{"id":32595,"date":"2025-02-07T11:00:00","date_gmt":"2025-02-07T03:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/?post_type=insight&#038;p=32595"},"modified":"2025-03-19T13:34:21","modified_gmt":"2025-03-19T05:34:21","slug":"analyzing-factor-performance-with-alphalens-price-and-volume-factors","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/","title":{"rendered":"Analyzing Factor Performance with Alphalens: Price and Volume Factors"},"content":{"rendered":"\n<p>Key Highlights of This Article<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center has-tablet-text-align-center has-mobile-text-align-center\"><img fetchpriority=\"high\" decoding=\"async\" width=\"4032\" height=\"3024\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash.jpg\" alt=\"Alphalens\" class=\"wp-image-31944\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash.jpg 4032w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-300x225.jpg 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-1024x768.jpg 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-150x113.jpg 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-768x576.jpg 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-1536x1152.jpg 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/tyler-prahm-lmV3gJSAgbo-unsplash-2048x1536.jpg 2048w\" sizes=\"(max-width: 4032px) 100vw, 4032px\" \/><figcaption class=\"wp-element-caption\">Photo by<a href=\"https:\/\/unsplash.com\/@lealea_leaa\" target=\"_blank\" rel=\"noopener\"> <strong>Tyler Prahm<\/strong><\/a> on<a href=\"https:\/\/unsplash.com\/photos\/black-leather-zip-up-jacket-on-white-textile-nsRBbE6-YLs?utm_content=creditCopyText&amp;utm_medium=referral&amp;utm_source=unsplash\" target=\"_blank\" rel=\"noopener\"> Unsplash<\/a><\/figcaption><\/figure>\n\n\n\n<h1 class=\"wp-block-heading\"><\/h1>\n\n\n\n<p><strong>Introduction to Alphalens and Price-Volume Factors<\/strong><\/p>\n\n\n\n<p><strong>Synthesizing Price-Volume Factors and Analyzing Their Performance with Alphalens<\/strong><\/p>\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-69f0f9cbe28b5\" 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-69f0f9cbe28b5\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Preface\" >Preface<\/a><\/li><\/ul><\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#What_Are_Price-Volume_Factors\" >What Are Price-Volume Factors?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Factors_Used_in_This_Study\" >Factors Used in This Study<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#1-Month_Turnover_Rate\" >1-Month Turnover Rate<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#1-Month_Price_Change\" >1-Month Price Change<\/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\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Introduction_to_Alphalens\" >Introduction to Alphalens<\/a><\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Single-Factor_Analysis\" >Single-Factor Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Explanation_of_the_Factor_Quantile_Bar_Chart\" >Explanation of the Factor Quantile Bar Chart<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#X-Axis\" >X-Axis:<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Y-Axis\" >Y-Axis:<\/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\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Interpretation\" >Interpretation:<\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Factor_Performance_Analysis\" >Factor Performance Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Comparison_of_Factor_Quantile_Bar_Charts\" >Comparison of Factor Quantile Bar Charts<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Conclusion\" >Conclusion<\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Factor_Synthesis\" >Factor Synthesis<\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Calculating_Factor_Weights\" >Calculating Factor Weights<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Factor_Synthesis_Method\" >Factor Synthesis Method<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Detailed_Method\" >Detailed Method:<\/a><\/li><\/ul><\/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\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Composite_Factor_Analysis\" >Composite Factor Analysis<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Conclusion-2\" >Conclusion<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Further_Reading\" >Further Reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/#Related_Links\" >Related Links<\/a><\/li><\/ul><\/nav><\/div>\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Preface\"><\/span><strong>Preface<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>In investment decision-making,&nbsp;<strong>price-volume factors<\/strong>&nbsp;are essential for investors to gain insights into market behavior. The relationship between price and trading volume supply and demand dynamics of an asset also reveals capital flows and shifts in market sentiment. These factors play a crucial role in capturing short-term opportunities and identifying potential risks in asset allocation.