Quantitative Models and Construction Methods 1. Model Name: High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model combines three types of high-frequency factors (price range, price-volume divergence, and regret avoidance) with equal weights to enhance the CSI 1000 Index. It aims to leverage the predictive power of high-frequency factors for stock selection[3][62][66] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with weights of 25%, 25%, and 50%, respectively[36][42][51] 2. Neutralize the combined factor by industry market capitalization[36][42][51] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[62][66] - **Model Evaluation**: The model demonstrates strong excess return performance both in-sample and out-of-sample, with a stable upward trend in the net value curve[39][66] 2. Model Name: High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - **Model Construction Idea**: This model integrates high-frequency factors with fundamental factors (consensus expectations, growth, and technical factors) to improve the performance of multi-factor investment portfolios[67][69] - **Model Construction Process**: 1. Combine the three high-frequency factors (price range, price-volume divergence, and regret avoidance) with fundamental factors (consensus expectations, growth, and technical factors) using equal weights[67][69] 2. Neutralize the combined factor by industry market capitalization[67][69] 3. Implement weekly rebalancing with a turnover buffer mechanism to reduce transaction costs[67][69] - **Model Evaluation**: The model shows improved performance metrics compared to the high-frequency-only strategy, with higher annualized returns and Sharpe ratios[69][71] --- Model Backtesting Results 1. High-frequency "Gold" Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 9.63% - Annualized Volatility: 23.82% - Sharpe Ratio: 0.40 - Maximum Drawdown: 47.77% - Annualized Excess Return: 9.85% - Tracking Error: 4.32% - IR: 2.28 - Maximum Excess Drawdown: 6.04%[63][66] 2. High-frequency & Fundamental Resonance Combination CSI 1000 Index Enhanced Strategy - Annualized Return: 13.80% - Annualized Volatility: 23.44% - Sharpe Ratio: 0.59 - Maximum Drawdown: 39.60% - Annualized Excess Return: 13.93% - Tracking Error: 4.20% - IR: 3.31 - Maximum Excess Drawdown: 4.52%[69][71] --- Quantitative Factors and Construction Methods 1. Factor Name: Price Range Factor - **Factor Construction Idea**: Measures the activity of stock transactions in different price ranges during the day, reflecting investors' expectations of future stock trends[3][33] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate transaction volume and number of transactions in high (80%) and low (10%) price ranges[33][36] 2. Combine sub-factors with weights of 25%, 25%, and 50%[36] 3. Neutralize the combined factor by industry market capitalization[36] - **Factor Evaluation**: The factor shows strong predictive power and stable performance, with a steadily upward excess net value curve[39] 2. Factor Name: Price-Volume Divergence Factor - **Factor Construction Idea**: Measures the correlation between stock price and trading volume. Lower correlation indicates a higher probability of future price increases[3][40] - **Factor Construction Process**: 1. Use high-frequency snapshot data to calculate the correlation between price and trading volume, as well as price and transaction count[40][42] 2. Combine sub-factors with equal weights[42] 3. Neutralize the combined factor by industry market capitalization[42] - **Factor Evaluation**: The factor's performance has been relatively flat in recent years but has shown good excess return this year[44] 3. Factor Name: Regret Avoidance Factor - **Factor Construction Idea**: Based on behavioral finance, this factor captures investors' regret avoidance emotions, such as the impact of selling stocks that later rebound[3][46] - **Factor Construction Process**: 1. Use tick-by-tick transaction data to identify active buy/sell directions[46] 2. Construct sub-factors like sell rebound ratio and sell rebound deviation, and apply restrictions on small orders and closing trades[46] 3. Combine sub-factors with equal weights and neutralize by industry market capitalization[46][51] - **Factor Evaluation**: The factor shows stable upward performance and strong excess return levels out-of-sample[53] 4. Factor Name: Slope Convexity Factor - **Factor Construction Idea**: Captures the impact of order book slope and convexity on expected returns, reflecting investor patience and supply-demand elasticity[3][54] - **Factor Construction Process**: 1. Use order book data to calculate the slope of buy and sell orders at different levels[54] 2. Construct sub-factors for low-level slope and high-level convexity, and combine them[54][58] 3. Neutralize the combined factor by industry market capitalization[58] - **Factor Evaluation**: The factor has shown stable performance since 2016, with relatively flat out-of-sample results[61] --- Factor Backtesting Results 1. Price Range Factor - Annualized Excess Return: 4.90% - IR: 1.13 - Maximum Excess Drawdown: 1.89%[36][39] 2. Price-Volume Divergence Factor - Annualized Excess Return: 5.59% - IR: 1.29 - Maximum Excess Drawdown: 2.13%[42][44] 3. Regret Avoidance Factor - Annualized Excess Return: -2.62% - IR: -0.61 - Maximum Excess Drawdown: 1.69%[46][53] 4. Slope Convexity Factor - Annualized Excess Return: -10.40% - IR: -2.35 - Maximum Excess Drawdown: 2.42%[58][61]

Core Viewpoint - NVIDIA Corporation (NASDAQ:NVDA) is considered one of the best quality stocks to buy before 2026, with Wall Street remaining optimistic despite challenges in the AI chip sector and geopolitical uncertainties regarding exports to China [1]. Group 1: Analyst Ratings and Price Targets - Wall Street's 12-month average price target for NVIDIA represents more than 38% upside from the current level [2]. - Bernstein's analyst Stacy Rasgon maintained a Buy rating with a $275 price target, citing a bullish outlook following a meeting with NVIDIA's investor relations [3]. - Jefferies analyst Blayne Curtis also reiterated a Buy rating with a $250 price target [5]. Group 2: Company Outlook and Competitive Position - NVIDIA's $500 billion outlook released in October is expected to see further upside due to recent deals with companies like Anthropic and OpenAI, as well as collaborations in the Middle East [3]. - NVIDIA acknowledges that Google is ahead in the AI chip space due to its TPU program, but believes its programmable platform solutions are superior for cloud infrastructure [4]. - The new Nemotron 3 Nano is capable of delivering 4x higher throughput than its predecessor, enhancing performance for multi-agent systems [5].