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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.

Nasdaq-100 Components Realized Volatility Streamgraph

Components
Historical price and realized volatility 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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Nasdaq-100 Components Realized Volatility Streamgraph

Left-hand side y-axis orders liquidity rank of individuals chart components.
Right-hand side y-axis coordinates measure the price of QQQ.

Components Realized Volatility

Component Last Value
AAPL 38.77976
ABNB 80.23598
ADBE 56.85513
ADI 58.80251
ADP 40.43938
ADSK 60.30239
AEP 27.02070
ALGN 73.14896
AMAT 72.16706
AMD 94.43710
AMGN 36.18420
AMZN 61.44410
ANSS 55.28298
ASML 56.41175
ATVI 19.29283
AVGO 55.69893
BIDU 64.43474
BIIB 41.26913
BKNG 49.87848
CDNS 58.05577
CEG -
CHTR 36.34898
CMCSA 45.22202
COST 35.10272
CPRT 50.10837
CRWD 80.92059
CSCO 33.05378
CSX 37.07067
CTAS 34.22040
CTSH 40.90831
DDOG 107.36236
DLTR 44.52089
DOCU 90.33557
DXCM 67.27306
EA 48.62436
EBAY 48.81582
EXC 38.35831
FAST 40.86419
FB 74.05828
FISV 40.69434
FTNT 74.80038
GILD 29.66886
GOOG 45.76347
GOOGL 46.07703
HON 37.49433
IDXX 57.11385
ILMN 59.91273
INTC 42.43882
INTU 57.61953
ISRG 49.49365
JD 63.44129
KDP 24.68595
KHC 34.62611
KLAC 73.95027
LCID 123.95766
LRCX 78.59868
LULU 60.39941
MAR 51.02402
MCHP 68.45625
MDLZ 27.41507
MELI 85.37649
MNST 38.15310
MRNA 113.57186
MRVL 89.29005
MSFT 45.58899
MTCH 68.04980
MU 70.01754
NFLX 84.57001
NTES 58.59326
NVDA 90.66829
NXPI 78.28543
ODFL 57.06030
OKTA 86.61473
ORLY 42.95110
PANW 68.85691
PAYX 42.43583
PCAR 40.24088
PDD 108.02312
PEP 28.21999
PYPL 79.26366
QCOM 72.25404
REGN 38.24157
ROST 44.67985
SBUX 35.57288
SGEN 55.41175
SIRI 41.53998
SNPS 61.14405
SPLK 72.66750
SWKS 64.09252
TEAM 90.43611
TMUS 41.96937
TSLA 90.65280
TXN 51.92491
VRSK 34.70341
VRSN 41.80692
VRTX 41.92377
WBA 35.77851
WDAY 63.44796
XEL 33.86823
XLNX 95.56741
ZM 85.81917
ZS 83.88804
All Realized Volatility Visualizations
Component 1 Month Ago 1 Week Ago Last Value
AAPL 38.77976 38.63915 30.15665
ABNB 80.23598 74.91322 62.64738
ADBE 56.85513 53.30568 43.12488
ADI 58.80251 52.87430 34.41449
ADP 40.43938 40.35793 25.41425
ADSK 60.30239 59.41653 43.89687
AEP 27.02070 24.82951 20.27990
ALGN 73.14896 75.38940 60.53973
AMAT 72.16706 81.04700 69.69853
AMD 94.43710 84.25646 68.55535
AMGN 36.18420 34.39002 22.94087
AMZN 61.44410 59.99830 30.19413
ANSS 55.28298 55.40529 39.81541
ASML 56.41175 58.28966 47.71653
ATVI 19.29283 48.56489 53.47010
AVGO 55.69893 55.14872 40.82248
BIDU 64.43474 66.49961 63.89147
BIIB 41.26913 48.51975 60.28146
BKNG 49.87848 44.78120 40.64123
CDNS 58.05577 57.79911 42.48022
CEG 0.00000 0.00000 0.00000
CHTR 36.34898 39.03388 33.00951
CMCSA 45.22202 44.76882 24.31322
COST 35.10272 36.12365 30.78648
CPRT 50.10837 45.16261 33.83816
CRWD 80.92059 87.72520 78.89935
CSCO 33.05378 31.59577 24.34058
CSX 37.07067 36.38901 26.06008
CTAS 34.22040 33.49074 34.10268
CTSH 40.90831 37.47762 27.63136
DDOG 107.36236 105.21427 87.44940
DLTR 44.52089 45.72376 33.20682
DOCU 90.33557 92.71438 72.99765
DXCM 67.27306 70.34514 62.55534
EA 48.62436 52.48833 38.55488
EBAY 48.81582 50.34281 40.90955
EXC 38.35831 37.86535 24.16003
FAST 40.86419 41.28302 32.51778
FB 74.05828 73.04523 40.50827
FISV 40.69434 40.96572 36.43275
FTNT 74.80038 74.24593 70.77929
GILD 29.66886 28.23682 19.93978
GOOG 45.76347 44.40252 32.01714
GOOGL 46.07703 44.94302 33.06965
HON 37.49433 35.39457 23.60873
IDXX 57.11385 57.19958 47.77476
ILMN 59.91273 73.20918 63.95001
INTC 42.43882 41.80179 32.49699
INTU 57.61953 56.36512 44.04553
ISRG 49.49365 49.79366 41.75277
JD 63.44129 73.88672 73.29949
KDP 24.68595 28.24174 24.39746
KHC 34.62611 29.75216 22.57869
KLAC 73.95027 78.10155 63.42233
LCID 123.95766 128.20626 112.91320
LRCX 78.59868 80.70474 61.16246
LULU 60.39941 60.20557 52.62542
MAR 51.02402 46.38428 40.78780
MCHP 68.45625 65.06026 45.11730
MDLZ 27.41507 26.27573 17.02948
MELI 85.37649 94.76805 83.45488
MNST 38.15310 40.39497 32.73779
MRNA 113.57186 103.10649 113.45499
MRVL 89.29005 91.78508 70.87906
MSFT 45.58899 48.63842 36.11950
MTCH 68.04980 70.14632 55.15211
MU 70.01754 63.63834 47.97591
NFLX 84.57001 83.24291 38.79382
NTES 58.59326 60.07166 55.40274
NVDA 90.66829 84.33221 66.47064
NXPI 78.28543 72.33270 43.17002
ODFL 57.06030 56.76749 39.80419
OKTA 86.61473 89.75477 69.84036
ORLY 42.95110 38.71327 29.11774
PANW 68.85691 72.80905 62.11700
PAYX 42.43583 42.63724 32.44219
PCAR 40.24088 41.13315 31.29551
PDD 108.02312 110.63263 103.08422
PEP 28.21999 26.12869 15.10019
PYPL 79.26366 79.64412 47.70435
QCOM 72.25404 70.64576 47.45050
REGN 38.24157 35.95867 32.63616
ROST 44.67985 46.97516 37.88399
SBUX 35.57288 36.39165 30.98403
SGEN 55.41175 52.25197 48.58447
SIRI 41.53998 38.68829 23.12639
SNPS 61.14405 59.48875 46.18373
SPLK 72.66750 70.22030 48.60390
SWKS 64.09252 60.86877 39.06918
TEAM 90.43611 88.48890 87.23993
TMUS 41.96937 45.41467 38.10636
TSLA 90.65280 91.97972 81.93932
TXN 51.92491 49.38162 30.44168
VRSK 34.70341 36.83071 31.31380
VRSN 41.80692 41.00330 28.57779
VRTX 41.92377 42.48801 26.05592
WBA 35.77851 32.50012 30.47287
WDAY 63.44796 63.46047 48.05589
XEL 33.86823 32.85037 22.75528
XLNX 95.56741 87.50715 66.32451
ZM 85.81917 84.52080 64.53840
ZS 83.88804 91.58436 85.29907