<\/p>\n\n\n\n<p>This article focuses on&nbsp;<strong>price-volume factors<\/strong>, exploring how these factors reflect the dynamic changes in assets and leveraging the&nbsp;<strong>Alphalens<\/strong>&nbsp;tool to analyze their performance and practical applications in the market. We will first introduce the concept and design logic of price-volume factors and then use&nbsp;<strong>alphabets-tej&nbsp;<\/strong>for analysis to evaluate their explanatory power and stability in predicting asset returns.<\/p>\n\n\n\n<p>In this series of articles, we have previously analyzed&nbsp;<strong>foreign&nbsp;capital&nbsp;<\/strong>and&nbsp;<strong>value factors<\/strong>, discussing the impact of foreign capital flows on the market and how valuation-related indicators affect long-term returns. This article serves as the final part of the series, further enhancing our comprehensive understanding of factor strategies and helping investors effectively utilize price-volume factors to capture market trends.<\/p>\n\n\n\n<p>To conduct similar factor analyses,&nbsp;you can leverage the&nbsp;<strong>alphabets-tej<\/strong>&nbsp;tool in&nbsp;<strong>TQuant Lab<\/strong>.&nbsp;This tool not only integrates&nbsp;<strong>TEJ data<\/strong>&nbsp;but also eliminates the cumbersome data processing steps, allowing you to efficiently examine factor performance and further support the development of investment strategies.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"What_Are_Price-Volume_Factors\"><\/span><strong>What Are Price-Volume Factors?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In the investment market,&nbsp;<strong>price-volume factors<\/strong>&nbsp;are essential for uncovering the relationship between asset prices and trading volume. They are often associated with key market dynamics indicators, such as&nbsp;<strong>trading volume<\/strong>,&nbsp;<strong>volume change rate<\/strong>, and&nbsp;<strong>price momentum<\/strong>. These indicators reflect the intensity of market demand for an asset, capital flows, and overall market sentiment.<\/p>\n\n\n\n<p>Price-volume factors are widely used to identify short-term opportunities and assess the persistence of trends. When applied to investment strategies, they allow investors to analyze the interaction between price and trading volume across different assets.&nbsp;By&nbsp;capturing assets with high trading volume or strong price momentum, investors can achieve excess returns and improve investment efficiency.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factors_Used_in_This_Study\"><\/span><strong>Factors Used in This Study<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-Month_Turnover_Rate\"><\/span><strong>1-Month Turnover Rate<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Calculation formula:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Calculate the cumulative trading volume over the past 20 days:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Based on the trading volume of each stock, compute the total trading volume over the past 20 trading days using a rolling window calculation.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Turnover rate calculation:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Divide the cumulative trading volume by the number of outstanding shares of the stock to obtain the 1-month turnover rate.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"1-Month_Price_Change\"><\/span><strong>1-Month Price Change<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p><strong>Calculation formula:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Calculate the change in closing price over 20 days:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Based on the closing price of each stock, compute the difference between the current closing price and the closing price from 20 trading days ago.<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><strong>Price change formula:<\/strong>\n<ul class=\"wp-block-list\">\n<li>Divide the price change by the closing price from 20 trading days ago and express the result as a percentage.