En este capítulo -que introduce la tematica de acciones-, Hector le cuenta a Gaston como fue el año 2010 para el MERVAL, cuales fueron las estrellas de ese año. Y lo mas importante, como empezar a ver si una accion esta barata.

## Posts Tagged ‘Fundamentals

Llegue de casualidad, via el blog *Zero Hedge*, a un *post –*escrito por Paul Wilmott– sobre *High Frequency Trading* y su impacto en la liquidez y en la volatilidad de los mercados (Como diría un profesor de economía que tuve, “¿pero de que mercados me hablan?” cuando exigía claridad en los términos).

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I am concerned about High-frequency Trading (HFT) for two main reasons: Reduction of the relationship between value and price; Potential for positive feedback.

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But feedback can be positive or negative.

Negative feedback is when an up move in a stock leads to a sell signal, and thus a fall in the price, and a down leads to a buy, and thus a rise in the price. This dampens volatility.

Positive feedback is when an up begets a buy, which causes the stock to rise again, causing another buy, etc. etc. And when a fall begets a sell, causing another fall, and further selling, and…

So which is it? Does HFT result in a reduction of volatility via negative feedback or an increase via positive feedback? This is an easy one. If you are a hedge fund manager which of the following would you prefer? A or B?

A. Low volatility. Shares go up or go down fairly predictably. No skill is required to make money, even by the man on the street. Hedge funds can’t charge large fees.

B. High volatility. Very difficult markets, experts needed and can charge large fees. If a fund does well they make a killing because of the enormous profit they have made for their clients. But they are just as likely to lose all their clients’ money, in which case…nothing bad happens to the fund manager.

Yes, we are in that familiar territory of moral hazard. Of course the funds want to increase volatility and they have found themselves in exactly the place they want to be to make this happen.(…)

### un poquito de leverage…

*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.

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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.

**Technical, Fundamental, and Combined Information for Separating Winners from Losers**

**Abstract: **

The main purpose of this paper is to use both fundamental and technical information to improve the technical momentum strategy. We examine how fundamental accounting information along with the technical information such as past returns and past trading volume data can be used by investors to separate momentum winners from losers. Previous research has shown that the technical momentum strategy based on the past winners and losers in terms of cumulative returns, generates significantly positive returns in the subsequent periods. This paper proposes a unified framework of incorporating the fundamental indicators FSCORE (Piotroski (2000)) and GSCORE (Mohanram (2005)) into the technical momentum strategy. We have developed three hypotheses to test whether combined momentum strategy outperform the technical momentum strategy or not. From the empirical results of these three hypotheses, we conclude that the combined momentum strategy outperforms technical momentum strategy by generating significantly larger returns for both growth and value stocks.

Link al Paper

En *Vox *hay una reseña de un trabajo que investiga el valor predictivo (sobre los retornos) de una variable demográfica: *el ratio entre adultos de mediana edad y jóvenes adultos.*

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fundamentals perform better in predicting returns as the predictive horizon gets longer. In particular, the dynamic dividend growth model (Campbell-Shiller 1988) suggests that the relevant fundamental to capture the information component in stock market returns is the dividend-price ratio. This variable regularly plays an important role in recent empirical literature that has replaced the long tradition of the efficient market hypothesis with a view of predictability of returns

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Intuitive reasoning hints at demography as an important variable to determine the long-run behaviour of the stock market, while it is difficult to imagine a relationship between high-frequency fluctuations in stock market prices and a slow-moving trend determined by demographic factors.

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In a recent CEPR Discussion Paper (Favero et al. 2010), we take the Geanakopoulos et al. model to the data via the conjecture that fluctuations in the middle-aged-to-young ratio could capture a slowly evolving mean in the dividend price ratio within the dynamic dividend growth model.

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El *paper *se llama Demographic Trends, the Dividend-Price Ratio and the Predictability of Long-Run Stock Market Returns.

### Paper Predicción y Ratios

**Do Decomposed Financial Ratios Predict Stock Returns and Fundamentals Better?**

**Abstract: **

We investigate the prediction of excess returns and fundamentals by financial ratios – dividend-price ratio, earnings-price ratio, and book-to-market ratio – by decomposing financial ratios into a cyclical component and a stochastic trend component. We find both components predict excess returns and fundamentals. The cyclical components predict increases in future stock returns, while the stochastic trend components predict declines in future stock returns, in particular, in long horizons. This helps explain previous findings that financial ratios in the absence of decomposition find weak predictive power in short horizons and some predictive power in long horizons. We also find both components predict fundamentals, consistent with present value models.

Link al Paper