The Study of Price to Operating Cash Flow Ratio

BY

Cheung Chi Lok 11008474

Finance Concentration

An Honors Degree Project Submitted to the School of Business in Partial Fulfillment

of the Graduation Requirement for the Degree of Bachelor of Business Administration (Honours)

Hong Kong Baptist University Hong Kong

April 2014

1

Table of Contents Acknowledgements ................................................................................................... 2

Abstract ..................................................................................................................... 3

Introduction ............................................................................................................... 4

A. Background .................................................................................................... 4

B. Objective of the Study .................................................................................... 5

Literature Review ...................................................................................................... 5

A. The Reliability of Operating Cash Flow .......................................................... 5

B. Intuitive Understanding of P/OCF Ratio ......................................................... 7

C. Previous Studies ............................................................................................. 9

Statements of Hypothesis .........................................................................................10

Methodology ............................................................................................................10

A. Data Collection ..............................................................................................10

B. Portfolio Management ................................................................................... 11

Empirical Result .......................................................................................................13

A. Number of Stocks of Portfolios ......................................................................13

B. Market Capitalization of Chosen Stocks ........................................................13

C. Evaluation of Portfolios’ Performance ...........................................................16

D. Comparisons of Performance between Portfolios and Benchmark ..................17

E. Descriptive Statistics .....................................................................................20

F. Abnormal Rate of Return and Sharpe Ratio ...................................................21

G. Relationship between P/OCF Ratio of Stocks and their Return Rates .............23

Discussion ................................................................................................................25

A. Confirmation of Statements of Hypothesis .....................................................25

B. Other Ways of Using of P/OCF Ratio ............................................................25

C. Weaknesses of the Ratio ................................................................................26

D. Limitation ......................................................................................................28

Conclusion ...............................................................................................................29

Reference .................................................................................................................30

Appendix ..................................................................................................................33

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Acknowledgements

I would like to take this opportunity to express my sincere gratitude to my

supervisor, Professor Joseph, K.W. Fung for priceless guidance and support

throughout the whole process of this project. His advice certainly provides me

insights into the project and facilitates my work on the research.

Besides, I would also like to express my heartfelt thanks go to my friends and

parents for their support and encouragement.

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Abstract

Purpose: The aim of this study is to investigate the robustness and usefulness of

P/OCF ratio in screening stocks to construct a portfolio which can outperforms the

market. The ratio is tested in the Hong Kong Exchange Market (HKEx) and the

constructed portfolio is expected to outperform the Hang Seng Index (HSI). The

purpose of the research is to contribute more findings on the P/OCF ratio in assisting

investors to make favourable investment decisions so that they are able to earn

abnormal gain over the market over time.

Methodology: After studying the existing research paper, portfolios are formed to

compare the return rates with the HSI so as to fulfil the purpose of the paper. All

stocks of HKEx go through the screening process and those meet the criterion are

chosen to form the portfolios. The information for computing the P/OCF of all

companies is publicly available.

Findings: The research shows that the constructed portfolios have better

performance than HSI. P/OCF is useful in forming portfolios outperforming the

market.

Keywords: P/OCF, accounting ratio, cash flow, Hong Kong

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Introduction

A. Background

For every investor in securities market, they would like to construct an investment

portfolio that can reward them an excess rate of return over the benchmark, which is

mainly the market index, in long run. To this end, they spend lots of time and effort

in stock analysis with an aim to choose the outperformed stocks.

To choose an outperformed stock, investors usually go through fundamental analysis

of the stocks, namely using financial ratios to evaluate a company’s profitability,

liquidity as well as solvency. However, it is not enough. In order to find out if the

chosen stocks are fairly-priced, investors also need to further compute the intrinsic

value of them with a valuation model, such as Dividend Discount Model. However,

these processes are quite complicated and cumbersome.

On the other hand, using a wide array of investment valuation multiplies to estimate

the attractiveness of a potential investment and get an idea of the stocks’ valuation is

much simpler and quicker. Price to earnings (P/E) ratio, price to book value (P/B)

ratio and dividend yield are some of the examples, which are commonly used.

This paper will take an in-depth look of a valuation metric – Price to Operating Cash

flow (P/OCF) ratio.

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B. Objective of the Study

The objective of this study is to examine the usefulness of the valuation metric,

P/OCF ratio, on stock picking. A long-short stock portfolio (i.e. mainly works on the

Hong Kong Exchange Market, HKEx) will be constructed based on this metric with

an aim to outperform the market.

