Backtest USO
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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.
United States Oil ETF Seasonality
Historical price and seasonality data
Seasonality
This multi-factor forecast for Paypal Holdings (PYPL) is based on a weighted average of five factor-dervied forecasts.
Backtest PYPL Left-hand side y-axis coordinates measure return in percentage.
USO Seasonal Returns Previous 17 Years
| Month |
Mean |
| January |
-1.55027 |
| February |
2.44798 |
| March |
-1.55172 |
| April |
1.55189 |
| May |
0.86292 |
| June |
2.89435 |
| July |
-1.55464 |
| August |
-1.64601 |
| September |
-1.34434 |
| October |
-1.62202 |
| November |
-3.08185 |
| December |
-0.54692 |
| Month |
Mean |
Median |
Win Freq |
| January |
-1.55027 |
-1.55027 |
31.25000 |
| February |
2.44798 |
2.59591 |
68.75000 |
| March |
-1.55172 |
3.91566 |
62.50000 |
| April |
1.55189 |
2.62032 |
64.71000 |
| May |
0.86292 |
-1.04444 |
41.18000 |
| June |
2.89435 |
3.17265 |
64.71000 |
| July |
-1.55464 |
0.09703 |
52.94000 |
| August |
-1.64601 |
-2.96994 |
35.29000 |
| September |
-1.34434 |
-2.49561 |
47.06000 |
| October |
-1.62202 |
-0.88669 |
47.06000 |
| November |
-3.08185 |
-0.68286 |
47.06000 |
| December |
-0.54692 |
2.55496 |
62.50000 |
USO is an exchange-traded security that seeks to track the daily percentage changes in its share price to reflect the daily percentage changes in the spot price of light, sweet crude oil. The goal of the security is to provide shareholders with an investment that closely correlates to movements in the underlying commodity. USO is priced and traded as a security on the NYSE.
Best month to buy USO
United States Oil ETF has tended to perform the best during the month of June, during which shares have historically returned an average of 2.9.
Worst month to buy USO
United States Oil ETF has tended to perform the worst during the month of November, during which shares have historically returned an average of -3.1.
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.
Traders often attempt to take advantage of seasonal patterns by holding long and short positions in assets simultaneously in the same or a related markets, such as equity sectors, 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.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.
