{"id":1123,"date":"2025-02-07T11:00:00","date_gmt":"2025-02-07T11:00:00","guid":{"rendered":"https:\/\/www.tejwin.com\/en\/insights\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/"},"modified":"2025-02-07T11:00:00","modified_gmt":"2025-02-07T11:00:00","slug":"analyzing-factor-performance-with-alphalens-price-and-volume-factors","status":"publish","type":"insight","link":"https:\/\/www.tejwin.com\/en\/insights\/analyzing-factor-performance-with-alphalens-price-and-volume-factors\/","title":{"rendered":"Analyzing Factor Performance with Alphalens: Price and Volume Factors"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Key Highlights of This Article<\/p>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center has-tablet-text-align-center has-mobile-text-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-31944\" height=\"3024\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/tyler-prahm-lmV3gJSAgbo-unsplash.jpg\" width=\"4032\"\/><figcaption class=\"wp-element-caption\">Photo by<a href=\"https:\/\/unsplash.com\/@lealea_leaa\" rel=\"noopener\" target=\"_blank\"> <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\" rel=\"noopener\" target=\"_blank\"> Unsplash<\/a><\/figcaption><\/figure>\n<h1 class=\"wp-block-heading\"><\/h1>\n<p class=\"wp-block-paragraph\"><strong>Introduction to Alphalens and Price-Volume Factors<\/strong><\/p>\n<p class=\"wp-block-paragraph\"><strong>Synthesizing Price-Volume Factors and Analyzing Their Performance with Alphalens<\/strong><\/p>\n<h3 class=\"wp-block-heading\"><strong>Preface<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">In investment decision-making,\u00a0<strong>price-volume factors<\/strong>\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<p class=\"wp-block-paragraph\">This article focuses on\u00a0<strong>price-volume factors<\/strong>, exploring how these factors reflect the dynamic changes in assets and leveraging the\u00a0<strong>Alphalens<\/strong>\u00a0tool 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\u00a0<strong>alphabets-tej\u00a0<\/strong>for analysis to evaluate their explanatory power and stability in predicting asset returns.<\/p>\n<p class=\"wp-block-paragraph\">In this series of articles, we have previously analyzed\u00a0<strong>foreign\u00a0capital\u00a0<\/strong>and\u00a0<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<p class=\"wp-block-paragraph\">To conduct similar factor analyses,\u00a0you can leverage the\u00a0<strong>alphabets-tej<\/strong>\u00a0tool in\u00a0<strong>TQuant Lab<\/strong>.\u00a0This tool not only integrates\u00a0<strong>TEJ data<\/strong>\u00a0but also eliminates the cumbersome data processing steps, allowing you to efficiently examine factor performance and further support the development of investment strategies.<\/p>\n<h2 class=\"wp-block-heading\"><strong>What Are Price-Volume Factors?<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">In the investment market,\u00a0<strong>price-volume factors<\/strong>\u00a0are essential for uncovering the relationship between asset prices and trading volume. They are often associated with key market dynamics indicators, such as\u00a0<strong>trading volume<\/strong>,\u00a0<strong>volume change rate<\/strong>, and\u00a0<strong>price momentum<\/strong>. These indicators reflect the intensity of market demand for an asset, capital flows, and overall market sentiment.<\/p>\n<p class=\"wp-block-paragraph\">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.\u00a0By\u00a0capturing assets with high trading volume or strong price momentum, investors can achieve excess returns and improve investment efficiency.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Factors Used in This Study<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>1-Month Turnover Rate<\/strong><\/h3>\n<p class=\"wp-block-paragraph\"><strong>Calculation formula:<\/strong><\/p>\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<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<h3 class=\"wp-block-heading\"><strong>1-Month Price Change<\/strong><\/h3>\n<p class=\"wp-block-paragraph\"><strong>Calculation formula:<\/strong><\/p>\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<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<p class=\"wp-block-paragraph\">The\u00a0<strong>closing price<\/strong>\u00a0and\u00a0<strong>trading volume<\/strong>\u00a0data used in this study are sourced from the\u00a0<strong>TEJAPI \u201cTrading Data \u2013 Stock Price Data\u201d<\/strong>\u00a0table (<strong>TWN\/APIPRCD<\/strong>), specifically from the\u00a0<strong>\u201cClosing Price\u201d<\/strong>\u00a0and\u00a0<strong>\u201cTrading Volume (in thousand shares)\u201d<\/strong>\u00a0columns.