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<p>The&nbsp;<strong>closing price<\/strong>&nbsp;and&nbsp;<strong>trading volume<\/strong>&nbsp;data used in this study are sourced from the&nbsp;<strong>TEJAPI &#8220;Trading Data &#8211; Stock Price Data&#8221;<\/strong>&nbsp;table (<strong>TWN\/APIPRCD<\/strong>), specifically from the&nbsp;<strong>&#8220;Closing Price&#8221;<\/strong>&nbsp;and&nbsp;<strong>&#8220;Trading Volume (in thousand shares)&#8221;<\/strong>&nbsp;columns.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Introduction_to_Alphalens\"><\/span><strong>Introduction to Alphalens<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The&nbsp;<strong>Alphalens-tej<\/strong>&nbsp;package in&nbsp;<strong>TQuant Lab<\/strong>&nbsp;is a Python toolkit for&nbsp;<strong>factor analysis<\/strong>. Its core functionality is to help investors examine and evaluate factor performance, enabling them to develop more effective factor strategies. For a detailed introduction, you can refer to&nbsp;<strong>Alphalens.ipynb<\/strong>.<\/p>\n\n\n\n<p>In&nbsp;<strong>quantitative investing<\/strong>, factors are indicators used to explain and predict asset returns. Common factors include&nbsp;<strong>price-to-earnings ratio (P\/E ratio)<\/strong>,&nbsp;<strong>price momentum<\/strong>, and&nbsp;<strong>trading volume<\/strong>.<\/p>\n\n\n\n<p>Alphalens provides a set of&nbsp;<strong>visualization tools and performance metrics<\/strong>, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Mean Period Wise Return by Factor Quantile<\/strong><\/li>\n\n\n\n<li><strong>Information Coefficient (IC)<\/strong><\/li>\n\n\n\n<li><strong>Cumulative Returns by Quantile<\/strong><\/li>\n<\/ul>\n\n\n\n<p>These tools help us better understand the predictive power and stability of factors. Using Alphalens, investors can quickly analyze the performance of various factors under different market conditions and identify the most suitable factor combinations for their strategies. Additionally,&nbsp;<strong>Alphalens is well integrated with TEJ data<\/strong>, making it particularly useful for conducting&nbsp;<strong>factor backtesting and visualization<\/strong>&nbsp;within&nbsp;<strong>TQuant Lab<\/strong>, thus enhancing the efficiency and convenience of factor research.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Single-Factor_Analysis\"><\/span><strong>Single-Factor Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Due to space limitations, this section will focus only on calculating the&nbsp;<strong>Information Coefficient (IC)<\/strong>&nbsp;and i<strong>nformation Ratio (IR,&nbsp;which is&nbsp;the risk-adjusted IC)<\/strong>&nbsp;and plotting&nbsp;<strong>bar charts of the mean return by factor quantile<\/strong>.<\/p>\n\n\n\n<p>The sample period used in this study spans from&nbsp;<strong>2014 to 2024<\/strong>, and the stock universe consists of the&nbsp;<strong>top 100 most extensive market-cap stocks<\/strong>&nbsp;listed on the exchange.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large caption-align-center\"><img decoding=\"async\" width=\"1024\" height=\"437\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-1024x437.png\" alt=\"\" class=\"wp-image-32652\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-1024x437.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-300x128.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-150x64.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-768x328.png 768w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-1536x656.png 1536w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0b\u53485.09.26-2048x874.png 2048w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">The&nbsp;<strong>IC and IR values<\/strong>&nbsp;for each factor and the&nbsp;<strong>weighted average return of factor values<\/strong>&nbsp;for holding periods of&nbsp;<strong>1 day, 5 days, 10 days, and 21 days<\/strong>.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img decoding=\"async\" width=\"1024\" height=\"338\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-658.png\" alt=\"Alphalens\" class=\"wp-image-32603\" style=\"object-fit:cover\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-658.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-658-300x99.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-658-150x50.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-658-768x254.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Bar chart of the&nbsp;<strong>mean return by factor quantile<\/strong>&nbsp;for the&nbsp;<strong>1-month turnover rate<\/strong>&nbsp;factor.