Global markets crashed after the 2008 recession is one of the best examples. In such a scenario, trend analysis of stocks using a comparison of average returns and market returns provides an investor with information to make optimal investment decisions.
financial data analysis, stock market analysis, stock performance, trend analysisThe annual average returns of a stock define the earning possibility of investor from a particular investment whereas the market return states the return deriving capacity of all stocks traded in the market.
financial data analysis, stock market analysis, stock performance, trend analysisAutoregressive Integrated Moving Average (ARIMA) is the statistical tool with a standard structure which though is simpler but provides skillful information about the stock market.
stock categorization, stock market analysis, stock market page intro, time series analysis, trend analysisStock market trend analysis or equity market trend analysis refers to the process of examining the current trends based on the past and current movement of the stocks in order to predict future trends.
financial data analysis, literature survey, stock categorization, stock market analysis, stock market page intro, trend analysisThis article shows a testing serial correlation of errors or time series autocorrelation in STATA. Autocorrelation problem arises when error terms in a regression model correlate over time or are dependent on each other.
correlation, STATA for data analysis, time series analysis, trend analysisThe previous article showed that the three-time series values Gross Domestic Product (GDP), Gross Fixed Capital Formation (GFC) and Private Final Consumption (PFC) are non-stationary. Therefore they may have long-term causality. The general assumption, in this case, is that consumption PFC affects GDP, therefore these variables might be cointegrated.
STATA for data analysis, time series analysis, trend analysisAfter performing Autoregressive Integrated Moving Average (ARIMA) modelling in the previous article: ARIMA modeling for time series analysis in STATA, the time series GDP can be modelled through ARIMA (9, 2, 1) .
STATA for data analysis, time series analysis, trend analysisIn the previous article, all possibilities for performing Autoregressive Integrated Moving Average (ARIMA) modeling for the time series GDP were identified as under. S. No ARIMA 1 (1,1,1) 2 (1,1,2) 3 (1,1,3) 4 (1,1,4) 5 (1,1,5) 6 (1,1,6) 7 (1,2,1) 8 (4,2,1) 9 (9,2,1) Table 1: ARIMA models as per ACF and PACF graphs.
STATA for data analysis, time series analysis, trend analysis