Group 1: Market Microstructure and Algorithmic Trading - Algorithmic trading is closely linked to market microstructure, which serves as the core logic for trading strategies and is influenced by the proliferation of algorithmic trading[1] - Key dimensions of market microstructure include liquidity, volatility, investor structure, and regulatory frameworks[2][5] Group 2: Liquidity Metrics - Liquidity is a critical factor affecting trading costs and is assessed through metrics such as TwSpread (relative spread), QuoteSize (market depth), and AccTurnover (transaction amount)[2][12] - TwSpread measures the relative price difference, with lower values indicating better liquidity and lower trading costs[14] - QuoteSize reflects the average number of buy and sell orders in the order book, with larger sizes indicating stronger liquidity[23] Group 3: Volatility Metrics - Volatility is an important parameter in algorithmic trading strategy design, assessed through TickPeriod (the average time between price changes) and ValidVolatility (effective price fluctuation)[3][39] - A smaller TickPeriod indicates higher volatility, while ValidVolatility increases with greater trading activity and price fluctuations[43][51] Group 4: Investor Structure - The structure of investors significantly impacts market microstructure, with metrics like AucVolRatioOpen and AucVolRatioClose indicating the proportion of trading volume during opening and closing auctions[4][62] - Higher auction volume ratios suggest greater participation from institutional investors, which can amplify market impacts during significant events[64] Group 5: Regulatory Impact - Regulatory frameworks play a crucial role in shaping market microstructure and must be accurately implemented in algorithmic trading systems[5][68] - Recent regulations have aimed to reduce transaction costs, such as the reduction of trading fees by 30% to 50% in 2023, which positively affects market activity[69]

（原标题：21独家｜刘俏、曾毓群、王庆将牵头资本市场学会微观专委会工作） 中国资本市场学会定位于打造资本市场理论研究、学术交流、决策咨询高端智库平台，广泛团结和凝聚行业机构、上市公司、高校和科研院所、 政府部门等各方面研究力量，围绕资本市场战略性基础性前瞻性重大课题开展研究交流和宣传。 在6月18日的2025陆家嘴论坛开幕式上，中国证监会、民政部和上海市人民政府联合举办了中国资本市场学会揭牌仪式。 中国资本市场学会理事会会长由证监会主席吴清亲自出任，执行副会长由证监会副主席李超担任。7月26日，中国资本市场学会成立大会暨第一届 第一次会员代表大会在上海召开。 大会确立了学会的组织架构，宣布推出官方学术期刊《资本市场研究》，并宣布学会将下设包括七大研究领域的专业委员会，包括宏观与产业、 市场稳定与风险防控、创新发展、市场微观结构、期货与衍生品、国际市场与对外开放、法治与投资者保护等。 21世纪经济报道记者 黎雨辰 9月18日，21世纪经济报道记者独家获悉，中国资本市场学会市场微观结构专业委员会架构已经清晰。 该专委会，由北京?学光华管理学院院?刘俏担任主任委员，宁德时代董事?曾毓群和上海重阳投资管理股份有限公司 ...

Quantitative Models and Construction Methods - **Model Name**: High-dimensional Memory (MEMO) Factor **Construction Idea**: This factor uses symbol processing to analyze the relationship between each order and subsequent orders, reflecting institutional contributions to trading[40][45] **Construction Process**: 1. Convert the trading direction of each order into a numerical sequence 2. Calculate the correlation coefficient between orders to measure their relationship 3. Stronger correlations indicate higher institutional involvement and better company quality[40][45] **Evaluation**: The factor effectively captures institutional trading behavior and demonstrates strong performance in identifying high-quality stocks[40][45] - **Model Name**: Strong Reversal (SR) Factor **Construction Idea**: Based on the principle that higher single-order transaction amounts lead to stronger reversals, this factor refines the ideal reversal factor at the minute level[46][48] **Construction Process**: 1. Use minute-level single-order transaction amounts 2. Segment the intraday 240-minute price fluctuations 3. Construct the strong reversal factor based on the ideal reversal factor[46][48] **Evaluation**: The factor improves upon daily frequency reversal factors and effectively captures intraday reversal opportunities[46][48] - **Model Name**: Lottery (LOTTERY) Factor **Construction Idea**: This factor identifies retail investor behavior by analyzing orders placed at limit-up or limit-down prices, reflecting the dominance of retail characteristics in trading[48][49] **Construction Process**: 1. Analyze the proportion of orders placed at limit-up or limit-down prices 2. Higher proportions indicate retail-dominated trading structures 3. Stocks with higher retail dominance often exhibit price deviations[48][49] **Evaluation**: The factor effectively captures retail investor behavior and highlights stocks with potential price anomalies[48][49] Model Backtesting Results - **MEMO Factor**: - IC: 0.045 - ICIR: 2.989 - Annualized Long-Short Return: 29.3%[39][40][45] - **SR Factor**: - IC: -0.043 - ICIR: -2.473 - Annualized Long-Short Return: 19.7%[39][46][48] - **LOTTERY Factor**: - IC: -0.054 - ICIR: -2.792 - Annualized Long-Short Return: 32.9%[39][48][49] High-Frequency Factor Tracking Results - **MEMO Factor**: - IC: 0.045 - ICIR: 2.989 - Annualized Long-Short Return: 29.3%[39][40][45] - **SR Factor**: - IC: -0.043 - ICIR: -2.473 - Annualized Long-Short Return: 19.7%[39][46][48] - **LOTTERY Factor**: - IC: -0.054 - ICIR: -2.792 - Annualized Long-Short Return: 32.9%[39][48][49]