How Company Information Affect the Short Selling in the Hong

How Company Information Affect the Short Selling in

the Hong Kong Stock Market?

Chu Chi Lok, Andy Ip Shuk Fan, Cheryl

Department of Economics & Finance

City University of Hong Kong

December 2007 ABSTRACT:

This report analyses the effect of the company disclosed information on the short selling volume

in Hong Kong stock market. Using the data at 28 April 2006 on the short sale volume in

individual stocks, we find that the financial ratios namely quick ratio, price-to-book ratio, time

interest ratios, inventory turnover and earnings per share have an inversely proportion to the

number of short selling in the market. Meanwhile, the ratios including ROA and price-to-sales

ratio have a positive relation to the number of short selling. Short sellers may also refer to

factors like changing in directorship and company news in making their investment decision.

Keywords: Short Selling

Table of Content 1. Introduction ............................................................................................................................. 3 2. Econometric Model ................................................................................................................. 5 3. Data ....................................................................................................................................... 10 4. Estimation Result .................................................................................................................. 12 5. Conclusion............................................................................................................................. 15 6. References ............................................................................................................................. 16 7. Appendices ............................................................................................................................ 17

EF5070 How company information affect the short selling in the HK stock market?

1. Introduction

Short selling is one of the investing techniques being used in the stock market. Since it

involves the selling of a stock that the seller does not own, sooner or later short sellers must

cover their short by buying back the same number of shares at the market price and returning to

their broker. Short sellers would like to see the price of the stock drops because they can buy it

back at the lower price and make a profit on the difference.

When it comes to investing, analyzing financial statement information is one of the

important elements in the fundamental analysis process. At the same time, the massive amount

of numbers in a company’s financial statements can be bewildering and intimidating to many

investors. However, through financial ratio analysis, investors can work with these numbers in

an organized fashion. So this is an interesting topic to know how short sellers would interpret

these numbers and make their shorting decision based on these figures.

In this report, short sales information for Hong Kong stocks is considered. The reasons are

that we are more familiar with Hong Kong stocks than foreign market stocks. Also, we would like

to explore more on the Hong Kong stock market which is being viewed with growing importance

and being treated as the financial gate of China to the rest of the world.

This report utilizes information on the short sales volume of Hong Kong stocks to analyze

how short sellers respond to those disclosed financial ratios and company information. It

attempts to see whether the selected financial ratios are significant or not in determining the

number of short selling.

The remainder of the report proceeds as follows. Session 2 discusses the econometric

3


model. Session 3 describes the data. Session 4 presents the estimation results. Session 5

presents some concluding comments.

4


2. Econometric Model

The dream of all short sellers is to find a stock that plunges to zero. Financial statement

analysis can assist them to detect the red flags and financial ratio analysis can help to make the

value on the financial statements more meaningful to the short sellers.

Short sellers would like to know if a company has enough resources to pay its debts that

are coming due in the near future. Liquidity ratios can assist to measure a company’s ability to

pay off its short-term debt obligations. Actually there are a variety of liquidity ratios. One helpful

ratio is the current ratio.

Formula: bilityCurrentLia

etCurrentAssioCurrentRat =

If this is less than 1.0, then a company could have liquidity problems. However, this ratio has

some limitations. For example, the current assets might be inflated which thus increase the

difficulty to sell the inventory. As a result, short sellers might instead focus on quick ratio.

Formula: bilityCurrentLiaInventoryetCurrentAssQuickRatio −

=

This is a more conservative measure of liquidity than the current ratio as it removes inventory

from the current assets used in the formula. By excluding the inventory, the quick ratio focuses

on the more-liquid assets of a company. If a company with a quick ratio less than 1.0, this should

raise a red flag for investors as it may be a sign that the company will have difficulty running its

operations and meeting its obligations. In turn, this may drive up the short selling volume. As

such, quick ratio is added as an explanatory variable in the model. It is expected that it would be

inversely proportional to the number of short selling.

One of the concerns to shareholders is how well the company utilized its resources in

generating profit and shareholder value. Return on assets (ROA) ratio illustrates how well

management is employing the company’s total assets to make a profit. The higher the return,

5


the more efficient management is in utilizing its asset base.

Formula: sTotalAsset

NetIncomeROA =

The ROA ratio is expressed as a percentage. In general, a company with ROA less than 4%, it

means that the company is not using its asset effectively, and the number of short selling might

increase. As such, ROA is included as an explanatory variable for short selling. It is expected

that it would be inversely proportional to the number of short selling.