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"349\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-659.png\" alt=\"Alphalens\" class=\"wp-image-32605\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-659.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-659-300x102.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-659-150x51.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-659-768x262.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Bar chart of the&nbsp;<strong>mean return by factor quantile<\/strong>&nbsp;for the&nbsp;<strong>1-month price change<\/strong>&nbsp;factor.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Explanation_of_the_Factor_Quantile_Bar_Chart\"><\/span><strong>Explanation of the Factor Quantile Bar Chart<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"X-Axis\"><\/span><strong>X-Axis:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The&nbsp;<strong>x-axis<\/strong>&nbsp;represents the&nbsp;<strong>factor quantiles<\/strong>, where stocks are grouped into&nbsp;<strong>10 categories<\/strong>&nbsp;based on their factor values.&nbsp;<strong>Quantile 1<\/strong>&nbsp;represents the group with the&nbsp;<strong>lowest factor values<\/strong>, while&nbsp;<strong>Quantile 10<\/strong>&nbsp;represents the group with the&nbsp;<strong>highest<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Y-Axis\"><\/span><strong>Y-Axis:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>The&nbsp;<strong>y-axis<\/strong>&nbsp;represents the&nbsp;<strong>mean return for each quantile<\/strong>, measured in&nbsp;<strong>basis points (bps)<\/strong>&nbsp;(1 bps = 0.01%). This value indicates the&nbsp;<strong>average return over different holding periods<\/strong>&nbsp;(1 day, 5 days, 10 days, and 21 days).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Interpretation\"><\/span><strong>Interpretation:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>This chart illustrates the&nbsp;<strong>average returns<\/strong>&nbsp;for different holding periods across various quantiles. If a factor possesses&nbsp;<strong>predictive power<\/strong>, we generally expect a&nbsp;<strong>monotonic relationship<\/strong>&nbsp;in the returns, where&nbsp;<strong>higher quantile groups (e.g., Quantile 10) yield higher average returns, while lower quantile groups (e.g., Quantile 1) yield lower returns<\/strong>. Such a pattern indicates that the factor can&nbsp;<strong>effectively<\/strong>&nbsp;construct&nbsp;<strong>long-short portfolios<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Performance_Analysis\"><\/span><strong>Factor Performance Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>From the&nbsp;<strong>IC, IR values, and the weighted mean return of factor values<\/strong>, the performance of the&nbsp;<strong>1-month turnover rate<\/strong>&nbsp;and&nbsp;<strong>1-month price change<\/strong>&nbsp;factors did not meet the expected stability standards. Generally, a&nbsp;<strong>factor is considered to have strong predictive ability when its IC value is more significant than 0.03 and its IR value exceeds 0.5<\/strong>.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>1-month turnover rate factor<\/strong>&nbsp;shows&nbsp;<strong>IC and IR values that only approach or slightly exceed the threshold in more extended holding periods (e.g., 21 days)<\/strong>.&nbsp;This&nbsp;suggests that the factor&nbsp;<strong>has some explanatory power for asset returns over longer horizons<\/strong>.<\/li>\n\n\n\n<li>However, for&nbsp;<strong>short-term holding periods (such as 1 day or 5 days)<\/strong>, the&nbsp;<strong>factor&#8217;s performance is highly volatile<\/strong>, and its correlation with asset returns is less clear, indicating&nbsp;<strong>limited short-term predictive ability<\/strong>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Comparison_of_Factor_Quantile_Bar_Charts\"><\/span><strong>Comparison of Factor Quantile Bar Charts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>1-month turnover rate factor<\/strong>&nbsp;exhibits a&nbsp;<strong>monotonic trend<\/strong>&nbsp;in<strong>&nbsp;short-term (1-day, 5-day) and long-term (10-day, 21-day) holding periods<\/strong>.<\/li>\n\n\n\n<li>In contrast, the&nbsp;<strong>1-month price change factor lacks a consistent monotonic pattern<\/strong>&nbsp;in its&nbsp;<strong>quantile returns<\/strong>, particularly in the&nbsp;<strong>middle quantiles<\/strong>, where the performance appears more erratic. Additionally, its performance varies significantly across holding periods, suggesting&nbsp;<strong>insufficient stability in capturing asset returns<\/strong>.