DOES VOLUME PRICE CONFIRMATION INDICATOR IMPROVE FAMOUS INVESTOR MODELS

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JIFE, Volume 24, Number 4, 2024 ISSN: 1555-6336
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DOES VOLUME PRICE CONFIRMATION INDICATOR IMPROVE FAMOUS INVESTOR MODELS
Matt Lutey, Morehouse College, U.S.A.
dx.doi.org/10.18374/JIFE-24-4.3
ABSTRACT
We apply the Volume Price Confirmation Indicator to a simplified version of the Famous Stock Investor
portfolios from the American Association of Individual Investors. Specifically, we use a streamlined
portfolio known as Abriged Can Slim, alongside CAN SLIM, Joel Greenblatt, Benjamin Graham, and
Warren Buffett methodologies. Our findings reveal that while each portfolio individually outperforms the
S&P 500 since 1999, incorporating the Volume Price Confirmation Indicator enhances their returns on a
risk-adjusted basis. The Volume Price Confirmation Indicator alone also surpasses the S&P 500 since
1999, independent of the Famous Investor methodologies.
Our analysis covers the period from 1999 to 2024, using the S&P 500 index to construct our portfolios.
We present results for annualized return, total return over one, three, and five-year periods, as well as
since inception. Additionally, we provide metrics such as R-squared, beta, and maximum drawdown. The
implementation code is also included. These results offer valuable insights for student investment funds
and portfolio managers, helping to bridge the gap between academic finance and practical investment
strategies.
Keywords: Technical Analysis, Fundamental Analysis, Famous Investor Portfolios.
1. INTRODUCTION
The Volume Price Confirmation Indicator (VPCI), engineered by Buff Dormeier, CMT, is designed to
identify stock trends. In technical analysis, stocks that exhibit clear trends are often more likely to perform
well, a principle akin to momentum investing. Trends are determined by supply and demand, as indicated
by volume, which measures the flow of money in or out of a stock. The VPCI is constructed using moving
averages, volume averages, and volume-weighted averages, incorporating both fast and slow periods in
its calculations.
Literature has demonstrated that both moving averages and volume can be predictive of stock prices. For
instance, Blume, Easley, and O’Hara (1999) highlight the predictive power of volume, while Brock et al.
(1999) discuss the predictive ability of moving averages and trading range breakouts. Later studies, such
as Han et al. (2014), show how moving averages can time portfolios sorted by volatility, and Arvamov et
al. (2021) use two moving averages to create a timing indicator based on the difference between fast and
slow moving averages.
In technical analysis, moving averages and price momentum are closely related. Stocks trading above
their moving averages are expected to continue rising, similar to momentum investing. Lo, Mamaysky,
and Wang (2000) explore how stocks with nonlinear price patterns on increasing volume exhibit
conditional pattern formation. Buff Dormeier, CMT, has applied technical analysis extensively in portfolio
management, focusing on volume as a leading indicator for prices. His work is detailed in his books on
volume (2011 and 2024).
Fundamental analysis is also prevalent in practitioner finance. For example, billionaire fund manager Bill
O’Neil collected stock data from the 1960s to the 1980s and developed the CAN SLIM method, which
involves selecting stocks that are leaders in their industry based on price, volume, institutional ownership,
sales, and earnings per share growth. The ‘M’ criterion, related to market direction, is more challenging to
scan. O’Neil founded Investor’s Business Daily (IBD), recently acquired by the Wall Street Journal.