The Price-Earnings (PE) ratio helps investors compare valuations among companies.

Formula: )(

PrEPSrShareEarningsPe

iceStockPE =

However, this could be a useless figure if a company does not have any earnings. An alternative

is to use Price-to-Sales ratio (PSR).

Formula: Sales

dingSharesOutsiceStockPSR tanPr ×=

According to Kenneth L. Fisher (1984), he thinks that companies reach overvaluation when the

PSR exceeds 3.0. So the number of short selling might increase. As such, PSR is included as

an explanatory variable in the model. It is expected that it would be directly proportional to the

number of short selling.

The Price-to-Book (P/B) ratio provides a guide to a company’s relative valuation. Investors

use this to compares a stock’s market price to its book value.

Formula: erShareBookValueP

eicePerSharStockBP Pr/ =

If a company’s market price is lower than its book value, this can mean that the company has

serious problem because the market has low opinion and valuation of the company. This might

drive up the short selling volume. As such, P/B ratio is included in the model as an explanatory

variable. It is expected that it would be inversely proportional to the number of short selling.

6


Banks like to use the times interest earned ratio (TIE) when structuring financials.

Formula: penseInterestEx

EBITTIE =

A typical ratio would be for a company to maintain 4.0 or better. If this number is getting close to

minimum, the possibility of the company going to bankruptcy is higher. Thus, this may increase

the short selling volume. As such, TIE is included in the model as explanatory variable. It is

expected to be inversely proportional to the number of short selling.

The sales of a company can be volatile. There might be a new product introduction or a

recession. Short sellers might look for adverse trends in sales because this could be a red flag

of the company. Inventory turnover ratio can assist the analysis by showing how many times a

company’s inventory is sold and replaced over a period.

Formula: Inventory

SalesurnoverInventoryT =

In general, if the turnover is too high, this indicates that a company will easily run out of stocks

which results ineffective buying, the number of short selling might increase. As such, inventory

turnover is included in the model as an explanatory variable. It is expected that this would be

directly proportional to the number of short selling.

Earnings represent the money that’s left over after a company pays all its bills. For many

investors, the growth of a company’s earnings is the most important factor when analyzing a

company. Earnings per Share (EPS) represents the portion of a company’s profit allocated to

each outstanding share of common stock. It serves as an indicator of a company’s profitability.

Formula: dingSharesAverageOus

ckeferredStonDividendsONetIncomeEPStan

Pr−=

In general, a higher value means the company has better performance in sales or with better

management, thus the price of the stock may rise and which may have negative effect on the

number of short selling. As such, EPS is added as an explanatory variable. It is expected this

7


would be inversely proportional to the number of short selling.

Changing directorship could mean there is a problem of the company before the change.

The officer will likely to say that the director is leaving to pursue other opportunities. However, if

the company is doing well, then short sellers might wonder why the director would leave. So this

can be a red flag indicator. Normally, after the management changes, it would mean to have

better management than before. As such, a dummy variable is added to capture the effect on

changing directorship of a company on the number of short selling. It is expected that this would

be inversely proportional to the number of short selling.

Good news about a company usually would have a positive effect on the stock price

intuitively. Therefore, this might decrease the number of short selling. As such, a dummy

variable is added to capture the effect on the good news of a company on the number of short

selling. It is expected that this would be inversely proportional to the number of short selling.

To summarize, the econometric model in this report is defined as below:

iiiii

iiiiii

enewsdirectorEPSturnoverinvTIEBPPSRROAratioquickshort

++++++++++=

10987

654321

)_ln()ln()/ln()ln()_ln()ln(

ββββββββββ

Definition of variables: Variable name in SAS Measure

Dependent Variable

ln(short i) n_SHORT Logarithm of short sales trading volume

Independent Variables

ln(quick_ratio i) r_QUICK_RATIO Logarithm of Quick Ratio

ROA i r_ROA Return on Assets

ln(PSR i) r_PRICE_TO_SALES Logarithm of Price-to-Sales Ratio

ln(P/Bi) r_PRICE_TO_BOOK Logarithm of Price-to-Book Ratio

ln(TIEi) r_TIMES_INT_EARNED Logarithm of Times Interest Earned

ln(inv_turnoveri) r_INV_TURNOVER Logarithm of Inventory Turnover

EPSi r_EPS Earnings per Share

Dummy Variables

directori d_DIRECTOR_2005 Does the company have any changes in directorship in 2005?

newi d_GOOD_NEWS Does the company have any good news on 27 April 2006?

