The document analyzes short sellers' behavior in the Japanese stock market. It discusses short sales processes, constraints on shorting, and short squeezes. The analysis examines actual short sale data from the JPX stock exchange over 22 months. It finds that short covering trades have modest positive returns and short sellers tend to cover positions when they become more profitable. The document also examines a case study of short positions in Toshiba stock.

1. The Analysis of Short Sellers’
Behaviour in Japanese Stock Market
Sardor Mirzaev

2. • Short sales
• Results from Boehmer et al. 2018
• Empirical Analysis
• Study Case
• Conclusions
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Overview

3. Broker
Short
seller
Market
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Short sales
Step 1
Step 4
Step 2
Step 3
Step 1. A short seller borrows a stock from a broker
Step 2. The short seller immediately sells the stock on the market
Step 3. The short seller buys the stock from the market
Step 4. The short seller returns the borrowed stock to the broker

4. • Short sales as tool to reduce the strong deviation from prices
Stabilizing the price by offering demand for stocks
• Short sales constrains
The policy of market for certain stocks. Overvaluation
• Short Squeeze
Heterogeneity of traders – certain shorts may not be profitable due to adverse price
movement forces the positions to be covered (to close positions as soon as possible to reduce
losses).
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Short sales

5. Short sales in JPX, time frame – 22 month, 4,133 reported short positions in 889 stocks, 176 institutions/short sellers
1. Using regression analysis, significant positive return on stock performance around large cover trades:
𝑦 = 𝛽0 +𝑏1 𝑥1 + 𝑏2 𝑥2+. . . +𝑏 𝑛 + 𝜀
the return is 0.32% on the covering day, which is not significant leading to fact that short sellers not only use private
information when establishing
2. Going short and closing position
• Cox proportional hazard model that allows for the correlation between the observations within each stock and for the
variation in the log hazard function across stocks:
ℎ 𝑡 = ℎ0 𝑡 ∙ 𝑒𝑥𝑝(𝑏𝑖 𝑥𝑖 + 𝑏2 𝑥2+. . . +𝑏 𝑛 𝑥 𝑛)
The hazard ratio of 0.87 implies that a 1% increase in the cumulative position return reduces the exit probability by
13%
An increasing position return implies an increasing loss to short sellers; therefore, the probability of observing a
covering decision declines with greater position losses.
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Boehmer et al. 2018

6. Main results
1. The average covering trade size is economically significant with positive returns
• Average covering trade size is 0.12% of
2. Short sellers use the private information when closing their positions.
• Privileges of private information
3. Covering is more likely when the short position is more profitable
• which could be interpreted as a “disposition” effect
4. Covering is more likely when the market is liquid
• Selling and buying quickly at sustainable prices
5. Informed short sellers use brokerages
• Local and international
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Boehmer et al. 2018

7. 12/13/2018 QBER Univeristy of Kiel 7
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Millions
Thousands
Total Shorted and Total Covered Positions
covered shorts only shorted
• Observation time frame : 01.08.2018 – 31.10.2018, 63 trading days
• Reported short selling positions – approx. JPY 73 Billion
• Reported covered positions 1218 (at least 0.25% outstanding)
• Information sensitivity
• Big amount of successful covered stocks are in short period of time
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Number of Sellers with Open and Closed Positions
Total positions Closed positions
Announcement of the trade tariffs on Chinese goods in US
Empirical Analysis

8. • The average covering trade 17% - significant
• The return of covered positions higher in t+1 and t+2
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Empirical Analysis
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0 1 2 3 4 5 6 7 8 9 10
Return ratio in days
closing short positions in days reported day
0.01%
1.01%
2.01%
3.01%
4.01%
5.01%
6.01%
7.01%
8.01%
9.01%
0 1 2 3 4 5 6 7 8 9 10
Return ratio of covered positions