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Introduction to Alphalens<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>Alphalens-tej<\/strong>\u00a0package in\u00a0<strong>TQuant Lab<\/strong>\u00a0is a Python toolkit for\u00a0<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\u00a0<strong>Alphalens.ipynb<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">In\u00a0<strong>quantitative investing<\/strong>, factors are indicators used to explain and predict asset returns. Common factors include\u00a0<strong>price-to-earnings ratio (P\/E ratio)<\/strong>,\u00a0<strong>price momentum<\/strong>, and\u00a0<strong>trading volume<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">Alphalens provides a set of\u00a0<strong>visualization tools and performance metrics<\/strong>, including:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Mean Period Wise Return by Factor Quantile<\/strong><\/li>\n<li><strong>Information Coefficient (IC)<\/strong><\/li>\n<li><strong>Cumulative Returns by Quantile<\/strong><\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">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,\u00a0<strong>Alphalens is well integrated with TEJ data<\/strong>, making it particularly useful for conducting\u00a0<strong>factor backtesting and visualization<\/strong>\u00a0within\u00a0<strong>TQuant Lab<\/strong>, thus enhancing the efficiency and convenience of factor research.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Single-Factor Analysis<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Due to space limitations, this section will focus only on calculating the\u00a0<strong>Information Coefficient (IC)<\/strong>\u00a0and i<strong>nformation Ratio (IR,\u00a0which is\u00a0the risk-adjusted IC)<\/strong>\u00a0and plotting\u00a0<strong>bar charts of the mean return by factor quantile<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">The sample period used in this study spans from\u00a0<strong>2014 to 2024<\/strong>, and the stock universe consists of the\u00a0<strong>top 100 most extensive market-cap stocks<\/strong>\u00a0listed on the exchange.<\/p>\n<figure class=\"wp-block-image aligncenter size-large caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" class=\"wp-image-32652\" height=\"437\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/E688AAE59C96-2025-02-11-E4B88BE58D885.09.26.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">The\u00a0<strong>IC and IR values<\/strong>\u00a0for each factor and the\u00a0<strong>weighted average return of factor values<\/strong>\u00a0for holding periods of\u00a0<strong>1 day, 5 days, 10 days, and 21 days<\/strong>.<\/figcaption><\/figure>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32603\" height=\"338\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-658.png\" style=\"object-fit:cover\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Bar chart of the\u00a0<strong>mean return by factor quantile<\/strong>\u00a0for the\u00a0<strong>1-month turnover rate<\/strong>\u00a0factor.<\/figcaption><\/figure>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32605\" height=\"349\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-659.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Bar chart of the\u00a0<strong>mean return by factor quantile<\/strong>\u00a0for the\u00a0<strong>1-month price change<\/strong>\u00a0factor.<\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\"><strong>Explanation of the Factor Quantile Bar Chart<\/strong><\/h2>\n<h3 class=\"wp-block-heading\"><strong>X-Axis:<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>x-axis<\/strong>\u00a0represents the\u00a0<strong>factor quantiles<\/strong>, where stocks are grouped into\u00a0<strong>10 categories<\/strong>\u00a0based on their factor values.\u00a0<strong>Quantile 1<\/strong>\u00a0represents the group with the\u00a0<strong>lowest factor values<\/strong>, while\u00a0<strong>Quantile 10<\/strong>\u00a0represents the group with the\u00a0<strong>highest<\/strong>.