1 (no shorting) and P5 (highest short-selling pressure), and<br>&nbsp;(2) the performance of a long\u2013short strategy constructed from these two portfolios.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Average_Return_Analysis\"><\/span>Average Return Analysis<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Table 2 reports the average daily returns for P1, P5, and the long\u2013short spread (P5 \u2212 P1) across various holding horizons (1-day, 5-day, 10-day, and 21-day). The results align closely with the core hypothesis of this study. Across all holding periods, P1 consistently outperforms P5 by a substantial margin, indicating that<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\"> <\/mark><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">stocks with heavier short-selling pressure tend to deliver significantly lower subsequent returns.<\/mark><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td>Holding Period<\/td><td class=\"has-text-align-right\" data-align=\"right\">1-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">5-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">10-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">21-day<\/td><\/tr><\/thead><tbody><tr><td>High SIR (P5)<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.741<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.923<\/td><td class=\"has-text-align-right\" data-align=\"right\">2.974<\/td><td class=\"has-text-align-right\" data-align=\"right\">3.017<\/td><\/tr><tr><td>Zero SIR (P1)<\/td><td class=\"has-text-align-right\" data-align=\"right\">5.968<\/td><td class=\"has-text-align-right\" data-align=\"right\">6.445<\/td><td class=\"has-text-align-right\" data-align=\"right\">6.418<\/td><td class=\"has-text-align-right\" data-align=\"right\">6.462<\/td><\/tr><tr><td>Spread<em>(<\/em> P5-P1\uff09<\/td><td class=\"has-text-align-right\" data-align=\"right\">-3.227<\/td><td class=\"has-text-align-right\" data-align=\"right\">-4.033<\/td><td class=\"has-text-align-right\" data-align=\"right\">-3.974<\/td><td class=\"has-text-align-right\" data-align=\"right\">-3.924<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><em>Unit<\/em><em>\uff1abps \u3000Data Period\uff1a Jan 2013 \u2013 Jul 2025 <\/em>&nbsp;&nbsp;&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\"><em>\u27a1\ufe0f <\/em><a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-capital-gain-overhang-part-1\/\" target=\"_blank\" rel=\"noreferrer noopener\"><strong><em>Unlock more factor insights \u2014 starting with CGO<\/em><\/strong><\/a><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>The figure 1 provides a clear visual representation of how average returns vary across the SIR portfolios. A distinctly monotonic pattern emerges: returns decline steadily from P1 to P5 across all holding horizons. This consistent downward slope reinforces the <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">negative relationship between SIR and future returns<\/mark><\/strong>\u2014stocks facing stronger short-selling pressure reliably underperform those with no observable short interest.<\/p>\n\n\n\n<p>Figure 1.: Average Returns Across SIR Quintile Portfolios<br><br><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"900\" height=\"297\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-730.png\" alt=\"\" class=\"wp-image-41716\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-730.png 900w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-730-300x99.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-730-150x50.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-730-768x253.png 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><\/figure>\n\n\n\n<p><em>(This figure compares the average future returns of the five SIR-sorted portfolios across different holding periods. . Unit\uff1abps \u3000Data Period\uff1a Jan 2013 \u2013 Jul 2025.)<\/em><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Cumulative_Return_Analysis\"><\/span>Cumulative Return Analysis<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>To visualize the long-term performance of the SIR factor, Figure 2 plots the cumulative returns of the highest-SIR portfolio (P5) and the zero-SIR portfolio (P1).<\/p>\n\n\n\n<p>As shown in the figure 2, the zero-SIR portfolio (P1)\u2014representing stocks with no observable short-selling pressure\u2014delivers a steadily rising cumulative return throughout the sample period. In contrast, the high-SIR portfolio (P5) lags persistently behind, with the performance gap between the two portfolios widening steadily over time. The cumulative return profile therefore offers clear and compelling evidence that <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">SIR functions as a stable, long-lasting sentiment signal<\/mark><\/strong> in Taiwan\u2019s equity market.<\/p>\n\n\n\n<p>Figure 2.<strong>Cumulative Returns of SIR Portfolios (P1 vs. P5)<\/strong><br><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"288\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-731.png\" alt=\"\" class=\"wp-image-41718\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-731.png 900w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-731-300x96.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-731-150x48.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-731-768x246.png 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><\/figure>\n\n\n\n<p><em>Holding period: 1D Data Period\uff1a Jan 2013 \u2013 Jul 2025<\/em><\/p>\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>This section evaluates the predictive power of the SIR factor using the Information Coefficient (IC). We calculate the daily Spearman rank correlation between SIR values and future returns across multiple holding periods. The IC provides a direct measure of how well the cross-sectional ranking of SIR aligns with the cross-sectional ranking of subsequent performance.