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]

Group 1 - The China Capital Market Society has established a clear structure for its Microstructure Professional Committee, led by Liu Qiao from Peking University's Guanghua School of Management, with Zeng Yuqun from CATL and Wang Qing from Shanghai Chongyang Investment Management as deputy chairs [1] - The main responsibilities of the Microstructure Professional Committee include researching investor structure and behavior, improving trading mechanisms, enhancing trading supervision, optimizing listed companies' market value management, and refining the price formation mechanism in the capital market to improve pricing efficiency [1] - Chongyang Investment, founded in 2009, is the only private securities management member of the China Capital Market Society, positioning itself as a leading subjective private equity firm in the market [1] Group 2 - The China Capital Market Society is aimed at creating a high-end think tank platform for theoretical research, academic exchange, and decision-making consultation in the capital market, uniting various industry institutions, listed companies, universities, research institutes, and government departments for research and communication on significant strategic and foundational topics [1] - The society's council is chaired by the Chairman of the China Securities Regulatory Commission, Wu Qing, with the Vice Chairman being Li Chao, the Vice Chairman of the CSRC [2] - The society's inaugural meeting established its organizational structure and announced the launch of the official academic journal "Capital Market Research," along with seven specialized committees covering various research areas, including macro and industry, market stability and risk prevention, innovative development, microstructure, futures and derivatives, international markets and openness, and legal protection for investors [2]

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]