Literature Review

There's an old saying about business that "cash is king" and cash flow is the blood of

the company, showing the importance of cash flow to a firm. With sufficient cash,

enterprises can take advantage of attractive business opportunities, make

acquisitions, do research and development, and reduce debt. On the other hand,

without cash, profits are meaningless. Many a profitable business has ended up in

bankruptcy because of liquidity problem and insolvency.

As Aswath Damodaran said in his book Investment Fables, “Accountants measure

earnings by subtracting accounting expenses from revenues. To the extent that some

of these expenses are non-cash expenses … and because accrual accounting …does

not always yield the same results as cash accounting, accounting earnings can be

very different from cash flows.” In short, cash flow is a more reliable basis in

evaluating a stock than accounting profit.

A. The Reliability of Operating Cash Flow

Simply speaking, cash flow, namely operating cash flow (OCF), refers to the amount

of cash a company generates from normal business operations. As it has been

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adjusted for non-cash flow items and change in working capital, it is a more accurate

measure of how much cash a company has generated (or used) than accounting

measures of profit such as net income or earnings before interest and taxes.

Investors prefer using OCF to measure a company's financial performance, instead

of accounting profits, including net income and earnings before interest and taxes.

The reason behind is that we should not rely solely on accounting figures, which are

susceptible to manipulation.

Based on management’s judgment and estimates, these figures can be adjusted

through different accounting gimmicks, mainly arbitrary inventory valuation and

one-time items. Moreover, accounting profits can also be altered by the depreciation

of tangible assets, and most importantly, the amortization of intangible assets, such

as goodwill and patent. Two identical companies can have rather different

accounting profits if the two companies adopt varied accounting methods.

In contrast, it is more difficult to hide accounting tricks and management

adjustments in the computation of cash flow so OCF can truly reflect the realness of

a company's performance.

Lastly, OCF takes into account the net change in working capital, which is certainly

critical for any business, but not the accounting profits such as Net Profit, and

Earnings before Interest, Taxes, Depreciation and Amortization (EBITDA).

All in all, P/OCF ratio is more reliable and preferable than some of the other

valuation methods, including P/E ratio and Enterprise Value to EBITDA.

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B. Intuitive Understanding of P/OCF Ratio

P/OCF ratio is the market capitalization of the company divided by its operating

cash flow. It shows how much investors are willing to pay per dollar of OCF. By

comparing the price and OCF per share of a company, we can make a valuation of it.

Assuming a company was currently trading at a P/OCF of 20, the interpretation is

that an investor was willing to pay $20 for a dollar of company’s operating cash

flow.

Generally speaking, a company with low market price and high OCF is attractive for

investors based on contrarian investing, which is a value-investing method looking

for stocks not fairly-priced and buying (short-selling) those appeared to be

undervalued (overvalued) by the market. In fact, contrarian investors believe the

idea of regression toward the mean or mean reversion, which refers to the tendency

for stock price to even out over time. When stocks’ prices are far from their mean

values, this phenomenon is temporary and their prices tend to move toward to the

average price.

When using P/OCF ratio as a criteria to choose stocks, we are looking for stocks

with low P/OCF ratio to buy and with high P/OCF ratio to short sell. Low (high)

P/OCF ratios typically mean the stocks are undervalued (overvalued) and prices will

soon increase (decrease) back to their average value. Thus, it can be concluded that

the lower (higher) the ratio, the relatively cheaper (more expensive) the stock is and

the higher (lower) return rate of it.

From another angle to analyze the multiple, a price of a stock can be computed by

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using Discounted Cash Flow model. The price of it is simply the present value of

discounted future cash flow, namely OCF.

푃표푟푃푉 = OCF1

(1 + 푟)+

OCF2(1 + 푟)

+⋯OCFn

(1 + 푟)

And it can be expressed as:

P =푂퐶퐹

(1 + 푟)

Where

P is the discounted present value of the future OCF,

OCF is the nominal value of an operating cash flow in a future period,

r is the interest rate or discount rate,

n is the time in years before the future OCF occurs.

This formula can also be written as:

푃푂퐶퐹

= 1

(1 + 푅푅푅)

Where, RRR is Required Rate of Return of investing in a stock

From this formula, it shows that P/OCF ratio is negatively related to RRR. The

smaller (larger) the ratio is, the higher (lower) the RRR. This means if the ratio of a

stock were low, then we would expect the return rate of a stock should be high, vice

versa. It is also consistent with the previous conclusion that the lower the ratio, the

relatively cheaper the stock is and the higher return rate of it. Hence, there should be

a negative relationship between the P/OCF ratio and return rate.