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>1-month turnover rate factor<\/strong>&nbsp;consistently demonstrates a&nbsp;<strong>strong monotonic trend across all holding periods<\/strong>, making it a&nbsp;<strong>reliable predictor of asset returns<\/strong>, especially in distinguishing between&nbsp;<strong>high-return and low-return assets<\/strong>.<\/li>\n\n\n\n<li>On the other hand, the&nbsp;<strong>1-month price change factor<\/strong>&nbsp;shows&nbsp;<strong>inconsistent performance<\/strong>, particularly in short-term holding periods, where&nbsp;<strong>both its predictive power and monotonicity are weak<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p>To improve the&nbsp;<strong>1-month price change factor<\/strong>, future optimizations could be considered. Alternatively, combining these two factors with&nbsp;<strong>complementary factors<\/strong>&nbsp;may enhance the&nbsp;<strong>overall predictive ability and stability<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Synthesis\"><\/span><strong>Factor Synthesis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In the previous section, we observed that the&nbsp;<strong>1-month turnover rate factor<\/strong>&nbsp;exhibited&nbsp;<strong>stable predictive ability and a strong monotonic trend across different holding periods<\/strong>, while&nbsp;the&nbsp;<strong>1-month price change factor<\/strong>&nbsp;showed&nbsp;<strong>greater volatility in short-term holding periods and lacked sufficient monotonicity<\/strong>. This section will c<strong>ombine these two factors<\/strong>&nbsp;to evaluate whether the&nbsp;<strong>synthesized factor<\/strong>&nbsp;can effectively enhance overall predictive ability and stability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Calculating_Factor_Weights\"><\/span><strong>Calculating Factor Weights<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>When synthesizing multiple factors, it is essential to assign&nbsp;<strong>appropriate factor weights<\/strong>, as different factors have&nbsp;<strong>varying explanatory power<\/strong>&nbsp;for future returns. Proper weighting helps improve both&nbsp;<strong>prediction accuracy and stability<\/strong>.<\/p>\n\n\n\n<p>This study uses the&nbsp;<strong>rank IC_IR<\/strong>&nbsp;method to calculate&nbsp;<strong>factor weights<\/strong>. The&nbsp;<strong>rank IC_IR<\/strong>&nbsp;is computed by dividing each factor\u2019s&nbsp;<strong>one-month IR ratio<\/strong>&nbsp;by the&nbsp;<strong>sum of the IR ratios<\/strong>&nbsp;of both factors. This&nbsp;<strong>risk-adjusted IC-based weighting method<\/strong>&nbsp;assigns higher weights to factors demonstrating&nbsp;<strong>more extraordinary predictive ability and stability<\/strong>.<\/p>\n\n\n\n<p>To simulate the&nbsp;<strong>signal delay effect in actual trading<\/strong>,&nbsp;we apply a&nbsp;<strong>lagging shift<\/strong>&nbsp;to the final weight data.&nbsp;Specifically, we&nbsp;<strong>shift the weights by one day<\/strong>, ensuring that only&nbsp;<strong>previous-period data is used<\/strong>, thereby avoiding the use of&nbsp;<strong>future information<\/strong>&nbsp;in the model.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"489\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-660.png\" alt=\"Alphalens\" class=\"wp-image-32608\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-660.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-660-300x143.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-660-150x72.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-660-768x367.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Daily Factor Weights<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Factor_Synthesis_Method\"><\/span><strong>Factor Synthesis Method<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The&nbsp;<strong>composite factor<\/strong>&nbsp;is calculated by taking the&nbsp;<strong>weighted average of individual factors<\/strong>&nbsp;based on their respective&nbsp;<strong>weight<\/strong>, resulting in a single factor&nbsp;<strong>representing the&nbsp;overall<\/strong>effect.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Detailed_Method\"><\/span><strong>Detailed Method:<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"373\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-661.png\" alt=\"Alphalens\" class=\"wp-image-32610\" style=\"object-fit:cover\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-661.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-661-300x109.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-661-150x55.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-661-768x280.