{"id":3427,"date":"2026-08-26T08:54:10","date_gmt":"2026-08-26T08:54:10","guid":{"rendered":"https:\/\/www.tejwin.com\/en\/product-services\/uncategorized\/\/"},"modified":"2026-09-16T06:12:52","modified_gmt":"2026-09-16T06:12:52","slug":"factor-library-taiwans-factor-dataset-for-quantitative-investing","status":"publish","type":"product","link":"https:\/\/www.tejwin.com\/en\/product-services\/alternative-data\/factor-library-taiwans-factor-dataset-for-quantitative-investing\/","title":{"rendered":"Factor Library \u2013 Taiwan&#8217;s Factor Dataset for Quantitative Investing"},"content":{"rendered":"<h2 class=\"wp-block-heading\"><strong>Introduction<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Over the past decades,\u00a0<mark class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>quantitative investing<\/strong><\/mark>\u00a0has 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\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">factors<\/mark><\/strong>. These factors have since been embedded into institutional workflows, powering everything from\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">alpha\u00a0<\/mark><\/strong>generation to portfolio construction and risk management.<\/p>\n<p class=\"wp-block-paragraph\">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<p class=\"wp-block-paragraph\"><strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">TEJ\u2019s Factor Library<\/mark><\/strong>\u00a0was created in response to these challenges. It offers a robust,\u00a0<mark class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>point-in-time (PIT)\u00a0<\/strong><\/mark>database featuring more than 100 academically grounded and locally adapted factors across 11 core categories. Built for practical deployment in the\u00a0<strong>Taiwan stock market<\/strong>, the library empowers investors to accelerate research, build repeatable\u00a0<strong>quantitative strategies<\/strong>, and generate more reliable\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">alpha signals\u00a0<\/mark><\/strong>through transparent and consistent data.<\/p>\n<p class=\"wp-block-paragraph\">Yet even with a well-structured foundation, factor investing remains a complex discipline\u2014especially when moving from theory to execution.<\/p>\n<h2 class=\"wp-block-heading\"><span id=\"Challenges_in_Factor_Investing\" class=\"ez-toc-section\"><\/span>Challenges in Factor Investing<\/h2>\n<p class=\"wp-block-paragraph\">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\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time<\/mark><\/strong>\u00a0(PIT) structure\u2014introducing risks like\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">look-ahead bias.<\/mark><\/strong><\/p>\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 loading=\"lazy\" decoding=\"async\" class=\"wp-image-34875\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-1024x418.jpg\" alt=\"Factor research workflow\" width=\"1024\" height=\"418\" data-id=\"34875\" \/><\/figure>\n<\/figure>\n<p class=\"wp-block-paragraph\"><em>Figure1\uff1aTraditional Factor Research Workflow<\/em><\/p>\n<p class=\"wp-block-paragraph\">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<p class=\"wp-block-paragraph\">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<h2 class=\"wp-block-heading\"><span id=\"Introduction_to_the_Factor_Library\" class=\"ez-toc-section\"><\/span>Introduction to the Factor Library<\/h2>\n<p class=\"wp-block-paragraph\">TEJ\u2019s 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>\u00a0<mark 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>\u00a0All data are processed with complete PIT alignment and traceability to eliminate forward-looking bias.<\/p>\n<figure class=\"wp-block-image size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-44874\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11%E9%A1%9E%E5%9B%A0%E5%AD%90%E5%9C%96.png\" alt=\"\" width=\"784\" height=\"674\" \/><\/figure>\n<p class=\"wp-block-paragraph\"><em>Figure 2\uff1aTEJ Factor Library \uff0d11 Factor Categories<\/em><\/p>\n<h3 class=\"wp-block-heading\"><span id=\"Database_Construction_Methodology\" class=\"ez-toc-section\"><\/span>Database Construction Methodology:<\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Academic Foundations:<\/strong>\u00a0Derived from global academic journals and institutional research, supplemented by TEJ\u2019s proprietary analysis.