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FINC6015 Market for trading
创建日期：2024-05-16 18:15:59
所属分类：金融Finance
标签： FINC6015
Market for trading

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FINC6015 Week 7: Solutions

Week 7 - What do we want from markets?
Question 1
What’s your view on the three forms of market efficiency as proposed by Eugene Fama?
a) If you can profit from analysing historical prices, does that mean that market is not
weak form efficient?
b) If you can profit from publicly available information, does that mean that market is
not semi-strong form efficient?
c) If you can profit from private and inside information, does that mean that market is
not strong form efficient?
Strictly speaking no. You must systemically and consistently profit from those
information subsets to be able to claim weak form; semi-strong form; and strong form
efficiency
The EMH does not say
• that crises will not occur
• that asset prices will not be extremely volatile,
• that people will not make mistakes, or lie, or cheat,
• that all assets will be correctly priced,
• or even that all security prices will be correct.
EMH says:
• that security prices in public, active and liquid security markets will be
efficient with respect to publicly available information, neither
systematically understating nor systematically overstating the price.
• At any point in time such prices represent the best estimate of value based on the
public information available at the time; they may prove to be badly wrong when
new information allows us to enjoy the benefit of hindsight, or when the private
information leaks out.
Question 2

“Everyone has an economic interest in how markets should be organized, where there is
a wide variety of opinions. The ultimate objective should be to create well functioning
markets”

In the context of the above statement, what would be the ideal market structure/market
design that the following people would prefer:

a) Exchanges
b) Informed traders
c) Uninformed traders


Exchanges want a market structure that creates more order flow and one that allows it
to sell their data services to the higher bidder (exchanges sell data services to HFT).
Student to give examples.

e.g., Let’s discuss product innovation at an exchange

Market for trading an asset Key features of the market
OTC fixed income markets Access: Intermediated by dealers
Transparency: Limited
Trading platform: Telephone-based
The innovation of electronic trading can bring an array of other possibilities within
practical reach.
Fixed income market (ET) Order books can become feasible, allowing customers
direct access as well as through dealers

Informed traders want to have a market structure where they have more advantages to
take advantage of uninformed traders; but at the same time operating in an
environment that it now filled with other informed traders. Students to give examples
(e.g., let’s create a market for trading high risk assets and low risk assets)

Uninformed traders want to trade fairly at low costs and not feel they are being taken
advantage of by more informed traders (that’s why we have regulations). Students to
give examples


Question 3

Different asset classes have different levels of liquidity, differing barriers to entry,
differing transaction costs and differing levels of information disclosure.

How might different asset classes differ in their level of market efficiency? Consider the
Australian Equity market, FX market, as against secluded OTC markets like those for
various Fixed Income Derivatives.

The Australian market has differing levels of liquidity for different stocks. Blue chip
stocks experience a high volume of trading. Less liquid stocks may be less efficiently
priced due to the lack of an active market. Barriers to entry for interested traders are
very low, with individuals able to trade either through a broker, or even CFD provider, at
low cost. Information disclosure is mandatory, through the ASX. As a result, market
efficiency is likely to be high for the Australian stock market.

The FX market is the world’s most liquid market. High volume currency pairs like
USDJPY, EURUSD, AUDUSD and so on experience multi-billion USD volumes. Barriers to
entry to the FX market are seemingly higher than the AU Equity Market, as the FX
market is OTC. In small quantities investors can access the market through
intermediaries. Transaction costs are exceedingly low for institutional entities accessing
the market in reasonable trading size. Most information relevant to the FX market is
publicly available, with the market mostly moved by macroeconomic data that is
released in a coordinated manner by government entities. As a result of these factors,
global FX markets, particularly for the most traded currency pairs, are likely to be
exceedingly efficient.