8


Interpretation of the coefficients:

2β = the percentage change in expected short selling volume when the Quick Ratio increases

by 1 percent, keeping the other variables constant.

3β = the percentage change in expected short selling volume when the ROA increases by 1

percent, keeping the other variables constant.

4β = the percentage change in expected short selling volume when the PSR increases by 1


5β = the percentage change in expected short selling volume when the P/B ratio increases by 1


6β = the percentage change in expected short selling volume when the TIE increases by 1


7β = the percentage change in expected short selling volume when the Inventory Turnover

increases by 1 percent, keeping the other variables constant.

8β = the proportional change in expected short selling volume when the EPS increases by 1

unit, keeping the other variables constant.

9β = the proportional change in expected short selling volume when there is changes in

directorship, keeping the other variables constant.

10β = the proportional change in expected short selling volume when there is good news about

the company, keeping the other variables constant.

9


3. Data

To be eligible for inclusion in this report a stock had to be designated eligible for short selling

and had to have short sales transaction in Hong Kong stock market on the sample date. This

criterion led to the inclusion of 114 stocks (see Table 1) in the sample and the short sales

information for the stock is sampled on 28 April 2006. This sample date is chosen because most

of the listed companies announced their annual report by this date. The short sales data,

changes in directorship and company good news information were all sourced from the Hong

Kong Stock Exchange website (see Figure 1). In addition to the level of short sales volume,

individual stock financial information was collected from financial websites including

SHKonline.com (See Figure 2) and Yahoo! Finance (See Figure 3).

Limitations of the report include the fact that not every financial ratio was readily available

and some of the information was incomplete. In some cases, information had to be interpreted

or extrapolated. For Quick Ratio, ROA, Inventory Turnover and EPS, they were all readily

available from the financial websites mentioned above. For PSR, P/B Ratio and TIE, we need to

calculate the respective values based on the collected financial information including historical

stock price, net profit, EPS, Inventory turnover, EBIT, interest paid and book value per share

accordingly.

We take log on the following variables including short sales volume, Quick Ratio, PSR, P/B,

TIE, Inventory Turnover to capture the percentage changes rather than the unit changes of each

of the variables. However, we do not take log on ROA and EPS. The reason is that ROA is

expressed in percentage already. Meanwhile there are negative valued data for the EPS

variable in our sample.

10


Some basic summary statistics of each of the variables in the model are listed as below:

Mean Std Deviation

ln(short) 12.72816 2.01977

ln(quick_ratio) 0.07967 0.88509

ROA 8.539828 6.1918

ln(PSR) 0.90377 1.11846 ln(P/B) 0.77923 0.77069 ln(TIE) 2.476128 1.96695 ln(inv_turnover) 2.38711 1.67002 EPS 1.560589 2.62902

11


4. Estimation Result

In this report, we analyzed several financial ratios and company information that might

influence the number of short selling and created a multi-regression model in which the log

number of short-selling depending on 9 explanatory variables. Two of which were dummy

variables.

Table 2 reports the SAS regression result of our model. The overall explanatory power of the

equation is 9.76%. From the SAS result, it reveals that r_QUICK_RATIO explanatory variable is

the most significant. This r_QUICK_RATIO represents the log of quick ratio of each stock. The

coefficient of the variable is negative and suggests that an increase in the quick ratio by 1

percent will lead to a decrease in the number of short selling by 0.65883 percent, holding other

variables constant. It matches with our intuition that when quick ratio increases, it means the

company has more liquidity. This is a positive sign of a company and thus the number of short

selling will decrease.

All other explanatory variables included in the model are found to be insignificant. This is

surprising at the first glance. However, after a second thought, it may due to the fact that short

sellers may not solely rely on a single ratio to make their decisions. They may make industry

comparisons with the financial ratios accordingly when they do their analysis. Also, they may

check the trends of how the ratios move instead of just focusing a value of a particular year.

Nevertheless, the signs of the estimated coefficients are still worth noting.

The sign of the estimated coefficients for r_PRICE_TO_SALES, r_PRICE_TO_BOOK,

r_TIMES_INT_EARNED, r_EPS, d_DIRECTOR_2005 are consistent with our intuition. Table 3

lists the summary of our estimation sign of the coefficients and the actual sign of the coefficients

12


in the model.