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Raw Factor Data<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"365\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-662.png\" alt=\"Alphalens\" class=\"wp-image-32612\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-662.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-662-300x107.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-662-150x53.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-662-768x274.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Composite Factor Data<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Composite_Factor_Analysis\"><\/span><strong>Composite Factor Analysis<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Similar to the&nbsp;<strong>single-factor analysis<\/strong>, we import the&nbsp;<strong>new composite factor data<\/strong>&nbsp;into&nbsp;<strong>Alphalens<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"380\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-663.png\" alt=\"Alphalens\" class=\"wp-image-32614\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-663.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-663-300x111.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-663-150x56.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-663-768x285.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Bar Chart of Mean Return by Quantile<\/figcaption><\/figure>\n\n\n\n<p><strong>X-Axis:<\/strong>&nbsp;Represents the&nbsp;<strong>factor quantiles (1 to 10)<\/strong>, where higher quantiles indicate&nbsp;<strong>higher factor values<\/strong>.<\/p>\n\n\n\n<p><strong>Y-Axis:<\/strong>&nbsp;Represents the&nbsp;<strong>mean return for each quantile<\/strong>, measured in&nbsp;<strong>basis points (bps)<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"374\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-664.png\" alt=\"Alphalens\" class=\"wp-image-32616\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/image-664.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-664-300x110.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-664-150x55.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/image-664-768x281.png 768w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Cumulative Return Line Chart<\/figcaption><\/figure>\n\n\n\n<p><strong>X-Axis:<\/strong>&nbsp;Represents the&nbsp;<strong>years<\/strong>, showing the&nbsp;<strong>time variation of cumulative returns<\/strong>.<\/p>\n\n\n\n<p><strong>Y-Axis:<\/strong>&nbsp;Represents the&nbsp;<strong>logarithmic cumulative return<\/strong>, where&nbsp;<strong>higher values indicate higher cumulative returns<\/strong>.<\/p>\n\n\n\n<p><strong>Color Legend:<\/strong>&nbsp;Different&nbsp;<strong>colored lines<\/strong>&nbsp;represent the&nbsp;<strong>cumulative returns of different quantiles<\/strong>.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" width=\"1310\" height=\"318\" src=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30.png\" alt=\"Alphalens\" class=\"wp-image-32624\" srcset=\"https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30.png 1310w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30-300x73.png 300w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30-1024x249.png 1024w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30-150x36.png 150w, https:\/\/www.tejwin.com\/wp-content\/uploads\/\u622a\u5716-2025-02-11-\u4e0a\u534811.39.30-768x186.png 768w\" sizes=\"(max-width: 1310px) 100vw, 1310px\" \/><figcaption class=\"wp-element-caption\">IC and IR Values of the Composite Factor and the Weighted Average Return of Factor Values (Holding Periods: 1 Day, 5 Days, 10 Days, 21 Days)<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><\/h2>\n\n\n\n<p>From the&nbsp;<strong>IC, IR values, and the weighted average return of the composite factor<\/strong>, we can see that the composite factor outperforms the&nbsp;<strong>1-month price change factor<\/strong>&nbsp;in&nbsp;<strong>all holding periods<\/strong>&nbsp;in terms of&nbsp;<strong>IC, IR, and average return<\/strong>. However, it still falls&nbsp;<strong>slightly behind the 1-month turnover rate factor<\/strong>. Additionally, it is observed that&nbsp;<strong>the longer the holding period, the better the performance of the composite factor<\/strong>.