Some Fixed Income Derivatives markets experience lower liquidity levels than the AU
equity market. Although total notional traded may be exceedingly large, a few dominant
players control the market. Block trades may also account for most of total traded
volume. Barriers to entry are particularly high in some such markets, with only a few
investment banks, commercial banks, hedge funds or supranational organisations
actively participating in OTC and inter-broker markets. Transaction costs may be quite
high, due to the illiquidity. Whilst key economic information is publicly available, volume
and price information can be quite difficult to access for some market participants. Some
examples of such markets include the Cross-Currency Basis market, FX Swap markets,
Exotic Interest Rate Swaps, Credit Default Swap markets and so on. Such markets may
be less efficient than the FX or Equity market. In fact, some assets listed on the CDS
market are functionally equivalent to assets listed on the Corporate Bond market and
yet, due to illiquidity, they trade with a ‘basis,’ a difference in price.


Question 4

Advocates of behavioural finance argue that the stock market sometimes overreacts and
sometimes under-reacts. What is the basis for this argument, and how do believers in
market efficiency counter the argument?

When stocks are grouped into winners and losers based on the most recent 5 years of
returns, it appears that the loser stocks perform better over the next 5 years than the
winner stocks do. In other words, the performance of the two groups reverses. This
suggests that the market was too optimistic about the winners and too pessimistic about
the losers. In other words, the market overreacted by bidding up winner stocks too high
and bidding down loser stocks too low.

In contrast, when winners and losers are sorted based on very recent (6-12 months)
performance, the winners continue to be winners and the losers continue to be losers
over the next 6-12 months. This suggests that the market under-reacted, that is, the
market did not fully incorporate just how well the winner stocks would perform and just
how badly the loser stocks would perform.

EMH believers say that this mixed pattern of apparent over and under-reaction is what
we expect in an efficient market. Only if stocks systematically overreact or under-react
would we conclude that markets are inefficient. If it appears that markets sometimes
overreact and sometimes under-react, that is analogous to a mutual fund manager who
sometimes beats the market and sometimes trails the market.


Question 5

What is the joint hypothesis problem as it relates to tests of capital market efficiency?
Carefully explain why a better asset pricing model will not solve this potential problem.

The joint hypothesis problem occurs because any test of market efficiency is a test of
both market efficiency and an asset pricing model. That is, to determine if returns are
inappropriately large or small requires a model of what is appropriate or fair. A better
asset pricing model cannot resolve this problem because it only redefines the notion of
what is a fair return. A return that deviates from the fair return may be due to an
inappropriate model or due to a market inefficiency.


Question 6

Suppose that we wished to conduct an event study on whether acquiring firms
experience share price reactions to takeover announcements. For our event study, we
will use for our sample the following three acquiring firms:
Company X: Merger announcement date January 15, 2016
Company Y: Merger announcement date February 15, 2016
Company Z: Merger announcement date April 10, 2016

Suppose we establish an 11-day testing period for returns around the event dates, the
event date plus five days before and five days after. The table below provides our three
acquiring firm stock prices during 12-day periods around merger announcement dates.

a) Compute one-day returns for each of 11 days for each of the three stocks.
Acquiring company daily stock returns, (Pt/Pt-1) – 1, are computed as follows:

(a)
Prices Returns
Day X Y Z X Y Z
-6 50.125 20 60.375
-5 50.125 20 60.5 0.000% 0.000% 0.207%
-4 50.25 20.125 60.25 0.249% 0.625% -0.413%
-3 50.25 20.25 60.125 0.000% 0.621% -0.207%
-2 50.375 20.375 60 0.249% 0.617% -0.208%
-1 50.25 20.375 60.125 -0.248% 0.000% 0.208%
0 52.25 21.375 60.625 3.980% 4.908% 0.832%
1 52.375 21.25 60.75 0.239% -0.585% 0.206%
2 52.25 21.375 60.75 -0.239% 0.588% 0.000%
3 52.375 21.5 60.875 0.239% 0.585% 0.206%
4 52.5 21.375 60.875 0.239% -0.581% 0.000%
5 52.375 21.5 60.875 -0.238% 0.585% 0.000%

