Behavioral Finance 1

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ASSIGNMENT- A

Attempt these five analytical questions

Q.1 Explain the methodology behind predictability of bonds.

Q.2 Explain the significance of Pro-EMH evidence.

Q.3 What do you mean by Noise -trader risk.

Q.4 What do you mean by bubbles.

Q.5 Explain the term systematic investor sentiment.

Assignment B

Attempt any two analytical questions

Q.6 Explain the significance of understanding Efficient Markets Hypothesis.

Q.7 Explain the term “Behavioral Finance.

Q.8 What do you mean by Arbitrage. How Long-short trades help a arbitrager to make money.

CASE STUDY

Q.9 Write down the your observation of below mention research paper.

Futures trading activity and predictable foreign exchange market movements

Abstract

In this paper, we examine the relation between futures trading activity by trader type and returns over short horizons in five foreign currency futures markets – British pound, Canadian dollar, Deutsche mark, Japanese yen, and Swiss franc. Transforming trading activity into a sentiment measure, we find that speculator sentiment is positively related to future returns. In contrast, hedger sentiment covaries negatively with future returns. We also find that extreme sentiment by trader type is more correlated with future market movements than moderate sentiment. Our results suggest that hedgers lose to speculators in these futures markets, on average. Based on equilibrium pricing models that futures risk premiums are determined by both market risk and hedging pressure, we show that the profits to speculators are in general compensation for bearing risk.

1. Introduction

The efficiency of foreign exchange (FX) markets has long been a central issue in international finance research. A large volume of literature applies technical trading rules in spot and futures FX markets and documents unexploited profit opportunities. Examples of this literature include Sweeney (1986), Taylor and Allen (1992),

Levich and Thomas (1993), Kho (1996), and LeBaron (1999). Other FX puzzles such as forward bias and deviations from uncovered interest parity raise further questions about the efficiency of FX markets. 1

More recent research applies tools from the market microstructure literature to study currency price dynamics in terms of order flow between various types of FX dealers. These studies find that the information structure between FX dealers influences the dynamics of prices and the patterns of trades. The observed correlation between order flow and currency returns is generally interpreted to mean that some agents possess private information (e.g., Lyons, 1995; Evans, 2002; Evans and Lyons, 2002).

This paper adds to the recent literature by examining whether a specific trader type consistently beats the market in five actively traded foreign currency futures markets that include the British pound (BP), Canadian dollar (CD), Deutsche mark (DM), Japanese yen (JY), and Swiss franc (SF). We thus provide a test of FX market efficiency in the futures context. To accomplish this, we examine the relation between futures returns and lagged net positions of speculators and hedgers. 2 To facilitate comparisons across markets and to allow for an intuitive measure, we construct a sentiment index based on net trader positions. We then focus on the profitability of sentiment-based timing strategies.

We find that investor sentiment by trader type varies systematically with returns over short horizons in the futures markets in our sample. However, the relation between sentiment and future returns differs for speculators and hedgers. Whereas speculator sentiment varies positively with future returns, hedger sentiment varies negatively with future returns. We also find that extreme sentiment is more correlated with future market movements than is moderate sentiment. Our results suggest that speculators profit from trading currency futures, but hedgers lose money, on average.

At first glance, our results appear to contradict the efficient markets hypothesis (EMH). However, if speculator sentiment varies with expected risk premiums, the superior performance of speculators does not necessarily imply market inefficiency. Various asset pricing studies have documented evidence of time varying risk premiums in currency futures markets (e.g., McCurdy and Morgan, 1991, 1992; Bessem- binder, 1992; Kho, 1996). Unless risk premiums implicit in sentiment-based timing strategies are properly addressed, concluding that the profits to speculators are un- usual may be premature.

To adjust for risk, we analyze the sources of speculative profits based on the equilibrium pricing model of Hirshleifer (1990) who shows that futures risk premiums are determined by both systematic market risk and hedging pressure. Market risk arises from the correlation of the futures price with a market portfolio. Hedging pressure results from risks that agents cannot, or do not want to trade because of market frictions. Hedgers participate in futures markets to reduce risk. Thus, their net supply of futures contracts, or hedging pressure, is related to risk premiums. Bessembinder (1992) and De Roon et al. (2000) provide empirical support for the combined role of systematic risk and hedging pressure in determining futures prices in broad markets.

