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Behavioural Finance
Creation date：2024-06-14 16:22:30
Belonging category：金融Finance
Tag Finance
Behavioural Finance

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Behavioural Finance

Efficient Market Hypothesis Behavioral Finance Behavioral Biases: Examples
Versions of the EMH The Behavioral Critique Limits to Arbitrage
Types of Stock Analysis Errors in Information Processing Technical Analysis
Efficient market hypothesis is
A. Resources are allocated efficiently
B. Prices react fast and without bias
C. I don’t know what EMH is
D. Prices react fast but with bias
Efficient Market Hypothesis (EMH)
• EMH says stock prices already reflect all available information
• A forecast about favorable future performance leads to favorable
current performance, as market participants rush to trade on
new information.
Result: Prices change until expected returns are exactly
commensurate with risk.
Efficient Market Hypothesis (EMH)
• New information is unpredictable; if it could be predicted, then
the prediction would be part of today’s information.
• Stock prices that change in response to new (unpredictable)
information also must move unpredictably.
• Stock price changes follow a random walk.
Figure 11.1 Cumulative Abnormal Returns Before
Takeover Attempts: Target Companies
Figure 11.2 Stock Price Reaction to CNBC Reports
Versions of the EMH
• Weak
• Semi-strong
• Strong
• All versions assert that prices should reflect available information
Types of Stock Analysis
• Technical Analysis - using prices and volume information to
predict future prices
- Success depends on a sluggish response of stock prices to
fundamental supply-and-demand factors.
- Weak form efficiency
• Relative strength
• Resistance levels
The difference between a martingale (random walk) and a
submartingale is the expected price change in a martingale
(random walk) is____, and the expected price change for a
submartingale is____.
A. Positive; zero
B. Positive; positive
C. Positive; negative
D. Zero; positive
E. Zero; zero
Types of Stock Analysis
• Fundamental Analysis - using economic and accounting information
to predict stock prices
- Try to find firms that are better than everyone else’s estimate.
- Try to find poorly run firms that are not as bad as the market thinks.
- Semi strong form efficiency and fundamental analysis
Behavioral Finance
Conventional Finance Behavioral Finance
• Prices are correct and
equal to intrinsic value
• Resources are
allocated efficiently
• Consistent with EMH
• What if investors don’t
behave rationally?
The Behavioral Critique
Two categories of irrationalities:
1. Investors do not always process information correctly
• Result: Incorrect probability distributions of future returns
2. Even when given a probability distribution of returns, investors
may make inconsistent or suboptimal decisions
• Result: They have behavioral biases
Which of the following do you prefer?
A. $1m with chance of winning of 100%
B. $1m with 89% chance; $0 with 1% chance and
$5m with 10% chance
Which of the following do you prefer?
A. $1m with 11% probability and $0 with 89%
probability or
B. $5m with 10% probability and $0 with 90%
probability.
Rational decision
CHOICE 1
A. $1m with chance of winning of 100%
$1M x 100% = $1M
B. $1m with 89% chance; nothing with 1% chance and $5m with 10% chance
$1M x 89% + $0 x1% +$5M X 10% = $1.39M
CHOICE 2
A. $1m with 11% probability and $0 with 89% probability
$1M x 11% + $0 x 89% = $110,000
B. $5m with 10% probability and nothing with 90% probability.
$5M x 10% + $0 x 90% = $500,000
Linda is 31 years old, single, outspoken, and very bright. She
majored in philosophy. As a student, she was deeply
concerned with issues of discrimination and social justice, and
also participated in anti-nuclear demonstrations.
Which is more probable?
A. Linda is a bank teller
B. Linda is a bank teller and is active in the feminist
movement
Conjunction fallacy
Choosing a very low probability of Linda being a bank teller, say
Pr(Linda is a bank teller) = 0.05 and a high probability that she
would be a feminist, say Pr(Linda is a feminist) = 0.95, then,
assuming independence, Pr(Linda is a bank teller and Linda is a
feminist) = 0.05 × 0.95 or 0.0475, lower than Pr(Linda is a bank
teller).
Proponents of EMH typically advocate
A. An active trading strategy.
B. Investing in an index fund
C. A passive investment strategy
D. An active trading strategy and investing in an
index fund
E. Investing in an index fund and a passive
investment strategy
Errors in Information Processing: Misestimating
True Probabilities
1. Forecasting Errors: Too much weight is placed on recent
experiences
2. Overconfidence: Investors overestimate their abilities and the
precision of their forecasts
3. Conservatism: Investors are slow to update their beliefs and
under react to new information
4. Sample Size Neglect and Representativeness: Investors are too
quick to infer a pattern or trend from a small sample
Behavioral Biases: Examples
1. Framing
• How the risk is described, “risky losses” vs. “risky gains,” can
affect investor decisions
2. Mental Accounting
• Investors may segregate accounts or monies and take risks
with their gains that they would not take with their principal
If you are faced with the following choice, which
alternative would you chose?
A. A sure gain of AUD 240
B. A 25% chance to gain AUD 250 and a 75%
chance to gain nothing.
If you are faced with the following choice, which
alternative would you chose?
A. A sure loss of AUD 750
B. A 75% chance to lose AUD 1000 and 25%
chance to lose nothing
Imagine you have bought a AUD 50 ticket to a concert. When
you show up at the door and realise you have lost your ticket,
do you buy another?
