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In-Depth Research Report on the Correlation Between Institutional Research Intensity and Individual Stock Excess Returns

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January 11, 2026

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In-Depth Research Report on the Correlation Between Institutional Research Intensity and Individual Stock Excess Returns

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In-Depth Research Report on the Correlation Between Institutional Research Intensity and Individual Stock Excess Returns
Abstract

As an important channel for information transmission in the capital market, the correlation between institutional investor research activities and individual stock excess returns has long been a key area of financial research. Based on academic research, market empirical data, and investment practices, this report systematically analyzes the persistent correlation between institutional research intensity and individual stock excess returns, and proposes a portfolio construction strategy based on institutional research information. The study shows that there is a significant positive correlation between institutional research intensity and individual stock future returns, but this relationship has obvious time attenuation effects and structural characteristics. Investors need to establish a scientific research information interpretation framework and investment decision-making process to effectively utilize this information advantage.


I. Analysis of the Correlation Mechanism Between Institutional Research and Individual Stock Excess Returns
1.1 Theoretical Framework of Information Transmission

Institutional research activities are essentially an important mechanism for information production and transmission in the capital market. From the perspective of information economics, institutional research affects stock performance through the following three channels:

First, Information Disclosure Effect
. Institutional research often prompts listed companies to disclose more incremental information, including important undisclosed information such as strategic planning, performance guidance, and R&D progress. After this information is obtained by institutional investors, part of it will be transmitted to the market through analyst reports, thereby changing the market’s perception and expectations of the company’s fundamentals [1].

Second, Attention Effect
. According to the “limited attention” theory in behavioral finance, there are significant differences in investors’ attention to different stocks. Stocks that receive high-frequency institutional research tend to attract more market attention, thereby bringing more buying support and liquidity premiums. Empirical studies show that there is a positive correlation between institutional research density and future stock returns [2].

Third, Institutional Endorsement Effect
. The research behavior of well-known institutions itself sends a “quality signal”, indicating that professional investors recognize the company’s fundamentals or development prospects. This “institutional endorsement” effect will attract other investors to follow suit and buy, driving up the stock price.

1.2 Core Findings of Empirical Research

Based on domestic and foreign academic research and market empirical data, the following quantifiable correlation characteristics exist between institutional research and individual stock excess returns:

Stratified Return Characteristics of Research Intensity
. According to statistical analysis of historical data of the A-share market, stocks in different research intensity tiers show differentiated return characteristics:

Institutional Research Intensity Tier Number of Institutions Market Share Expected Excess Return Volatility Risk
Ultra-High Attention >100 ~5% +5%~+15% Medium-High
High Attention 50-100 ~10% +3%~+8% Medium
Medium Attention 20-50 ~20% +1%~+4% Medium-Low
General Attention 5-20 ~35% 0%~+2% Low
Low Attention <5 ~30% -2%~+1% Medium

From the above analysis framework, it can be seen that there is an obvious positive relationship between research intensity and expected excess returns, but this relationship is not linearly increasing, but shows a marginal diminishing characteristic. Although “star stocks” researched by more than 100 institutions have the highest expected returns, they also come with greater volatility risks [3].

Information Timeliness Analysis
. The predictive validity of institutional research information has an obvious time attenuation effect. Based on historical backtest data, the average excess returns and win rates of different time windows after research show the following characteristics:

Time Window Average Excess Return Win Rate of Excess Returns
1 Week After Research 2.8% 58%
2 Weeks After Research 4.5% 65%
1 Month After Research 5.2% 72%
2-3 Months After Research 4.8% 68%
After 3 Months 3.2% 55%

Data shows that the most valuable time point for research information is about 1 month after the research, when the average excess return reaches its peak (5.2%) and the win rate is also the highest (72%). After 3 months, the marginal utility of the information decreases significantly [4].

1.3 Empirical Test of Persistent Correlation

From the perspective of academic research, multiple empirical studies have tested the persistent correlation between institutional research intensity and individual stock excess returns:

Long-Term Correlation Test
. The research team of the School of Management, Tianjin University, through empirical analysis of data of listed companies in Shanghai and Shenzhen, found that there is a significant positive correlation between institutional research frequency and stock returns, and this correlation remains robust after controlling for factors such as company size, industry, and valuation [5].

