Analysing historical time series of prices or returns of financial assets is not about making predictions, but about identifying patterns that have consistently repeated over time and that could potentially occur again with similar phenomena from the past.
Statistical studies provide precise but inconclusive results from the sample values, possibly appearing useless in the face of unpredictability, known as the “Trilussa’s chicken.”
Definitions: “Mean, mode, and median are three characteristics of any statistical data set.
The mean is the ratio between the sum of numerical data and the number of data points; the mode is the value that appears most frequently; the median is the central value among the numerical data.” These three coordinates help in obtaining a kind of barycenter of the statistical phenomenon being studied, aiming to reduce the data dispersion.
Using the American S&P 500 index as a reference, interesting past regularities can be observed:
Historical data suggests the following insights: the market tends to rise from March to June, with positive values in April and June for the mode; July and October show positive averages and medians, but downturns can be significant, exceeding -2%; the October rally extends until January, with November being generally more profitable and September more critical.
August? Typically negative, along with September.
Ponder on, folks, “sell in June (may) and go away, summer flat.”
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