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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.

Invesco QQQ Trust Series 1 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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QQQ Seasonality Chart

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

See seasonal chart with year by year performance

QQQ Seasonal Returns

QQQ Seasonal Probabilities

QQQ Seasonal Returns Previous 24 Years

Month Mean Median Win Freq
January 0.60677 0.68135 60.87000
February -0.62730 -0.51402 47.83000
March 1.29844 2.08468 66.67000
April 1.90433 2.29954 62.50000
May 0.52004 2.29418 54.17000
June 0.37840 0.54446 58.33000
July 2.09538 2.36564 70.83000
August 1.16258 1.58899 58.33000
September -2.15491 -0.26202 45.83000
October 3.42834 4.55792 66.67000
November 2.28025 2.28025 79.17000
December 0.85868 0.74360 56.52000
All Seasonality Visualizations

QQQ Seasonal Returns Previous 24 Years

Month Mean
January 0.60677
February -0.62730
March 1.29844
April 1.90433
May 0.52004
June 0.37840
July 2.09538
August 1.16258
September -2.15491
October 3.42834
November 2.28025
December 0.85868

About Invesco QQQ Trust Series 1

About Invesco QQQ Trust Series 1

QQQ is an exchange-traded security designed to track the performance of 100 of the largest domestic and international nonfinancial companies listed on the NASDAQ Stock Market. The trust's adviser periodically adjusts securities to maintain a correspondence between the composition and weights of securities in the trust and stocks in the index. QQQ seeks investment results that correspond generally to the price and yield performance of this index.

When is the best month to buy QQQ?

Invesco QQQ Trust Series 1 has performed the best during the month of October, during which shares have returned an average of 3.43% over the last 24 years.

When is the worst month to buy QQQ?

Invesco QQQ Trust Series 1 has performed the worst during the month of September, during which shares have returned an average of -2.15% over the last 24 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 Invesco QQQ Trust Series 1 QQQ over the last 24 years.
Components
Historical price and seasonality data
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QQQ
Guest Commentary
Robson Chow is a hedge fund manager
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