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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.

Health Care 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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XLV Seasonality Chart

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

See seasonal chart with year by year performance

XLV Seasonal Returns

XLV Seasonal Probabilities

XLV Seasonal Returns Previous 25 Years

Month Mean Median Win Freq
January 0.84821 1.02958 62.50000
February -0.35792 0.26698 54.17000
March 1.44259 1.44259 62.50000
April 1.79488 1.54157 58.33000
May 0.87077 1.65287 70.83000
June 0.45443 0.10309 50.00000
July 1.29042 1.29042 62.50000
August -0.13197 1.18776 54.17000
September -1.21103 -0.52581 41.67000
October 0.82237 0.83478 60.87000
November 1.88891 1.94582 73.91000
December 2.11654 2.11654 75.00000
All Seasonality Visualizations

XLV Seasonal Returns Previous 25 Years

Month Mean
January 0.84821
February -0.35792
March 1.44259
April 1.79488
May 0.87077
June 0.45443
July 1.29042
August -0.13197
September -1.21103
October 0.82237
November 1.88891
December 2.11654

About Health Care Select Sector SPDR Fund

About Health Care Select Sector SPDR Fund

XLV invests in shares of companies that make up the Health Care Select Sector Index. The fund replicates the index by investing substantially all of its assets in securities that are listed in it. XLV includes companies from the following industries: pharmaceuticals, health care equipment and supplies, health care providers and services, biotechnology, life sciences tools and services.

When is the best month to buy XLV?

Health Care Select Sector SPDR Fund has performed the best during the month of December, during which shares have returned an average of 2.12% over the last 25 years.

When is the worst month to buy XLV?

Health Care Select Sector SPDR Fund has performed the worst during the month of September, during which shares have returned an average of -1.21% 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 Health Care Select Sector SPDR Fund XLV over the last 25 years.
Components
Historical price and seasonality data
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XLV
Guest Commentary
Robson Chow is a hedge fund manager
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