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Market-Wide_Information_Coefficient\"><\/span>Market-Wide Information Coefficient<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>Table 3. <strong>Summary Statistics of Information Coefficients (IC) Across Holding Periods<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><thead><tr><td>Holding Period<\/td><td class=\"has-text-align-right\" data-align=\"right\">1-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">5-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">10-day<\/td><td class=\"has-text-align-right\" data-align=\"right\">21-day<\/td><\/tr><\/thead><tbody><tr><td>IC Mean<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0005<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0046<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0017<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0068<\/td><\/tr><tr><td>IC Std<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1437<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1479<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1422<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1345<\/td><\/tr><tr><td>Risk Adjusted IC<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0036<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0311<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0119<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0506<\/td><\/tr><tr><td>IC &lt; 0\uff08%\uff09<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.4946<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.4792<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.4834<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.5267<\/td><\/tr><tr><td>IC &lt; 0.03\uff08%\uff09<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3972<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.39<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3969<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.4271<\/td><\/tr><tr><td>IC &lt; 0.05\uff08%\uff09<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3396<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3382<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3461<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3661<\/td><\/tr><tr><td>IC t-value<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1998<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.7205<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.6591<\/td><td class=\"has-text-align-right\" data-align=\"right\">-2.7969<\/td><\/tr><tr><td>IC p-value<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.8416<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>0.0855<\/strong>*<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.5099<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>0.0052<\/strong>***<\/td><\/tr><tr><td>IC Skewness<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.1195<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.1391<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.1112<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0799<\/td><\/tr><tr><td>IC Kurtosis<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1331<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.005<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.059<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.3288<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p><em>Data Period\uff1a Jan 2013 \u2013 Jul 2025.&nbsp; Significance levels: *** p&lt;0.01, ** p&lt;0.05, * p&lt;0.1.<\/em><\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Table 3 summarizes the IC statistics for the entire market. Interestingly, the results present a markedly different picture from the return spread analysis. Across the 1-day to 10-day horizons, the average ICs are relatively small\u2014and in some cases even positive\u2014suggesting weak or unstable monotonic relationships between SIR and future returns at the full-market level.<\/p>\n\n\n\n<p>This observation does not imply that the SIR factor is ineffective. Rather, it indicates that the factor\u2019s predictive signal may not manifest uniformly across all stocks, and its effect may be masked by heterogeneous dynamics among different market segments.<\/p>\n\n\n\n<p>Indeed, the distribution of IC values hints at this underlying complexity: although institutional short-selling is expected to predict negative returns, its impact appears uneven when evaluated across the full universe of stocks.<\/p>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h4 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Size-Based_Heterogeneity_Analysis\"><\/span>Size-Based Heterogeneity Analysis<span class=\"ez-toc-section-end\"><\/span><\/h4>\n\n\n\n<p>To further investigate the instability observed in the market-wide IC results, we conduct a size-segmented analysis. At the end of each month, all stocks are sorted into five equal-sized groups based on market capitalization. We then compute the IC within each size group separately. The results are presented in Figure 3.<\/p>\n\n\n\n<p><strong>Figure 3.Average IC of SIR by Market Capitalization Groups<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"308\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-732.png\" alt=\"\" class=\"wp-image-41721\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-732.png 900w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-732-300x103.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-732-150x51.