Impact of Macroeconomic Announcements on US Equity Prices: 2009–2013

Returns of several US equity exchange‐traded funds on the days of major macroeconomic announcements are examined for the period of January 2009 to July 2013. The ARMA+GARCH model with external linear regression terms that describe announcement events and their surprises is used. It is found that mean daily returns may be notably higher on the announcement days than those for the buy‐and‐hold strategy, though their difference may be not statistically significant. The ISM Manufacturing Reports, Non‐Farm Payrolls, International Trade Balance, Index of Leading Indicators, Housing Starts, and Jobless Claims turn out to be the most statistically significant factors in the model. Three trading strategies that realize daily returns on the various macroeconomic announcement days are compared with the buy‐and‐hold strategy. The choice of announcements with statistically significant regression coefficients yields higher mean daily returns and better Sharpe ratios but possibly lower compound returns. Transaction costs may significantly affect profitability of these trading strategies. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimating the Out‐of‐Sample Predictive Ability of Trading Rules: A Robust Bootstrap Approach

In this paper, we provide a novel way to estimate the out‐of‐sample predictive ability of a trading rule. Usually, this ability is estimated using a sample‐splitting scheme, true out‐of‐sample data being rarely available. We argue that this method makes poor use of the available data and creates data‐mining possibilities. Instead, we introduce an alternative.632 bootstrap approach. This method enables building in‐sample and out‐of‐sample bootstrap datasets that do not overlap but exhibit the same time dependencies. We show in a simulation study that this technique drastically reduces the mean squared error of the estimated predictive ability. We illustrate our methodology on IBM, MSFT and DJIA stock prices, where we compare 11 trading rules specifications. For the considered datasets, two different filter rule specifications have the highest out‐of‐sample mean excess returns. However, all tested rules cannot beat a simple buy‐and‐hold strategy when trading at a daily frequency. Copyright © 2015 John Wiley & Sons, Ltd.     

A neural network versus Black–Scholes: a comparison of pricing and hedging performances

An Erratum has been published for this article in Journal of Forecasting 22(6‐7) 2003, 551 The Black–Scholes formula is a well‐known model for pricing and hedging derivative securities. It relies, however, on several highly questionable assumptions. This paper examines whether a neural network (MLP) can be used to find a call option pricing formula better corresponding to market prices and the properties of the underlying asset than the Black–Scholes formula. The neural network method is applied to the out‐of‐sample pricing and delta‐hedging of daily Swedish stock index call options from 1997 to 1999. The relevance of a hedge‐analysis is stressed further in this paper. As benchmarks, the Black–Scholes model with historical and implied volatility estimates are used. Comparisons reveal that the neural network models outperform the benchmarks both in pricing and hedging performances. A moving block bootstrap is used to test the statistical significance of the results. Although the neural networks are superior, the results are sometimes insignificant at the 5% level. Copyright © 2003 John Wiley & Sons, Ltd.     

Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach

We present and apply singular spectrum analysis (SSA), a relatively new, non‐parametric and data‐driven method for signal extraction (trends, seasonal and business cycle components) and forecasting of UK tourism income. Our results show that SSA slightly outperforms SARIMA and time‐varying‐parameter state space models in terms of root mean square error, mean absolute error and mean absolute percentage error forecasting criteria. Copyright © 2011 John Wiley & Sons, Ltd.