We adopt a two-stage procedure to investigate whether the profits to speculators are attributable to market risk premiums, hedging pressure, or rewards to superior forecasting ability. In the first stage, we adjust futures returns for time varying market risk. After controlling for market risk, we capture hedging pressure effects in the negative relation between future returns and hedger sentiment. The second stage allows us to disentangle the rewards to superior forecasting ability from the premium associated with hedging pressures. Taking both market risk and hedging pressure into consideration, we find that speculative profits disappear in the BP, CD, JY, and SF markets, but not for the DM futures if we use classical t-statistics. When we employ Bayesian inference procedures to adjust for sample size (as in Jeffreys (1961) and Connolly (1991)), the apparent speculative profits in the DM futures market also disappear.

Our finding that the relation between speculator sentiment and returns remains positive and significant after accounting for market risk and becomes insignificant after hedging pressure is accounted for suggests that hedging pressure is an important risk in currency futures markets, which has been ignored in the prior studies (e.g., McCurdy and Morgan, 1992; Levich and Thomas, 1993; Kho, 1996). Failure to consider this risk is likely to result in misleading inferences with regard to market efficiency. 3 Our results also suggest that classical hypothesis tests with fixed significance levels lead to excessive rejection of the null hypothesis even if sample sizes are only moderately large.

The remainder of this article is organized as follows. Section 2 provides the data and empirical design. Section 3 presents the empirical results. Brief conclusions are provided in Section 4.

2. Data and methodology

2.1. Data

This study analyzes weekly trader position data on the BP, CD, DM, JY, and SF futures contracts traded on the International Monetary Market division of the Chi- cago Mercantile Exchange from January 1993 to March 2000. 4 The sample period is chosen because the COT data were unavailable on a weekly basis prior to the end of 1992. The five foreign currency futures contracts are selected because they represent the most active currency futures markets in terms of overall open interest and trading volume, and because they have been extensively studied in the literature.

The data on trader positions come from the CFTCÕs COT reports and are pro- vided by Pinnacle Data Corp., New York. The COT data provide a decomposition of positions held by categorized traders. In each market, the CFTC defines large traders as those holding a futures position exceeding the reporting threshold, and large traders are further classified as either commercial or noncommercial. A trader is classified as a commercial trader if he/she engages in a business activity hedged by the use of a futures contract. A trader is regarded as a noncommercial trader if he/ she takes futures positions for reasons other than hedging. Following the literature, we interpret noncommercial traders as speculators, and commercial traders as hedgers. 5 The positions by trader type in the COT reports represent closing positions aggregated for all outstanding contracts, filed by futures commission merchants, clearing members, and foreign brokers. This trader position information, which has been published in the CFTCÕs COT reports every Friday since October 1992, relates to closing positions on the preceding Tuesday.

We also collect weekly settlement prices for these futures contracts over the sam- ple period from Datastream International. A return is computed as the first differ- ence in logarithmic Tuesday’s settlement prices. When the prices are missing on Tuesday, Wednesday prices are used. To obtain a representative futures return se- ries, we use the settlement price of the contract closest to expiration, except within the delivery month in which case we use the price of the second nearest contract. Our use of weekly data reduces potential biases due to nonsynchronous trading.

PanelAofTable1presentssummarystatisticsforfuturescontractsandtheir weekly returns over the sample period. The average returns are positive for the BP (0.017%) and JY (0.047%) futures, but negative for the CD ()0.034%), DM ()0.057%), and SF ()0.028%) futures. DM, JY and SF returns exhibit larger stan- dard deviations than the other futures. Most return series show positive skewness and excess kurtosis, indicating nonnormality in returns. The Bera-Jarque’s test con- firms this formally. 6

We use net positions (the long open interest less the short open interest) as a proxy fortradingactivity.PanelBofTable1presentssummarystatisticsforthismeasure. Contrary to the conventional assumption that speculators are net long and hedgers are net short, panel B shows the opposite for all contractsexcept BP futures. In

absolute terms, average net positions are largest for JY futures, with speculators short 13,610 contracts and hedgers long 21,760 contracts. Net positions are smallest for BP futures, with speculators long 390 contracts and hedgers short 530 contracts, on average. The DM futures market (142,320 contracts) has the highest weekly trad- ing volume, and the CD futures market (40,070 contracts) has the lowest.