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Y-Axis:<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>y-axis<\/strong>\u00a0represents the\u00a0<strong>mean return for each quantile<\/strong>, measured in\u00a0<strong>basis points (bps)<\/strong>\u00a0(1 bps = 0.01%). This value indicates the\u00a0<strong>average return over different holding periods<\/strong>\u00a0(1 day, 5 days, 10 days, and 21 days).<\/p>\n<h3 class=\"wp-block-heading\"><strong>Interpretation:<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">This chart illustrates the\u00a0<strong>average returns<\/strong>\u00a0for different holding periods across various quantiles. If a factor possesses\u00a0<strong>predictive power<\/strong>, we generally expect a\u00a0<strong>monotonic relationship<\/strong>\u00a0in the returns, where\u00a0<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\u00a0<strong>effectively<\/strong>\u00a0construct\u00a0<strong>long-short portfolios<\/strong>.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Factor Performance Analysis<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">From the\u00a0<strong>IC, IR values, and the weighted mean return of factor values<\/strong>, the performance of the\u00a0<strong>1-month turnover rate<\/strong>\u00a0and\u00a0<strong>1-month price change<\/strong>\u00a0factors did not meet the expected stability standards. Generally, a\u00a0<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<ul class=\"wp-block-list\">\n<li>The\u00a0<strong>1-month turnover rate factor<\/strong>\u00a0shows\u00a0<strong>IC and IR values that only approach or slightly exceed the threshold in more extended holding periods (e.g., 21 days)<\/strong>.\u00a0This\u00a0suggests that the factor\u00a0<strong>has some explanatory power for asset returns over longer horizons<\/strong>.<\/li>\n<li>However, for\u00a0<strong>short-term holding periods (such as 1 day or 5 days)<\/strong>, the\u00a0<strong>factor\u2019s performance is highly volatile<\/strong>, and its correlation with asset returns is less clear, indicating\u00a0<strong>limited short-term predictive ability<\/strong>.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Comparison of Factor Quantile Bar Charts<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>The\u00a0<strong>1-month turnover rate factor<\/strong>\u00a0exhibits a\u00a0<strong>monotonic trend<\/strong>\u00a0in<strong>\u00a0short-term (1-day, 5-day) and long-term (10-day, 21-day) holding periods<\/strong>.<\/li>\n<li>In contrast, the\u00a0<strong>1-month price change factor lacks a consistent monotonic pattern<\/strong>\u00a0in its\u00a0<strong>quantile returns<\/strong>, particularly in the\u00a0<strong>middle quantiles<\/strong>, where the performance appears more erratic. Additionally, its performance varies significantly across holding periods, suggesting\u00a0<strong>insufficient stability in capturing asset returns<\/strong>.<\/li>\n<\/ul>\n<h3 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h3>\n<ul class=\"wp-block-list\">\n<li>The\u00a0<strong>1-month turnover rate factor<\/strong>\u00a0consistently demonstrates a\u00a0<strong>strong monotonic trend across all holding periods<\/strong>, making it a\u00a0<strong>reliable predictor of asset returns<\/strong>, especially in distinguishing between\u00a0<strong>high-return and low-return assets<\/strong>.<\/li>\n<li>On the other hand, the\u00a0<strong>1-month price change factor<\/strong>\u00a0shows\u00a0<strong>inconsistent performance<\/strong>, particularly in short-term holding periods, where\u00a0<strong>both its predictive power and monotonicity are weak<\/strong>.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">To improve the\u00a0<strong>1-month price change factor<\/strong>, future optimizations could be considered. Alternatively, combining these two factors with\u00a0<strong>complementary factors<\/strong>\u00a0may enhance the\u00a0<strong>overall predictive ability and stability<\/strong>.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Factor Synthesis<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">In the previous section, we observed that the\u00a0<strong>1-month turnover rate factor<\/strong>\u00a0exhibited\u00a0<strong>stable predictive ability and a strong monotonic trend across different holding periods<\/strong>, while\u00a0the\u00a0<strong>1-month price change factor<\/strong>\u00a0showed\u00a0<strong>greater volatility in short-term holding periods and lacked sufficient monotonicity<\/strong>. This section will c<strong>ombine these two factors<\/strong>\u00a0to evaluate whether the\u00a0<strong>synthesized factor<\/strong>\u00a0can effectively enhance overall predictive ability and stability.