{"id":34900,"date":"2025-05-08T18:57:54","date_gmt":"2025-05-08T10:57:54","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=news&#038;p=34900"},"modified":"2026-05-11T17:19:57","modified_gmt":"2026-05-11T09:19:57","slug":"factor-library","status":"publish","type":"news","link":"https:\/\/www.tejwin.com\/en\/news\/factor-library\/","title":{"rendered":"Factor Library \u2013 Taiwan&#8217;s Factor Dataset for Quantitative Investing"},"content":{"rendered":"\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"576\" data-id=\"34868\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-1024x576.jpg\" alt=\"\" class=\"wp-image-34868\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-1024x576.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-300x169.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-150x84.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-768x432.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library-1536x864.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/TEJ-Factor-Library.jpg 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/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-6a401901ba8e8\" 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-6a401901ba8e8\"  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\/news\/factor-library\/#Introduction\" >Introduction<\/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\/news\/factor-library\/#Challenges_in_Factor_Investing\" >Challenges in Factor Investing<\/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\/news\/factor-library\/#Introduction_to_the_Factor_Library\" >Introduction to the Factor Library<\/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\/news\/factor-library\/#Database_Construction_Methodology\" >Database Construction Methodology:<\/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\/news\/factor-library\/#i\" >&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Factor_Library_%E2%80%93_Use_Cases_and_Applications\" >Factor Library \u2013 Use Cases and Applications<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Key_Applications\" >Key Applications<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Example_Multi-Factor_Stock_Selection\" >Example: Multi-Factor Stock Selection<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Key_Benefits_of_TEJ_Factor_Library\" >Key Benefits of TEJ Factor Library<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Localized_Factor_Design\" >Localized Factor Design<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Point-in-Time_PIT_Data_Integrity\" >Point-in-Time (PIT) Data Integrity<\/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\/news\/factor-library\/#Ready-to-Use_Factor_Dataset\" >Ready-to-Use Factor Dataset<\/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\/news\/factor-library\/#Conclusion\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.tejwin.com\/en\/news\/factor-library\/#Further_Reading\" >Further Reading:<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><strong>Introduction<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Over the past decades, <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>quantitative investing<\/strong><\/mark> has evolved from academic theory into a core pillar of modern investment management. Foundational models such as CAPM, APT, and the Fama-French multifactor framework have shaped how investors identify and quantify systematic return drivers\u2014known as <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">factors<\/mark><\/strong>. These factors have since been embedded into institutional workflows, powering everything from <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">alpha <\/mark><\/strong>generation to portfolio construction and risk management.<\/p>\n\n\n\n<p>However, the proliferation of factors\u2014often inconsistently defined or statistically fragile\u2014has given rise to what researchers call the \u201cfactor zoo.\u201d This phenomenon underscores the urgent need for structured, high-quality data and disciplined implementation frameworks.<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">TEJ\u2019s Factor Library<\/mark><\/strong> was created in response to these challenges. It offers a robust, <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>point-in-time (PIT) <\/strong><\/mark>database featuring more than 100 academically grounded and locally adapted factors across 11 core categories. Built for practical deployment in the <strong>Taiwan stock market<\/strong>, the library empowers investors to accelerate research, build repeatable <strong>quantitative strategies<\/strong>, and generate more reliable <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">alpha signals <\/mark><\/strong>through transparent and consistent data.<\/p>\n\n\n\n<p>Yet even with a well-structured foundation, factor investing remains a complex discipline\u2014especially when moving from theory to execution.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Challenges_in_Factor_Investing\"><\/span>Challenges in Factor Investing<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The factor investing process\u2014from data collection to strategy construction\u2014is complex and resource-intensive (see Figure 1). Analysts must source data from multiple providers or crawlers, deal with inconsistent formats, and often face the lack of a <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time<\/mark><\/strong> (PIT) structure\u2014introducing risks like <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">look-ahead bias.