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Although the IBD company offers a list of highly rated CAN SLIM stocks, their exchange-traded fund
(ETF) has underperformed, despite positive results in their paper.
Studies by Lutey et al. (2013, 2014, 2018) have simplified and automated the CAN SLIM method for
various indices, such as the S&P 500, Nasdaq 100, and Dow Jones, showing superior risk-adjusted
returns. Lutey and Mukherjee (2023) compared the CAN SLIM system from the American Association of
Individual Investors (AAII) with other renowned investor models, including those of Warren Buffett,
Benjamin Graham, and Joel Greenblatt, and developed a streamlined version called ‘Abridged CAN
SLIM’ (ACS). Their portfolios outperformed the S&P 500 benchmark while selecting stocks from both U.S.
and foreign primary stock indices. However, these studies faced challenges in student investment fund
implementation, including issues with the number of stocks passing each month and rebalancing thinly
traded stocks.
We aim to combine the VPCI strategy of Buff Dormeier (2011) with the simplified famous investor models
and the ACS model from Lutey and Mukherjee (2023). We apply these combined methodologies to a
smaller universe of stocks, making it more practical for student investment funds and practitioners, using
the S&P 500 index. We adopt the simplified famous investor models from Lutey and Mukherjee (2023),
following the detailed steps outlined in their paper. We apply these steps to the S&P 500 and integrate the
VPCI rules by eliminating stocks with a VPCI reading below -0.5. Our portfolios are rebalanced monthly.
We compare the performance of portfolios with and without the VPCI threshold against the S&P 500.
2. METHODOLOGY
The Volume Price Confirmation Indicator (VPCI) is calculated using a combination of volume-weighted
moving averages, simple moving averages, and volume moving averages, along with the relationships
between these indicators. For our application to the AAII.com 'Guru' portfolios, as outlined by Lutey and
Mukherjee (2023), our buy rule is to select stocks with a VPCI reading above -0.5, thereby excluding
stocks with low trend strength.
The VPCI indicators utilize both fast and slow periods in their construction. Specifically, we use a 5-period
indicator for the fast component and a 20-period indicator for the slow component. These indicators are
applied to each of the following moving averages:
• Simple Moving Average (SMA)
• Volume Moving Average (volavg)
• Volume Weighted Moving Average (vwma)
The equations for calculating these indicators are provided below.
(1)
(2)
(3)

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The indicators above are applied to make the Volume Price Ratio (VPR), Volume Multiplier (VM) and
Volume Price Confirmation (VPC).
VPR = VWMA(5)/MA(5)
(4)
VM = VolAvg(13)/VolAvg(52)
(5)
VPC = VWMA(20)-MA(20)
(6)
These three indicators are then combined to make the volume price confirmation indicator (VPCI). Blend
in the literature review here.
VPCI = VPR*VM*VPC
(7)
The model rules applied to all stocks (similar to CRSP share codes 10 and 11) are detailed in Lutey and
Mukherjee (AFL, 2023). We apply this methodology to the portfolios within the S&P 500 index to enhance
its practicality for student fund implementation. We then incorporate the Volume Price Confirmation
Indicator (VPCI) into these portfolios to assess its impact on returns.
Our analysis presents two groups of portfolios: one with the VPCI applied and one without. We evaluate
both groups based on various performance metrics, including annualized alpha, annualized return,
returns over one, three, and five years, total return, Sharpe ratio, Sortino ratio, maximum drawdown,
standard deviation, R-squared, and beta. We compare these metrics to determine which portfolio may be
more suitable for student investment fund implementation. Detailed results and Portfolio123.com
implementation code are provided in the appendix.
The data variables and definitions are outlined in Lutey and Mukherjee (2023), which also includes the
Portfolio123.com implementation code for all 'Guru' models. Our analysis extends this by integrating the
VPCI.
We find that the VPCI-enhanced 'Guru' portfolios are comparable to both the 'VPCI only' portfolio and the
S&P 500 portfolio, as well as the 'Guru' portfolios without VPCI. Similarly, the 'VPCI only' portfolio is
comparable to the S&P 500 portfolio.

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3. RESULTS
Figure 1. All Famous Investor Models with VPCI compared to both the VPCI and SP500 portfolios.