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C. Previous Studies

There are some related studies examining the robustness of the P/OCF ratio. A study

“Contrarian Investment, Extrapolation and Risk” (Josef Lakonishok, Andrei

Schleifer and Robert W. Vishny, 1994) is conducted based on the American Stock

Exchange and the New York Stock Exchange from 1968 to1990. Stocks are divided

into ten selections, regarded as deciles, by the P/CF ratio. The conclusion of this

study reveals that the average annual return of the tenth-decile stocks with low P/CF

is 11% higher than that of the first-decile stocks with high P/CF, which shows the

low P/CF stocks outperform those with high P/CF.

Another study “Back-test showing the power of Price to Free Cash Flow in the

Investment Process 1950-2009” (Peter George Psaras, 2010) examines the

robustness of P/FCF ratio. Though this measure is not truly the same as P/OCF ratio,

the result generated should not be hugely different from each other, and the

conclusion could still be representative to a large extent. This study back tests the

ratio in choosing stocks from the Dow Jones Industrial Average (DJIA) for 1950 to

2009. Stocks with P/FCF ratio smaller than 15 are held and then sold after a year.

This selection process has been repeated for 60 years. The performance is

remarkable with an average annual return for 21%, which is nearly 14% higher than

that if DJIA.

Also, a study “Further evidence on the predictability of international equity returns”

(A. Michael Keppler, 1991) has tested the usefulness of the ratio across 18 countries’

indexes (including HSI) from 1970 to 1989. It has found that the country index with

the lowest P/CF produced a return for 19% on average while that of the index with

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highest P/CF was only 4.7%.

For the abovementioned studies, all studies have found that the ratio is useful in

choosing outperforming stocks (or indexes).

Statements of Hypothesis

This study aims to test the robustness of P/OCF ratio in screening stocks to construct

a portfolio that can outperform the market. In this study, the ratio is tested under the

HKEx. I hypothesis that the constructed portfolio with all stocks in HKEx screened

by P/OCF can outperform the market, the Hang Seng Index (HSI).

H1: The constructed portfolio can outperform the HSI.

Methodology

A. Data Collection

The sample companies are selected from all of the Hong Kong listed corporations in

the HKEx for a period from 2002 to 2012 as the base for constructing the portfolios.

The data is calculated and collected manually. The data collected mainly includes

the companies’ OCF and their market cap for figuring out the P/OCF ratios for each

company as well as their respective closing stock price at the end of each calendar

year for computing their return rates of each year.

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B. Portfolio Management

This study deletes the bottom one-third of stocks based on their market

capitalization to eliminate any penny stocks, which may face higher chance of

bankruptcy.

At the end of each year, P/OCF ratio of each company will be computed as a

criterion for choosing stocks. For simplicity, those companies with negative OCF are

excluded from the dataset for constructing a portfolio.

The approach here is to put stocks into order from largest P/OCF ratio to lowest and

separate them into percentiles. Four portfolios are constructed according to different

level of percentiles and are shown in Table 1.

Table 1: Construction of Portfolios based on P/OCF Ratio

Portfolio Long Position (in percentile) Short Position (in percentile) 1st 1% >99% 2nd 1-3% 97-99% 3rd 3-5% 95-97% 4th 5-10% 90-95%

For first portfolio, I take long position of stocks with the 1st percentile of lowest

P/OCF ratio and short position of stocks above the 99th percentile; and long stocks

between 1st and 3rd percentile and short stocks between 97th and 99th percentile for

second portfolio; and long stocks between 3rd and 5th percentile and short stocks

between 95th and 97th percentile for third portfolio; and, lastly, long stocks between

5th and 10th percentile and short stocks between 90th and 95th percentile for fourth

portfolio.

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With this arrangement, we are able to study the return distribution along the

percentiles of stocks, and find out whether the lower ratio of a stock, the higher the

return of it. It is expected that the first portfolio should have the highest return,

followed by the second and third one, while the fourth portfolio should have the

lowest one.

Besides, the return rates of the portfolios are equal-weighted by averaging the

returns of all stocks in the portfolios. And portfolios are rebalanced annually. For

simplicity, it is assumed that there is no transaction cost.

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Empirical Result

A. Number of Stocks of Portfolios

After screening by P/OCF ratio, the number of stocks for each portfolio covering the

period from 2002 to 2013 has been shown in Table 2. Both long and short positions

share the equal number of stocks. The first portfolio has the smallest number of

stocks with an average number of 11.5 stocks while the fourth portfolio has the

largest amount of stocks, 57.5 stocks on average.