9. Positive future returns are higher after covering by brokerage firms
Dominant share of successful covered positions are executed by international brokerage firms and financial
institutions
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0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00
JPM Securities Japan Co Ltd.
Credit Suisse Securities (Europe) Limited
AQR Capital Management, LLC
Deutsche Bank Aktiengesellschaft, LondonvUK
Integrated Core Strategies (Asia) Pte. Ltd.
Deutsche Bank Aktiengesellschaft, London
Nomura International plc
Anchor Bolt Capital, LP
Citigroup Global Markets Limited
Integrated Core Strategies (Asia) Pte. Ltd.
Aristeia Capital, L.L.C.
Nomura International plc
Nomura International plc
GOLDMAN SACHS INTERNATIONAL
JPM Securities Japan Co Ltd.
THOUSANDS
The Sellers with Covered Shares
Empirical Analysis

10. The rate of closing is relatively small than opened positions( 22% on average)
• Postponing closing time, the seller will face risks to bear loses in short term, when short
covering most effective.
• Sellers cover short at time t and most shorted stocks have been reported at t+2, t+4 days
• Going short later at tome t+3 and postponing closing time, the seller loses and faces short
squeeze
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Empirical Analysis

11. 12/13/2018 QBER Univeristy of Kiel 11
Case study
We consider shares of Toshiba shorted by Merrill Lynch International
• reports their outstanding positions on 1st of August
• going short on 27th of July, and covering on 1st of August
• Eventually going again short on 3rd and covering on 6th August
The sum of covered stocks in these trading days composes 126,2 million JPY.
While the price of the stock recovers on 7th of August, which is aliened with E. Fama’s theory of Efficient
Market Hypothesis, with semi-strong-form of efficiency
We suppose that international players are better informed about expected changes and hold private
information
• Significant covered positions were at t+1 and reported at t+3

12. Price decrease was observed with 26 times at least 1% and 14 times at lest 5% respectively in 62
trading days
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Case study
26.5
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The price changes of Toshiba Corp , in USD
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The price changes of Toshiba Corp , in USD
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19/07/201808/08/201828/08/201817/09/201807/10/201827/10/201816/11/2018
inUSD
Price Change in linear regression

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Case study
y = -0.0751x + 30.636
R² = 0.7385
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0 5 10 15 20 25 30 35
AxisTitle
Axis Title
Price change ( 1.Aug-15.Sept)

14. Covered positions = Outstanding positions – last reported short positions
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Empirical Analysis
0
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0 1 2 3 4 5 6 7 8 9 10
Ratio dates
cover date report date
2018/09/11 2018/09/07 0.65% 42,988,583 42,988 2018/09/05 0.66% 0.01% 18,645.22 2 4
2018/09/25 2018/09/20 0.66% 43,140,965 43,140 2018/09/07 0.65% 0.01% 18,645.22 13 5
2018/09/26 2018/09/21 0.42% 631,191 631 2018/09/20 0.52% 0.10% 186,452.19 1 5
2018/09/27 2018/09/25 6.57% 42,856,301 428,563 2018/09/21 0.65% -5.92% 11,037,969.64- 4 2
2018/09/28 2018/09/26 0.65% 4,295,803 42,958 2018/09/25 6.57% 5.92% 11,037,969.64 1 2
2018/10/01 2018/09/27 0.66% 4,318,803 43,188 2018/09/26 0.65% 0.01% 18,645.22 1 4
2018/10/02 2018/09/28 0.65% 4,287,688 42,876 2018/09/27 0.66% 0.01% 18,645.22 1 4
Date of Report
Date of
Calculation
Ratio of Short
Positions to
Shares
Outstanding
Number of
Short Positions
in Shares
Number of Short
Positions in
Trading Units
Date of
Calculation in
Previous
Reporting
Ratio of Short
Positions in
Previous
Reporting
ratio Value USD short date
report
date

15. Does forecasting of the
price suggest
to open or close positions?
ARIMA model
Box.test(fitlnstock $resid, lag = 15, type = "Ljung-Box")
Box-Ljung test
alpha = 5, p-value = 0.5409
alpha = 10, p-value = 0.1035
alpha = 15, p-value = 0.2104
The forecasted price 29.6
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Case study
01/08/18 15/08/18 01/09/18 15/09/18 20/09/18