The positive coefficient of r_PRICE_TO_SALES suggests that when Price-to-Sales ratio

increases, the company becomes more over-valued. Thus the number of short selling will

increase.

The coefficient for r_PRICE_TO_BOOK is negative. It means that when Price-to-Book ratio

decreases, it can be a problem for the company fundamentally. Thus it will increase the number

of short selling. However, a lower Price-to-Book ratio could also mean that the stock is

undervalued. In this case, the number of short selling would decrease. In our sample, the

dominated factor is the former which thus has a negative effect on the coefficient.

The negative coefficient of r_TIMES_INT_EARNED suggests that when Time Interest

Earned ratio increases, the chance of the company going bankruptcy is lower. Thus the short

sales volume will decrease. The negative coefficient of r_EPS suggests that when the Earnings

per Share ratio increases, the profitability of the company is relatively higher. Therefore, the

short sales volume will decrease. The negative coefficient of d_DIRECTOR_2005 shows that

when there are changes in the directorship in the company, investors would think that the

changes in directorship will lead to a better management. Thus the number of short selling will

decrease.

The sign of the estimated coefficients for r_ROA, r_INV_TURNOVER, d_GOOD_NEWS

are not consistent with our intuition. Originally, we think that r_ROA would have a negative

coefficient because a higher ROA would mean that the company can use the assets more

effectively to generate profit. However, our results show that r_ROA has a positive coefficient.

The difference may due to the reason that different industries would have different ROA ratios.

13


We cannot compare the ROA among companies directly. For example, some companies are

‘lighter’ in the sense that their value are based on things such as trademarks, brand names and

patents, which accounting rules do not recognize as assets. As a result, the assets in these

companies will be understated, and its ROA may get a questionable boost. So a high ROA does

not guarantee the company can generate profit from its assets effectively.

The coefficient of r_INV_TURNOVER is negative and this is different from our estimation.

This suggests that a low turnover implies a poor sales and therefore with excess inventory. This

would mean a red flag for the short sellers and thus increase the number of short selling.

The coefficient of d_GOOD_NEWS is positive and this is a bit counter-intuitive. This

suggests that if there is good news about the company, the number of short selling would

increase. We captured the news on 27 April 2006, the market may be very efficient enough to

absorb the news within few hours to a day. In this sense, the price of the stock would be

overvalued in a short term, thus the number of short selling will increase.

14


5. Conclusion

This report tries to study the effect of financial ratios and company information on the short

selling volume in the Hong Kong stock market. The results suggest that the financial ratios

namely quick ratio, price-to-book ratio, time interest ratios, inventory turnover and earnings per

share have an inversely proportion to the number of short selling in the market. Meanwhile, the

ratios including ROA and price-to-sales ratio have a positive relation to the number of short

selling. Short sellers may also refer to factors like changing in directorship and company news in

making their investment decision for certain extend.

However, with the low level of significance and explanatory power found in the report, it is

believed that short selling decision may not solely depend on financial ratios or company

information. Further study on factors other than that might need to be considered if we want to

find the determinants of the short sales volume. For example, investors may like to offset their

existing long positions by selling short against the box. In this case, they may short only to

reduce the ex-dividend effect or to hedge. This kind of short may not be driven by the company

disclosed information directly.

This report is subject to several limitations that include the fact that information from the data

sources can sometimes be incomplete. In some cases, information has to be interpreted or

extrapolated and this can lead to bias. Using specific sample date for the short sales volume but

with the fact that different companies announced their financial ratios on different date, this may

also lead to the data discrepancies.

15


6. References Kenneth L. Fishers, 1984. Super Stocks. Dow Jones. Tom T, 2004. What is Short Selling? McGraw-Hill.

16


7. Appendices Table 1: List of stocks with short sales transactions on the Hong Kong Stock Exchange on 28 April 2006 and included in the research Stock Code Stock Name Stock

Code Stock Name Stock Code Stock Name

1 CHEUNG KONG 308 CHINA TRAVEL HK 883 CNOOC 2 CLP HOLDINGS 316 OOIL 902 HUANENG POWER 3 HK & CHINA GAS 321 TEXWINCA HOLD 906 CHINA NETCOM 4 WHARF HOLDINGS 323 MAANSHAN IRON 939 CCB 5 HSBC HOLDINGS 330 ESPRIT HOLDINGS 941 CHINA MOBILE 6 HK ELECTRIC 338 SHANGHAI PECHEM 980 LIANHUA 8 PCCW 358 JIANGXI COPPER 981 SMIC