Table 2: Total Number of stocks for each Portfolio (including both long and short position)

Portfolio 1st 2nd 3rd 4th

2002 8 16 16 40 2003 10 20 20 50 2004 10 20 20 50 2005 12 24 24 60 2006 12 24 24 60 2007 12 24 24 60 2008 14 28 28 70 2009 12 24 24 60 2010 12 24 24 60 2011 12 24 24 60 2012 12 24 24 60 2013 12 24 24 60

Average 11.5 23 23 57.5

B. Market Capitalization of Chosen Stocks

From Table 4, the majority of stocks chosen by P/OCF ratio are small-cap stocks.

Even for the fourth portfolio, which has the highest average market capitalization of

all stocks, it is only about HK$10 billion, which only accounts for, on average,

6.38% of the total market capitalization of all stocks in HKEx.

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Small-cap stocks are also considered as fast-growing companies, which are able to

raise their profits at a faster pace than large-cap stocks, or mature companies.

Small-cap stocks can, therefore, grant the investors higher capital gain. However,

they are also risky investments. Because the venture or businesses of these

fast-growing firms are unproven most of the time and may easily fail, the return

rates of small-cap stocks are extremely volatile.

Concerning the small-cap stocks in HKEx, most of them are small-to-medium

enterprises whose profitability are not stable. Some of them even do not have

substantive businesses and investors always speculate on these stocks based on

‘news’, mainly a brand-new development plans. In short, the returns of small-cap

stocks in HKEx are highly fluctuated.

As the portfolios cover large portion of small-cap stocks, it is expected that the

portfolios should have higher rate of return (or loss) with great volatility, which can

be supported by Table 7 and be further discussed under the topic of Descriptive

Statistics.

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Table 4: Market Capitalization (HK$ in billion) of the stocks of portfolios

Portfolio/ 1st 2nd 3rd 4th

Year Average*1 % of Total M. Cap*2 Average % of Total M. Cap Average % of Total M. Cap Average % of Total M. Cap 2002 0.27 0.06% 2.86 1.36% 4.13 1.97% 1.39 1.66% 2003 0.36 0.10% 2.32 1.27% 2.38 1.30% 17.69 16.70% 2004 0.31 0.05% 5.73 1.86% 2.13 0.69% 30.1 24.39% 2005 7.39 1.30% 5.29 1.87% 2.74 0.97% 4.39 3.87% 2006 4.63 0.67% 3.56 1.04% 4.49 1.31% 7.72 5.61% 2007 0.69 0.05% 13.12 2.06% 3.77 0.59% 8 3.14% 2008 1.01 0.04% 8.07 0.66% 2.92 0.24% 14.83 3.04% 2009 3.94 0.32% 39.14 6.34% 10.33 1.67% 7.45 3.02% 2010 2.08 0.35% 6.42 2.13% 15.08 5.01% 4.73 3.93% 2011 1.84 0.21% 1.13 0.26% 14.15 3.22% 3.33 1.89% 2012 0.54 0.07% 1.67 0.45% 6.39 1.71% 4.51 3.01% 2013 16.94 1.73% 7.44 1.52% 11.16 2.27% 12.34 6.29% Average 3.333333333 0.41% 8.0625 1.74% 6.639167 1.75% 9.706667 6.38%

*1: It is the average market capitalization of all stocks *2: It is the percentage of total market capitalization of stocks in portfolio to the total market capitalization of all stocks in HK

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C. Evaluation of Portfolios’ Performance

The portfolios’ performance from 2002 to 2013 was shown in Table 5 and the return

for both long and short position could also be found.

As mentioned under the topic of Portfolio Management in Methodology, it is

predicted that the first portfolio should have the highest return, followed by the

second and third one, while the fourth portfolio should have the lowest. But the results

are not consistent with this prediction. The third portfolio has the greatest average

return, rather than the first portfolio, which is also higher than that of the first and

second portfolios.

In theory, assuming a long position, a stock with a relatively low (high) P/OCF ratio

should have a higher (lower) return than another. However, there are reasons why

market overvalues or undervalues stocks. There may be some issues bothering the

market so much that the ‘seemingly undervalued overvalued’ stocks continue to be

‘undervalued overvalued’. On the other hand, if a stock had a bright future growth,

market would be eager to give a price premium for it so the stock would continue to

be overvalued. That’s why, this theory cannot be applied to those stocks with extreme

value of P/OCF ratio. This can explain why the first portfolio fails to perform better

than the second and third ones.

In addition, the average returns of short position for all portfolios are negative and

hence the poor performance of short position has limited the overall return of the

portfolios. This may be due to a weakness of the ratio, which will be further explained

under the topic of Discussion.

