Analysis of Personalized Investment Research Systems and Alignment with Industry Practices

• According to FT Adviser [1], investment advisers need to enhance soft skills and emotional intelligence to better support clients.
• Morningstar (via Washington Post) [2] highlights three core investment principles: market unpredictability, global diversification, and bonds as portfolio stabilizers.
• Institutional Investor [3] notes that investors tend to favor familiar assets, creating challenges for capital flow into innovative sectors.
• Industry research (Dongfang Caifu) [4] identifies data processing limitations, insufficient analyst capabilities, and ineffective methods as key issues in current投研 systems, with AI integration and process optimization as potential solutions.
• Shanghai Lixin University [5] points out that herd mentality and other human weaknesses impact investment decisions, requiring improved self-awareness and independent thinking.

• Reddit users [7] discuss a personalized投研 system centered on portfolio diversification and individual stock growth, with 50-60% deep-tracked stocks, 20-30% trading opportunities, and 20% cash for short-term gains. Bottom-up selection (stock pool + financial due diligence + valuation) is primary, with top-down for industry beta.
• Xueqiu user “兜兜麦肯” [6] shares a detailed system: portfolio allocation across stable growth, rapid growth, turnaround plays, and trading opportunities; bottom-up selection with financial screening (cash flow, shareholding structure);重仓 stocks require 1:3+ risk-reward ratio and 12-month catalysts; risk management includes 15-20 stocks diversification and stop-loss rules (e.g., 6% drop + break below support triggers 1/3 position cut).

The personalized投研 systems discussed on social media align with industry research in emphasizing diversification and risk management (e.g.,15-20 stocks vs. Morningstar’s global diversification [2]). However, retail investors’ focus on bottom-up selection and short-term catalysts contrasts with institutional advice on global asset allocation and long-term principles [2]. Common challenges like data limitations and human biases (herd mentality) are acknowledged in both research [4,5] and social media [6,7], but retail systems often lack the AI integration and formal training recommended by industry studies [4]. The personalized system’s emphasis on financial due diligence (e.g., cash flow, shareholding structure) addresses some industry-identified issues (like data gaps) [4], but its reliance on individual judgment highlights the need for soft skills and emotional intelligence as noted by FT Adviser [1].