11 HANG SENG BANK 363 SHANGHAI IND H 991 DATANG POWER 12 HENDERSON LAND 386 SINOPEC CORP 992 LENOVO GROUP 13 HUTCHISON 388 HKEX 1038 CKI HOLDINGS 14 HYSAN DEV 392 BEIJING ENT 1055 CHINA SOUTH AIR 16 SHK PPT 440 DAH SING 1070 TCL MULTIMEDIA 17 NEW WORLD DEV 480 HKR INT'L 1109 CHINA RES LAND 18 ORIENTAL PRESS 493 GOME 1111 LCH BANK 19 SWIRE PACIFIC A 494 LI & FUNG 1114 BRILLIANCE CHI 23 BANK OF E ASIA 511 TVB 1138 CHINA SHIP DEV 41 GREAT EAGLE H 525 GUANGSHEN RAIL 1168 SINOLINK HOLD 44 HAECO 551 YUE YUEN IND 1171 YANZHOU COAL 66 MTR CORPORATION 576 ZHEJIANGEXPRESS 1175 FU JI CATERING 69 SHANGRI-LA ASIA 583 SCMP GROUP 1199 COSCO PACIFIC 78 REGAL INT'L 589 PORTS 1200 MIDLAND HOLDING 83 SINO LAND 598 SINOTRANS 2038 FIH 96 WING LUNG BANK 604 SHENZHEN INVEST 2318 PING AN 101 HANG LUNG PPT 635 PLAYMATES 2319 MENGNIU DAIRY 123 GUANGZHOU INV 669 TECHTRONIC IND 2328 PICC P&C 144 CHINA MER HOLD 682 CHAODA MODERN 2332 HUTCH TELECOM 148 KINGBOARD CHEM 683 KERRY PPT 2337 FORTE 165 CHINA EB LTD 688 CHINA OVERSEAS 2388 BOC HONG KONG 168 TSINGTAO BREW 700 TENCENT 2600 CHALCO 177 JIANGSU EXPRESS 709 GIORDANO INT'L 2628 CHINA LIFE 179 JOHNSON ELEC H 728 CHINA TELECOM 2698 WEIQIAO TEXTILE 183 CIFH 753 AIR CHINA 2777 R&F PROPERTIES 203 DENWAY MOTORS 754 HOPSON DEV HOLD 2868 BJ CAPITAL LAND 267 CITIC PACIFIC 762 CHINA UNICOM 2878 SOLOMON SYSTECH 270 GUANGDONG INV 763 ZTE 2883 CHINA OILFIELD 291 CHINA RESOURCES 809 GLOBAL BIO-CHEM 2899 ZIJIN MINING 293 CATHAY PAC AIR 828 DYNASTY WINES 3328 BANKCOMM 302 WING HANG BANK 857 PETROCHINA 3383 AGILE PROPERTY

17


Table 2: Regression result on the list of stocks with short sale records on 28 Apr 2006

18


19

Table 3: Summarized regression output for the model

Parameter Estimate

Actual sign of the

coefficient

Expected sign of the coefficient

Intercept 12.93082 Log(Quick Ratio) -0.65883 - - ROA 0.07622 + - Log(PSR) 0.22432 + + Log(P/B Ratio) -0.18116 - - Log(TIE) -0.15464 - - Log(Inventory Turnover) -0.1166 - + EPS -0.11036 - - Change Directorship in 2005 -0.25279 - - Goods News on 27 Apr 06 0.10172 + - R-squared 0.0976 Adjusted R-squared -0.0092

Figure 1: Short sales volume on the Hong Kong Stock Exchange Hong Kong Stock Exchange http://www.hkex.com.hk/hkexsearch.acsp

http://www.hkex.com.hk/hkexsearch.acsp


Figure 2: Financial information of listed companies (Quick Ratio, ROA, Inventory Turnover, Net Profit, EPS, EBIT, Interest Paid, Book Value per Share) SHKonline.com http://www.shkonline.com

21

http://www.shkonline.com/


22

Figure 3: Historical prices of listed companies on 28 Apr 2006 Yahoo! Finance http://finance.yahoo.com

http://finance.yahoo.com/

Business

How Company Information Affect the Short Selling in the Hong