{"id":656,"date":"2016-02-12T14:08:53","date_gmt":"2016-02-12T14:08:53","guid":{"rendered":"http:\/\/quantpedia.com\/?p=656"},"modified":"2025-06-04T14:08:36","modified_gmt":"2025-06-04T12:08:36","slug":"an-examination-of-the-turn-of-the-month-effect","status":"publish","type":"post","link":"https:\/\/vvv.quantpedia.com\/es\/an-examination-of-the-turn-of-the-month-effect\/","title":{"rendered":"An Examination of The Turn-of-the-Month-Effect"},"content":{"rendered":"

\n\tA new related paper has been added to:<\/strong>
\n\t
\n\t#41 – Turn of the Month in Equity Indexes<\/a><\/strong><\/p>\n

\n\tAutores: <\/strong>Giovanis<\/p>\n

\n\tT\u00edtulo: <\/strong>The Turn-of-the-Month-Effect: Evidence from Periodic Generalized Autoregressive Conditional Heteroskedasticity (PGARCH) Model<\/p>\n

\n\tLink:<\/strong> http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2584213<\/a><\/p>\n

\n\tAbstracto:<\/strong>
\n\t
\n\tThe current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.<\/p>\n

\n\tFragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n

\n\t"<\/p>\n

\n\tThe purpose of this paper is to investigate the turn of the month effect in stock market indices around the globe and to test its pattern, which can be used for the optimum asset allocation with result the maximization of profits. Because each stock market behaves differently and presents different turn of the month effect patterns, the trading strategy should be formed in this way where the buy and sell signals and actions will be varied in each stock market index. Haugen and Jorion (1996) suggested that calendar effects should not be long lasting, as market participants can learn from past experience. Hence, if the turn of the month effect exists, trading based on exploiting this calendar anomaly pattern of returns should yield extraordinary profits – at least for a short time. Yet such trading strategies affect the market in that further profits should not be possible: the calendar effect should break down.<\/p>\n

\n\tHowever, the results show that the turn of the month effect is persistent in 19 out of 20 stock market indices during the whole period examined. Moreover, sub-sample periods have been explored too supporting the same concluding remarks. In addition, when the post financial crisis period sample 2010-2013 is excluded from the analysis, the turn of the month effect is present in all stock market indices."<\/p>\n\n

\n\n\n\n

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\n\tA new related paper has been added to:<\/strong>
\n\t
\n\t#41 – Turn of the Month in Equity Indexes<\/a><\/strong><\/p>\n

\n\tAutores: <\/strong>Giovanis<\/p>\n

\n\tT\u00edtulo: <\/strong>The Turn-of-the-Month-Effect: Evidence from Periodic Generalized Autoregressive Conditional Heteroskedasticity (PGARCH) Model<\/p>\n

\n\tLink:<\/strong> http:\/\/papers.ssrn.com\/sol3\/papers.cfm?abstract_id=2584213<\/a><\/p>\n

\n\tAbstracto:<\/strong><\/p>\n

\tThe current study examines the turn of the month effect on stock returns in 20 countries. This will allow us to explore whether the seasonal patterns usually found in global data; America, Australia, Europe and Asia. Ordinary Least Squares (OLS) is problematic as it leads to unreliable estimations; because of the autocorrelation and Autoregressive Conditional Heteroskedasticity (ARCH) effects existence. For this reason Generalized GARCH models are estimated. Two approaches are followed. The first is the symmetric Generalized ARCH (1,1) model. However, previous studies found that volatility tends to increase more when the stock market index decreases than when the stock market index increases by the same amount. In addition there is higher seasonality in volatility rather on average returns. For this reason the Periodic-GARCH (1,1) is estimated. The findings support the persistence of the specific calendar effect in 19 out of 20 countries examined.<\/p>\n

\n\tFragmentos destacados del art\u00edculo de investigaci\u00f3n acad\u00e9mica:<\/strong><\/p>\n

\n\t"<\/p>\n

\n\tThe purpose of this paper is to investigate the turn of the month effect in stock market indices around the globe and to test its pattern, which can be used for the optimum asset allocation with result the maximization of profits. Because each stock market behaves differently and presents different turn of the month effect patterns, the trading strategy should be formed in this way where the buy and sell signals and actions will be varied in each stock market index. Haugen and Jorion (1996) suggested that calendar effects should not be long lasting, as market participants can learn from past experience. Hence, if the turn of the month effect exists, trading based on exploiting this calendar anomaly pattern of returns should yield extraordinary profits – at least for a short time. Yet such trading strategies affect the market in that further profits should not be possible: the calendar effect should break down.<\/p>\n

\n\tHowever, the results show that the turn of the month effect is persistent in 19 out of 20 stock market indices during the whole period examined. Moreover, sub-sample periods have been explored too supporting the same concluding remarks. In addition, when the post financial crisis period sample 2010-2013 is excluded from the analysis, the turn of the month effect is present in all stock market indices."<\/p>\n

\n

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