<\/p>\n\n\n\n<p>Furthermore, the&nbsp;<strong>bar chart of mean return by quantile&nbsp;<\/strong>shows that the&nbsp;<strong>composite factor exhibits a relatively stable monotonic increasing trend across quantiles<\/strong>, outperforming the&nbsp;<strong>1-month price change factor<\/strong>. However, compared to the&nbsp;<strong>1-month turnover rate factor<\/strong>, the composite factor still shows some gaps in&nbsp;<strong>monotonicity and stability<\/strong>. Overall, by integrating the characteristics of both factors, the&nbsp;<strong>composite factor balances predictive power and stability. However,<\/strong>&nbsp;further optimization is required to&nbsp;<strong>match the performance of the best-performing factor<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Conclusion-2\"><\/span><strong>Conclusion<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Through this analysis, we demonstrated&nbsp;<strong>how to use Alphalens to evaluate the performance of price-volume factors&nbsp;<\/strong>and apply them to&nbsp;<strong>practical investment strategies<\/strong>. The two price-volume factors analyzed in this study are the&nbsp;<strong>1-month turnover rate<\/strong>&nbsp;and the&nbsp;<strong>1-month price change<\/strong>.<\/p>\n\n\n\n<p>From the&nbsp;<strong>single-factor IC and IR analysis<\/strong>, we found that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>1-month turnover rate factor<\/strong>&nbsp;exhibits&nbsp;<strong>strong predictive ability and a clear monotonic trend across all holding periods<\/strong>, particularly in&nbsp;<strong>more extended holding periods (e.g., 21 days)<\/strong>, where it performs relatively better.<\/li>\n\n\n\n<li>However, the&nbsp;<strong>1-month price change factor&nbsp;<\/strong>has&nbsp;<strong>limited predictive power<\/strong>, showing&nbsp;<strong>insufficient monotonicity and stability<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p>We&nbsp;<strong>synthesized the two price-volume factors using weighted averaging<\/strong>. The results indicate that:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The&nbsp;<strong>composite factor outperforms the 1-month price change factor<\/strong>&nbsp;in terms of&nbsp;<strong>IC, IR, and monotonicity<\/strong>.<\/li>\n\n\n\n<li>However, it&nbsp;<strong>slightly underperforms the 1-month turnover rate factor<\/strong>.<\/li>\n\n\n\n<li>The\u00a0<strong>best performance of the composite factor occurs at the 21-day holding period<\/strong>, but it is not significantly better than other holding periods.\u00a0This\u00a0suggests that while the\u00a0<strong>composite factor maintains a certain level of stability across different holding periods<\/strong>, the factor synthesis method did not yield\u00a0<strong>powerful improvements<\/strong>. Future research could explore\u00a0<strong>alternative synthesis methods<\/strong>\u00a0to construct a more\u00a0<strong>effective composite factor<\/strong>.<\/li>\n<\/ul>\n\n\n\n<p><strong>Important Reminder<\/strong>: This analysis is for reference only and does not constitute any product or investment advice.<\/p>\n\n\n\n<p>We welcome readers interested in various trading strategies to consider purchasing relevant solutions from&nbsp;<a href=\"https:\/\/www.tejwin.com\/en\/solution\/quantitative-finance-solution\/\" class=\"ek-link\"><strong><mark style=\"background-color:#ffdf88\" class=\"has-inline-color\">Quantitative Finance Solution<\/mark><\/strong><\/a>. With our high-quality databases, you can construct a trading strategy that suits your needs.<\/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-100 has-custom-font-size\" style=\"font-size:22px\"><a class=\"wp-block-button__link has-background wp-element-button\" href=\"https:\/\/www.tejwin.com\/en\/databank-solution\/financial-data\/\" style=\"border-radius:16px;background:linear-gradient(135deg,rgb(243,224,131) 0%,rgb(102,197,166) 50%,rgb(51,132,181) 100%)\"><strong>Access to Comprehensive Quantitative Data<\/strong><br><strong>Start Building Portfolios That Outperform the Market Today!<\/strong><\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:22px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p class=\"has-text-align-center\" style=\"font-size:32px\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-vivid-cyan-blue-color\"><strong><em>&#8220;Taiwan stock market data, TEJ collect it all.&#8221;<\/em><\/strong><\/mark><\/p>\n\n\n\n<p>The characteristics of the Taiwan stock market differ from those of other European and American markets. Especially in the first quarter of 2024, with the <strong><mark style=\"background-color:rgba(0, 0, 0, 0);color:#c05d5d\" class=\"has-inline-color\">Taiwan Stock Exchange reaching a new high of 20,000 points<\/mark><\/strong> due to the rise in TSMC&#8217;s stock price, global institutional investors are paying more attention to the performance of the Taiwan stock market.