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In this paper, we make use of the replicating asset for statistical arbitrage trading, where the replicating asset is constructed by a portfolio that mimics the returns from a factor model. Using the replicating asset in the context of statistical arbitrage has never been done before in the literature. A novel optimal statistical arbitrage trading model is applied, and we derive the average transaction length and return for the Berkshire A stock and its replicating asset. The results show that the statistical arbitrage method proposed by Bertram (2010) is profitable by using the replicating asset. We also compute the average returns under different transaction costs. For the statistical arbitrage using the replicating asset of the factor model, average annual returns were at least 33%. Robustness is examined with the S&P500. Our results can provide hedge fund managers with a new technique for conducting statistical arbitrage. [ABSTRACT FROM AUTHOR]

In this article, we apply the threshold autoregressive model to examine oil price thresholds and the relationship between oil prices and the returns of 25 benchmark portfolios formed on size and book-to-market. Our results show that the relationship between the change in oil prices and portfolio returns reverses depending on whether the current oil price is above or below the estimated oil price threshold. Additional results show that this threshold effect is concentrated in the small stock portfolios. Finally, our finding that there is a threshold effect of oil price changes on stock returns is consistent with the theoretical model of investor sentiment presented by Barberis, Shleifer, and Vishny (1998) and is intuitively consistent with the economic dynamics and structure of the oil industry. [ABSTRACT FROM AUTHOR]

No  prior research has (1) studied the relation between gold and stocks for the four severe bear markets since 1960s, (2) used different segments of stock markets simultaneously for analysis and (3) implemented a system of equations to control for exogenous and endogenous variables to investigate the role of gold for investments hedge in these severe bear market periods, and compare the results with its role in nonbear market periods. Results show that gold was a good instrument for hedging stock market risk for only two of the four severe bear market periods analysed. For nonbear market periods, except for small-cap stocks, gold also did not offer good risk hedging. The findings are of interest, as it coincides with the fact that small-cap stocks are the riskiest and most volatile investment even during economic good times, and gold is found to offer a risk-hedging power for this segment of the stock market. [ABSTRACT FROM AUTHOR]

We test and implement portfolio strategies for three major asset pricing models, under uniform diagnostic measures using the PACAP data set containing all current listing and de-listing of firms for the local stock exchange in several Pacific Basin countries. Compared to the often used MSCI database that include only a subset of the (large) firms in the local markets, the more complete coverage of our database allows for more robust testing of current multifactor asset pricing models since the possible effects of additional factors such as size and book to market may not show up correctly using less comprehensive data sets. Our data set also provides a natural packet of nonUS data for addressing the issue of whether the results of recent asset pricing research are sample specific. Our overall results provide multi-country (sample nonspecific) support for the additional asset pricing risk factors of the Fama-French three-factor model but not for the momentum factor of the Carhart model. We additionally find that the size risk factor is more prominent than value risk factor in the Pacific Basin markets. Finally, we find strong evidence that portfolio strategies implemented to capture value and size effects are profitable in the Pacific Basin stock markets. [ABSTRACT FROM AUTHOR]

We study whether dividend yield (DY) can predict aggregate stock returns while controlling for the effects of structural breaks in the parameters and bias induced by autocorrelation in the predictor variable. To do so we apply the Bai and Perron (BP) (1998, 2000) methodology to test for structural breaks and the bias-adjusted predictability test of Lewellen (2004). We show that although DY predicts market returns during the period 1946 to 1989, there exist 'natural' subsamples bounded by statistically detectable structural breaks that can last for long periods of time (up to 11 years in duration) when DY does not show significant forecasting power. This has important implications in that even if in the long-run DY actually provides strong predictive ability, investors should be mentally prepared for long dry spells of unpredictability with respect to DY. [ABSTRACT FROM AUTHOR]