<\/mark><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-2 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large is-style-default\"><img decoding=\"async\" width=\"1024\" height=\"418\" data-id=\"34875\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-1024x418.jpg\" alt=\"Factor research workflow\" class=\"wp-image-34875\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-1024x418.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-300x122.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-150x61.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-768x313.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-1536x626.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2.jpg 1920w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n\n<p><em>Figure1\uff1aTraditional Factor Research Workflow<\/em><\/p>\n\n\n\n<p>Preprocessing involves missing value handling, outlier detection, and aligning data by release timing\u2014all technically demanding tasks. Designing factor logic requires extensive literature review, adapting definitions for local markets, and ensuring statistical validity. These challenges consume significant time and resources and introduce errors that can hinder research and replication.<\/p>\n\n\n\n<p>A well-structured factor database that incorporates PIT processing, academic rigor, and transparent methodology can greatly streamline the process and help investors focus on strategy innovation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction_to_the_Factor_Library\"><\/span>Introduction to the Factor Library<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>TEJ&#8217;s Factor Library is a structured, point-in-time database designed to explain asset risks and returns through factor characteristics. It currently covers 11 major factor categories:<strong> <mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">Momentum, Dividend Yield, Value, Growth, Quality, Liquidity, Volatility, Size, Sentiment, Credit Risk and machine Learning<\/mark>.<\/strong> All data are processed with complete PIT alignment and traceability to eliminate forward-looking bias.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"784\" height=\"674\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11\u985e\u56e0\u5b50\u5716.png\" alt=\"\" class=\"wp-image-44874\" style=\"width:840px;height:auto\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11\u985e\u56e0\u5b50\u5716.png 784w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11\u985e\u56e0\u5b50\u5716-300x258.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11\u985e\u56e0\u5b50\u5716-150x129.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11\u985e\u56e0\u5b50\u5716-768x660.png 768w\" sizes=\"(max-width: 784px) 100vw, 784px\" \/><\/figure>\n\n\n\n<p><em>Figure 2\uff1aTEJ Factor Library \uff0d11 Factor Categories<\/em><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Database_Construction_Methodology\"><\/span>Database Construction Methodology:<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Academic Foundations:<\/strong> Derived from global academic journals and institutional research, supplemented by TEJ&#8217;s proprietary analysis.<\/li>\n\n\n\n<li><strong>Data Source:<\/strong> Built on TEJ&#8217;s investment-grade database, fully <strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time.<\/mark><\/strong><\/li>\n\n\n\n<li><strong>Localization:<\/strong> Adjusted from academic definitions for <strong>relevance to Taiwan&#8217;s stock market<\/strong>.<\/li>\n\n\n\n<li><strong>Factor Count:<\/strong> Over 100 factors, including academic and machine-learning-based factors.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"i\"><\/span> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-table is-style-stripes\" style=\"font-style:normal;font-weight:400;text-decoration:none\"><table class=\"has-pale-cyan-blue-background-color has-background\"><thead><tr><th><strong>Category<\/strong><\/th><th><strong>Description<\/strong><\/th><\/tr><\/thead><tbody><tr><td><strong>Momentum<\/strong><\/td><td>Captures the persistence in both price and fundamental performance of a firm.<\/td><\/tr><tr><td><strong>Dividend Yield<\/strong><\/td><td>Captures the excess returns associated with high-dividend stocks and reflects a firm\u2019s dividend policy and capital return strategy.<\/td><\/tr><tr><td><strong>Value<\/strong><\/td><td>Reflects undervaluation relative to fundamentals and potential for excess returns.<\/td><\/tr><tr><td><strong>Growth<\/strong><\/td><td>Reflects the growth potential of a company\u2019s earnings and revenues, and captures excess returns from high-growth stocks.<\/td><\/tr><tr><td><strong>Quality<\/strong><\/td><td>Reflects a company\u2019s financial strength and operational soundness, and captures excess returns from high-quality stocks.<\/td><\/tr><tr><td><strong>Liquidity<\/strong><\/td><td>The liquidity factor measures trading ease. Stocks with lower liquidity often entail higher costs, leading to potential excess returns.