CSS Analytics tiene un post donde muestra la relación entre fundamentals y análisis técnico vía el Efecto Apalancamiento.
The “leverage effect” and subsequent theory originated from early studies done by Fisher Black (of Black-Scholes fame). Black found that stock volatility tended to rise when stock prices went down and that volatility fell when prices went up. The economic rationale behind this effect is rooted in the firm’s capital structure. As a stock rises, the percentage of equity to debt rises, and the firm becomes less risky since the debt holders claims to the company value are more limited. Conversely, as the stock falls, the percentage of equity to debt falls, and the increased share of debt holder claims make the firm’s equity more risky. Thus a falling stock price should lead to an increase in future volatility.
Well most educated finance students understand first of all the principle of balance sheet leverage: when you add debt to the balance sheet, a given % increase in sales has a disproportionately larger increase in the % increase in earnings because you are netting out a fixed interest cost. If we all agree that 1) the market tends to discount future GDP and hence revenues, and 2) that firm value is also a function of earnings growth and total earnings, then the firms that will experience the greatest percentage change in firm value when the market forecasts a recovery should mathematically be the most leveraged firms.
This affects the technical trader in many ways whether you trade short-term or not. It means that the biggest winning stocks will have strong velocity relative to volatility (think sharpe ratio) only while the market is rising. When the market peaks, in fact, their velocity relative to volatility will have peaked at extraordinary levels. At this point the firm’s debt to equity is often at unsustainable levels–leaving it vulnerable to the largest proportionate changes in firm leverage–and hence future volatility/downside risk.