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Calculating Factor Weights<\/strong><\/h3>\n<p class=\"wp-block-paragraph\">When synthesizing multiple factors, it is essential to assign\u00a0<strong>appropriate factor weights<\/strong>, as different factors have\u00a0<strong>varying explanatory power<\/strong>\u00a0for future returns. Proper weighting helps improve both\u00a0<strong>prediction accuracy and stability<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">This study uses the\u00a0<strong>rank IC_IR<\/strong>\u00a0method to calculate\u00a0<strong>factor weights<\/strong>. The\u00a0<strong>rank IC_IR<\/strong>\u00a0is computed by dividing each factor\u2019s\u00a0<strong>one-month IR ratio<\/strong>\u00a0by the\u00a0<strong>sum of the IR ratios<\/strong>\u00a0of both factors. This\u00a0<strong>risk-adjusted IC-based weighting method<\/strong>\u00a0assigns higher weights to factors demonstrating\u00a0<strong>more extraordinary predictive ability and stability<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">To simulate the\u00a0<strong>signal delay effect in actual trading<\/strong>,\u00a0we apply a\u00a0<strong>lagging shift<\/strong>\u00a0to the final weight data.\u00a0Specifically, we\u00a0<strong>shift the weights by one day<\/strong>, ensuring that only\u00a0<strong>previous-period data is used<\/strong>, thereby avoiding the use of\u00a0<strong>future information<\/strong>\u00a0in the model.<\/p>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32608\" height=\"489\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-660.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Daily Factor Weights<\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\"><strong>Factor Synthesis Method<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">The\u00a0<strong>composite factor<\/strong>\u00a0is calculated by taking the\u00a0<strong>weighted average of individual factors<\/strong>\u00a0based on their respective\u00a0<strong>weight<\/strong>, resulting in a single factor\u00a0<strong>representing the\u00a0overall<\/strong>effect.<\/p>\n<h3 class=\"wp-block-heading\"><strong>Detailed Method:<\/strong><\/h3>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32610\" height=\"373\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-661.png\" style=\"object-fit:cover\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Raw Factor Data<\/figcaption><\/figure>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32612\" height=\"365\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-662.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Composite Factor Data<\/figcaption><\/figure>\n<h2 class=\"wp-block-heading\"><strong>Composite Factor Analysis<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Similar to the\u00a0<strong>single-factor analysis<\/strong>, we import the\u00a0<strong>new composite factor data<\/strong>\u00a0into\u00a0<strong>Alphalens<\/strong>.<\/p>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32614\" height=\"380\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-663.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Bar Chart of Mean Return by Quantile<\/figcaption><\/figure>\n<p class=\"wp-block-paragraph\"><strong>X-Axis:<\/strong>\u00a0Represents the\u00a0<strong>factor quantiles (1 to 10)<\/strong>, where higher quantiles indicate\u00a0<strong>higher factor values<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\"><strong>Y-Axis:<\/strong>\u00a0Represents the\u00a0<strong>mean return for each quantile<\/strong>, measured in\u00a0<strong>basis points (bps)<\/strong>.<\/p>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32616\" height=\"374\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/image-664.png\" width=\"1024\"\/><figcaption class=\"wp-element-caption\">Cumulative Return Line Chart<\/figcaption><\/figure>\n<p class=\"wp-block-paragraph\"><strong>X-Axis:<\/strong>\u00a0Represents the\u00a0<strong>years<\/strong>, showing the\u00a0<strong>time variation of cumulative returns<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\"><strong>Y-Axis:<\/strong>\u00a0Represents the\u00a0<strong>logarithmic cumulative return<\/strong>, where\u00a0<strong>higher values indicate higher cumulative returns<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\"><strong>Color Legend:<\/strong>\u00a0Different\u00a0<strong>colored lines<\/strong>\u00a0represent the\u00a0<strong>cumulative returns of different quantiles<\/strong>.