A. Yes
B. No
Now imagine say you hadn’t bought the ticket yet, and you
show up at the door of the concert hall to buy your ticket.
Unfortunately, you realised you have lost AUD 50 in cash since
you walked from your car. LUCKILY, you still have enough in
your wallet though to cover the cost of the ticket. Do you buy
the ticket?
A. Yes
B. No
Behavioral Biases: Examples
3. Regret Avoidance
• Investors blame themselves more when an unconventional or
risky bet turns out badly
4. Prospect Theory
• Conventional view: Utility depends on level of wealth
• Behavioral view: Utility depends on changes in current wealth
Traditional finance presumes that investors_____, and
behavioral finance presumes that they______.
A. Are irrational; are irrational
B. Are rational; may not be rational
C. Are rational; are rational
D. May not be rational; may not be rational
E. May not be rational; are rational
Limits to Arbitrage
• Behavioral biases would not matter if rational arbitrageurs could
fully exploit the mistakes of behavioral investors
• Fundamental Risk:
-“Markets can remain irrational longer than you can remain solvent”
- Intrinsic value and market value may take too long to converge
Limits to Arbitrage
• Implementation Costs:
- Transactions costs and restrictions on short selling can limit
arbitrage activity
• Model Risk:
- What if you have a bad model and the market value is actually
correct?
Technical Analysis:Trends and Corrections
• Momentum and moving averages
- The moving average is the average level of prices over a
given interval of time, where the interval is updated as time
passes
• Bullish signal: Market price breaks through the moving
average line from below, it is time to buy
• Bearish signal: When prices fall below the moving
average, it is time to sell
Figure 12.3 Moving Average for INTC
Technical Analysis: Relative Strength
• Relative strength
- Measures the extent to which a security has out- or
underperformed either the market as a whole or its particular
industry
Case category
Tag cloud
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CMP7000A CMP-7038B Math 263 LAW3016 CS371 STAT 334 FIN0008 ACC3004 STAT 371 ​COMP1921 MTH201 ACCT2000 UN3412 CIV6000 MATHS 3021 MATE7014 ECON1 CIBC6035 LAW121G COMM 321 CSC8204R TTM2033 CSCI 2600 MAE 115 LNG302 CENV6175W1 ECON30019 CPS2015 BST969 DDES9015 AMATH242 CS 1151 N1592 ACCT600 EG503G SMM519 EC318 Ecstatistics EEE 471 MARK2051 Topology COM00037H ACCT3000 MTHM501 EG501V COM00055H BUSN9085 GGR273 MATH 2B ITD102 MA214 ECON0060 ECE3114 ECMM447 Mechanics CCT361H5S BSAN3210 WELF2001 Fin3001 RESP5001 STA 137 Linguistics 101 CSCI368 CMPSC497 LUBS3655 OMBA 5355 BUSM2562 ​BFF5902 CYBR7001 CULT2005 ECON 7720 MTRX3760 FSE598 ​LUE1001 MGSC 7100 CS3240 COSC7500 ACCT3091 INFOGR TRAN 2080 DESIGN CIVE215001 CIVE2360 CPSC 8430 FNCE2003 Stoichiometry INFS6023 Quantitative Method Math 21D ECE5179 Design ​ECON8022 Chemical DMS 213 ADM1370 BFF5902 BFF2701 AGRI10051 AMN400 ECON7322 ​FINC5090 ​MATH1052 ​SciComp Elec4622 ​FINS5516 ENG 101 MMM143 CS431CA2 BSAN2201 EBSC7070 ES9v9-10 PLIN0009 DATA 8 MATH 101 BCPM0097 ​MAT 237 BISM2201 EEE8099 Physics 11 ECE2238B Biostat 234 ​ECON30009 INF10025 CHEM252 CCMA4008 CIVE 3007 STAT0031 ELEC ENG 3108 MGMT90140 CHEM 252 STAT3016 MMM095 ​MMGT6016 Legal STAT 311 ELEC9764 COMP 370 TCS 3053 EECS 1021 FIT1051 C/C++ EE3C COMP2003J COM398 COMP51915 Simulator FIN42330 8PRO102 PGD ELEC5160 XJCO1921 COMP5123M INFS 2042 CHE1148H ECA5313 IN3329 Microsoft Modeling B15 2TT COMP3687 COMP2432 DMS2030 KAN4TSF CFKG CSE 5322 CSE 445 STATS 3DA3 CHC5223 COMP1048 EL3105 EECS 3221 CS231 ME 4434 ITP4510 ITP4501 CSCI 5708 Controller EECE 5699 CPEN 231L CPN programs CSC8016 MATU9D2 ENGN4213 2. SVM. (10 points) Consider an SVM classifier using RBF kernel, and finish the following python code to search for the optimum kernel parameter γ. You are required to calculate the RBF function and search yourself, instead of using functions in Scikit-Learn (sklearn). def parameter search(gamma list ,Xtr,ytr,Xts,yts ,...): """ Parameters gamma list: A list of RBF kernel parameter gamma to search for Xtr: Training data, shape (ntr,nrow,ncol) ytr: Training labels, shape (ntr,) Xts: Test data, shape (nts,nrow,ncol) yts: Test labels, shape (nts,) ... Add other parameters as needed Returns: gamma optimum: The gamma with minimum error on Xts, yts """ ... return gamma optimum
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