<\/td><\/tr><tr><td><strong>Volatility<\/strong><\/td><td>Measures the uncertainty in stock prices or returns, and captures the excess returns associated with low-risk stocks (as measured by volatility, beta, or idiosyncratic risk).<\/td><\/tr><tr><td><strong>Size<\/strong><\/td><td>Captures the relationship between a firm\u2019s market capitalization and its returns.<\/td><\/tr><tr><td><strong>Sentiment<\/strong><\/td><td>Captures the impact of investor behavior and psychological expectations on stock prices.<\/td><\/tr><tr><td><strong>Credit Risk<\/strong><\/td><td>Measures the probability of corporate default or bankruptcy.<\/td><\/tr><tr><td><strong>Machine Learning<\/strong><\/td><td>Utilizes statistical algorithms and AI techniques to extract non-linear features and complex patterns from high-dimensional data, aiming to enhance asset pricing or return prediction accuracy.<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><strong><em>Table 1: Description of the 11 Categories in the Factor Library<\/em><\/strong> (2026 updated)<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Library_%E2%80%93_Use_Cases_and_Applications\"><\/span>Factor Library \u2013 Use Cases and Applications<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The value of the Factor Library extends beyond data provision\u2014it enables diverse applications across the investment lifecycle. Depending on the strategy, investors can deploy single or multiple factors for stock selection, risk assessment, and model construction.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_Applications\"><\/span>Key Applications<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Factor-Based Stock Selection:<\/strong> By filtering stocks based on one or multiple factor metrics, investors can identify equities with specific desired characteristics. This helps narrow down the investable universe and improves the precision and efficiency of the stock selection process.<\/li>\n\n\n\n<li><strong>Quantitative Investing:<\/strong> Researchers can use factor data to develop entirely new investment strategies or refine existing ones. By integrating selected factors into systematic models, they can better capture specific risk premia and <strong>alpha signals<\/strong>\u2014ultimately enhancing portfolio return potential.<\/li>\n\n\n\n<li><strong>Risk Analysis:<\/strong> Factor data can be used to assess the underlying risk profile of individual securities or entire portfolios. This enables investors to strengthen their risk control frameworks and make more informed asset allocation decisions.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Example_Multi-Factor_Stock_Selection\"><\/span>Example: Multi-Factor Stock Selection<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Stock Universe Definition: <\/strong>Based on liquidity and size.<\/li>\n\n\n\n<li><strong>Factor Testing &amp; Selection<\/strong>: Identify effective factors.<\/li>\n\n\n\n<li><strong>Model Construction<\/strong>: Standardize and weight selected factors (e.g., Z-score method).<\/li>\n\n\n\n<li><strong>Strategy Execution<\/strong>: Define rebalancing and portfolio rules; execute trades accordingly.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-3 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"457\" data-id=\"34885\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-1024x457.png\" alt=\"\" class=\"wp-image-34885\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-1024x457.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-300x134.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-150x67.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-768x343.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-1536x686.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy.png 1964w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n<\/figure>\n\n\n\n<p><em>Figure 3: Cumulative Return of a Factor Strategy \u2013 highlighting how factor data supports performance backtesting to discover alpha-generating strategies.<\/em><\/p>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Key_Benefits_of_TEJ_Factor_Library\"><\/span>Key Benefits of TEJ Factor Library<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In today&#8217;s market environment\u2014characterized by an explosion of factors and widening information gaps\u2014researchers often find themselves bogged down by labor-intensive processes such as data preparation, validation, and ongoing maintenance. These challenges make it difficult to focus on strategic optimization and backtesting. The TEJ Factor Library was explicitly designed to solve these pain points. Its data service emphasizes academic rigor, practical relevance, and completeness in update frequency, data structure, and usability. It also serves as a high-quality <strong>market data service<\/strong> that facilitates advanced <strong>quantitative data analysis<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Localized_Factor_Design\"><\/span>Localized Factor Design<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Factors are specifically designed for the Taiwan market, incorporating local trading behaviors such as margin financing, broker activity, and institutional flows, enabling the capture of market microstructure and behavioral patterns often missing in global datasets, and allowing investors to identify Taiwan-specific inefficiencies and generate more differentiated alpha signals.