Core Viewpoint - The Sina Finance App provides a solution for Chinese investors to track major U.S. institutional holdings through its "U.S. Giant Holdings" feature, offering authoritative data, Chinese visualization, and free access, thus breaking down barriers to information access [1][2][6]. Group 1: Information Accessibility - The app addresses the challenge of understanding SEC's 13F reports, which are lengthy and filled with jargon, by structuring the data and presenting it in an easily digestible format [2][9]. - Users can access data on major institutions like Berkshire Hathaway, Bridgewater, BlackRock, and ARK Invest without any subscription fees, making professional resources more accessible [2][12]. Group 2: Timeliness of Data - The app updates its data within 24 hours of the release of 13F reports, ensuring users receive timely notifications about significant changes in institutional holdings [3][10]. - Users have reported improved investment outcomes by acting on timely information, allowing them to follow institutional trends more closely [3][10]. Group 3: Comprehensive Analysis - The app combines holdings data with market trends and expert analysis, helping users understand not just what institutions are buying, but also the rationale behind those decisions [4][11]. - The inclusion of an AI assistant allows users to quickly extract key insights from reports, making it easier for those who may not be proficient in English to grasp institutional strategies [4][11]. Group 4: Tool Adaptability - The app offers features tailored to different types of investors, such as alerts for significant changes in holdings for short-term traders and historical data for long-term investors [5][12]. - Compared to other platforms, the app maintains neutrality by not requiring users to link trading accounts, thus providing unbiased analysis [5][13]. Group 5: Global Investment Landscape - The app's comprehensive coverage includes not only U.S. institutions but also top asset management firms from Europe and Asia, providing a global perspective on holdings [6][13]. - In the context of global asset allocation, the app emphasizes the importance of information efficiency in investment decisions, enabling Chinese investors to compete effectively in the U.S. market [6][13].

Quantitative Models and Construction Methods 1. Model Name: Chartist-Fundamentalist Model - **Model Construction Idea**: This model integrates the behaviors of chartists and fundamentalists to explain the coexistence of constant mispricing and oscillatory mispricing in stock markets. It reconciles the views of Grossman & Stiglitz (1980) and Lo & Farmer (1999) by considering the dynamic interactions between these two types of traders and the role of market makers[4][17][20] - **Model Construction Process**: - **Market Maker's Price Adjustment**: The market maker adjusts prices based on excess demand using the equation: $$ P_{t+1} = P_{t} + \alpha(D_{t}^{C} + D_{t}^{F} + D_{t}^{R} - N) \tag{1} $$ where \( \alpha > 0 \) is the price adjustment parameter, \( D_{t}^{C} \) and \( D_{t}^{F} \) represent the demand from chartists and fundamentalists, \( D_{t}^{R} \) is non-speculative demand, and \( N \) is the total stock supply[24][26] - **Chartists' Behavior**: Chartists extrapolate past price trends into the future, formalized as: $$ D_{t}^{C} = \beta(P_{t} - P_{t-1}) \tag{3} $$ where \( \beta > 0 \) is the market reaction coefficient of chartists[27] - **Fundamentalists' Behavior**: Fundamentalists trade based on deviations from fundamental value \( F_t \), with their demand defined as: $$ D_{t}^{F} = \begin{cases} \gamma(F_{t} - P_{t}) & \text{if } P_{t} - F_{t} > h \\ 0 & \text{if } -h \leq P_{t} - F_{t} \leq h \\ \gamma(F_{t} - P_{t}) & \text{if } P_{t} - F_{t} < -h \end{cases} \tag{4} $$ where \( \gamma > 0 \) measures the market influence of fundamentalists, and \( h \) is the threshold for mispricing[27] - **Fundamental Value Dynamics**: The fundamental value follows a random walk: $$ F_{t+1} = F_{t} + \delta_{t}, \quad \delta_{t} \sim N(0, \sigma_{\delta}^2) \tag{5} $$[28] - **Price Evolution Equation**: Combining the above equations, the price evolution is expressed as: $$ P_{t+1} = \begin{cases} (1 + \alpha\beta - \alpha\gamma)P_{t} - \alpha\beta P_{t-1} + \alpha\gamma F_{t} & \text{if } P_{t} - F_{t} > h \\ (1 + \alpha\beta)P_{t} - \alpha\beta P_{t-1} & \text{if } -h \leq P_{t} - F_{t} \leq h \\ (1 + \alpha\beta - \alpha\gamma)P_{t} - \alpha\beta P_{t-1} + \alpha\gamma F_{t} & \text{if } P_{t} - F_{t} < -h \end{cases} \tag{6} $$[29] - **Model Evaluation**: The model successfully explains the coexistence of constant and oscillatory mispricing, highlighting the dynamic nature of market efficiency and the role of trader interactions[4][17][85] --- Model Backtesting Results 1. Chartist-Fundamentalist Model - **Parameter Region R1**: When both chartists' and fundamentalists' market influence are low, prices converge to a non-fundamental fixed point, resulting in constant mispricing[21][22][66] - **Parameter Region R2**: With moderate market influence, prices either converge to a non-fundamental fixed point or exhibit endogenous oscillatory dynamics[21][22][66] - **Parameter Region R3**: When fundamentalists' market influence is high, prices either converge to a non-fundamental fixed point or diverge[21][22][66] - **Parameter Region R4**: When chartists' market influence is high, prices exhibit divergent dynamics[21][22][66] - **Impact of Fundamental Shocks**: Random shocks to the fundamental value can cause transitions between fixed-point dynamics and oscillatory dynamics, with the latter becoming dominant as the parameter \( c \) increases[78][79][80]