About Realized Volatility

Realized volatility (as derived from the square root of variance) is a measurement of the standard deviation of returns of an asset over a given time period, typically annualized.

How Realized Volatility is Measured

Realized volatility can be measured many ways. The classical way of calculating realized volatility is by taking the log returns of close to close prices.

Per Euan Sinclair, “there is no uncertainty due to measurement. But there is uncertainty over whether the measure is truly representative of the underlying reality.”

The streamgraph visualization above displays realized volatility over the previous 21 days by applying the Yang-Zhang method of calculating realized volatility. This measurement utilizes more data points than the typical close-to-close estimator, which results in a measurement that is considered more accurate.

We measure realized volatility for each component of a particular index or ETFs in order to help understand volatility dynamics, and anomalies underneath the surface.

How to Read the Streamgraph

The streamgraph is a data visualization that enables the representation of many timeseries in an efficient manner. The Tradewell realized volatility streamgraph shows the change in realized volatility through time across multiple datasets, displaced around a central axis (the 0-line).

The streamgraph highlights three main attributes of realized volatility:

1. The overall level of realized volatility at the index or etf level relative to history.
If you notice the streamgraph expanding and then contracting, that behavior is representative of individual component volatility expanding and contracting. The widest part of the streamgraph represents the period with the most volatility across components, while the narrowest part of the streamgraph represents the period with the least volatility across components.

2. Anomalies in individual components realized through time.
When companies have large moves, ie volatility increases significantly due to earnings, unexpected events or otherwise, the streamgraph will immediately highlight those anomalies visually — the width of an individual securities contribution to the streamgraph will widen considerably and the individual component ticker will be displayed on the streamgraph on the Date where realized volatility was highest for Nasdaq-100. As volatility clusters, it is common to see the width persist after an anomalous move in a particular security.

3. The level of realized volatility of individual components relative to other components that comprise Nasdaq-100.

About the Streamgraph Coordinates

The X-axis displays trading days by date, and the Y-axis contains the component realized volatility. The absolute distance between each line on the chart is the 21-day realized volatility for Nasdaq-100.

Nasdaq-100

The Nasdaq-100 is a stock market index that tracks the 100 largest non-financial companies listed on the Nasdaq. The index is composed of stocks that are weighted by their market capitalizations.
All Realized Volatility Visualizations

Realized Volatility

This chart shows the component realized volatility of QQQover the past year of trading.
Components
Historical price and realized volatility data
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PYPL forecast 2025 logo
AAPL
Guest Commentary
Robson Chow is a hedge fund manager
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