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D. Comparisons of Performance between Portfolios and Benchmark

Based on Table 6, when comparing their average return with benchmark, which is the

average return of HSI, for the period from 2002 to 2013, all the portfolios outperform

the HSI. We can see that the average return of HSI is only about 10% over the period

while the fourth portfolio, even with the lowest one among the four, has 16%. And the

third portfolio has the most outstanding return for 23%.

Most importantly, their geometric returns are much higher than that of HSI. When

compared to that of HSI for only 6%, the third portfolio has an impressive result for

22%. These results no doubt show the robustness of P/OCF ratio in choosing

outperforming stocks, which confirms the first statement of hypothesis.

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Table 5: Each Portfolio’s Performance from 2002 to 2013 Portfolio/ Year

1st 2nd 3rd 4th Long Short Total Long Short Total Long Short Total Long Short Total

2002 -25.92% 9.32% -8.30% -28.63% 23.37% -2.63% -16.96% 50.16% 16.60% 24.01% 19.05% 21.53% 2003 142.16% 37.93% 90.04% 118.16% -39.23% 39.46% 87.23% -21.71% 32.76% 67.82% -16.08% 25.87% 2004 96.85% 9.80% 53.33% 29.42% -9.98% 9.72% 89.70% 13.35% 51.52% 48.21% 12.64% 30.42% 2005 21.42% -62.81% -20.69% 4.51% 11.12% 7.82% 87.60% -4.92% 41.34% 5.28% 7.20% 6.24% 2006 121.56% -67.28% 27.14% 38.87% -10.08% 14.40% 58.79% -20.26% 19.26% 56.33% -33.93% 11.20% 2007 139.44% -140.56% -0.56% 38.47% -89.65% -25.59% 114.93% -47.98% 33.47% 88.18% -44.90% 21.64% 2008 -42.84% 63.61% 10.39% -17.50% 72.06% 27.28% -46.28% 64.88% 9.30% -56.50% 66.71% 5.10% 2009 51.87% 3.07% 27.47% 234.85% -103.01% 65.92% 148.30% -68.08% 40.11% 154.93% -114.56% 20.19% 2010 14.58% -38.94% -12.18% 15.97% -22.48% -3.25% 21.82% 9.13% 15.47% 41.93% -20.99% 10.47% 2011 -37.50% 37.53% 0.02% -7.44% 45.57% 19.06% -17.03% 43.59% 13.28% -21.73% 32.65% 5.46% 2012 118.49% 19.14% 68.82% 43.46% 9.28% 26.37% 23.31% -2.84% 10.24% 66.08% -35.32% 15.38% 2013 35.55% -39.64% -2.05% 63.29% -32.39% 15.45% 57.61% -54.40% 1.61% 76.90% -38.16% 19.37%

Average return

52.97% -14.07% 19.45% 44.45% -12.12% 16.17% 50.75% -3.26% 23.75% 45.95% -13.81% 16.07%

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Table 6: Comparison of Average and Geometric Return between portfolios and Hang Seng Index (HSI) Year/ Portfolio 1st 2nd 3rd 4th HSI

Return Cumulative Return Cumulative Return Cumulative Return Cumulative Return Cumulative 2002 -8.30% 91.70% -4.52% 97.37% 3.93% 116.60% 12.73% 121.53% -18.21% 81.79% 2003 90.04% 174.27% 57.17% 135.79% 60.53% 154.80% 38.29% 152.97% 34.92% 110.34% 2004 53.33% 267.20% 24.26% 148.99% 35.34% 234.55% 33.06% 199.51% 13.15% 124.86% 2005 -20.69% 211.91% -1.69% 160.64% 15.52% 331.52% 10.88% 211.96% 4.54% 130.53% 2006 27.14% 269.43% 18.65% 183.77% 18.89% 395.38% 15.05% 235.70% 34.20% 175.17% 2007 -0.56% 267.92% -17.25% 136.74% 3.04% 527.73% 12.34% 286.72% 39.31% 244.03% 2008 10.39% 295.74% 21.65% 174.04% 16.71% 576.81% 10.91% 301.35% -48.27% 126.24% 2009 27.47% 376.98% 53.10% 288.77% 48.12% 808.17% 34.05% 362.18% 52.02% 191.91% 2010 -12.18% 331.06% -6.23% 279.38% 2.45% 933.22% 6.46% 400.10% 5.32% 202.11% 2011 0.02% 331.13% 12.72% 332.63% 12.94% 1057.15% 9.20% 421.93% -19.97% 161.74% 2012 68.82% 559.00% 40.52% 420.35% 28.41% 1165.38% 21.89% 486.82% 22.91% 198.79% 2013 -2.05% 547.56% 9.62% 485.28% 6.41% 1184.14% 19.37% 581.21% 2.87% 204.49%

Average return 19.45% / 16.17% / 23.75% / 16.07% / 10.23% / Geometric return / 15% / 14.07% / 22.87% / 15.79% / 6.14%

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E. Descriptive Statistics

The descriptive statistics of all portfolios are shown in Table 7, and standard deviation,

skewness and kurtosis of portfolios would be explained in detail.