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Table 1. ‘Guru’ Models with VPCI on SP500
Model
Alpha
Annualized
Return
One
Year
Three
Year
Five
Year
Total
Sharpe
Sortino
Max
Drawdown
Standard
Deviation
R2
Beta
ACS
7.48%
12.86%
23.66%
42.99%
154.85%
2,038.77%
0.68
1.02
-44.01%
17.23%
0.35
0.67
CAN
SLIM
4.55%
9.93%
25.24%
38.97%
99.24%
999.86%
0.65
0.89
-48.72%
13.79%
0.55
0.66
Greenblatt
3.27%
10.86%
14.41%
24.04%
82.06%
1,260.08%
0.62
0.83
-48.78%
16.15%
0.90
1.00
Graham
4.54%
11.99%
23.95%
36.12%
151.07%
1,657.62%
0.65
0.90
-54.83%
17.09%
0.81
1.00
Buffet
1.99%
8.96%
22.67%
27.87%
92.93%
778.62%
0.49
0.65
-60.94%
16.92%
0.73
0.94
VPCI only
1.83%
9.54%
13.49%
16.37%
59.79%
903.92%
0.52
0.70
-58.39%
17.43%
0.88
1.06
Table 2. ‘Guru’ Models on SP500 without VPCI
Model
Alpha
Annualized
Return
One
Year
Three
Year
Five
Year
Total
Sharpe
Sortino
Max
Drawdown
Standard
Deviation
R2
Beta
ACS
6.60%
12.19%
23.31%
51.65%
128.21%
1,741.37%
0.65
0.96
-45.43%
17.27%
0.40
0.71
CAN
SLIM
3.88%
9.40%
24.47%
31.17%
77.08%
872.79%
0.61
0.82
-48.93%
13.96%
0.57
0.69
Greenblatt
3.13%
10.83%
16.23%
23.75%
87.75%
1,249.75%
0.61
0.82
-48.54%
16.35%
0.91
1.02
Graham
3.73%
11.42%
29.53%
44.93%
152.20%
1,444.68%
0.61
0.83
-54.87%
17.28%
0.83
1.03
Buffet
0.79%
8.10%
19.73%
8.04%
75.83%
618.35%
0.44
0.58
-56.57%
16.80%
0.79
0.97
SP500
7.68%
25.57%
26.23%
86.14%
551.20%
0.44
0.58
-55.19%
15.34%
The ACS portfolio demonstrates higher alpha, lower risk, and lower beta compared to other portfolios. Among the CAN SLIM, Greenblatt, Graham,
and Buffett methodologies, all show higher alphas than the S&P 500 and the VPCI-only portfolios. The VPCI-only portfolio achieves an alpha of
1.83% on its own.
In terms of risk-adjusted performance, both Sharpe Ratios and Sortino Ratios improve for all 'Guru' models when compared to their respective
portfolios without the VPCI indicator. However, only the Graham model surpasses the S&P 500 over the past year. None of the portfolios with the
VPCI indicator outperform the S&P 500 over the last year, but all portfolios exceed it over the last three years, five years, and since inception. The
objective is to achieve strong performance across all time periods, which these portfolios do. Adding the VPCI indicator enhances the performance
of all portfolios over all periods since inception.

