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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.

Technology 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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XLK Seasonality Chart

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

See seasonal chart with year by year performance

XLK Seasonal Returns

XLK Seasonal Probabilities

XLK Seasonal Returns Previous 25 Years

Month Mean Median Win Freq
January 0.84253 0.84253 50.00000
February -1.12338 0.21296 54.17000
March 1.85221 2.19314 66.67000
April 1.59798 1.59798 66.67000
May 0.70619 1.92505 58.33000
June 0.16277 -0.06045 45.83000
July 1.77821 2.16356 66.67000
August 0.94498 1.73784 62.50000
September -2.45425 -0.15505 50.00000
October 3.09945 4.01935 66.67000
November 2.46515 2.76963 75.00000
December 0.70997 1.85609 58.33000
All Seasonality Visualizations

XLK Seasonal Returns Previous 25 Years

Month Mean
January 0.84253
February -1.12338
March 1.85221
April 1.59798
May 0.70619
June 0.16277
July 1.77821
August 0.94498
September -2.45425
October 3.09945
November 2.46515
December 0.70997

About Technology Select Sector SPDR Fund

About Technology Select Sector SPDR Fund

XLK is an exchange traded fund whose portfolio is comprised of US technology sector stocks. This investment seeks to provide the same performance as stocks in the Technology Select Sector Index. The fund typically invests at least 95% of its assets in the securities that comprise this index.

When is the best month to buy XLK?

Technology Select Sector SPDR Fund has performed the best during the month of October, during which shares have returned an average of 3.1% over the last 25 years.

When is the worst month to buy XLK?

Technology Select Sector SPDR Fund has performed the worst during the month of September, during which shares have returned an average of -2.45% 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 Technology Select Sector SPDR Fund XLK 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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