Table 7: Descriptive Statistic for all Portfolios Portfolio 1st 2nd 3th 4th

Count 12 12 12 12 Mean 19.45% 16.17% 23.75% 16.07%

Median 5.20% 14.92% 17.93% 17.37% Standard Deviation 34.90% 23.05% 15.54% 8.04%

Kurtosis -0.122171 1.4182877 -1.01772 -1.120858 Skewness 0.945029 0.4509403 0.419141 0.1063731

Range 110.74% 91.51% 49.91% 25.32% Minimum -20.69% -25.59% 1.61% 5.10% Maximum 90.04% 65.92% 51.52% 30.42%

Concerning standard deviation of portfolio’s return, it can be regarded as a measure of

risk. An asset faces greater risk if its return fluctuates more dramatically than the

others. The first portfolio has the highest standard deviation, which means it faces the

greatest risk, while the fourth portfolio faces the lowest. In general, the volatilities of

all the portfolios are quite high, which is consistent with the previous observation that

the portfolios consist of larger portion of small-cap stocks.

Regarding skewness, it is a measure of asymmetry. A normal distribution should have

a value of zero. The values of skewness for all the portfolios are larger than zero,

which means that the distribution is positively skewed. With positive skewness, the

standard deviation may overestimate risk because extreme positive returns increase

the estimate of volatility.

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With regard to kurtosis, it is a measure of the degree of fat tails and with value of

three for normal distribution. All values of the portfolios are smaller than three,

indicating a relatively fat distribution of returns. Therefore, there are high frequencies

of extreme returns, which is also coherent with the characteristics of small-cap stocks.

(Note: The Descriptive Statistics for all stocks of both long and short position can be

found in Appendix A and B for reference.)

F. Abnormal Rate of Return and Sharpe Ratio

Abnormal return is defined as difference between the stock or portfolio’s actual return

and the expected return, of which, in this study, HSI is used as a benchmark for the

expected return. It is the measure of a portfolio’s performance independent of the

market.

퐴푏푛표푟푚푎푙푅푒푡푢푟푛 = 퐴푐푡푢푎푙푅푒푡푢푟푛 − 퐸푥푝푒푐푡푒푑푅푒푡푢푟푛

Table 8: Abnormal Return of Portfolios (including both long and short position)

Portfolio 1st 2nd 3rd 4th

Long Position 52.97% 44.45% 50.75% 45.95% Abnormal Return 42.74% 34.22% 40.52% 35.72%

Short Position -14.07% -12.12% -3.26% -13.81% Abnormal Return -24.30% -22.35% -13.49% -24.04%

Total 19.45% 16.17% 23.75% 16.07% Abnormal Return 9.22% 5.94% 13.52% 5.84%

Note: HSI’s average return from 2002 to 2013 (10.23%) is used as benchmark

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From Table 8, the abnormal returns of all portfolios in total level (consisting of both

long and short position) are positive, ranging from 5% to 13%. The portfolio

constructed based on P/OCF ratio obviously outperforms the market, showing the

robustness and usefulness of the multiple in screening stocks, which further confirms

the statement of hypothesis.

When only considering the abnormal return generated by the long position, the result

is far more favourable. It ranges from 34% to 42.74%. On the other hand, the

performance of the short position is much poorer and it totally underperforms the

market.

Investors are usually not only interested in the abnormal return of a portfolio over a

benchmark index but also the risk of the portfolio they invest in. This suggests the

importance of the trade-off between returns and risk. Hence, Sharpe ratio would be

useful in measuring and comparing the attraction of different portfolios. The higher

the Sharpe ratio of a portfolio, the more attractive it is.

푆ℎ푎푟푝푒푟푎푡푖표 = 푅푖푠푘푃푟푒푚푖푢푚

푆퐷

Where

Risk Premium is the abnormal return over HSI,

SD is the standard deviation of the abnormal return.

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The following table has shown the Sharpe ratio of all portfolios:

Portfolio 1st 2nd 3rd 4th

Abnormal return 9.22% 5.94% 13.52% 12.40% SD 34.90% 23.05% 15.54% 25.16%

Sharpe ratio 0.264264 0.2575925 0.870048 0.4926681

The third portfolio has the highest Sharpe ratio so it would the best portfolio to invest.

