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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.

SPDR S&P 500 ETF Trust 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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SPY Seasonality Chart

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

See seasonal chart with year by year performance

SPY Seasonal Returns

SPY Seasonal Probabilities

SPY Seasonal Returns Previous 30 Years

Month Mean Median Win Freq
January 0.46730 1.43288 58.62000
February 0.00024 0.60669 60.00000
March 1.35268 1.66529 70.00000
April 2.04522 1.36102 76.67000
May 0.83676 1.59012 66.67000
June 0.06945 0.49415 66.67000
July 1.39743 1.53120 60.00000
August -0.06592 0.45561 63.33000
September -0.52282 0.08812 53.33000
October 1.98331 2.26001 66.67000
November 2.24059 2.71861 76.67000
December 1.28529 1.31548 72.41000
All Seasonality Visualizations

SPY Seasonal Returns Previous 30 Years

Month Mean
January 0.46730
February 0.00024
March 1.35268
April 2.04522
May 0.83676
June 0.06945
July 1.39743
August -0.06592
September -0.52282
October 1.98331
November 2.24059
December 1.28529

About SPDR S&P 500 ETF Trust

About SPDR S&P 500 ETF Trust

SPY is an investment trust that seeks to match the price and yield performance of the S&P 500 Index. As such, SPY holds a portfolio of common stocks in proportion to their weighting in the index. The goal of this exchange-traded security is to provide investment returns broadly comparable to those of the S&P 500 Index.

When is the best month to buy SPY?

SPDR S&P 500 ETF Trust has performed the best during the month of November, during which shares have returned an average of 2.24% over the last 30 years.

When is the worst month to buy SPY?

SPDR S&P 500 ETF Trust has performed the worst during the month of September, during which shares have returned an average of -0.52% over the last 30 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 SPDR S&P 500 ETF Trust SPY over the last 30 years.
Historical price and seasonality data
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Guest Commentary
Robson Chow is a hedge fund manager
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Any hypothetical performance shown is based on the retrospective application of a model developed with hindsight. While these results are presented for illustrative purposes only, it's important to note that there may be limitations built into any projection.

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