16. 1. Going short are significantly profitable .Short covering perform positive return
2. Some short sellers are privately informed about positive future events and have timing
ability in covering positions. We observed that short sellers have timing ability when they
open short positions and close them.
3. Disclosure of covered positions doesn’t affect on the volume of short sellers activity
4. We found out positive price reaction to short coverings. Short sellers help incorporate
negative information into prices.
5. Short sellers face short squeeze, however short sellers don’t short when the information is
already in the market.
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Conclusion

17. Motivations going short:
• To profit in bearish market.
o Without short-selling it can be difficult to make money from a down market
• To hedge the downside risk of a long position in the same security or a related one with short positions
• Position of private info
o the seller is good at finding at companies where something is going wring which you believe will eventually
result in a decrease in the stock price.
▪ Adverse price movements
▪ Short sale constraints and Overpricing
▪ Price appreciation short sale constraint
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Conclusion

18. Pitfalls:
• Shares are difficult to borrow have a “hard to borrow ” fee.
• A short seller can anytime close out short position for a difficult-to-borrow stock, because the
lenders are demanding it back. This can lead to unprecedented losses for short seller.
• The short seller also has to pay to the lenders spin-offs and bonus share issues
• The short seller is responsible for making dividend payments to the lender on the shorted
stock
• If short positions are kept open over an extended period, the interested payable can be add
up noticeably
• When the heavily shorted stock moves sharply higher, due to positive development in the
market, the traders rush to buy the stock to cover their positions which are typically further
moves the price even higher.
• Recall risk – unable to sell the stock and cover with profit
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Conclusion

19. Further research to improve the study of behaviour of the sellers:
• “Quantifying Trading Behaviour in Financial Markets Using Google Trends” Tobias Preis et al. in
2013
o Better understanding of collective human behaviour
o Google Trends data may have also been able to anticipate certain future trends
o Findings of patterns that may be interpreted as “early warning signs” of stock market moves by
analysing changes in Google query volumes for search terms related to finance
• Google Trends search query volumes for certain terms can be used in the construction of
profitable trading strategies.
o the collection of these traced data helps to predict the behaviour of stock prices, allowing sellers to
review these analysis as additional factors to open short positions on certain stocks in anticipated
date
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Conclusion

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21. • He analysed closing prices of the Dow Jones Industrial Average (DJIA)
o Time series of closing prices p(t (DJIA) on the first day of trading in each week t covering the period
from 5 January 2004 until 22 February 2011. The colour code corresponds to the relative search
volume changes for the search term debt, with Δt = 3 weeks. Search volume data are restricted to
requests of users localized in the United States of America
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Conclusion

22. • Comparing strategies:
1. Google Trends strategy portfolio using search volume data
Sell at price p(t) on Monday if ∆𝑛 𝑡 + 1, ∆𝑡 > 0 and buy at p(t+1) on next Monday
Buy at p(t) on Monday if ∆𝑛 𝑡 + 1, ∆𝑡 < 0 and sell at p(t+1) on next Monday.
The symmetric impacts on the cumulative return R of a strategy's portfolio
R= log 𝑝 𝑡 − 𝑙𝑜𝑔 𝑝 𝑡 + 1 for short positions,
R= 𝑙𝑜𝑔 𝑝 𝑡 + 1 − log 𝑝 𝑡 for large positions
2. Buy and hold strategy
Position trading, is an investment strategy where an investor buys stocks and holds them for a long
time
3. Random investment strategy
3.1. momentum investing
3.2. investment based on the relative strength indicator of stocks
3.3. Up and down persistency, where investment one day is the opposite of market direction the day
prior
3.4. Investing based on the moving average convergence/divergence of the stock
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Conclusion

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Conclusion
Cumulative performance of an investment strategy based on Google Trends data
The Google Trends strategy using the search volume of the term debt would have yielded a profit of 326%.