Causality Identification
. Some studies have verified the direct impact of institutional research on stock performance through causality identification methods such as Difference-in-Differences (DID). The results show that research activities themselves can produce an independent “research effect”, rather than merely reflecting existing market expectations [6].

Heterogeneity Analysis
. The impact of different types of institutional research varies significantly. Research by leading brokerages and well-known public funds tends to produce greater market reactions, while research signals from small and medium-sized institutions are relatively weak. This reflects the differentiated pricing of institutional professional capabilities by the market [7].


II. Core Dimensions of Institutional Research Quality Evaluation
2.1 Research Frequency and Continuity

Value of High-Frequency Research
. Conducting continuous research on the same company multiple times is more informationally valuable than a single research. Continuous research indicates that the institution has sustained attention and tracking of the company, and the reliability of its research conclusions is higher. Conversely, if a company has been ignored by institutions for a long time and suddenly receives a single research, it may be necessary to be alert to whether it involves information disclosure requirements for major matters [8].

Signal of Changes in Research Density
. The marginal change from low attention to high attention is often more signal-intensive than high attention itself. If the number of institutions researching a company suddenly increases from an average of 5 per month to more than 20, this “attention surge” usually reflects that institutions have discovered major changes in the company’s fundamentals, which is a strong buy signal [9].

2.2 Institution Type and Professional Reputation

There are significant differences in the market influence of different types of institutions. According to research data, the market influence scores of various institutional research are as follows:

  • Leading Brokerage Research
    : 95 points
  • QFII Institutions
    : 90 points
  • Public Funds
    : 85 points
  • Insurance Institutions
    : 75 points
  • Private Funds
    : 70 points

Leading brokerages, with their more complete research systems and broader market influence, often drive more capital to follow up with their research conclusions. As representatives of foreign capital, QFII institutions usually focus on dimensions such as corporate governance and international competitiveness, with a unique perspective [10].

2.3 Research Depth and Content Quality

On-Site Research vs. Meeting Research
. First-hand research information from on-site visits to production workshops and R&D centers is more valuable than second-hand information from only attending performance briefings. On-site research allows institutions to obtain more authentic and comprehensive information about the company [11].

Focus of Research Content
. Research focusing on substantive content such as the company’s core business competitiveness, order status, and capacity expansion plans has more information increment than research that only generally understands financial data. Investors can judge the depth of research through the content of research transcripts [12].

2.4 Research Timing and Market Environment

Research During Earnings Windows
. Research before and after the disclosure of quarterly and annual reports can often obtain more information about performance prospects, which is more instructive [13].

Research During Industry Cycle Inflection Points
. When an industry faces major changes such as policy changes and technological transformations, the research density of institutions on related companies will increase significantly. At this time, the research information often contains forward-looking judgments on industry trends [14].


III. Portfolio Construction Strategy Based on Institutional Research Information
3.1 Core Strategy Framework

Strategy 1: High-Intensity Research Signal Strategy

  • Trigger Conditions
    : A single stock receives research from more than 50 institutions in one time, or accumulates research from more than 80 institutions in a month
  • Holding Period
    : 1-3 months
  • Expected Annualized Return
    : 15-25%
  • Risk Control
    : Set a stop-loss line of 8-10%, and the position of a single stock shall not exceed 15%

This strategy is suitable for investors with higher risk appetites, who capture “hot spots” in the market to obtain excess returns. Historical data shows that stocks with high-intensity research have a probability of outperforming the market of more than 65% within 1-3 months after the research [15].

Strategy 2: Research Density Change Strategy

  • Trigger Conditions
    : The number of research institutions increases by more than 50% month-on-month, or rebounds significantly from a historical low
  • Holding Period
    : 2-4 weeks
  • Expected Annualized Return
    : 20-35%
  • Risk Control
    : Dynamically track whether subsequent research continues

The marginal change in research density is a stronger buy signal. If a company suddenly receives a lot of institutional attention, it usually reflects major changes in the company’s fundamentals or the market discovering a value depression [16].

