{"id":35319,"date":"2025-05-28T20:00:00","date_gmt":"2025-05-28T12:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=35319"},"modified":"2025-07-11T09:57:19","modified_gmt":"2025-07-11T01:57:19","slug":"factor-research-idiosyncratic-volatility","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/","title":{"rendered":"Factor Research \u2013 Idiosyncratic Volatility | Part 1"},"content":{"rendered":"\n<figure class=\"wp-block-image size-large is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1024x576.png\" alt=\"Factor Research \u2013 The Foundations of Idiosyncratic Volatility\" class=\"wp-image-37298\" style=\"width:841px;height:auto\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1024x576.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-300x169.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-150x84.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-768x432.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217-1536x864.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u5b98\u7db2_factor-\u7cfb\u5217.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-69f0f9ce4c372\" 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-69f0f9ce4c372\"  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-idiosyncratic-volatility\/#Preface\" >Preface<\/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-idiosyncratic-volatility\/#The_Low-Volatility_Anomaly\" >The Low-Volatility Anomaly<\/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-research-idiosyncratic-volatility\/#The_Idiosyncratic_Volatility_Puzzle\" >The Idiosyncratic Volatility Puzzle<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Factor_Analysis\" >Factor Analysis&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Data_Source_and_Sample_Period\" >Data Source and Sample Period&nbsp;<\/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-idiosyncratic-volatility\/#Construction_of_Idiosyncratic_Volatility_IVOL\" >Construction of Idiosyncratic Volatility (IVOL)&nbsp;<\/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-idiosyncratic-volatility\/#Descriptive_Statistics_of_IVOL\" >Descriptive Statistics of IVOL&nbsp;<\/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-idiosyncratic-volatility\/#Relationship_Between_IVOL_and_Fundamental_Characteristics\" >Relationship Between IVOL and Fundamental Characteristics&nbsp;<\/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-idiosyncratic-volatility\/#Insights_for_Investment_Screening\" >Insights for Investment Screening&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Result_Analysis\" >Result Analysis<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Cumulative_Performance_Comparison\" >Cumulative Performance Comparison&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Interpretation\" >Interpretation&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Information_Coefficient_IC_Analysis\" >Information Coefficient (IC) Analysis<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Key_Insights\" >Key Insights<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Turnover_Analysis\" >Turnover Analysis&nbsp;<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Key_Observations\" >Key Observations&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Interpretation-2\" >Interpretation&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Conclusion\" >Conclusion&nbsp;<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-research-idiosyncratic-volatility\/#Further_Reading\" >Further Reading:<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Preface\"><\/span><strong>Preface<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In recent years, the low-volatility anomaly has gained widespread attention for challenging traditional asset pricing theory. This article takes a closer look at one key driver behind the anomaly\u2014<mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>Idiosyncratic Volatility (IVOL)<\/strong><\/mark>\u2014through a comprehensive analysis of the Taiwan stock market. Using point-in-time data from the TEJ Factor Library, we investigate the statistical behavior of IVOL, its relationship with stock characteristics, and its implications for cross-sectional return prediction.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Low-Volatility_Anomaly\"><\/span><strong>The Low-Volatility Anomaly <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Traditional asset pricing theory suggests a positive relationship between risk and return\u2014higher risk should yield higher expected returns. Yet empirical evidence often contradicts this: stocks with lower historical volatility tend to outperform, even after adjusting for risk. This phenomenon is known as the <strong>low-volatility anomaly<\/strong>.&nbsp;<\/p>\n\n\n\n<p>Early research by Black (1972) observed that the link between beta and realized returns was flatter than theory predicts. Later studies confirmed that <strong>low-volatility stocks<\/strong> consistently generate excess returns across markets and time periods, even after controlling for known factors like size, value, and momentum. These findings challenge the capital asset pricing model (CAPM) and have led to the rise of <strong>Smart Beta<\/strong> strategies and <strong>low-volatility ETFs<\/strong>.