G. Relationship between P/OCF Ratio of Stocks and their Return Rates

As mentioned in the Literature Review, there should be a negative relationship

between P/OCF ratio of stocks and their return rates.

Hence, a regression analysis would be run between the ratios of the stocks picked for

the portfolio and their respective rates of return in the following year for the period

from 2002 to 2012. From this, we can figure out if there is truly a negative correlation

between P/OCF ratio of stocks and their return rates.

Since there are a number of extreme values of P/OCF ratio, it makes the regression

result of the relationship between P/OCF ratio and their respective return rates invalid.

From Table 9, it shows that the range of the value of P/OCF ratio is so large, around

34,000.

In order to minimize the extreme value or noises, this study has taken a log of the

value of P/OCF ratio and the range of the Log P/OCF ratio is only 6.49, which is

more appropriate. The regression result could be much more meaningful. In short,

statistically, it makes more sense to run a regression between the Log P/OCF ratio of

stocks and their respective return rates.

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Table 9: Descriptive Statistics between P/OCF Ratio and Log P/OCF Ratio

P/OCF Ratio Log P/OCF Ratio

Count 1380 1380 Mean 326.633616 1.254089969 Median 20.96679931 1.116461418 Kurtosis 204.0741667 -1.228014935 Skewness 12.61926015 0.224863782 Range 34084.76762 6.492843806 Minimum 0.010957631 -1.960283328 Maximum 34084.77858 4.532560477

The regression result in Table 10 shows that the relationship between P/OCF ratios the

return rates of stocks is very weak. On the other hand, there is slightly negative

relationship, which is statistically significant, between Log P/OCF ratio and the stocks’

return rates.

As mentioned above, the Log P/OCF ratio is a better choice than the P/OCF ratio for

regression analysis, we can conclude that P/OCF and stocks’ return rates is slightly

negatively correlated.

Table 10: Result of Regression Model (N = 1380)

Variables Coefficients P-Value

P/OCF Ratio 7.18E-05 0.0215 Log P/OCF Ratio -0.3072 5.08E-12

Adjusted R2

0.03414

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Discussion

A. Confirmation of Statements of Hypothesis

With regard to the statement of hypothesis, all constructed portfolios generate higher

average returns and geometric returns than the market, and, most importantly, positive

abnormal returns. It, therefore, confirms the hypothesis that P/OCF valuation multiple

is useful in screening stocks to construct portfolios outperforming the market.

B. Other Ways of Using of P/OCF Ratio

In this study, I hold long (short) position for stocks with low (high) P/OCF ratio as

compared to the market as a whole. It is only one of the methods in adopting the ratio

to construct a portfolio. Certainly, there are more ways to make use of the multiple.

Another method is that we can long (short-sell) a stock when it has low (high) ratio in

relationship to its own financial position in the past. For example, we can compare its

current ratio with the average value of the ratio in past three or five. As long as the

current one is lower (higher) than that of past, we expect that its current price will

tend to move toward its historical average price so we buy (short-sell) the stocks.

Also, we can compare the ratio of a company to average value of its industry. For

instance, if the ratio of a stock is far lower (higher) than the average value of its

industry, it is relatively cheaper (more expensive) than its competitors. We then should

buy (short-sell) the stock.

Lastly, we can choose stocks based on their extent in the change of P/OCF ratio from

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last year to current year. We can buy (short-sell) a stock if its P/OCF decreases

(increases) by the greatest extent. Using the change in ratio may even be a better

measure than merely the absolute value of ratio alone for screening stocks.

C. Weaknesses of the Ratio

There are two major weaknesses of the ratio in screening stocks. To start with, using

the ratio for contrarian investing cannot be applied to every stock. As mentioned

before, there are reasons why market overvalues or undervalues stocks, and herd

behaviour is a hardship for contrarian investing. Investing in a manner differing from

the market could be disastrous. For example, we may short sell a ‘seemingly

overvalued’ stock but its price continues to jump up due to its rosy concept of future

development, of which the market is willing to pay a high premium for it. And it does

not return back to the mean value, of which the contrarian investors have expected.

Eventually, we would suffer a tremendous loss.

Another more crucial weakness of P/OCF is that it fails to screen stocks for

short-selling. In the study, those stocks with the greatest P/OCF are being short-sold

because they are believed to be overvalued. In theory, it would be much better to

short-sell stocks with negative OCF, which, however, are excluded from the dataset

for constructing a portfolio.