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-732-768x263.png 768w\" sizes=\"(max-width: 900px) 100vw, 900px\" \/><\/figure>\n\n\n\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p>Stocks are evenly divided into five groups by daily market capitalization (1 = smallest, 5 = largest). The figure shows the average IC between SIR and future returns for each group across different holding periods; Sample period: Jan 2013 \u2013 Jul 2025<\/p>\n\n\n\n<p>Figure 3 reveals a striking and intuitive pattern:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>For <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">mid- and large-cap stock<\/mark>s<\/strong>, higher short-selling pressure (higher SIR) reliably predicts <strong>lower<\/strong> future returns. The negative relationship is strong and consistent, indicating that SIR functions as an effective sentiment factor within this segment of the market.<\/li>\n\n\n\n<li>Among <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">small-cap stocks<\/mark><\/strong>, the effect reverses entirely: higher SIR values are associated with <strong>higher<\/strong> future returns. This positive relationship is statistically significant and suggests that short-selling in smaller firms may trigger speculative trading dynamics\u2014such as increased short-squeeze risk\u2014that generate upward price pressure.<\/li>\n<\/ol>\n\n\n\n<p>These opposing forces explain why the aggregate IC appears weak and unstable. The negative ICs from mid- and large-cap stocks are counteracted by the positive ICs from small-cap stocks, resulting in near-zero averages at the full-market level.<\/p>\n\n\n\n<p>In summary, <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-luminous-vivid-orange-color\">the predictive power of SIR is highly size-dependent<\/mark><\/strong>. It behaves as a meaningful negative sentiment factor only among medium- and large-sized firms, while exhibiting an inverted effect among small caps.<\/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=\"From_SBL-Based_Sentiment_Insights_to_Strategy_Construction\"><\/span>From SBL-Based Sentiment Insights to Strategy Construction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Our findings uncover a distinctive structure behind the Short Interest Ratio (SIR) in Taiwan\u2019s market. While the return spreads across SIR portfolios clearly show that institutional short-selling contains meaningful negative information, the market-wide IC results reveal that this signal is far from uniform. SIR is strongly predictive among mid- and large-cap stocks, yet reverses among small caps, where speculative dynamics and short-squeeze risks often dominate. This interplay explains why SIR appears weak on a broad market basis, despite its strong performance within the segments where informed trading truly occurs.<\/p>\n\n\n\n<p>In other words, SIR is not a universal sentiment factor\u2014but when applied to the right part of the market, it becomes a powerful indicator of institutional expectations.<\/p>\n\n\n\n<p>With these insights established, the next question is straightforward: Can SIR improve real investment strategies?<\/p>\n\n\n\n<p>In <strong><a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-applying-sir-to-strengthen-momentum-strategies-in-the-taiwan-market-sir-part-2\/\" target=\"_blank\" rel=\"noreferrer noopener\">Part 2<\/a>,<\/strong> we test how SIR interacts with established factors such as 52-week high momentum, how it performs under realistic transaction costs, and whether filtering out short-selling pressure can materially enhance portfolio returns.<\/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\"><em><strong>\u27a1\ufe0f <\/strong><\/em><strong><a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-applying-sir-to-strengthen-momentum-strategies-in-the-taiwan-market-sir-part-2\/\" target=\"_blank\" rel=\"noreferrer noopener\">Click to continue to Part 2 and see how SIR performs in live strategy backtests.<\/a><\/strong><a id=\"_msocom_1\"><\/a><\/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=\"Psf6w2qVqw\"><a href=\"https:\/\/www.tejwin.com\/en\/news\/tej-at-neudata-ny-summit-2025\/\">Highlighting Taiwan\u2019s Data Advantage: TEJ Joins the Neudata NY Data Summit<\/a><\/blockquote><iframe class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"Highlighting Taiwan\u2019s Data Advantage: TEJ Joins the Neudata NY Data Summit &#8212; TEJ\" src=\"https:\/\/www.tejwin.com\/en\/news\/tej-at-neudata-ny-summit-2025\/embed\/#?secret=ckUgAWl4KS#?secret=Psf6w2qVqw\" data-secret=\"Psf6w2qVqw\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Taiwan\u2019s short-selling signals are often misleading because the market operates under a dual-track system: retail investors short stocks through margin accounts, while institutional investors use securities borrowing and lending (SBL). Only SBL-based short selling reflects informed institutional sentiment, while margin shorting introduces noise. This study isolates SBL to construct the Short Interest Ratio (SIR) and evaluates its ability to predict cross-sectional returns and reveal size-dependent patterns in informed short-selling behavior.<\/p>\n","protected":false},"featured_media":42040,"template":"","tags":[3063,3540,2987],"insight-category":[690],"class_list":["post-41712","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-backtesting-2","tag-factor-library","tag-quant","insight-category-data-analysis"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/41712","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":39,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/41712\/revisions"}],"predecessor-version":[{"id":42484,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/41712\/revisions\/42484"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/42040"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=41712"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=41712"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=41712"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}