Notes on the Book 'Firing Your Stock Analyst'

Harry Domash, originally focused on U.S. equities research; content adapted for the A-share market. Information provided is solely a summary of the book’s content and does not constitute investment advice. Core Logic of the Book Full analytical sequence: initial stock screening → preliminary review to eliminate low-quality companies → in-depth item-by-item analysis → establishing three-tier target price ranges (bullish, neutral, bearish) → defining clear buy and sell conditions. Stocks failing the preliminary review are discarded outright—no resources are expended on deep analysis. Eleven Core Analytical Tools 1. Deconstructing Analyst Forecasts Do not take buy/overweight ratings at face value. Focus instead on consensus revenue and earnings forecasts, comparing each quarter’s actual financial results against market expectations. Stocks that consistently miss expectations by wide margins receive penalty points. In the A-share market, brokerage research reports tend overall to be optimistic; therefore, pay special attention to trends in upward or downward revisions—not the textual rating itself. 2. Valuation Analysis: Calculating Implied Growth Rate Use P/E, P/S, and P/B ratios for two-tiered comparisons: against the company’s own historical data and against peers in the same industry. Implied growth rate: reverse-engineering the annual profit growth rate priced into the current stock price. An excessively high implied growth rate signals that market expectations have already been overextended, increasing risk. For cyclical industries, avoid using single-year profits from peak or trough years; instead, normalize profits. In the A-share market, non-recurring profit-adjusted metrics are preferred for valuation; an implied growth rate exceeding 30% is considered high-risk. 3. Target Price Ranges Avoid setting a single target price. Instead, define three tiers—bullish, neutral, and bearish—calculated by multiplying normalized EPS with appropriate valuation multiples. Given the A-share market’s high volatility, price ranges should be set with appropriate breadth. 4. Industry Analysis Assess industry growth rate, competitive landscape, and the company’s position within its industry—distinguishing genuine industrial growth from speculative theme-chasing. In the A-share market, additionally consider impacts from industrial policy and regulatory developments. 5. Business Plan Scorecard Identify the source of earnings growth: prioritize organic growth driven by core operations, and remain wary of earnings artificially inflated by continuous M&A activity. In the A-share market, closely examine goodwill as a percentage of net assets; high ratios trigger penalty points. 6. Management Assessment Evaluate dividend policy, share buybacks, equity dilution via new share issuance, executive compensation, and history of financial statement restatements. In the A-share market, also verify major shareholders’ share reductions, share pledges, and related-party transactions. 7. Financial Health Analyze current ratio, quick ratio, and total debt; compare cash holdings against debt levels to identify solvency risks. In the A-share market, distinguish between operating liabilities and interest-bearing debt, and watch for the “high cash & high debt” anomaly. 8. Profitability Track gross margin, net margin, and ROE; observe 3–5 year trends in revenue and non-recurring net profit; verify alignment between operating cash flow and net profit. Strong net profit coupled with persistently weak operating cash flow constitutes a major red flag.

The money you earn in the stock market may just be noise: expectations versus intrinsic value behind stock price fluctuations

However, moving averages have an inherent drawback: lag. For example, a 10-day moving average incorporates nine past candlesticks and only one current day’s data. By the time the moving average generates a signal, the move may already be halfway complete. 3. Multi-Factor Cross-Validation A signal from a single factor is often just noise. But when multiple factors—e.g., 'low P/E' + 'revenue growth' + 'increased Northbound fund holdings'—all align simultaneously, the probability of coincidental noise drops significantly. Signals derived from multiple factors are generally more robust and better at filtering out noise than those based on one or only a few factors. 4. Out-of-Sample Testing Split your data into a 'training set' and a 'test set'. Parameters optimized on the training set must then be validated on the unseen test set. If the strategy remains effective on the test set, it likely reflects a genuine pattern—not overfitted noise. This is the fundamental idea behind walk-forward validation. The Greatest Harm Caused by Noise: Overfitting What is the biggest threat noise poses to quantitative strategies? Overfitting. Your backtest may 'discover' a pattern—but that pattern could merely be a random artifact of noise. For instance, you find in historical data across 5,000 stocks that 'prices rise on Day 4 after three consecutive down days, with a 55% probability.' Yet if this 55% is merely a statistical illusion caused by noise, the strategy will fail in live trading. How do you distinguish true signals from noise? ・ Does this pattern hold across different time periods? ・ Does it hold across different stock universes? ・ Is there a sound logical foundation—or is it purely statistical? ・ Does the result change drastically with minor parameter adjustments? If all answers are 'yes,' the pattern is more likely a true signal. If several questions cannot be answered affirmatively, it is most likely noise. Practical Summary: One Table to Understand Key Concepts Concept One-Sentence Explanation Significance for Quantitative Strategies Expectation Current price reflects the market’s consensus view of the future—not today’s intrinsic value A good company ≠ a good stock; price rises only on positive surprises. Expectation Gap Deviation between actual results and market expectations The real engine of short-term price movement—not how much was earned, but whether it exceeded or fell short of expectations. Intrinsic Value Present value of all future profitability The long-term anchor of price; prices oscillate around value. DDM Model Stock price = Dividend / (Required return − Growth rate) The foundational model underlying all valuation methods—if you understand DDM, you understand valuation. Noise Short-term price fluctuations unrelated to fundamentals The primary source of interference—and the chief culprit behind backtest overfitting. Mean Reversion Prices eventually revert when deviating too far from intrinsic value The profit source for many strategies: capturing the convergence of price toward value. Small-Cap Noise Smaller market cap → higher proportion of noise Backtest results for small caps require downward adjustment; large-cap signals are more reliable. Investment vs. Speculation Investment profits from value; speculation profits from volatility Strategy design must align with its intended profit source. Noise Filtering Extending time horizons, moving averages, multi-factor models, out-of-sample testing The first line of defense against overfitting. Final Thoughts So, my personal view is: If we aim to invest in a stock for the long term, we need our own method to estimate that 'invisible' value line. The more accurate our valuation, the higher the certainty of profit. If we choose to speculate, opportunities still exist.

Truist cuts Westlake Chemical stock price target on challenging conditions

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Truist Securities lowered Westlake Chemical's price target to $90 from $104, citing HIP business challenges and a PVC plant closure in Germany. Despite updated margin assumptions for commodity chemicals, the firm maintains a Buy rating following strong Q2 2026 results.

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