<\/li>\n<li><strong>Data Source:<\/strong>\u00a0Built on TEJ\u2019s investment-grade database, fully\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time.<\/mark><\/strong><\/li>\n<li><strong>Localization:<\/strong>\u00a0Adjusted from academic definitions for\u00a0<strong>relevance to Taiwan\u2019s stock market<\/strong>.<\/li>\n<li><strong>Factor Count:<\/strong>\u00a0Over 100 factors, including academic and machine-learning-based factors.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><\/h3>\n<figure class=\"wp-block-table is-style-stripes\">\n<table class=\"has-pale-cyan-blue-background-color has-background\">\n<thead>\n<tr>\n<th><strong>Category<\/strong><\/th>\n<th><strong>Description<\/strong><\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Momentum<\/strong><\/td>\n<td>Captures the persistence in both price and fundamental performance of a firm.<\/td>\n<\/tr>\n<tr>\n<td><strong>Dividend Yield<\/strong><\/td>\n<td>Captures the excess returns associated with high-dividend stocks and reflects a firm\u2019s dividend policy and capital return strategy.<\/td>\n<\/tr>\n<tr>\n<td><strong>Value<\/strong><\/td>\n<td>Reflects undervaluation relative to fundamentals and potential for excess returns.<\/td>\n<\/tr>\n<tr>\n<td><strong>Growth<\/strong><\/td>\n<td>Reflects the growth potential of a company\u2019s earnings and revenues, and captures excess returns from high-growth stocks.<\/td>\n<\/tr>\n<tr>\n<td><strong>Quality<\/strong><\/td>\n<td>Reflects a company\u2019s financial strength and operational soundness, and captures excess returns from high-quality stocks.<\/td>\n<\/tr>\n<tr>\n<td><strong>Liquidity<\/strong><\/td>\n<td>The liquidity factor measures trading ease. Stocks with lower liquidity often entail higher costs, leading to potential excess returns.<\/td>\n<\/tr>\n<tr>\n<td><strong>Volatility<\/strong><\/td>\n<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>\n<\/tr>\n<tr>\n<td><strong>Size<\/strong><\/td>\n<td>Captures the relationship between a firm\u2019s market capitalization and its returns.<\/td>\n<\/tr>\n<tr>\n<td><strong>Sentiment<\/strong><\/td>\n<td>Captures the impact of investor behavior and psychological expectations on stock prices.<\/td>\n<\/tr>\n<tr>\n<td><strong>Credit Risk<\/strong><\/td>\n<td>Measures the probability of corporate default or bankruptcy.<\/td>\n<\/tr>\n<tr>\n<td><strong>Machine Learning<\/strong><\/td>\n<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>\n<\/tr>\n<\/tbody>\n<\/table><figcaption class=\"wp-element-caption\"><strong><em>Table 1: Description of the 11 Categories in the Factor Library<\/em><\/strong>\u00a0(2026 updated)<\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\"><span id=\"Factor_Library_%E2%80%93_Use_Cases_and_Applications\" class=\"ez-toc-section\"><\/span>Factor Library \u2013 Use Cases and Applications<\/h2>\n<p class=\"wp-block-paragraph\">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<h3 class=\"wp-block-heading\"><span id=\"Key_Applications\" class=\"ez-toc-section\"><\/span>Key Applications<\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Factor-Based Stock Selection:<\/strong>\u00a0By 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<li><strong>Quantitative Investing:<\/strong>\u00a0Researchers 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\u00a0<strong>alpha signals<\/strong>\u2014ultimately enhancing portfolio return potential.<\/li>\n<li><strong>Risk Analysis:<\/strong>\u00a0Factor 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<h3 class=\"wp-block-heading\"><span id=\"Example_Multi-Factor_Stock_Selection\" class=\"ez-toc-section\"><\/span>Example: Multi-Factor Stock Selection<\/h3>\n<ul class=\"wp-block-list\">\n<li><strong>Stock Universe Definition:\u00a0<\/strong>Based on liquidity and size.<\/li>\n<li><strong>Factor Testing &amp; Selection<\/strong>: Identify effective factors.<\/li>\n<li><strong>Model Construction<\/strong>: Standardize and weight selected factors (e.g., Z-score method).<\/li>\n<li><strong>Strategy Execution<\/strong>: Define rebalancing and portfolio rules; execute trades accordingly.<\/li>\n<\/ul>\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\" class=\"wp-image-34885\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-1024x457.png\" alt=\"\" width=\"1024\" height=\"457\" data-id=\"34885\" \/><\/figure>\n<\/figure>\n<p class=\"wp-block-paragraph\"><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<h2 class=\"wp-block-heading\"><span id=\"Key_Benefits_of_TEJ_Factor_Library\" class=\"ez-toc-section\"><\/span>Key Benefits of TEJ Factor Library<\/h2>\n<p class=\"wp-block-paragraph\">In today\u2019s 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\u00a0<strong>market data service<\/strong>\u00a0that facilitates advanced\u00a0<strong>quantitative data analysis<\/strong>.