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We replicate this analysis by applying the models to a broader universe that includes all U.S. stocks and foreign primary stocks.
Table 3. Model Portfolios on All Stocks with VPCI
Table 4. Model Portfolios on All Stocks without VPCI
Model
Alpha
Annualized
Return
One
Year
Three
Year
Five
Year
Total
Sharpe
Sortino
Max
Drawdown
Standard
Deviation
R2
Beta
ACS
8.16%
14.40%
15.39%
10.46%
77.74%
2,991.95%
88.00%
113.00%
-47.29%
14.42%
58.00%
71.00%
CAN
SLIM
12.35%
17.89%
7.24%
18.85%
360.56%
6,547.01%
84.00%
125.00%
-53.88%
19.91%
32.00%
73.00%
Greenblatt
4.27%
12.10%
11.02%
13.16%
78.15%
1,744.36%
61.00%
82.00%
-56.44%
18.85%
76.00%
107.00%
Graham
16.44%
22.98%
41.15%
116.09%
269.79%
19,476.18%
101.00%
145.00%
-44.08%
21.08%
40.00%
87.00%
Buffet
11.12%
19.09%
11.76%
54.58%
117.11%
8,513.62%
80.00%
110.00%
-54.34%
22.71%
57.00%
112.00%
Stocks
above $1
4.01%
11.41%
3.47%%
-18.01%
29.75%
1,472.97%
53.00%
77.00%
-59.16%
20.35%
59.00%
102.00%
Model
Alpha
Annualized
Return
One
Year
Three
Year
Five
Year
Total
Sharpe
Sortino
Max
Drawdown
Standard
Deviation
R2
Beta
ACS
8.78%
15.07%
15.45%
12.22%
98.02%
3,493.18%
91.00%
117.00%
-46.15%
14.71%
57.00%
72.00%
CAN
SLIM
12.71%
18.29%
6.75%
20.24%
401.06%
7,148.00%
85.00%
127.00%
-54.56%
20.05%
31.00%
73.00%
Greenblatt
4.08%
11.86%
10.16%
13.73%
78.45%
1,643.61%
60.00%
81.00%
-57.21%
18.85%
75.00%
107.00%
Graham
15.60%
21.82%
43.97%
82.74%
117.63%
15,264.31%
95.00%
136.00%
-48.89%
21.49%
37.00%
85.00%
Buffet
10.39%
18.25%
13.36%
36.17%
118.73%
7,097.56%
78.00%
108.00%
-53.32%
22.36%
56.00%
109.00%
VPCI only
0.01%
7.48%
2.84%
-
20.77%
24.92%
529.50%
37.00%
50.00%
-63.72%
19.51%
68.00%
105.00%

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All models outperform the S&P 500 over the long term. Specifically:
• Over five years: The Graham, Buffett, CAN SLIM (without VPCI), and CAN SLIM, Graham, and
Buffett (with VPCI) models all beat the S&P 500.
• Over three years: The CAN SLIM, Graham, and Buffett (without VPCI), as well as the Graham
and Buffett (with VPCI) models, outperform the S&P 500.
• Over one year: Only the Graham model (both with and without VPCI) surpasses the S&P 500.
All models exhibit positive alpha, except for the VPCI-only model, when applied to all stocks, both with
and without the VPCI filter. The VPCI filter improves performance for the ACS and CAN SLIM models, but
it detracts from the Greenblatt, Graham, and Buffett models.
Among the models, the Graham model delivers the highest returns both with and without the filter (after
excluding penny stocks). Lutey and Mukherjee (2023) show that the CAN SLIM model outperforms all
other models when applied to all stocks without any technical filter or penny stock exclusion.
Excluding penny stocks reduces the number of stocks available for purchase each month. The models
with penny stocks and the filter, as well as those updated with only penny stocks and no filter, show
varying results. In the long run, all models are good investments, outperforming the market. However, in
the short term, performance may differ. Removing penny stocks makes the Graham strategy the best
investment across all available stocks, while the ACS model (followed by Graham) performs best on the
S&P 500.
Unless better access to penny stock data is available, it is advisable to use a price cutoff of $1 or focus on
S&P 500 constituents. Further improvements could be made by incorporating technical analysis, such as
the Volume Price Confirmation Indicator (VPCI).
4. LIMITATIONS
The limitations for the study are included in sample analysis. Although the methodology described works
across multiple timeframes and multiple assets (i.e. different famous investor criteria, different major stock
indexes) the analysis is done at a point in time in the sample. It is expected that the results would
continue to hold out of the sample but to obtain a true out of the sample result it would require waiting an
additional 3-5 years (or more) to obtain the real time results. Past performance may not prove future
performance, but it provides a good expectation of what might work in the future.
5. CONCLUSION
We have refined the universe of stocks for the famous investor model portfolios from Lutey and
Mukherjee (2023), originally based on the American Association of Individual Investors (AAII.com). By
incorporating the Volume Price Confirmation Indicator (VPCI) into these 'Guru' portfolios, we find that the
portfolios outperform the S&P 500 when applied to S&P 500 constituents. The VPCI indicator enhances
returns and risk-adjusted returns compared to portfolios without the indicator, nearly doubling the return
from 1999 to 2024. The model integrates both technical and fundamental data, offering a robust
screening tool for student investment funds.
Investors interested in implementing this model can use the sample code provided in the appendix,
available on Portfolio123.com. This model generates a list of real-time, passing companies each month,
making it a valuable tool for portfolio managers and student investment funds.