&nbsp;<\/p>\n\n\n\n<p>Behavioral explanations include the <strong>lottery preference<\/strong>\u2014investors overpay for high-volatility stocks in hopes of outsized gains, leading to long-term underperformance. Others cite <strong>overconfidence<\/strong> and <strong>representativeness bias<\/strong>, which further inflate expectations for volatile growth stocks.&nbsp;<\/p>\n\n\n\n<p>In Taiwan, ETFs like Cathay TIP TAIEX+ Low Volatility Select 30 ETF<strong>(<\/strong>00701<strong>)<\/strong> and Yuanta Taiwan High Dividend Low Volatility ETF(00713) have gained popularity by combining low volatility and high dividend yields, offering stable returns to risk-averse investors.&nbsp;<\/p>\n\n\n\n<p>However, recent literature suggests these returns may be explained by <strong>other factor exposures<\/strong>. Novy-Marx (2014) and Fama &amp; French (2015) show that once profitability and investment factors are included, the anomaly weakens. Others argue that performance depends on market cycles, making the low-volatility premium inconsistent.&nbsp;<\/p>\n\n\n\n<p>Still, some anomalies\u2014especially those tied to <strong>idiosyncratic risk<\/strong>\u2014resist easy explanation. One such case is the <strong>Idiosyncratic Volatility Puzzle<\/strong>.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\"><img decoding=\"async\" width=\"1024\" height=\"107\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-1024x107.png\" alt=\"\" class=\"wp-image-35334\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-1024x107.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-300x31.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-150x16.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-768x80.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1-1536x160.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/CTA_Factor-Library-1.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"The_Idiosyncratic_Volatility_Puzzle\"><\/span>The Idiosyncratic Volatility Puzzle<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>While systematic risk is central to asset pricing, <strong>idiosyncratic volatility (IVOL)<\/strong>\u2014risk unique to an individual stock\u2014is theoretically diversifiable and should not influence returns. Yet empirical evidence shows otherwise: stocks with higher IVOL often deliver significantly lower future returns.&nbsp;<\/p>\n\n\n\n<p>Ang et al. (2006, 2009) documented this in U.S. and global markets, finding that high-IVOL stocks underperform even after accounting for major risk factors. This contradicts the idea that only systematic risk is rewarded.&nbsp;<\/p>\n\n\n\n<p>Follow-up studies suggest <strong>mispricing<\/strong> and <strong>arbitrage limits<\/strong> drive this anomaly. Stocks with high IVOL are often hard to short, overpriced, and exhibit characteristics such as <strong>small size, low profitability, high investment spending, and growth bias<\/strong>. These traits hinder efficient pricing and contribute to long-term underperformance (Stambaugh et al., 2014; Detzel et al., 2023).&nbsp;<\/p>\n\n\n\n<p>This study extends the literature by examining whether this puzzle holds in <strong>Taiwan\u2019s equity market<\/strong>. Using comprehensive, point-in-time data from the <strong>TEJ Factor Library<\/strong>, we assess the relationship between IVOL and future stock returns, and evaluate how this factor could be applied in portfolio strategies.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Analysis\"><\/span>Factor Analysis&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To determine whether the negative return premium associated with <strong>Idiosyncratic Volatility (IVOL)<\/strong>, observed in the U.S., is also present in Taiwan\u2019s equity market, we conduct an empirical analysis using <strong>portfolio sorting techniques<\/strong>. The analysis evaluates IVOL\u2019s predictive power through return statistics, information coefficients, and turnover metrics.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Data_Source_and_Sample_Period\"><\/span><strong>Data Source and Sample Period&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The dataset is sourced from <strong>Taiwan Economic Journal (TEJ)<\/strong> :&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Price &amp; Trading Data<\/strong>: returns, market capitalization, PE ratio, PB ratio, and yield.&nbsp;<\/li>\n\n\n\n<li><strong>Institutional Holdings<\/strong>: foreign shareholding, margin trading.&nbsp;<\/li>\n\n\n\n<li><strong>Beta Values<\/strong>: CAPM beta (monthly);.&nbsp;<\/li>\n\n\n\n<li><strong>Market Factor Data <\/strong>: risk premiums, SMB, HML, risk-free rate.&nbsp;<\/li>\n\n\n\n<li><strong>Factor Indicators <\/strong>: daily IVOL values.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>Sample period: <strong>Jan 2005 \u2013 Mar 2025<\/strong><br>Scope: All listed and OTC common stocks in Taiwan.