b) Suppose that we have decided to use the mean adjusted return method to compute
excess or abnormal stock returns. Here, we will compute mean daily returns for
each security for a period outside our 11-day testing period. Suppose we compute
average daily returns and standard deviations for each of the stocks for 180-day
periods prior to the testing periods (the raw returns data are not given here).
Suppose that we have found normal or expected daily returns along with standard
deviations as follows:
Compute excess returns for each stock for each of the 11 days.
c) For each of the 11 days in the analysis, compute average residuals for the three
stocks. Then for each day, compute a standard deviation of residuals for the three
stocks. Finally, compute normal deviates for each of the 11 dates based on the
averages and standard deviations for the three stocks.
(b) (c)
Excess returns (residuals)
Day X Y Z Average Std Dev
Normal
Deviate
-6
-5 -0.047% -0.052% 0.199% 0.033% 0.143% 0.23
-4 0.203% 0.573% -0.421% 0.118% 0.503% 0.24
-3 -0.047% 0.569% -0.216% 0.102% 0.413% 0.25
-2 0.202% 0.565% -0.216% 0.184% 0.391% 0.47
-1 -0.295% -0.052% 0.200% -0.049% 0.247% -0.20
0 3.934% 4.856% 0.823% 3.204% 2.113% 1.52
1 0.193% -0.637% 0.198% -0.082% 0.480% -0.17
2 -0.285% 0.536% -0.008% 0.081% 0.418% 0.19
3 0.193% 0.533% 0.198% 0.308% 0.195% 1.58
4 0.192% -0.633% -0.008% -0.150% 0.431% -0.35
5 -0.285% 0.533% -0.008% 0.080% 0.416% 0.19
d) Are the average residuals for any of the dates statistically significant at the 95%
level?
We shall assume that the residuals follow a t-distribution and we will perform a one-
tail test with a 95% level of significance. Given 1 = 3-2 degrees of freedom, the
critical value for each test will be t0.95,1 = 6.314. Based on the computations in c)
above, we find that none of the residual t-statistics (normal deviates) exceed 6.314.
Thus, we do not reject the null hypothesis and we may not conclude at the 95%
level of confidence that any residual differs from zero.
e) Compute cumulative average residuals for each of the 11 dates.
f) Compute standard deviations and normal deviates for each of the 11 dates.
(e) (f)
Cumulative residuals
Day
Cumul.
Residual X
Cumul.
Residual Y
Cumul.
Residual Z
Cumul. Avg
Residual Std Dev
Normal
Deviate
-6
-5 -0.05% -0.05% 0.20% 0.03% 0.143% 0.23
-4 0.16% 0.52% -0.22% 0.15% 0.372% 0.41
-3 0.11% 1.09% -0.44% 0.25% 0.774% 0.33
-2 0.31% 1.66% -0.65% 0.44% 1.160% 0.38
-1 0.02% 1.60% -0.45% 0.39% 1.078% 0.36
0 3.95% 6.46% 0.37% 3.59% 3.061% 1.17
1 4.14% 5.82% 0.57% 3.51% 2.684% 1.31
2 3.86% 6.36% 0.56% 3.59% 2.909% 1.23
3 4.05% 6.89% 0.76% 3.90% 3.070% 1.27
4 4.24% 6.26% 0.75% 3.75% 2.788% 1.35
5 3.96% 6.79% 0.74% 3.83% 3.028% 1.27
g) Does there appear to be statistically significant evidence of abnormal acquiring firm
returns around announcement dates?
Normal deviates do not exceed the critical value of 6.314. Therefore, there does not
appear to be statistical evidence at the 95% confidence level of abnormal returns
around announcement date.
Question 7
Some trading strategies are dependent upon specific market efficiency views. What view
of market efficiency is likely to be held by a: ___________________
a) Technical Trader
b) Value Trader
c) Index Fund Investor
d) CFO currently engaging in insider trading
