Posts Tagged ‘pronosticos

20
Jun
11

Gráfico du Jour: Algunos pronósticos 2012…

(Fuente: JP Morgan, via FT Alphaville)

 

07
Dec
10

Gráfico du Jour: Pronostico del Petroleo

(Fuente: Bloomberg, via Infectious Greed)

20
Sep
10

Gráfico du Jour: Pronósticos

(Fuente: NYT)

18
Aug
10

Paper: Recomendaciones y sesgos en las estimaciones

‘Self-Fulfilling’ Stock Recommendations

Abstract:
This paper tests the hypothesis that analysts report biased earnings estimates in order to enhance their stock recommendation performance. In particular, we argue that analysts with optimistic (pessimistic) stock recommendations tend to issue negatively (positively) biased earnings forecasts so that the underlying firms are more likely to beat (miss) the consensus forecasts and thus have higher (lower) stock returns after these recommendations are issued. Consistent with this hypothesis, we find that average stock recommendations prior to earnings announcements significantly and positively predict subsequent earnings surprises. In addition, the predictability is substantially stronger when the net benefits associated with such strategic behavior are larger, for example, among firms with lower analyst coverage.

Link al Paper

14
Jul
10

Paper: Cash y Retornos del Portfolio

Cash Position Forecasts and Stock Market Portfolio Returns

Abstract:
Several articles in highly regarded news outlets over the last decade have argued that firms holding relatively more cash are favored by investors. The contention is those firms holding cash will have better access to good investment prospects. This view contradicts the Jensen (1986) free cash flow proposition. This study examines the investment returns of portfolios created according to trading strategies based on expected “good” news versus “bad” news predictions of cash holdings. Results indicate that firms holding more cash than expected have superior investment returns.

Link al Paper

18
May
10

Gráfico du Jour: Pronostico vs Actual, Ganancias S&P 500

(Fuente: HBR/McKinsey, via Infectious Greed)

27
Apr
10

Pronósticos, una tentación a promediar

Vox tiene un articulo clave sobre pronósticos económicos y metodología econométrica (y supuestos). Haciendo hincapié en la disyuntiva entre promediar modelos o partir de un modelo general hacia uno más especifico.

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The exchange emphasises not just that economic variables are important in forecasts, but that econometric issues matter. If GDP is trend stationary, the implications are very different for forecasting than if GDP is a random walk with drift – one will correct to some equilibrium, the other won’t. Economic nuances matter too – what other variables make up this equilibrium relationship? Historically, there has been such a steady state, but whether that is the same one to which we will soon correct is unclear, and bad forecasts may result.

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Averaging forecasts is motivated by the perceived difficulty of choosing a single model. It is often not clear in advance which model will forecast best, thus we take the insurance of averaging over a number of models. We forecast worse than the best model (the premium), but we forecast better than the worst model (the pay out) by smoothing forecast errors.
However, is this a sensible strategy? Do we do better by averaging? The alternative to averaging forecasts is to select one particular forecast, but framed in the context of 2K possible models for K variables in a dataset, choosing the “right” model seems an impossible task when K is at all large.

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Hendry (1995) emphasised a general-to-specific strategy of starting with the most general empirical model possible. This general model would incorporate variables from all economic theory and previous econometric work. It should also be well specified, satisfying the assumptions placed on the statistical model. From there, a search would begin for the simplest model that still satisfied the statistical assumptions.

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Por ultimo, en el post se trata el tema de mercados predictivos como una forma de promediar modelos (Ejemplo: Intrade).

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Prediction markets, particularly on frequent similar events such as soccer matches, provide an excellent natural “simulation” study to determine forecast accuracy. By considering thousands of sports matches, we can ascertain the accuracy of prediction markets.

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Of course, trusting prediction markets to forecast well in the future relies on the assumption that the event they are predicting is stationary; that soccer matches do not suddenly change in their nature overnight – the goals aren’t widened or the rules drastically altered. In that non-stationary situation, it would be impossible to know whether InTrade would continue to forecast well.

(…)

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Al final de la nota hay una completa lista de bibliográfica sobre el tema.




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