The momentum effect on Taiwanese hotel stocks

Chen, Ming‐Hsiang; Zhang, Zhao; Wang, Keling; Lv, Wan Qing

doi:10.1016/j.ijhm.2017.09.011

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…Moreover, the findings of this paper are consistent with Chen et al (2007) and Leung and Lee (2006) concerning the existence of financial anomalies among the HT stocks. While this paper found positive and insignificant momentum in the US HT, Chen et al (2018) found negative and significant momentum over the short, intermediate and long-term horizons using a sample of Taiwanese hotels.…”

Section: Resultscontrasting

confidence: 57%

“…This forms a total of nine portfolios at the intersections between J and K. For instance, J3-K3 is the strategy that is formed according to the returns of the previous three months and held for three after formation. Finally, the figures in parentheses are the t-statistics found positive and insignificant momentum in the US HT, Chen et al (2018) found negative and significant momentum over the short, intermediate and long-term horizons using a sample of Taiwanese hotels.…”

Section: Momentum Investingmentioning

confidence: 99%

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Momentum investing: evidence from the US tourism and hospitality

Ahmed

2021

EJMBE

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PurposeThe present research aims to examine a range of momentum trading strategies for the tourism and hospitality sector.Design/methodology/approachThe paper followed the methodology of Jegadeesh and Titman (1993) to construct the portfolios. In this methodology, all portfolios were formed and evaluated by their cumulative stock returns over the past J periods and holding the position for the next K periods. In total, nine formation and holding periods were used, represented by 3, 6 and 12. For example, strategy 3–3 (that is, strategy with J = 3 and K = 3) refers to the strategy that stocks are ranked based on their previous three months and then held for the next three months.FindingsThe findings demonstrated that none of these momentum investing strategies was profitable. Most of the results, however, show positive, but insignificant momentum returns. This finding can be interpreted as price reversal over a horizon of three to twelve months in the US hospitality and tourism sector. These results are robust to size, different formation and holding combinations, beta and turnover.Research limitations/implicationsRegarding the research limitations, this paper only considers the US tourism and hospitality sector. Therefore, the extension of results to other developed and developing markets should be taken carefully. Also, this paper relies only on the methodology of Jegadeesh and Titman (1993). Other methodologies could be suitable avenues for future research.Practical implicationsInvestors and portfolio managers who seek for earning abnormal returns by investing in the US HT stocks can attain their hopes by constructing portfolios based on existing guidelines in the literature and adopting a short-term reversal trading strategy or by buying past losers and selling past winners of the US tourism and hospitality stocks.Originality/valueThis research contributes to the hospitality finance literature by offering the investors who are interested in the US hospitality and tourism sector an uncomplicated trading rule that uses real return data and is expected to generate actual returns. Moreover, the momentum strategy of Jegadeesh and Titman (1993) is never used in the hospitality finance literature.

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Section: Resultscontrasting

confidence: 57%

Section: Momentum Investingmentioning

confidence: 99%

Momentum investing: evidence from the US tourism and hospitality

Ahmed

2021

EJMBE

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“…A panel quantile regression analysis is also carried out. This approach has been found to be of value in the tourism industry field (Akron etal., 2020; Assaf and Tsionas, 2018; Chen, 2016; Chen etal., 2018) as it provides “an overall assessment of the covariate effects at different quantiles (τ) of the dependent variable” (Yuan and Yin, 2010: 105). As a brief description of the quantile regression analysis, consider the following conditional quantile function:…”

Section: Methodsmentioning

confidence: 99%

“…Quantile regressions have been increasingly used in hospitality and tourism studies (e.g. Akron etal., 2020; Assaf and Tsionas, 2018; Chen, 2016; Chen etal., 2018). Note that unlike least squares estimators that are based on the conditional mean of data dispersions, quantile regressions can deal with information about different parts of data distribution including the tails (Chiang etal., 2010).…”

Section: Introductionmentioning

confidence: 99%

Understanding the effects of economic policy uncertainty on US tourism firms’ performance

et al. 2021

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This study analyzes the impact of economic policy uncertainty (EPU) on the performance of US tourism firms using a sample of 296 publicly traded tourism companies from 2000 to 2018 with a sample of 3068 firm-year observations. Estimation results of panel regressio tests based on the system-generalized method of moments indicate that EPU has a negative impact on return on assets (ROA), return on equity (ROE), and Tobin’s Q. Our results are consistent for different variable specifications. We also find that firm size and leverage play a moderating role in the relationship between EPU and firm performance. Panel quantile regression results show that the impact of EPU on US tourism firm performance is asymmetric. Specifically, low-performing (25% quantile of ROA and ROE) firms are less affected by EPU, and for the case of Tobin’s Q, EPU does not affect firms with a high growth opportunity (100% quantile of Tobin’s Q).

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“…Following Peng et al (2010) and Chen et al (2018), we also utilized the quantile regression method to analyze the effects of sentiment and uncertainty on different performance levels of the T&L stock portfolio. Quantile regression (QR) has several advantages compared with the standard OLS procedure.…”

Section: Rationale Research Hypotheses and Methodsmentioning

confidence: 99%

Sentiment, Confidence and Uncertainty: The Case of Tourism and Leisure Stocks

Aharon

2020

Cornell Hospitality Quarterly

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This study tests the possible effect of sentiment on the performance of the tourism and leisure (T&L) industry. Using a portfolio consisting of U.S. publicly traded firms, we reveal that the Consumer Sentiment Index (CSI) and the Consumer Confidence Index (CCI) are perhaps better candidates for capturing a contemporaneous relationship or making predictions about the performance of the T&L stocks than the Economic Policy Uncertainty Index (EPU) or the Volatility Index (VIX). Macroeconomic variables have a limited contribution to the establishment of contemporaneous relationships and none of them is suitable for making predictions, which underscores the importance of addressing and paying attention to the issue of sentiment. Our results are robust using different empirical procedures such as ordinary least squares (OLS), GARCH, and quantile regressions. The findings may have important implications for policy makers at both the state and firm levels in light of increased uncertainty periods such as the ongoing coronavirus epidemic.

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The momentum effect on Taiwanese hotel stocks

Cited by 10 publications

References 36 publications

Momentum investing: evidence from the US tourism and hospitality

Momentum investing: evidence from the US tourism and hospitality

Understanding the effects of economic policy uncertainty on US tourism firms’ performance

Sentiment, Confidence and Uncertainty: The Case of Tourism and Leisure Stocks

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