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The datasets from which these PYPL forecasts are drawn originate from FactSet. They represent the aggregated estimates made available to academics or practitioners via the Institutional Brokers’ Estimate System (IBES). Although this seems like a fair way of predicting future profits given that they have some level expertise in investment banking, studies show there's still an optimism bias present among these professionals.

Regression-based models suffer from the use of past earnings in a linear or exponential framework. This can lead to bias because these models assume that future performance will mirror historical trends exactly, whereas business cycle dynamics and seasonality may introduce randomness over time periods.

While there is a clear consensus that a factor-based approach to investment is rewarded over time, it goes without saying that the implementation of factor investing strategies, especially in the world of long-only money-management, is rarely subject to the same consensus. Index providers who offer funds that generally contain a small number of stocks in relation to the size and risk level they are designed for, often do so by selecting certain conditions or factors within each company.

For example, some commercial indexes aim at proportionality between price movements and dividends paid out over time while others look exclusively on liquidity considerations alone; yet still more restrict their selection criteria based around corporate governance issues like transparency reports rating various aspects such as soundness levels among others relevant metrics available about any given firm when deciding whether it should be included into an investor’s portfolio.

Financial Select Sector SPDR Fund Seasonality

Components
Historical price and seasonality data
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Seasonality

This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
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XLF Seasonality Chart

Left-hand side y-axis coordinates measure return in percentage.

See seasonal chart with year by year performance

XLF Seasonal Returns

XLF Seasonal Probabilities

XLF Seasonal Returns Previous 25 Years

Month Mean Median Win Freq
January -0.66949 0.10098 54.17000
February -1.05121 -0.61713 50.00000
March 1.89036 0.26918 50.00000
April 3.11746 2.78676 66.67000
May 0.38989 1.76262 58.33000
June -1.44134 -1.33434 37.50000
July 1.79883 2.35951 66.67000
August 0.19010 0.19010 50.00000
September -0.91287 -0.86923 45.83000
October 2.18921 2.31676 69.57000
November 1.23793 2.18458 60.87000
December 1.63383 2.28639 75.00000
All Seasonality Visualizations

XLF Seasonal Returns Previous 25 Years

Month Mean
January -0.66949
February -1.05121
March 1.89036
April 3.11746
May 0.38989
June -1.44134
July 1.79883
August 0.19010
September -0.91287
October 2.18921
November 1.23793
December 1.63383

About Financial Select Sector SPDR Fund

About Financial Select Sector SPDR Fund

XLF is an exchange traded fund whose portfolio is comprised of US financial services stocks. This investment seeks to provide the same performance as stocks in the Financial Select Sector Index. This index includes securities of companies from the following industries: diversified financial services; insurance; banks; capital markets; mortgage real estate investment trusts ("REITs"); consumer finance, thrifts and mortgage finance.

When is the best month to buy XLF?

Financial Select Sector SPDR Fund has performed the best during the month of April, during which shares have returned an average of 3.12% over the last 25 years.

When is the worst month to buy XLF?

Financial Select Sector SPDR Fund has performed the worst during the month of June, during which shares have returned an average of -1.44% over the last 25 years.
About Market Seasonality
Seasonality can be defined as the predictable changes that occur over a one-year period in an economy, market or business, based on the seasons of the calendar year.

Academic research supports the notion that seasonal pricing patterns occur with regularity in futures contracts of commodities with fixed maturities, most notably in the natural gas and crude oil markets.

For example, Ewald, Haugom, Stordal, Lien and Wu find evidence for seasonality in futures products that appears distinct from the seasonal patterns in spot price for the respective commodities.

Traders often attempt to take advantage of seasonal patterns through spread trades that hold long and short positions in assets of differing maturities simultaneously or across related assets in financial products such as equity sector ETFs, index futures or commodities.

Investors in individual equities may take seasonality into account when when analyzing the impact that seasonal changes may have on the fortunes of particular companies. For example, for many businesses, sales can vary depending on the season. In such cases, the share prices of business that experience higher profits during specific seasons may simultaneously register significant gains while later giving them back during off-peak periods.
All Seasonality Visualizations

Seasonality

This chart shows the seasonal tendencies  of the share price of Financial Select Sector SPDR Fund XLF over the last 25 years.
Components
Historical price and seasonality data
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XLF
Guest Commentary
Robson Chow is a hedge fund manager
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