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Point-in-Time_PIT_Data_Integrity\"><\/span>Point-in-Time (PIT) Data Integrity<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>All factors are constructed under a strict Point-in-Time framework, preserving historical data versions and aligning with actual data availability, ensuring the elimination of look-ahead bias and consistency between backtesting and live trading environments, thereby improving the reliability and credibility of research results and reducing model risk in investment decision-making.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Ready-to-Use_Factor_Dataset\"><\/span>Ready-to-Use Factor Dataset<span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The dataset provides 116 pre-calculated factors across 11 categories in a standardized, research-ready format, removing the need for data cleaning, factor construction, and alignment across multiple sources, and enabling researchers to focus on alpha generation rather than data engineering while significantly accelerating the overall research process.<\/p>\n\n\n\n<figure class=\"wp-block-table is-style-regular has-medium-font-size\"><table class=\"has-background\" style=\"background:linear-gradient(135deg,rgb(255,245,203) 0%,rgba(51,168,181,0) 100%)\"><tbody><tr><td><strong>Factor Code<\/strong><\/td><td>mom52wh<\/td><\/tr><tr><td><strong>Factor Name<\/strong><\/td><td>Momentum Factor (52-Week High)<\/td><\/tr><tr><td><strong>English Name<\/strong><\/td><td>52-Week High Momentum (MOM52WH)<\/td><\/tr><tr><td><strong>Category<\/strong><\/td><td>Momentum<\/td><\/tr><tr><td><strong>Subcategory<\/strong><\/td><td>Price Momentum<\/td><\/tr><tr><td><strong>Expected Direction<\/strong><\/td><td>Positive<\/td><\/tr><tr><td><strong>Reference<\/strong><\/td><td>George, T.J., &amp; Hwang, C. (2004). <em>The 52-Week High and Momentum Investing<\/em>. Journal of Finance, 59(5), 2145\u20132176.<\/td><\/tr><tr><td><strong>Calculation Method<\/strong><\/td><td>Adjusted closing price of the day divided by the highest adjusted price over the past 252 trading days.<\/td><\/tr><\/tbody><\/table><figcaption class=\"wp-element-caption\"><em>Table 2: Sample Factor Description<\/em><\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span>Conclusion<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>TEJ&#8217;s Factor Library empowers investment teams with high-quality, standardized, and traceable factor data, bridging the gap from data acquisition to live strategy execution. It&#8217;s not just a research tool, but a strategic asset\u2014enabling alpha discovery, model backtesting, and risk management.<\/p>\n\n\n\n<p>By combining academic insights with local market practices, and supporting over 100 factors across 11 categories with PIT structure and daily updates, TEJ provides the robust infrastructure required to navigate the expanding world of <strong>factor investing<\/strong>. In an era of market uncertainty and data explosion, only those with access to verifiable and flexible factor systems can stay ahead in the quant investing landscape.<\/p>\n\n\n\n<p>TEJ&#8217;s commitment to innovation, accuracy, and usability positions the Factor Library as an indispensable resource for investors aiming to transform data into performance.<\/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\/affiliated-companies-disclosures\/\">Unlocking Market Insights: Comparing Three Strategies Based on Directors&#8217; Shareholding Data<\/a><\/li>\n<\/ul>\n\n\n\n<p class=\"has-medium-font-size\"><\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-75\"><a class=\"wp-block-button__link has-white-color has-text-color has-background has-link-color has-medium-font-size has-custom-font-size wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/tej-factor-white-paper-2025\/\" style=\"background-color:#003399\"><strong>Unlock Taiwan Alpha\uff0d<\/strong><br><strong>Download TEJ Facfor White Paper<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>TEJ Factor Library provides a comprehensive set of pre-calculated quantitative factors designed for the Taiwan equity market.<br \/>\nTEJ Factor Library covers over 100 localized factors across 11 major categories, enabling investors and researchers to efficiently conduct factor-based analysis, backtesting, and strategy development.<br \/>\nUnlike raw data inputs, TEJ Factor Library delivers ready-to-use signals, significantly reducing the time required for data cleaning, factor construction, and alignment.<\/p>\n","protected":false},"featured_media":34868,"parent":0,"menu_order":0,"template":"","tags":[3442,2926,3540,3421],"news-category":[764,688],"class_list":["post-34900","news","type-news","status-publish","has-post-thumbnail","hentry","tag-alpha-2","tag-factor-investing","tag-factor-library","tag-quantitative-strategy","news-category-product-introduction-en","news-category-product-news"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/news\/34900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/news"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/34868"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=34900"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=34900"},{"taxonomy":"news-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/news-category?post=34900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}