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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.

Consumer Discretionary 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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XLY Seasonality Chart

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

See seasonal chart with year by year performance

XLY Seasonal Returns

XLY Seasonal Probabilities

XLY Seasonal Returns Previous 25 Years

Month Mean Median Win Freq
January 0.05939 0.01220 50.00000
February -0.19924 -0.19924 50.00000
March 2.16726 2.13516 58.33000
April 2.78108 2.51440 75.00000
May -0.08744 0.12960 54.17000
June -0.65516 -0.06308 50.00000
July 1.72532 1.81284 66.67000
August 0.08099 -0.08736 45.83000
September -0.92322 -0.70318 41.67000
October 2.05933 2.12488 62.50000
November 2.45239 2.58303 79.17000
December 1.61039 2.32350 79.17000
All Seasonality Visualizations

XLY Seasonal Returns Previous 25 Years

Month Mean
January 0.05939
February -0.19924
March 2.16726
April 2.78108
May -0.08744
June -0.65516
July 1.72532
August 0.08099
September -0.92322
October 2.05933
November 2.45239
December 1.61039

About Consumer Discretionary Select Sector SPDR Fund

About Consumer Discretionary Select Sector SPDR Fund

XLY is an exchange traded fund whose portfolio is comprised of US consumer-discretionary stocks. This investment seeks to provide the same performance as stocks in the Consumer Discretionary Select Sector Index. The index includes securities of companies from the following industries: retail, hotels, restaurants and leisure; textiles, apparel and luxury goods; household durables; automobiles.

When is the best month to buy XLY?

Consumer Discretionary Select Sector SPDR Fund has performed the best during the month of April, during which shares have returned an average of 2.78% over the last 25 years.

When is the worst month to buy XLY?

Consumer Discretionary Select Sector SPDR Fund has performed the worst during the month of September, during which shares have returned an average of -0.92% 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 Consumer Discretionary Select Sector SPDR Fund XLY 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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