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Construction_of_Idiosyncratic_Volatility_IVOL\"><\/span><strong>Construction of Idiosyncratic Volatility (IVOL)<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>IVOL is computed following <strong>Hou, Xue, and Zhang (2020)<\/strong>:&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>For each stock on day <em>t<\/em>, we regress the past 21 days of daily <strong>excess returns<\/strong> (vs. the risk-free rate) on the <strong>Fama-French three-factor model<\/strong>;&nbsp;<\/li>\n\n\n\n<li>The <strong>standard deviation of regression residuals<\/strong> is defined as IVOL for that day.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>To ensure reliability:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Stocks must be listed for at least 21 days;&nbsp;<\/li>\n\n\n\n<li>At least 15 out of the past 21 days must have valid trading data.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#ffe9ae\"><em>\ud83d\udccdThis standardized methodology is implemented across the<strong> <a href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/\">TEJ Factor Library<\/a>,<\/strong> which supports systematic factor research and portfolio construction with clean historical signals.&nbsp;<\/em><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Descriptive_Statistics_of_IVOL\"><\/span>Descriptive Statistics of IVOL&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>We sort all stocks into 10deciles daily based on their IVOL values and calculate summary statistics over the full sample period.&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-group is-layout-constrained wp-block-group-is-layout-constrained\">\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong>Decile<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Min<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Max<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Mean<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Std Dev<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>Count<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\"><strong>% of Total<\/strong>&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">1&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00000&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01707&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00548&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00212&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">746,835&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.05%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">2&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00424&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01994&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00810&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00234&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">742,751&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">3&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00548&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02233&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00999&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00262&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">742,168&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">4&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00671&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02471&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01183&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00284&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">742,329&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">5&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00792&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02697&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01380&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00300&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">742,907&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.00%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">6&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00934&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02973&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01600&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00312&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">741,330&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.98%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">7&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01112&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.03211&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01863&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00322&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">741,653&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.98%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">8&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01357&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.03514&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02199&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00332&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">742,271&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.99%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">9&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