Efficient market hypothesis and forecasting

By Allan G. Timmermann, Clive W. J. Granger. 2004

541 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/4add3b2edca863087c1230ab2be7cc2ab1c60f3d

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Citations in the corpus

An artificial neural network (p, d, q) model for timeseries forecasting (2010, background): “They claim that the evolution of any exchange rate follows the theory of efficient market hypothesis (EMH) (Timmermann & Granger, 2004).”
Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold–Mariano Tests (2015, unknown): “(See, among many others, Granger and Timmermann 2004.)”
How Costly is it to Ignore Breaks When Forecasting the Direction of a Time Series? (2004, background): “Timmermann and Granger (2002) argue that certain forms of predictability in financial returns are likely to self-destruct as a consequence of the efficient market hypothesis once the predictable patterns are publicized and sufficient capital is allocated towards exploiting them.”
Financial modeling using Gaussian process models (2011, background): “Article [7] reveals that a model able to predict ﬁnancial returns should adapt to market changes quickly and catch local dependencies in price movements.”
Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction (2011, background): “…such a setup; see H4), the limited representativeness of the datasets in size, structure and heterogeneity, and the exclusion of certain performance metrics that assess the final impact on decision making, e.g., the inventory costs arising from operational forecasting (Timmermann & Granger, 2004).”
Judging the judges through accuracy-implication metrics: The case of inventory forecasting (2010, background): “In the area of economics and finance, for example, Timmermann and Granger (2004) highlighted the need to evaluate forecast results using utility functions.”
The Profitability of Technical Analysis: A Review (2004, background): “That is, as suggested by Timmermann and Granger (2004), it may be improper to apply the nonlinear approach that was not available until recent years to reveal the profitability of technical trading rules.”
Predicting the direction of stock market prices using tree-based classifiers (2019, methodology): “Early models used in stock forecasting involved statistical methods such as time series modeling and multivariate analysis (Gencay 1999; Timmermann and Granger 2004; Bao and Yang 2008).”
The Evolution of Stock Market Efficiency Over Time: A Survey of the Empirical Literature (2011, background): “In the forecasting literature, temporary forecastability (Timmermann and Granger, 2004) and temporary market inefficiencies (Park and Irwin, 2007) are two leading explanations given for the counterintuitive positive forecasting results of technical trading rules during certain time periods.”
Improved Forecasting of Mutual Fund Alphas and Betas (2007, background): “A more closely related article is Timmermann and Granger (2004).”
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2010, unknown): “,N and N = 15), where each model is based on one of the 15 variables from Welch and Goyal (2008) for which quarterly data are available for 1947:1–2005:4.”