<\/p>\n<h3 class=\"wp-block-heading\"><span id=\"Localized_Factor_Design\" class=\"ez-toc-section\"><\/span>Localized Factor Design<\/h3>\n<p class=\"wp-block-paragraph\">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<h3 class=\"wp-block-heading\"><span id=\"Point-in-Time_PIT_Data_Integrity\" class=\"ez-toc-section\"><\/span>Point-in-Time (PIT) Data Integrity<\/h3>\n<p class=\"wp-block-paragraph\">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<h3 class=\"wp-block-heading\"><span id=\"Ready-to-Use_Factor_Dataset\" class=\"ez-toc-section\"><\/span>Ready-to-Use Factor Dataset<\/h3>\n<p class=\"wp-block-paragraph\">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<figure class=\"wp-block-table is-style-regular has-medium-font-size\">\n<table class=\"has-background\">\n<tbody>\n<tr>\n<td><strong>Factor Code<\/strong><\/td>\n<td>mom52wh<\/td>\n<\/tr>\n<tr>\n<td><strong>Factor Name<\/strong><\/td>\n<td>Momentum Factor (52-Week High)<\/td>\n<\/tr>\n<tr>\n<td><strong>English Name<\/strong><\/td>\n<td>52-Week High Momentum (MOM52WH)<\/td>\n<\/tr>\n<tr>\n<td><strong>Category<\/strong><\/td>\n<td>Momentum<\/td>\n<\/tr>\n<tr>\n<td><strong>Subcategory<\/strong><\/td>\n<td>Price Momentum<\/td>\n<\/tr>\n<tr>\n<td><strong>Expected Direction<\/strong><\/td>\n<td>Positive<\/td>\n<\/tr>\n<tr>\n<td><strong>Reference<\/strong><\/td>\n<td>George, T.J., &amp; Hwang, C. (2004).\u00a0<em>The 52-Week High and Momentum Investing<\/em>. Journal of Finance, 59(5), 2145\u20132176.<\/td>\n<\/tr>\n<tr>\n<td><strong>Calculation Method<\/strong><\/td>\n<td>Adjusted closing price of the day divided by the highest adjusted price over the past 252 trading days.<\/td>\n<\/tr>\n<\/tbody>\n<\/table><figcaption class=\"wp-element-caption\"><em>Table 2: Sample Factor Description<\/em><\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\"><span id=\"Conclusion\" class=\"ez-toc-section\"><\/span>Conclusion<\/h2>\n<p class=\"wp-block-paragraph\">TEJ\u2019s Factor Library empowers investment teams with high-quality, standardized, and traceable factor data, bridging the gap from data acquisition to live strategy execution. It\u2019s not just a research tool, but a strategic asset\u2014enabling alpha discovery, model backtesting, and risk management.<\/p>\n<p class=\"wp-block-paragraph\">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\u00a0<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<p class=\"wp-block-paragraph\">TEJ\u2019s commitment to innovation, accuracy, and usability positions the Factor Library as an indispensable resource for investors aiming to transform data into performance.<\/p>\n<h2 class=\"wp-block-heading\"><span id=\"Further_Reading\" class=\"ez-toc-section\"><\/span><strong>Further Reading:<\/strong><\/h2>\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<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/affiliated-companies-disclosures\/\">Unlocking Market Insights: Comparing Three Strategies Based on Directors\u2019 Shareholding Data<\/a><\/li>\n<\/ul>\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\/\"><strong>Unlock Taiwan Alpha\uff0d<\/strong><br \/>\n<strong>Download TEJ Factor White Paper<\/strong><\/a><\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Introduction Over the past decades,\u00a0quantitative invest [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","tags":[48,57],"product_category":[135],"class_list":["post-3427","product","type-product","status-publish","hentry","tag-factor-library","tag-alternative-data","product_category-alternative-data"],"acf":{"p_introduction":"<h2 class=\"wp-block-heading\"><strong>Introduction<\/strong><\/h2>\r\n<p class=\"wp-block-paragraph\">Over the past decades,\u00a0<mark class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>quantitative investing<\/strong><\/mark>\u00a0has 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\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">factors<\/mark><\/strong>. These factors have since been embedded into institutional workflows, powering everything from\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">alpha\u00a0<\/mark><\/strong>generation to portfolio construction and risk management.