.01682&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.04014&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02666&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00361&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">741,538&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">9.98%&nbsp;<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\">10&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.02173&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">1.54585&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.03749&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.00908&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">744,226&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">10.02%&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><strong>Table 1: Summary Statistics by IVOL Decile<\/strong>&nbsp;<br>(<em>Sample Period: Jan 2005 \u2013 Mar 2025<\/em>)&nbsp;<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n<\/div>\n\n\n\n<p>\ud83d\udccc Stocks in the top decile exhibit significantly higher IVOL dispersion (Std Dev = 0.00908), indicating greater heterogeneity among high-risk names.&nbsp;<\/p>\n\n\n\n<p>This IVOL breakdown forms the basis for later return and risk analysis.&nbsp;<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Relationship_Between_IVOL_and_Fundamental_Characteristics\"><\/span><strong>Relationship Between IVOL and Fundamental Characteristics<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>To better understand how idiosyncratic volatility reflects underlying firm attributes, we examine monthly IVOL decile groups based on end-of-month values. For each decile, we compute the <strong>median value<\/strong> of various fundamental indicators.&nbsp;<\/p>\n\n\n\n<p>This analysis reveals distinct patterns linking IVOL levels with valuation, size, liquidity, and institutional participation.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Negative correlation with <strong>market cap and dividend yield<\/strong>&nbsp;<\/li>\n\n\n\n<li>Positive correlation with<strong> PE ratio, PB ratio, and total volatility<\/strong>&nbsp;<\/li>\n\n\n\n<li>High-IVOL stocks are less liquid and less institutionally held, but exhibit higher margin trading&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>These characteristics align with prior studies describing high-IVOL stocks as <strong>small, speculative, and low-quality<\/strong>\u2014ideal candidates for exclusion in portfolio construction.&nbsp;<\/p>\n\n\n\n<p><strong>Table 2: IVOL Deciles and Median Firm Characteristics<\/strong>&nbsp;<br>(<em>Sample Period: Jan 2005 \u2013 Mar 2025; grouped by monthly IVOL rankings<\/em>)&nbsp;<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-28f84493 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column has-black-color has-text-color has-background has-link-color wp-elements-b58395c1e074d9d750566816cbf51fdb\" style=\"background-color:#ffe6b6;font-style:normal;font-weight:400;flex-basis:100%\">\n<figure class=\"wp-block-table\">\n<table style=\"width: 100%; height: 391px;\">\n<tbody>\n<tr style=\"height: 23px;\">\n<td style=\"height: 23px; width: 18.9286%;\"><strong>Indicator Type<\/strong>\u00a0<\/td>\n<td style=\"height: 23px; width: 20.9524%;\"><strong>Indicator<\/strong>\u00a0<\/td>\n<td style=\"height: 23px; width: 60%;\"><strong>Observation<\/strong>\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 23px;\">\n<td style=\"height: 92px; width: 18.9286%;\" rowspan=\"3\"><strong>Price &amp; Size<\/strong>\u00a0<br \/><br \/><\/td>\n<td style=\"height: 23px; width: 20.9524%;\">Close Price\u00a0<\/td>\n<td style=\"height: 23px; width: 60%;\">Lowest in highest IVOL decile\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 23px;\">\n<td style=\"height: 23px; width: 20.9524%;\">Market CAP<\/td>\n<td style=\"height: 23px; width: 60%;\">Negatively correlated (higher IVOL = smaller firms)\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 46px; width: 20.9524%;\">Amihud Illiquidity\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Highest in top IVOL decile; suggests price impact is greater per dollar traded\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 69px; width: 18.9286%;\" rowspan=\"2\"><strong>Valuation &amp; Profit<\/strong>\u00a0<\/td>\n<td style=\"height: 46px; width: 20.9524%;\">P\/E ratio, P\/B ratio\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Positively correlated (higher IVOL = higher valuation multiples)\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 23px;\">\n<td style=\"height: 23px; width: 20.9524%;\">Dividend Yield\u00a0<\/td>\n<td style=\"height: 23px; width: 60%;\">Negatively correlated (high IVOL = lower cash payouts)\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 23px;\">\n<td style=\"height: 69px; width: 18.9286%;\" rowspan=\"2\"><strong>Risk<\/strong>\u00a0<\/td>\n<td style=\"height: 23px; width: 20.9524%;\">Total Volatility\u00a0<\/td>\n<td style=\"height: 23px; width: 60%;\">Generally increasing with IVOL\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 46px; width: 20.9524%;\">CAPM_Beta\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Positively