<\/p>\n<figure class=\"wp-block-image aligncenter size-full caption-align-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"Alphalens\" class=\"wp-image-32624\" height=\"318\" src=\"https:\/\/www.tejwin.com\/en\/wp-content\/uploads\/2026\/08\/E688AAE59C96-2025-02-11-E4B88AE58D8811.39.30.png\" width=\"1310\"\/><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<h2 class=\"wp-block-heading\"><\/h2>\n<p class=\"wp-block-paragraph\">From the\u00a0<strong>IC, IR values, and the weighted average return of the composite factor<\/strong>, we can see that the composite factor outperforms the\u00a0<strong>1-month price change factor<\/strong>\u00a0in\u00a0<strong>all holding periods<\/strong>\u00a0in terms of\u00a0<strong>IC, IR, and average return<\/strong>. However, it still falls\u00a0<strong>slightly behind the 1-month turnover rate factor<\/strong>. Additionally, it is observed that\u00a0<strong>the longer the holding period, the better the performance of the composite factor<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">Furthermore, the\u00a0<strong>bar chart of mean return by quantile\u00a0<\/strong>shows that the\u00a0<strong>composite factor exhibits a relatively stable monotonic increasing trend across quantiles<\/strong>, outperforming the\u00a0<strong>1-month price change factor<\/strong>. However, compared to the\u00a0<strong>1-month turnover rate factor<\/strong>, the composite factor still shows some gaps in\u00a0<strong>monotonicity and stability<\/strong>. Overall, by integrating the characteristics of both factors, the\u00a0<strong>composite factor balances predictive power and stability. However,<\/strong>\u00a0further optimization is required to\u00a0<strong>match the performance of the best-performing factor<\/strong>.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Through this analysis, we demonstrated\u00a0<strong>how to use Alphalens to evaluate the performance of price-volume factors\u00a0<\/strong>and apply them to\u00a0<strong>practical investment strategies<\/strong>. The two price-volume factors analyzed in this study are the\u00a0<strong>1-month turnover rate<\/strong>\u00a0and the\u00a0<strong>1-month price change<\/strong>.<\/p>\n<p class=\"wp-block-paragraph\">From the\u00a0<strong>single-factor IC and IR analysis<\/strong>, we found that:<\/p>\n<ul class=\"wp-block-list\">\n<li>The\u00a0<strong>1-month turnover rate factor<\/strong>\u00a0exhibits\u00a0<strong>strong predictive ability and a clear monotonic trend across all holding periods<\/strong>, particularly in\u00a0<strong>more extended holding periods (e.g., 21 days)<\/strong>, where it performs relatively better.<\/li>\n<li>However, the\u00a0<strong>1-month price change factor\u00a0<\/strong>has\u00a0<strong>limited predictive power<\/strong>, showing\u00a0<strong>insufficient monotonicity and stability<\/strong>.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">We\u00a0<strong>synthesized the two price-volume factors using weighted averaging<\/strong>. The results indicate that:<\/p>\n<ul class=\"wp-block-list\">\n<li>The\u00a0<strong>composite factor outperforms the 1-month price change factor<\/strong>\u00a0in terms of\u00a0<strong>IC, IR, and monotonicity<\/strong>.<\/li>\n<li>However, it\u00a0<strong>slightly underperforms the 1-month turnover rate factor<\/strong>.<\/li>\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<p class=\"wp-block-paragraph\"><strong>Important Reminder<\/strong>: This analysis is for reference only and does not constitute any product or investment advice.<\/p>\n<p class=\"wp-block-paragraph\">We welcome readers interested in various trading strategies to consider purchasing relevant solutions from\u00a0<a class=\"ek-link\" href=\"\/en\/solution\/quantitative-finance-solution\/\"><strong><mark class=\"has-inline-color\" style=\"background-color:#ffdf88\">Quantitative Finance Solution<\/mark><\/strong><\/a>. With our high-quality databases, you can construct a trading strategy that suits your needs.