The main reason for the exclusion is that it is difficult to interpret the meaning of a

stock with negative P/OCF. In one hand, assuming same stock price for two stocks,

we should short-sell the stocks with greater negative OCF. It means that we should

short-sell the stocks with larger (negative) P/OCF. On the other hand, assuming two

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stocks having same amount of negative OCF, the stock with higher price should be

short-sold and hence we should choose the stocks with smaller (negative) P/OCF to

short-sell.

As the two meanings of a negative value of the ratio contradict with each other, we

are not able to determine whether we should short-sell a stock with a higher or lower

negative P/OCF. There is no precise implied meaning of a stock with negative P/OCF.

In short, the ratio fails to be used as a criterion for selecting stocks to short sell. A

compromising method is to just short-sell stocks with high (positive) P/OCF.

But from Table 5, the performance for this compromising method is disappointing.

The average returns are negative for the short position of all portfolios and they even

generate abnormal losses (Table 8).

Due to this weakness, the overall returns of the portfolios are dragged down by the

short position. Therefore, it is not recommended to short-sell any stocks based on the

P/OCF ratio even those with high (positive) P/OCF. And if we just take long position

of stocks, the portfolios could even generate more decent return rates according to the

long position of Table 8.

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D. Limitation

This study has some limitations in examining the usefulness of P/OCF ratio in

choosing stocks or constructing portfolio. Though the conclusion has supported from

the statistics, it may cause some variances in the results.

The first critical limitation is the small sample size in terms of time horizon. The

study on using P/OCF ratio only has 12 observations, which are the annual returns of

portfolios from 2002 to 2013. The sample size is too small in terms of time horizon.

The result is not representative enough to reflect the truthfulness or usefulness of

P/OCF ratio to construct an investment portfolio. The statement of hypothesis is,

therefore, still in question. In order to increase the reliability of the results, the sample

size should be enlarged in terms of time horizon.

Another limitation is that assuming no transaction costs is not realistic. In reality, the

transaction costs for investing in an index, such as exchange-traded fund of HSI,

should be lower than that of buying stocks to form a portfolio. Most importantly, the

costs to short-sell stocks are much higher. Assuming the costs of buying and

short-selling stocks is around 1% to 1.5% while that of investing in HSI is about

0.5% , the performance of the four constructed portfolios would be restrained. The

abnormal returns of the portfolios over the HSI would, therefore, be reduced by nearly

1% annually. In spite of the costs incurred, the portfolios could still outperform the

market but in a smaller extent.

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Conclusion

The prior researches on P/OCF ratio mainly focus on the foreign stock exchange

market, rather than HK stock market. Hence, I extend my study to examine whether

this ratio would also be a decent investment criterion in HK stock market.

In this paper, we can see that P/OCF ratio is useful and successful in choosing stocks

from HKEx to form a portfolio outperforming the HSI. It can generate abnormal

return rates over time. However, because of its weakness, investors are not

recommended to make use of the multiple in screening stocks to short-sell.

This study provides another angle for investors to examine the robustness of P/OCF

ratio in HK stock market. It is worthwhile for others researchers to investigate the

other ways of using this ratio as an investment criterion and if it can be applied in

other markets.

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Appendix

Appendix A: Descriptive Statistics for all stocks of long position covering the whole

period from 2002 to 2013

Portfolio 1% 3% 5% 10%

Count 63 126 126 315 Mean 53.5% 43.1% 49.5% 41.7% Median 8.3% 8.6% 8.6% 9.2% Standard Deviation 121.1% 131.4% 112.8% 122.7% Kurtosis 2.2660717 30.47928 2.811532 20.67495 Skewness 1.5624135 4.474559 1.692202 3.627221 Range 541.4% 1160.0% 557.8% 1162.1% Minimum -77.3% -83.5% -84.8% -87.1% Maximum 464.1% 1076.5% 472.9% 1075.0%

Appendix B: Descriptive Statistics for all stocks of short position covering the whole

period from 2002 to 2013

Portfolio 1% 3% 5% 10%

Count 63 126 126 315 Mean -12.3% -9.6% 1.0% -11.6% Median 23.4% 14.4% 4.9% 4.1% Standard Deviation 135.8% 95.4% 63.6% 83.9% Kurtosis 18.4862537 9.308545 5.232154 10.87591 Skewness -3.9424574 -2.72259 -1.72527 -2.5718 Range 878.5% 601.6% 377.5% 653.7% Minimum -788.9% -510.1% -288.1% -558.7% Maximum 89.6% 91.6% 89.4% 95.0%