correlated (high IVOL = higher market sensitivity)\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 46px; width: 18.9286%;\"><strong>Institutional Holding<\/strong>\u00a0<\/td>\n<td style=\"height: 46px; width: 20.9524%;\">Foreign Ownership\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Declines in higher IVOL groups, implying lower institutional participation\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 92px; width: 18.9286%;\" rowspan=\"2\"><strong>Short Interest<\/strong>\u00a0<\/td>\n<td style=\"height: 46px; width: 20.9524%;\">Securities Lending Ratio\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Lower in top decile; high-IVOL stocks less shorted by institutions\u00a0<\/td>\n<\/tr>\n<tr style=\"height: 46px;\">\n<td style=\"height: 46px; width: 20.9524%;\">Margin Short Ratio\u00a0<\/td>\n<td style=\"height: 46px; width: 60%;\">Positively correlated (high-IVOL stocks more shorted by retail investors)\u00a0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<figcaption class=\"wp-element-caption\"><\/figcaption>\n<\/figure>\n<\/div>\n<\/div>\n\n\n\n<p>\ud83d\udccc <em>Amihud Illiquidity<\/em> is defined as the average of |daily return| \/ (daily turnover in million TWD) over the past month.&nbsp;<br><em>Securities Lending Ratio<\/em> and <em>Margin Short Ratio<\/em> are measured as the daily short balance over market cap.&nbsp;<\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><em><strong>Figure 1: Median Characteristics by IVOL Decile<\/strong>&nbsp;<br><\/em>(<em>Each subplot displays one variable\u2019s median by decile; X-axis = IVOL deciles ranked monthly<\/em>)&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"1920\" height=\"1080\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en.png\" alt=\"\" class=\"wp-image-35380\" style=\"width:834px;height:auto\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en.png 1920w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en-300x169.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en-1024x576.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en-150x84.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en-768x432.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_en-1536x864.png 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/><\/figure>\n\n\n\n<p>\ud83d\udc49 <em>These plots visually reinforce that high-IVOL stocks tend to be small-cap, high-valuation, low-liquidity stocks with minimal institutional support.<\/em>&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Insights_for_Investment_Screening\"><\/span><strong>Insights for Investment Screening<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Stocks with high IVOL tend to exhibit <strong>lower quality fundamentals<\/strong>\u2014they are smaller, more expensive, less liquid, and have weaker institutional confidence. This aligns with the findings of Novy-Marx (2014), who highlighted that <strong>high-volatility stocks resemble speculative growth names with poor long-term returns<\/strong>.&nbsp;<\/p>\n\n\n\n<p>From a practical standpoint, <strong>screening out the top IVOL deciles<\/strong> could serve as a <strong>risk filter<\/strong>, helping to improve overall portfolio quality and consistency.&nbsp;<\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#ffe9ae\">\ud83e\udde0 <strong>Application Note<\/strong>&nbsp;<br><em>The <strong>TEJ Factor Library<\/strong> enables monthly IVOL rankings and fundamental cross-sectional analysis through a unified point-in-time database\u2014crucial for avoiding survivorship and look-ahead biases in systematic strategies.&nbsp;<\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Result_Analysis\"><\/span><strong>Result Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To assess IVOL\u2019s effectiveness as a return signal, we analyze the performance of IVOL-based decile portfolios under varying holding periods: 1-day, 5-day, 10-day, and 21-day horizons.&nbsp;<\/p>\n\n\n\n<p>Results show a clear pattern: <strong>stocks with lower IVOL consistently outperform those with higher IVOL<\/strong>, regardless of holding period. The return difference between the top (high-IVOL) and bottom (low-IVOL) deciles is negative, indicating the <strong>return premium favors low-IVOL stocks<\/strong>.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-background has-fixed-layout\" style=\"background-color:#ffe9ae\"><thead><tr><th><strong>Holding Period<\/strong>&nbsp;<\/th><th class=\"has-text-align-center\" data-align=\"center\"><strong>Top Quantile<\/strong><br><strong>(High IVOL)<\/strong>&nbsp;<\/th><th class=\"has-text-align-center\" data-align=\"center\"><strong>Bottom Quantile<\/strong><br><strong>(Low IVOL)<\/strong>&nbsp;<\/th><th class=\"has-text-align-center\" data-align=\"center\"><strong>Long-Short Spread (Top \u2013 Bottom)&nbsp;<\/strong><\/th><\/tr><\/thead><tbody><tr><td>1 Day (1D)&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\">6.144 <\/td><td class=\"has-text-align-center\" data-align=\"center\">7.330<\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>&#8211;<\/strong>1.186<\/td><\/tr><tr><td>5 Days (5D)&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\">4.400 <\/td><td class=\"has-text-align-center\" data-align=\"center\">6.175&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>&#8211;<\/strong>2.435<\/td><\/tr><tr><td>10 Days (10D)&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\">4.285&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\">5.790<\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>&#8211;<\/strong>2.242<\/td><\/tr><tr><td>21 Days (21D)&nbsp;<\/td><td class=\"has-text-align-center\" data-align=\"center\">4.551<\/td><td class=\"has-text-align-center\" data-align=\"center\">5.673<\/td><td class=\"has-text-align-center\" data-align=\"center\"><strong>&#8211;<\/strong>2.012&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><strong>Table 4: Average Daily Returns by IVOL Decile and Long-Short Spread<\/strong>&nbsp;<br>(<em>Unit: basis points, Sample Period: Jan 2005 \u2013 Mar 2025<\/em>)&nbsp;<\/figcaption><\/figure>\n\n\n\n<p>The negative spreads indicate that <strong>longing low-IVOL stocks while shorting high-IVOL stocks yields a consistent return edge<\/strong>, particularly in short- to medium-term horizons.&nbsp;<\/p>\n\n\n\n<div style=\"height:31px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-text-align-center\"><strong>Figur2 : Average Return by IVOL Decile \u2013 All Holding Periods&nbsp;<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"373\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-1024x373.png\" alt=\"\" class=\"wp-image-35384\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-1024x373.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-300x109.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-150x55.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-768x280.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1-1536x560.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_3-1.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>This visual confirms a broadly monotonic decline in average return as IVOL increases, though small deviations exist across middle deciles.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Cumulative_Performance_Comparison\"><\/span><strong>Cumulative Performance Comparison&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>To evaluate long-term performance, we compare the cumulative return trajectories of the lowest and highest IVOL deciles under a 1-day holding strategy.&nbsp;<\/p>\n\n\n\n<p><em><strong>Figure 3: <\/strong>Cumulative Return Curve of IVOL Deciles (Top vs. Bottom Quantile)<br>(This figure shows the cumulative returns of the 10th (Top) and 1st (Bottom) IVOL deciles under a 1-day holding period; Sample Period: Jan 2005 \u2013 Mar 2025)<br><\/em><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"373\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-1024x373.png\" alt=\"\" class=\"wp-image-35386\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-1024x373.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-300x109.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-150x55.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-768x280.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4-1536x560.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/ivol_4.png 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Notably, the <strong>low-IVOL portfolio exhibits smoother growth and shallower drawdowns<\/strong>, highlighting its <strong>defensive nature<\/strong>, especially during volatile market conditions.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Interpretation\"><\/span><strong>Interpretation<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The return analysis suggests that IVOL contains valuable <strong>negative predictive power<\/strong>. High-IVOL stocks tend to underperform not just on average, but consistently across different time frames.&nbsp;<\/p>\n\n\n\n<p>This implies IVOL may be better suited as a <strong>screening factor<\/strong>\u2014filtering out volatile underperformers\u2014rather than a direct alpha signal for long-only selection.&nbsp;<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Information_Coefficient_IC_Analysis\"><\/span><strong>Information Coefficient (IC) Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To further evaluate IVOL\u2019s predictive ability, we calculate the <strong>Spearman rank correlation<\/strong> between IVOL values and subsequent stock returns\u2014known as the <strong>Information Coefficient (IC)<\/strong>.&nbsp;<\/p>\n\n\n\n<p>This approach assesses whether <strong>stocks with lower IVOL rankings tend to deliver higher returns<\/strong>, and whether the relationship is statistically consistent over time.