&nbsp;<\/p>\n\n\n\n<p><strong><mark style=\"background-color:rgba(0, 0, 0, 0);color:#0978b8\" class=\"has-inline-color\">Taiwan Economical Journal (TEJ)<\/mark><\/strong>, a financial database established in Taiwan for over 30 years, serves local financial institutions and academic institutions, and has long-term cooperation with internationally renowned data providers, providing high-quality financial data for five financial markets in Asia.&nbsp;<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color has-black-color\">Complete Coverage<\/mark><\/strong>: Includes all listed companies on stock markets in Taiwan, China, Hong Kong, Japan, Korea, etc.&nbsp;<\/li>\n\n\n\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color\">Comprehensive Analysis of Enterprises<\/mark><\/strong>: Operational aspects, financial aspects, securities market performance, ESG sustainability, etc.&nbsp;<\/li>\n\n\n\n<li><strong><mark style=\"background-color:#ebc766\" class=\"has-inline-color\">High-Quality Database<\/mark><\/strong>: TEJ data is cleaned, checked, enhanced, and integrated to ensure it meets the information needs of financial and market analysis.&nbsp;<\/li>\n<\/ul>\n\n\n\n<p>With TEJ&#8217;s assistance, you can access relevant information about major stock markets in Asia, such as securities market, financials data, enterprise operations, board of directors, sustainability data, etc., providing investors with timely and high-quality content. Additionally, TEJ offers advisory services to help solve problems in theoretical practice and financial management!<\/p>\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>This study presents a<strong>&nbsp;workflow from single-factor analysis to composite factor construction and backtesting<\/strong>. In future applications, we can consider:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Introducing additional complementary factors<\/strong>&nbsp;to enhance factor interactions.<\/li>\n\n\n\n<li><strong>Exploring different weighting methods<\/strong>&nbsp;to optimize the performance of composite factors.<\/li>\n\n\n\n<li><strong>Incorporating actual trading costs<\/strong>&nbsp;such as&nbsp;<strong>slippage and transaction fees<\/strong>&nbsp;to evaluate the actual feasibility of the strategy.<\/li>\n<\/ul>\n\n\n\n<p>By implementing these enhancements,&nbsp;<strong>multi-factor strategies can become more adaptive and robust<\/strong>,&nbsp;<strong>supportinginvestment decision-making in dynamic markets<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Further_Reading\"><\/span>Further Reading<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/www.tejwin.com\/en\/insight\/analyzing-factor-performance-with-alphalens\/#\">Analyzing Factor Performance with Alphalens: Foreign Capital Factors<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.tejwin.com\/en\/insight\/seeking-alpha\/\">Seeking Alpha<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><span class=\"ez-toc-section\" id=\"Related_Links\"><\/span>Related Links<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p><a href=\"https:\/\/github.com\/tejtw\/TQuant-Lab\" target=\"_blank\" rel=\"noopener\">TQuant Lab Github<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/tquant.tejwin.com\" target=\"_blank\" rel=\"noopener\">TQuant Lab \u9996\u9801<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In investment decision-making,\u00a0price-volume factors\u00a0are essential for investors to gain insights into market behavior. The relationship between price and trading volume supply and demand dynamics of an asset also reveals capital flows and shifts in market sentiment. These factors play a crucial role in capturing short-term opportunities and identifying potential risks in asset allocation.<\/p>\n","protected":false},"featured_media":33130,"template":"","tags":[3442,3164,2926,2988],"insight-category":[3509,1356],"class_list":["post-32595","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-alpha-2","tag-data-analysis-2","tag-factor-investing","tag-quantitative-analysis","insight-category-fintech-en","insight-category-tquant-lab-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/32595","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":4,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/32595\/revisions"}],"predecessor-version":[{"id":33706,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/32595\/revisions\/33706"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/33130"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=32595"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=32595"},{"taxonomy":"insight-category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight-category?post=32595"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}