<\/p>\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=\"\/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<div aria-hidden=\"true\" class=\"wp-block-spacer\" style=\"height:22px\"><\/div>\n<p class=\"has-text-align-center wp-block-paragraph\" style=\"font-size:32px\"><mark class=\"has-inline-color has-vivid-cyan-blue-color\" style=\"background-color:rgba(0, 0, 0, 0)\"><strong><em>\u201cTaiwan stock market data, TEJ collect it all.\u201d<\/em><\/strong><\/mark><\/p>\n<p class=\"wp-block-paragraph\">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 class=\"has-inline-color\" style=\"background-color:rgba(0, 0, 0, 0);color:#c05d5d\">Taiwan Stock Exchange reaching a new high of 20,000 points<\/mark><\/strong> due to the rise in TSMC\u2019s stock price, global institutional investors are paying more attention to the performance of the Taiwan stock market.\u00a0<\/p>\n<p class=\"wp-block-paragraph\"><strong><mark class=\"has-inline-color\" style=\"background-color:rgba(0, 0, 0, 0);color:#0978b8\">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.\u00a0<\/p>\n<ul class=\"wp-block-list\">\n<li><strong><mark class=\"has-inline-color has-black-color\" style=\"background-color:#ebc766\">Complete Coverage<\/mark><\/strong>: Includes all listed companies on stock markets in Taiwan, China, Hong Kong, Japan, Korea, etc.\u00a0<\/li>\n<li><strong><mark class=\"has-inline-color\" style=\"background-color:#ebc766\">Comprehensive Analysis of Enterprises<\/mark><\/strong>: Operational aspects, financial aspects, securities market performance, ESG sustainability, etc.\u00a0<\/li>\n<li><strong><mark class=\"has-inline-color\" style=\"background-color:#ebc766\">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.\u00a0<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">With TEJ\u2019s 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<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-3e41869c 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=\"\/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<p class=\"wp-block-paragraph\">This study presents a<strong>\u00a0workflow from single-factor analysis to composite factor construction and backtesting<\/strong>. In future applications, we can consider:<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>Introducing additional complementary factors<\/strong>\u00a0to enhance factor interactions.<\/li>\n<li><strong>Exploring different weighting methods<\/strong>\u00a0to optimize the performance of composite factors.<\/li>\n<li><strong>Incorporating actual trading costs<\/strong>\u00a0such as\u00a0<strong>slippage and transaction fees<\/strong>\u00a0to evaluate the actual feasibility of the strategy.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">By implementing these enhancements,\u00a0<strong>multi-factor strategies can become more adaptive and robust<\/strong>,\u00a0<strong>supportinginvestment decision-making in dynamic markets<\/strong>.<\/p>\n<h2 class=\"wp-block-heading\">Further Reading<\/h2>\n<p class=\"wp-block-paragraph\"><a href=\"\/en\/insight\/analyzing-factor-performance-with-alphalens\/#\">Analyzing Factor Performance with Alphalens: Foreign Capital Factors<\/a><\/p>\n<p class=\"wp-block-paragraph\"><a href=\"\/en\/insight\/seeking-alpha\/\">Seeking Alpha<\/a><\/p>\n<h2 class=\"wp-block-heading\">Related Links<\/h2>\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/github.com\/tejtw\/TQuant-Lab\" rel=\"noopener\" target=\"_blank\">TQuant Lab Github<\/a><\/p>\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/tquant.tejwin.com\" rel=\"noopener\" target=\"_blank\">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":1122,"template":"","tags":[47,52,67,97],"insight_category":[12,16],"class_list":["post-1123","insight","type-insight","status-publish","has-post-thumbnail","hentry","tag-factor-investing","tag-quantitative-analysis","tag-alpha","tag-data-analysis","insight_category-factor-investing","insight_category-quant-data-science"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight\/1123","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"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media\/1122"}],"wp:attachment":[{"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/media?parent=1123"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/tags?post=1123"},{"taxonomy":"insight_category","embeddable":true,"href":"https:\/\/www.tejwin.com\/en\/wp-json\/wp\/v2\/insight_category?post=1123"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}