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REFERENCES:
Blume, L., Easley, D., & O'hara, M. (1994). Market statistics and technical analysis: The role of
volume. The journal of finance, 49(1), 153-181.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic
properties of stock returns. The Journal of finance, 47(5), 1731-1764.
Dormeier, B. P. (2011). Market Volume Is the Force. Pearson Education.
Dormeier, B. P. (2024). The Volume Factor. Morgan James Publishing
Han, Y., Yang, K., & Zhou, G. (2013). A new anomaly: The cross-sectional profitability of technical
analysis. Journal of Financial and Quantitative Analysis, 48(5), 1433-1461.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational
algorithms, statistical inference, and empirical implementation. The journal of finance, 55(4),
1705-1765.
Lutey, M., Hassan, M. K., & Rayome, D. (2018). An application of CAN SLIM investing in the Dow Jones
benchmark. Asian Journal of Economic Modelling, 6(3), 274-286.
Lutey, M., Crum, M., & Rayome, D. (2013). Outperforming the broad market: An application of the CAN
SLIM strategy. ASBBS eJournal, 9(1), 90-96.
Lutey, M., Crum, M., & Rayome, D. (2014). OPBM II: An Interpretation of the CAN SLIM Investment
Strategy. Journal of Accounting & Finance (2158-3625), 14(5).
Lutey (2022). Accumulated Capital Weighted Dollar Volume and Volume Price Confirmation Indicator
Factor Model. Journal of Academy of Business and Economics 22 (3), 26-37
Mukherjee, T., & Lutey, M. (2023). Towards a Simplified Can Slim Model. Applied Finance Letters, 12(1),
44-54.

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5. CODE STEPS AND APPENDIX
Table 5. VPCI Portfolio 123.com Inputs.
Description and Code for
‘VPCI’ Indicator Inputs
Abbreviation
Definition
Portfolio123.com Code
Simple Moving Average Fast
‘SMA fast’
Arithmetic average of price
over 5 periods
SMA(5)
Simple Moving Average
Slow
‘SMA slow’
Arithmetic average of price
over 20 periods
SMA(20)
Volume Moving Average
Fast
‘VMA fast’
Arithmetic average of
volume over 5 periods
AvgVol(5)
Volume Moving Average
Slow
‘VMA slow’
Arithmetic average of
volume over 20 periods
AvgVol(20)
Volume Weighted Moving
Average Fast
‘VWMA fast’
Summation of price
multiplied by volume over 5
periods divided by the
summation of volume over 5
periods
VMA(5)
Volume Weighted Moving
Average Slow
‘VWMA slow’
Summation of price
multiplied by volume over 20
periods divided by the
summation of volume over
20 periods
VMA(20)
Table 6. VPCI Portoflio123.com Construction
Description and Code for
Portfolio 123.com ‘VPCI’
Indicator Construction
Abbreviation
Definition
Portfolio123.com Code
Volume Price Confirmation
‘$VPC’
VWMAslow minus SMAslow
VMA(20)-SMA(20)
Volume Price Ratio
‘$VPR’
VWMAfast divided by
SMAfast
VMA(5)/SMA(5)
Volume Multiplier
‘$VM’
VMAfast divided by
VMAslow
VMA(20)-SMA(20)
Volume Price Confirmation
Indicator
‘$VPCI’
VPC multiplied by VPR
multiplied by VM.
$VPC*$VPR*$VM