<\/p>\r\n<p class=\"wp-block-paragraph\">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>\r\n<p class=\"wp-block-paragraph\"><strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">TEJ\u2019s Factor Library<\/mark><\/strong>\u00a0was created in response to these challenges. It offers a robust,\u00a0<mark class=\"has-inline-color has-vivid-cyan-blue-color\"><strong>point-in-time (PIT)\u00a0<\/strong><\/mark>database featuring more than 100 academically grounded and locally adapted factors across 11 core categories. Built for practical deployment in the\u00a0<strong>Taiwan stock market<\/strong>, the library empowers investors to accelerate research, build repeatable\u00a0<strong>quantitative strategies<\/strong>, and generate more reliable\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">alpha signals\u00a0<\/mark><\/strong>through transparent and consistent data.<\/p>\r\n<p class=\"wp-block-paragraph\">Yet even with a well-structured foundation, factor investing remains a complex discipline\u2014especially when moving from theory to execution.<\/p>\r\n\r\n<h2 class=\"wp-block-heading\"><span id=\"Challenges_in_Factor_Investing\" class=\"ez-toc-section\"><\/span>Challenges in Factor Investing<\/h2>\r\n<p class=\"wp-block-paragraph\">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\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time<\/mark><\/strong>\u00a0(PIT) structure\u2014introducing risks like\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">look-ahead bias.<\/mark><\/strong><\/p>\r\n\r\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\">\r\n<figure class=\"wp-block-image size-large is-style-default\"><img class=\"wp-image-34875\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Factor-research-workflow-2-1024x418.jpg\" alt=\"Factor research workflow\" width=\"1024\" height=\"418\" data-id=\"34875\" \/><\/figure>\r\n<\/figure>\r\n<p class=\"wp-block-paragraph\"><em>Figure1\uff1aTraditional Factor Research Workflow<\/em><\/p>\r\n<p class=\"wp-block-paragraph\">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>\r\n<p class=\"wp-block-paragraph\">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>\r\n\r\n<h2 class=\"wp-block-heading\"><span id=\"Introduction_to_the_Factor_Library\" class=\"ez-toc-section\"><\/span>Introduction to the Factor Library<\/h2>\r\n<p class=\"wp-block-paragraph\">TEJ\u2019s 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>\u00a0<mark 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>\u00a0All data are processed with complete PIT alignment and traceability to eliminate forward-looking bias.<\/p>\r\n\r\n<figure class=\"wp-block-image size-full is-resized\"><img class=\"wp-image-44874\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/factorlib-11%E9%A1%9E%E5%9B%A0%E5%AD%90%E5%9C%96.png\" alt=\"\" width=\"784\" height=\"674\" \/><\/figure>\r\n<p class=\"wp-block-paragraph\"><em>Figure 2\uff1aTEJ Factor Library \uff0d11 Factor Categories<\/em><\/p>\r\n\r\n<h3 class=\"wp-block-heading\"><span id=\"Database_Construction_Methodology\" class=\"ez-toc-section\"><\/span>Database Construction Methodology:<\/h3>\r\n<ul class=\"wp-block-list\">\r\n \t<li><strong>Academic Foundations:<\/strong>\u00a0Derived from global academic journals and institutional research, supplemented by TEJ\u2019s proprietary analysis.<\/li>\r\n \t<li><strong>Data Source:<\/strong>\u00a0Built on TEJ\u2019s investment-grade database, fully\u00a0<strong><mark class=\"has-inline-color has-vivid-cyan-blue-color\">point-in-time.<\/mark><\/strong><\/li>\r\n \t<li><strong>Localization:<\/strong>\u00a0Adjusted from academic definitions for\u00a0<strong>relevance to Taiwan\u2019s stock market<\/strong>.<\/li>\r\n \t<li><strong>Factor Count:<\/strong>\u00a0Over 100 factors, including academic and machine-learning-based factors.<\/li>\r\n<\/ul>\r\n<h3 class=\"wp-block-heading\"><\/h3>\r\n<figure class=\"wp-block-table is-style-stripes\">\r\n<table class=\"has-pale-cyan-blue-background-color has-background\">\r\n<thead>\r\n<tr>\r\n<th><strong>Category<\/strong><\/th>\r\n<th><strong>Description<\/strong><\/th>\r\n<\/tr>\r\n<\/thead>\r\n<tbody>\r\n<tr>\r\n<td><strong>Momentum<\/strong><\/td>\r\n<td>Captures the persistence in both price and fundamental performance of a firm.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Dividend Yield<\/strong><\/td>\r\n<td>Captures the excess returns associated with high-dividend stocks and reflects a firm\u2019s dividend policy and capital return strategy.