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"has-text-align-center\"><strong>Table 4: Summary Statistics of IVOL Information Coefficients (IC)&nbsp;<\/strong>(Sample Period: Jan 2005 \u2013 Mar 2025)&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\" style=\"font-style:normal;font-weight:400\"><table class=\"has-fixed-layout\"><thead><tr><th><strong>Metric<\/strong>&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\">1D<\/th><th class=\"has-text-align-right\" data-align=\"right\">5D<\/th><th class=\"has-text-align-right\" data-align=\"right\">10D<\/th><th class=\"has-text-align-right\" data-align=\"right\">21D<\/th><\/tr><\/thead><tbody><tr><td><strong>IC Mean<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0361&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0639&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0754&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0872&nbsp;<\/td><\/tr><tr><td><strong>IC Std Dev<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1315&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1267&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1218&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1225&nbsp;<\/td><\/tr><tr><td><strong>Risk-Adjusted IC<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.2745&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.5040&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.6193&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.7121&nbsp;<\/td><\/tr><tr><td><strong>% IC &lt; 0<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">60.73%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">69.79%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">73.04%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">76.75%&nbsp;<\/td><\/tr><tr><td><strong>% IC &lt; -0.03<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">51.42%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">60.50%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">64.28%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">68.86%&nbsp;<\/td><\/tr><tr><td><strong>% IC &lt; -0.05<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">44.66%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">53.88%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">58.14%&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">63.31%&nbsp;<\/td><\/tr><tr><td><strong>IC t-stat<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-19.32***&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-35.48***&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-43.59***&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-50.13***&nbsp;<\/td><\/tr><tr><td><strong>IC p-value<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0&nbsp;<\/td><\/tr><tr><td><strong>Skewness<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0695&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0055&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.0326&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1090&nbsp;<\/td><\/tr><tr><td><strong>Kurtosis<\/strong>&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.2128&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1033&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.1560&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">-0.0483&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\">*** denotes statistical significance at the 0.001 level.&nbsp;<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_Insights\"><\/span><strong>Key Insights<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The <strong>mean IC values are consistently negative<\/strong>, particularly beyond 5-day horizons.&nbsp;<\/li>\n\n\n\n<li>Over <strong>70% of IC observations are below zero<\/strong> at the 10- and 21-day horizons,, indicating strong directional consistency.&nbsp;<\/li>\n\n\n\n<li><strong>Risk-adjusted ICs<\/strong> fall below -0.5 for 5D, 10D, and 21D, suggesting stable signal strength.&nbsp;<\/li>\n\n\n\n<li>T-values are highly significant across all holding periods.&nbsp;<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Turnover_Analysis\"><\/span><strong>Turnover Analysis&nbsp;<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>To evaluate IVOL\u2019s practicality in real-world portfolio management, we examine the <strong>turnover rates<\/strong> of the high- and low-IVOL decile portfolios. Turnover reflects how frequently positions are changed and directly impacts transaction costs and strategy implementability.&nbsp;<\/p>\n\n\n\n<p><strong>Table 5: Average Turnover Rates by Holding Period and IVOL Decile<\/strong>&nbsp;(<em>Sample Period: Jan 2005 \u2013 Mar 2025<\/em>)&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><strong>Holding Period<\/strong>&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\"><strong>Top Decile (High IVOL)<\/strong>&nbsp;<\/th><th class=\"has-text-align-right\" data-align=\"right\"><strong>Bottom Decile (Low IVOL)<\/strong>&nbsp;<\/th><\/tr><\/thead><tbody><tr><td>1 Day (1D)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.088&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.076&nbsp;<\/td><\/tr><tr><td>5 Days (5D)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.259&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.237&nbsp;<\/td><\/tr><tr><td>10 Days (10D)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.389&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.378&nbsp;<\/td><\/tr><tr><td>21 Days (21D)&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.561&nbsp;<\/td><td class=\"has-text-align-right\" data-align=\"right\">0.590&nbsp;<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Turnover is calculated as the proportion of stocks replaced at each rebalance point.