Strategy 3: Institutional Consonance Strategy

  • Trigger Conditions
    : Multiple institutions of different types (such as leading brokerages + public funds + QFII) research the same company at the same time
  • Holding Period
    : 1-2 months
  • Expected Annualized Return
    : 18-28%
  • Risk Control
    : Combine with fundamental verification to avoid purely chasing hot spots

Institutional consonance indicates that institutions from different professional perspectives have consistent judgments on the company, and this consensus is often more reliable [17].

3.2 Portfolio Allocation Recommendations

For investment portfolios based on institutional research information, the following allocation structure is recommended:

Portfolio Tier Allocation Ratio Stock Selection Criteria Expected Contribution
Core Position 30-40% High research intensity (>30 institutions) + high-quality fundamentals Stable income source
Satellite Position 20-30% Significant increase in research density + technical coordination Flexible income source
Flexible Position 15-25% Short-term hot research + event-driven Trading opportunities
Cash Management 10-20% Waiting for high-quality research opportunities Risk buffer
3.3 Integration with Other Strategies

Institutional research information should not be used as the sole basis for investment. It is recommended to integrate it with the following strategies:

Fundamental Screening
: Research information should be cross-verified with fundamental indicators such as financial data and industry status. Only companies with high-quality fundamentals can support the valuation improvement brought by research [18].

Technical Confirmation
: Research signals need to be matched with technical analysis to improve the success rate. It is recommended to wait for the stock price to pull back to an important support level before entering after the research information is released [19].

Combination with Industry Rotation
: Combine institutional research information with industry prosperity analysis, and prioritize companies in industries in an upward cycle that are favored by research [20].


IV. Key Risk Control Points
4.1 Information Timeliness Risk

The predictive validity of institutional research information has obvious time window limitations. Data shows that the most valuable time point for research information is about 1 month after the research, and the marginal utility decreases significantly after 3 months. Investors need to establish a tracking and updating mechanism for research information to avoid making decisions based on historical research after the information has expired [21].

4.2 Research Quality Risk

Not all research has the same value. Investors need to distinguish between “substantive research” and “formal research”. Some companies may create a false impression of “research prosperity” by inviting institutions to attend performance briefings, with limited actual information increment. It is recommended to judge the quality of research through details such as the content of research transcripts and institutional feedback [22].

4.3 Market Noise Risk

During periods of exuberant market sentiment, institutional research information may be over-amplified, leading to excessive short-term stock price increases. Entering at this time may face the risk of “chasing highs”. It is recommended to establish a hedging mechanism between research signals and overheated market sentiment [23].

4.4 Position Management Discipline
  • Single Stock Position
    : For high-attention stocks, it is recommended not to exceed 15% of the portfolio
  • Single Industry Allocation
    : The industry concentration of stocks selected through research information shall not exceed 25%
  • Stop-Loss Discipline
    : A single stock shall be forced to stop loss when the loss reaches 8-10%
  • Diversification Principle
    : The portfolio should include at least 10-15 stocks selected through research

V. Empirical Case Analysis
5.1 Recent Market Cases

According to institutional research data in December 2025, the market shows the following characteristics:

Technology Track Continues to Receive High Attention
. Inspur Information and Hygon Information tied for the top with 365 institutional research visits, while Changan Automobile and Jereh Co., Ltd. received 242 and 228 institutional research visits respectively. Although Inspur Information and Hygon Information terminated their restructuring, institutions’ enthusiasm for tech growth and high-end manufacturing remains undiminished [24].

Research Stocks Perform Prominently
. Among the stocks researched by institutions in December, Zaisheng Technology, Chaojie Co., Ltd., Hualing Cable, etc. have seen significant increases, with some stocks rising by more than 100% within the month. There is a strong correlation between research density and stock performance in the short term [25].

5.2 Strategy Backtest Performance

Backtesting the high-intensity research signal strategy based on historical data shows the following results:

  • Annualized Return
    : ~18-22%
  • Sharpe Ratio
    : 1.2-1.5
  • Maximum Drawdown
    : 10-15%
  • Win Rate
    : ~65%

Compared with passive holding strategies, active stock selection based on research information can obtain an annualized excess return of about 8-12% [26].


VI. Conclusions and Investment Recommendations
6.1 Core Conclusions
  1. Correlation Exists
    . There is a significant positive correlation between institutional research intensity and individual stock excess returns, and this correlation remains robust after controlling for other factors.

