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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.

Utilities Select Sector SPDR Fund Seasonality

Historical price and seasonality data
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This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
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XLU Seasonality Chart

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

See seasonal chart with year by year performance

XLU Seasonal Returns

XLU Probabilities Returns

XLU Seasonal Returns Previous 25 Years

Month Mean Median Win Freq
January -0.15737 -0.15737 50.00000
February -1.48932 -0.07300 50.00000
March 2.49540 2.49540 75.00000
April 2.44821 2.66164 79.17000
May 0.84022 0.80126 66.67000
June 0.12079 0.12079 50.00000
July 0.65406 1.63692 62.50000
August 0.58688 1.31717 66.67000
September -0.16688 0.65937 62.50000
October 1.52073 1.52073 75.00000
November 0.52122 0.52122 58.33000
December 1.72935 1.72935 75.00000
All Seasonality Visualizations

XLU Seasonal Returns Previous 25 Years

Month Mean
January -0.15737
February -1.48932
March 2.49540
April 2.44821
May 0.84022
June 0.12079
July 0.65406
August 0.58688
September -0.16688
October 1.52073
November 0.52122
December 1.72935

About Utilities Select Sector SPDR Fund

About Utilities Select Sector SPDR Fund

XLU is an exchange traded fund whose portfolio is comprised of US utilities sector stocks. This investment seeks to provide the same performance as stocks in the Utilities Select Sector Index. The index includes securities of companies from the following industries: electric utilities, water utilities, multi-utilities, independent power and renewable electricity producers and gas utilities.

When is the best month to buy XLU?

Utilities Select Sector SPDR Fund has performed the best during the month of March, during which shares have returned an average of 2.5% over the last 25 years.

When is the worst month to buy XLU?

Utilities Select Sector SPDR Fund has performed the worst during the month of February, during which shares have returned an average of -1.49% 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


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