Three-Dimensional Framework Analysis of Stock Price Drivers

This analysis is based on the stock price driver framework published by a Snowball user on November 15, 2025, which systematically decomposes stock price influencing factors into three time dimensions [Snowball Post]:

• Short-term dimension: Risk Appetite
  - Emotional fluctuations lead to mispricing, providing opportunities for contrarian investment
• Medium-term dimension: Liquidity
  - Macroeconomic capital conditions determine market valuation levels and style switching
• Long-term dimension: Fundamentals
  - Corporate profitability and moats are the cornerstones of value regression

: Current market data fully validates the driving effect of risk appetite on short-term stock prices. Major A-share indices have all shown a downward trend, reflecting market sentiment with low risk appetite: Shanghai Composite Index fell 0.97% in a single day, Shenzhen Component Index fell 1.93%, ChiNext Index fell 2.82%, and CSI 300 fell 1.57% [0]. This generally declining market environment is a typical manifestation of the “emotional fluctuations leading to mispricing” mentioned in the framework, providing potential opportunities for contrarian investors.

: Sector performance shows obvious liquidity-driven characteristics and signs of style switching. The energy sector performed the strongest, rising 3.12%; the technology sector rose 2.03%, utilities rose 2.16%; while the communication services sector performed the weakest, falling 2.22% [0]. This differentiation reflects the reallocation of funds among different sectors, perfectly embodying the liquidity-driven market style switching mechanism.

: Taking Kweichow Moutai (600519.SS) as an example, although its stock price fell by 0.94%, its P/E ratio of 20.30x shows a more reasonable valuation level compared to Apple’s 36.47x [0]. This confirms the core role of long-term fundamental factors in value discovery—corporate profitability and moats are indeed the cornerstones of value regression.

This three-dimensional framework not only has theoretical value but also shows strong practical guiding significance in the current market environment. The current market presents typical structural characteristics: weak short-term risk appetite, medium-term liquidity differentiation, and long-term fundamental support coexist. This dynamic balance of three factors provides investors with a clear allocation approach.

The universality of the framework has been verified in different markets and sectors. A-shares overall performed weakly but with obvious sector differentiation—traditional defensive sectors such as energy and utilities were favored by funds, while growth sectors such as communication services faced capital outflow pressure [0]. This differentiation phenomenon reflects changes in liquidity allocation preferences under different market environments.

The three time dimensions do not act independently but interact and reinforce each other. When short-term risk appetite declines, it is often accompanied by the reallocation of medium-term liquidity, while long-term fundamentals become the ultimate determinant of value regression. This multi-dimensional interaction forms a complete logical chain for market operation.

Investors should note that current market differentiation may have a significant impact on portfolio allocation:

1. Liquidity Risk
   : The sharp decline (-2.22%) of the communication services sector [0] may indicate the risk of rapid capital withdrawal; it is necessary to closely monitor the sustainability of sector rotation.

2. Valuation Risk
   : High valuations of U.S. tech stocks (Apple’s P/E ratio of 36.47x) [0] face correction pressure in an environment of tightening liquidity; cross-market valuation differences need to be treated cautiously.

3. Sentiment Risk
   : The general market decline reflects a decrease in risk appetite, which may trigger a chain reaction; it is necessary to be alert to the diffusion effect of worsening sentiment.

1. Contrarian Investment Opportunities
   : Mispricing caused by market sentiment fluctuations provides entry opportunities for contrarian investors, especially for targets with stable fundamentals but short-term overselling.

2. Style Switching Gains
   : Medium-term liquidity-driven style switching provides profit space for active allocation; the relative strength of defensive sectors such as energy and utilities is worthy of attention.

3. Value Regression Potential
   : High-quality targets supported by long-term fundamentals are more investment-worthy after market adjustments, such as companies with strong moats like Moutai.

• Policy Changes
  : Impact of monetary and fiscal policy adjustments on liquidity
• Geopolitics
  : Impact of Sino-US relations and global trade situation on risk appetite
• Economic Data
  : Guidance of inflation, employment, GDP and other data on market expectations
• Corporate Performance
  : Repricing of fundamentals during the earnings season of listed companies

The current A-share market presents typical characteristics of three driving factors: low short-term risk appetite leading to general index declines, medium-term liquidity differentiation triggering sector rotation, and long-term fundamental support providing a basis for value investment. This structural feature provides investors with a clear analysis framework and decision-making basis.

This stock price driver framework provides investors with core logic to navigate cycles. By identifying the dominant factors in different time dimensions, investors can conduct asset allocation and risk management more accurately. Historical patterns show that the dynamic balance of the three-factor framework usually determines market trends; investors should include this in their systematic analysis considerations.

Market data [0] shows that the average trading volume of the Shanghai Composite Index is 61.75 billion yuan, with relatively stable capital participation; sector rotation is obvious, with energy, technology, and utilities leading the gains; Moutai’s P/E ratio is 20.30x and EPS is 71.75 yuan, showing strong fundamental support. These data points provide empirical support for the effectiveness of the framework.