a) Technical Trader
A technical trader is likely to believe that the market is not even weak form efficient,
as he/she believes that future stock price movements can be predicted based on
knowledge of past changes.
b) Value Trader
A value trader may or may not believe that the market is weak form efficient. They
will not, however, believe that the market is semi-strong form efficient or above. If
the market was semi-strong form efficient then their research would be futile, all
publicly available information would already be ‘priced in.’
c) Index Fund Investor
An index fund manager is likely to believe that the market is either semi-strong form
efficient, or even strong form efficient. To be more specific, they are likely to believe
that an investor is unable to earn excess risk-adjusted returns after transaction
costs are considered, and this is probably going to manifest as a belief in semi-
strong form efficiency.
d) CFO currently engaging in insider trading
A CFO engaged in insider trading is highly likely to believe that the market is *not*
strong form efficient. Insider trading would not be profitable in a market that reflects
all public and private information.
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PHIL2617 PHYS3116 MIE250 BCPM0008 ECN 620 QBUS6830 ECSE-415 ECON5102 MGMT20005 MAT 653 ECO 480 COMP 5/600 CSSE 5600 ME 571 7CCMMS61 CS 550 COMP2865 MATH3075 STATS 506 QTM 220 ISYS2120 FIT5106 STAT 153 MKTG3112 ECON2102 ELE2018 COMP3031 MSIN0010 SCC461 MATH237 CS246 CSC3065 CSE 101 ICTWEB411 COMP9313 ECOS 2025 FN620 MATH11111 MATH 3018 KE1068 FINA 6112 C++ MISM 6202 MSIN0105 CSC8631 MECO6935 COMP4121 ACCT2011 DPBS1150 EEL 3701C F19PL OPM 661 CSE 310 CS918 BIOL30001 ACCT3563 BANK3600 MET CS622 MATH367 ACCT5907 Continuing COMP5048 COMP61021 CS3334 GSOE9210 CSCI-570 COMP 5322 HW 2 ALY-6020 IEOR 4706 DATA 200 EBUS504 CGE12412 COMP4108 7SSMM800 ECMM171P MATE50002 CS225 RS 6003 BH2274 ELEC3111 MAT6669 ECON-UA 227 PHIL 1012 7SSMM603 TB028A P6 ECS708 CSC 427 CS6476 CSE 250A A1A2 ITS61504 EECS 376 model HW6 UV0010 ECON 6074 MG4F7 CMP-7030Y INFO3008 BFIP013 ECON 5068 MATH6002 CS3483 PSTAT 131 C31FM CSOR W4246 AAEC 5307 DATA ELEC362 COMSM0047 ECN6006 MA20224 BST351 MATH 323 MATH323 AA2021 COMP3811 COMP2013 IB9X60 INVP001 EMATM0051 COMP220 BLAW1002 E20 TCP8001 ECON61001 EART40005 ECONM1022 MATH 365 MATH 367 ELEC6218 COMP 5812M EDUC 5061M ELEC ENG 1101 equation MATH6174 ST334 C4 EC9570 A33648 EBUS603 ECON47815 stata WFMS0001 PEM102 INF6060 ionically ELECTRICAL MATH 375 MMW 12 MKTG860 ECON60401 ALY6010 SOST20131 Introduction COM3524 MME2 GGR305 JNB 538 EECS101 COMM5030 IEOR E4601 2B CSC336 EN2315 LAW 432 CSC263 MATH2001 OLET1143 CSCB63 SIT772 COMM1170 STAT 441 MS930 INFT3050 GLBH0030 Stat 120B MAST10007 MGEC61 Machine GGR 252H1S ECON7030 Financial Marketing MGMT30019 ECON5120 SDSC 8009 CSC165H1S DSCI 551 3081W ACM41000 ECA 5304 MATH 1064 ECSE444 DATA 604 COMP7105 MIE1615 ECON4004 ENV200H1S EC481 MKT 306 EC1B1 MATH08051 ALY6140 CS260C CSE30 MSIN0226 CS4103 CSC3064 AM16 COMP11212 CSE4101 PSTAT 174 Math 380W CNT 5106C STAT 310 FIN2028 CS5014 STAT 425 COMP 4102A ELEC2140 MANF9544 QRM 9 QRM 8 ECON 457 COMPSCI 4091 MATH 523 DNSC-4211 PEAS 6000 MSIN0180 COMP2119 A2A HT 2022 IB93F0 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