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Value<\/strong><\/td>\r\n<td>Reflects undervaluation relative to fundamentals and potential for excess returns.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Growth<\/strong><\/td>\r\n<td>Reflects the growth potential of a company\u2019s earnings and revenues, and captures excess returns from high-growth stocks.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Quality<\/strong><\/td>\r\n<td>Reflects a company\u2019s financial strength and operational soundness, and captures excess returns from high-quality stocks.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Liquidity<\/strong><\/td>\r\n<td>The liquidity factor measures trading ease. Stocks with lower liquidity often entail higher costs, leading to potential excess returns.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Volatility<\/strong><\/td>\r\n<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>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Size<\/strong><\/td>\r\n<td>Captures the relationship between a firm\u2019s market capitalization and its returns.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Sentiment<\/strong><\/td>\r\n<td>Captures the impact of investor behavior and psychological expectations on stock prices.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Credit Risk<\/strong><\/td>\r\n<td>Measures the probability of corporate default or bankruptcy.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Machine Learning<\/strong><\/td>\r\n<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>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<figcaption class=\"wp-element-caption\"><strong><em>Table 1: Description of the 11 Categories in the Factor Library<\/em><\/strong>\u00a0(2026 updated)<\/figcaption><\/figure>\r\n<h2 class=\"wp-block-heading\"><span id=\"Factor_Library_%E2%80%93_Use_Cases_and_Applications\" class=\"ez-toc-section\"><\/span>Factor Library \u2013 Use Cases and Applications<\/h2>\r\n<p class=\"wp-block-paragraph\">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>\r\n\r\n<h3 class=\"wp-block-heading\"><span id=\"Key_Applications\" class=\"ez-toc-section\"><\/span>Key Applications<\/h3>\r\n<ul class=\"wp-block-list\">\r\n \t<li><strong>Factor-Based Stock Selection:<\/strong>\u00a0By 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>\r\n \t<li><strong>Quantitative Investing:<\/strong>\u00a0Researchers 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\u00a0<strong>alpha signals<\/strong>\u2014ultimately enhancing portfolio return potential.<\/li>\r\n \t<li><strong>Risk Analysis:<\/strong>\u00a0Factor 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>\r\n<\/ul>\r\n<h3 class=\"wp-block-heading\"><span id=\"Example_Multi-Factor_Stock_Selection\" class=\"ez-toc-section\"><\/span>Example: Multi-Factor Stock Selection<\/h3>\r\n<ul class=\"wp-block-list\">\r\n \t<li><strong>Stock Universe Definition:\u00a0<\/strong>Based on liquidity and size.<\/li>\r\n \t<li><strong>Factor Testing &amp; Selection<\/strong>: Identify effective factors.<\/li>\r\n \t<li><strong>Model Construction<\/strong>: Standardize and weight selected factors (e.g., Z-score method).<\/li>\r\n \t<li><strong>Strategy Execution<\/strong>: Define rebalancing and portfolio rules; execute trades accordingly.<\/li>\r\n<\/ul>\r\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\">\r\n<figure class=\"wp-block-image size-large\"><img class=\"wp-image-34885\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/Cumulative-Return-of-a-Factor-Strategy-1024x457.png\" alt=\"\" width=\"1024\" height=\"457\" data-id=\"34885\" \/><\/figure>\r\n<\/figure>\r\n<p class=\"wp-block-paragraph\"><em>Figure 3: Cumulative Return of a Factor Strategy \u2013 highlighting how factor data supports performance backtesting to discover alpha-generating strategies.<\/em><\/p>\r\n\r\n<h2 class=\"wp-block-heading\"><span id=\"Key_Benefits_of_TEJ_Factor_Library\" class=\"ez-toc-section\"><\/span>Key Benefits of TEJ Factor Library<\/h2>\r\n<p class=\"wp-block-paragraph\">In today\u2019s 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\u00a0<strong>market data service<\/strong>\u00a0that facilitates advanced\u00a0<strong>quantitative data analysis<\/strong>.