&nbsp;<\/em><\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_Observations\"><\/span><strong>Key Observations<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Across all holding periods, <strong>turnover levels are moderate<\/strong>, with <strong>21-day portfolios turning over about 6.7x to 7x annually<\/strong>.&nbsp;<\/li>\n\n\n\n<li>The <strong>difference in turnover between high- and low-IVOL groups is minor<\/strong>, suggesting comparable implementation burden.&nbsp;<\/li>\n\n\n\n<li>High turnover could be a concern in more volatile universes, but here, <strong>IVOL-based strategies remain within an operationally feasible range<\/strong>.&nbsp;<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Interpretation-2\"><\/span><strong>Interpretation<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>While IVOL-based strategies do involve regular position changes, the magnitude is manageable for most institutional portfolios, especially under <strong>monthly or biweekly rebalancing<\/strong>.&nbsp;<\/p>\n\n\n\n<p>When applying IVOL as a filter rather than a ranking signal, turnover can be further reduced without compromising risk control benefits.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong>&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>While Part 1 of this series established the empirical foundations of Idiosyncratic Volatility (IVOL) and its negative return implications in the Taiwan equity market, the next step is turning these into actionable strategies. In Part 2: Factor Strategy \u2013 Idiosyncratic Volatility, we move from theory to application\u2014constructing and backtesting both single-factor and filter-enhanced models to evaluate how IVOL can be used to improve portfolio performance.<\/p>\n\n\n\n<p class=\"has-background\" style=\"background-color:#ffe9ae\">\ud83d\udc49 <a href=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-idiosyncratic-volatility\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/factor-strategy-idiosyncratic-volatility\/\">Continue reading to explore how IVOL transforms from a statistical signal into a practical risk-control tool in factor-based investing.<\/a><\/p>\n\n\n\n<div style=\"height:22px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-buttons 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 has-custom-width wp-block-button__width-100 has-custom-font-size\" style=\"font-size:21px\"><a class=\"wp-block-button__link has-background has-text-align-center wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/contact\/\" style=\"border-radius:16px;background:linear-gradient(135deg,rgb(160,209,216) 0%,rgb(51,145,181) 50%,rgb(50,95,191) 100%)\"><strong>Want to Learn More About Our Databases and Solutions?<br>Contact Us and Get the Free Trial Today!<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Further_Reading\"><\/span><strong>Further Reading:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/how-dividend-policy-affects-investment-an-event-study-analysis-of-key-factors\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/how-dividend-policy-affects-investment-an-event-study-analysis-of-key-factors\/\">How Dividend Policy Affects Investment: An Event Study Analysis of Key Factors<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/charles-brandes-value-investing-principles-building-a-portfolio-with-a-margin-of-safety-nemo\/\" data-type=\"link\" data-id=\"https:\/\/www.tejwin.com\/en\/insight\/charles-brandes-value-investing-principles-building-a-portfolio-with-a-margin-of-safety-nemo\/\">Charles Brandes&#8217; Value Investing Principles : Building a Portfolio with a Margin of Safety<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In recent years, the low-volatility anomaly has gained widespread attention for challenging traditional asset pricing theory. This article takes a closer look at one key driver behind the anomaly\u2014Idiosyncratic Volatility (IVOL)\u2014through a comprehensive analysis of the Taiwan stock market. Using point-in-time data from the TEJ Factor Library, we investigate the statistical behavior of IVOL, its relationship with stock characteristics, and its implications for cross-sectional return prediction.\u00a0<\/p>\n","protected":false},"featured_media":37298,"template":"","tags":[3442,3231,2962,2987,3421],"insight-category":[690],"class_list":["post-35319","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-alpha-2","tag-factors-research","tag-market-data","tag-quant","tag-quantitative-strategy","insight-category-data-analysis"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/35319","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":27,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/35319\/revisions"}],"predecessor-version":[{"id":42961,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/35319\/revisions\/42961"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/37298"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=35319"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=35319"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=35319"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}