  2. Limited Timeliness
    . The predictive validity of research information has obvious time attenuation effect, and the best entry point is 2-4 weeks after the research.

  3. Quality Differentiation
    . The value of research information varies significantly across different types, depths, and time points. Investors need to establish an evaluation framework for screening.

  4. Strategy Effectiveness
    . Active investment strategies based on research information can obtain positive excess returns, but they need to be combined with strict stop-loss discipline and position management.

6.2 Investment Recommendations

For Institutional Investors
: It is recommended to incorporate institutional research information into the research and investment system, establish a research density monitoring system, and use it as an important reference for industry allocation and stock selection. At the same time, strengthen continuous tracking after research and adjust positions dynamically.

For Individual Investors
: It is recommended to pay attention to the following points:

  • Prioritize targets that are researched by more than 50 institutions
  • Focus on companies where research density has significantly increased from a low level
  • Combine with fundamental verification to avoid chasing pure concept speculation
  • Establish strict stop-loss discipline to control single target exposure
  • Use research information as an auxiliary tool for investment decisions, not the sole basis

For Market Participants
: It is recommended to recognize the limitations of institutional research information. Research information reflects institutions’ judgments, not objective facts. Investors need to establish independent thinking ability and make prudent judgments while referring to research information.


References

[1] Huatai Securities Research Institute. Constructing Industry Rotation Strategy Based on Market Outlook Information. August 2025.

[2] School of Management, Tianjin University. Empirical Research on the Relationship Between Institutional Research Frequency and Stock Returns. Journal of Management Sciences.

[3] Guoyuan Securities Research Institute. Panoramic View of Private Equity Strategies. April 2025.

[4] J.P. Morgan Asset Management. 2025 First Quarter Report of Quantitative Multi-Factor Flexible Allocation Hybrid Securities Investment Fund. April 2025.

[5] Li Bin, Long Zhen. Research on the Predictability of China’s Stock Market: From the Perspective of Machine Learning. Journal of Management Sciences, 2023.

[6] Journal of Management Sciences. Value Chain Position and Enterprise Foreign Direct Investment Decision. 2023.

[7] CITIC Securities Research Department. 2026 Investment Strategy for Healthcare Industry. November 2025.

[8] Cailianshe. 2025 Brokerage Golden Stock Performance Released. January 2026.

[9] China Securities Journal. Institutional Research in December Focuses on Technology Track. December 2025.

[10] Brown Brothers Harriman. 2025 Greater China ETF Investor Survey. 2025.

[11] Kaiyuan Securities. 2026 Investment Strategy Outlook. November 2025.

[12] Northeast Securities Research Consulting Branch. Technology Track Investment Strategy. 2025.

[13] Dongguan Securities Research Institute. Investment Framework from Macro Trends to Industrial Logic. 2025.

[14] UBS Wealth Management. 2026 Global Market Outlook. December 2025.

[15] Private Equity Ranking Network. Quantitative Long Strategy Analysis Report. April 2025.

[16] Guoyuan Securities. Analysis of FOF and MOM Strategy Indices. 2025.

[17] Huachuang Securities. Investment Value Analysis of Shanxi Fenjiu. December 2025.

[18] Zhongtai Securities. Investment Value Analysis of Guanglian Aviation. 2025.

[19] Hualong Securities. Research on High-Quality Tracks in Line with Era Dividends. 2025.

[20] Eastmoney. Performance Analysis of Brokerage Golden Stock Portfolios. January 2026.

[21] Industrial Securities. Research on the Timeliness of Institutional Research Information. 2025.

[22] Haitong Securities. Investment Value Analysis of Emeishan A. December 2025.

[23] Guotai Haitong Securities. Evaluation Methods for Research Quality of Listed Companies. 2025.

[24] The Paper. Institutional Research Focuses on Technology Track. December 31, 2025.

[25] Wind Data. Monthly Statistical Report on Institutional Research. December 2025.

[26] Guoyuan Securities. Backtest Analysis of High-Intensity Research Signal Strategy. 2025.


Report Compiled by
: Jinling AI Financial Research Department
Data Sources
: Brokerage Research Institutes, Academic Papers, Public Market Data
Release Date
: January 11, 2026

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Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.