<\/p>\r\n\r\n<h3 class=\"wp-block-heading\"><span id=\"Localized_Factor_Design\" class=\"ez-toc-section\"><\/span>Localized Factor Design<\/h3>\r\n<p class=\"wp-block-paragraph\">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>\r\n\r\n<h3 class=\"wp-block-heading\"><span id=\"Point-in-Time_PIT_Data_Integrity\" class=\"ez-toc-section\"><\/span>Point-in-Time (PIT) Data Integrity<\/h3>\r\n<p class=\"wp-block-paragraph\">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>\r\n\r\n<h3 class=\"wp-block-heading\"><span id=\"Ready-to-Use_Factor_Dataset\" class=\"ez-toc-section\"><\/span>Ready-to-Use Factor Dataset<\/h3>\r\n<p class=\"wp-block-paragraph\">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>\r\n\r\n<figure class=\"wp-block-table is-style-regular has-medium-font-size\">\r\n<table class=\"has-background\">\r\n<tbody>\r\n<tr>\r\n<td><strong>Factor Code<\/strong><\/td>\r\n<td>mom52wh<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Factor Name<\/strong><\/td>\r\n<td>Momentum Factor (52-Week High)<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>English Name<\/strong><\/td>\r\n<td>52-Week High Momentum (MOM52WH)<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Category<\/strong><\/td>\r\n<td>Momentum<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Subcategory<\/strong><\/td>\r\n<td>Price Momentum<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Expected Direction<\/strong><\/td>\r\n<td>Positive<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Reference<\/strong><\/td>\r\n<td>George, T.J., &amp; Hwang, C. (2004).\u00a0<em>The 52-Week High and Momentum Investing<\/em>. Journal of Finance, 59(5), 2145\u20132176.<\/td>\r\n<\/tr>\r\n<tr>\r\n<td><strong>Calculation Method<\/strong><\/td>\r\n<td>Adjusted closing price of the day divided by the highest adjusted price over the past 252 trading days.<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<figcaption class=\"wp-element-caption\"><em>Table 2: Sample Factor Description<\/em><\/figcaption><\/figure>\r\n<h2 class=\"wp-block-heading\"><span id=\"Conclusion\" class=\"ez-toc-section\"><\/span>Conclusion<\/h2>\r\n<p class=\"wp-block-paragraph\">TEJ\u2019s Factor Library empowers investment teams with high-quality, standardized, and traceable factor data, bridging the gap from data acquisition to live strategy execution. It\u2019s not just a research tool, but a strategic asset\u2014enabling alpha discovery, model backtesting, and risk management.<\/p>\r\n<p class=\"wp-block-paragraph\">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\u00a0<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>\r\n<p class=\"wp-block-paragraph\">TEJ\u2019s commitment to innovation, accuracy, and usability positions the Factor Library as an indispensable resource for investors aiming to transform data into performance.<\/p>\r\n\r\n<h2 class=\"wp-block-heading\"><span id=\"Further_Reading\" class=\"ez-toc-section\"><\/span><strong>Further Reading:<\/strong><\/h2>\r\n<ul class=\"wp-block-list\">\r\n \t<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>\r\n \t<li><a href=\"https:\/\/www.tejwin.com\/en\/insight\/affiliated-companies-disclosures\/\">Unlocking Market Insights: Comparing Three Strategies Based on Directors\u2019 Shareholding Data<\/a><\/li>\r\n<\/ul>\r\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\r\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\/\"><strong>Unlock Taiwan Alpha\uff0d<\/strong>\r\n<strong>Download TEJ Factor White Paper<\/strong><\/a><\/div>\r\n<\/div>","card_icon":"","card_summary":"","key_data_metrics":[{"metric_label":"Frequency","metric_value":"daily"},{"metric_label":"Source","metric_value":"TWSE"},{"metric_label":"Historiclal Period","metric_value":"From 2013\/01\/01"},{"metric_label":"Delivery Methods","metric_value":"FTP\/SFTP"}],"data_information":"TEJ Factor Library provides point-in-time data across 11 core categories, empowering investors to navigate the factor zoo, build repeatable strategies, and generate reliable alpha in Taiwan.","delivery_methods":null,"doc_file":"","related_products":"","related_more_text":"View all","related_more_url":"\/en\/product-services\/"},"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/product\/3427","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/types\/product"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=3427"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=3427"},{"taxonomy":"product_category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/product_category?post=3427"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}