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Table 7. ‘Guru’ Model Portfolio123.com Implementation
1
MODEL
STEPS
PORTFOLIO 123.COM
ACS
Model
1. EPS growth is above 15% 5-year
average, and EPS growth is above 25% in the
most recent quarter compared to the same
quarter one year prior.
1. EPSExclXorGr%5Y>=15And EPSExclXorGr%PQ > 25
2. Price is within 10% of a new high
2. Price >= 0.9 * PriceH
3. VPCI is above -.5
3. $VPCI > -.5
CAN
SLIM
1. Percentile rank for % institutional
ownership between 10 and 50
1. Frank(" Inst%Own",#all,#desc)>=10 and Frank("
Inst%Own",#all,#desc)<50
2. EPS growth (latest qtr.) Percentile rank
in top 35%
2. Frank(" EPSExclXorGr%PYQ")>=65
3. Share price % gain in last 240 trading
days ranks in the top 35%
3. Frank("Close(0)/Close(240)")>=65
4. Distance between the current price and
the 12-month high ranks in top 50%
4. Frank(" Price/ PriceH")>=50
5. VPCI is above -.5
5. $VPCI > -.5
Greenblatt
1. Choose liquid stocks not trading over
the counter (OTC).
1. Universe(NOOTC)
2. Market cap is at least $50 million.
2. MktCap>=50
3. U.S. stocks only, no ADR
3. Universe($ADR)=false
4. No financial or utility companies or
REITs
4. !GICS(FINANC) !GICS(UTILIT) !GICS(reoper)
5. 5-year return on investment is in the
top 35%
5. Frank(" ROI%5YAvg")>=65
6. VPCI is above -.5
6. $VPCI > -.5
Graham
1. No thinly traded over the counter
(OTC) stocks. Choose more liquid stocks.
1. Universe(NOOTC)
2. Current ratio is at least 1.5
2. CurRatioQ>=1.5
3. Long-term debt is less than 110% of
working capital.
3. DbtLTQ<=(CurAstQ- CurLiabQ)*1.10
4. Last four quarters of EPS positive
4. EPSExclXor(0,qtr)>0 and
EPSExclXor(1,qtr)>0 and
EPSExclXor(2,qtr)>0 and
1
The model ‘Guru’ portfolio steps and implementation code are from Lutey and Mukherjee (2023) We added the Volume Price Confirmation Indicator (VPCI) criteria to the portfolios.

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EPSExclXor(3,qtr)>0
5. Last five years of EPS positive
5. EPSExclXor(0,ann)>0and EPSExclXor(1,ann)>0 and
EPSExclXor(2,ann)>0 and
EPSExclXor(3,ann)>0and
EPSExclXor(4,ann)>0
6. Annual EPS grew over the past year
and past five years.
6. EPSExclXor(0,ann)>EPSExclXor(1,ann)
andEPSExclXor(0,ann)>EPSExclXor(4,ann)
7. Company has paid dividends within the
past year
7. DivPSTTM>0
8. VPCI is above -.5
8. $VPCI > -.5
Buffet
1. Stocks in the top 75% of EPS
compared to the industry.
1. Frank("EPSExclXorGr%5Y",#industry)>25
2. Annual EPS has been better in the last
three years than the last 7.
2. EPSExclXor(2,ann)>=EPSExclXor(6,ann)
3. EPS grew over the past year and the
past seven years.
3. EPSExclXor(0,ann)>EPSExclXor(1,ann)
4. ROE last 12 months better than the
industry median
4. EPSExclXor(0,ann)>EPSExclXor(6,ann)
5. ROE 5 year-average better than the
industry
5. ROE%5YAvg>FMedian("ROE%5YAvg",#industry)
6. Sustainable growth rate in the top 15%
compared to industry peers.
6. Frank("SusGr%",#industry)>85
7. Debt to equity lower than the industry
7. DbtTot2EqQ <= DbtTot2EqQInd
8. Net profit margin higher than the
industry
8. NPMgn%TTM >= NPMgn%TTMInd
9. Operating profit margin higher than the
industry
9. OpMgn%TTM >= OpMgn%TTMInd
10. VPCI is above -.5
10. $VPCI > -.5