2.2. Measuring investor sentiment

We construct a sentiment index that is similar to the COT index in the market place (e.g., Briese, 1994). A sentiment index for a trader type is constructed on the basis of the current net position and its historical extreme values. The primary difference between our sentiment index measure and the COT index is that we measure sentiment using historical extreme values in a moving window prior to the current date (see Eq. (1) below). The forward-looking nature of this measure allows us to use the sentiment index for forecasting purposes. Since our sentiment index is constructed using actual trader positions, it differs from the sentiment indexes in previous studies that are based on analysts’ opinions (e.g., Clarke and Statman, 1998; Fisher and Statman, 2000). The sentiment index for trader type i in week t, Siit, is given by

where NOIit represents net positions of trader type i in week t, and i denotes spec- ulators and hedgers. denote the maximum and mini- mum net positions over the three years prior to week t for trader type i. 7

An advantage of using a sentiment index rather than the number of long or short positions in studying return predictability is that the sentiment index is fairly intuitive and facilitates comparisons across markets, irrespective of their nature and size. Moreover, the sentiment index makes our analysis comparable to other studies in equity markets, such as Solt and Statman (1988), Clarke and Statman (1998), and Fisher and Statman (2000). Except Fisher and Statman (2000) who find that the sentiments of small investors and Wall Street strategists are reliable contrary indicators for future S&P 500 stock returns, the other studies find that various sentiment indexes are hardly useful in predicting future stock returns.

PanelCofTable1presentssummarystatisticsforsentimentbytradertype.The average sentiment level is lower for speculators than for hedgers with the exception of the DM futures, where the reverse is true. The weekly standard deviation of sentiment index ranges from 24% to 28% across these markets. The lower part of panel C shows the correlations between sentiments of trader types. For all these markets, speculator sentiment is highly negatively correlated with hedger sentiment, with the strongest correlation (in absolute terms) of) 0.968 in the SF futures. These strong correlations between speculator and hedger sentiments are not surprising given that the two types of large traders account for more than 70-80% of total open interest in a typical futures market.

2.3. Sentiment and future returns

To assess whether sentiment by trader type covaries with future market movements, we follow Solt and Statman (1988) and Fisher and Statman (2000) by examining the relation between the level of sentiment by trader type and subsequent returns for each futures market. The empirical model is of the following form:

where RjtþK represents the percentage return for market j over the subsequent K weeks, K 1/4 2, 4, and 8, and i represents speculators and hedgers. 8 OLS estimation of Eq. (2) produces consistent parameter estimates, but the usual OLS standard errors are incorrect due to the overlapping weekly observations of futures returns. We use Newey and West’s (1987) procedures to adjust for heteroskedastic and autocorrelated errors in the regressions.

Classical t-statistics with a fixed significance level can lead to excessive rejection of a null hypothesis if the sample size is large (e.g., Connolly, 1991). We thus compute posterior odds based on the procedures of Jeffreys (1961) and Connolly (1991). An important feature of the posterior odds approach is that it is free of sample size-re- lated distortions. Assuming equal prior odds, the posterior odds of H0 relative to H1, PðH0Þ=PðH1Þ, are approximately equal to

where H0 and H1 denote the null and alternative hypotheses respectively, n denotes the sample size, and t is the classical t-statistic.

The correlation between the level of sentiment and future returns is typically weak (e.g., Clarke and Statman, 1998; Fisher and Statman, 2000). However, several market practitioners contend that extreme sentiment is a more useful predictor of future returns (e.g., Briese, 1994; Investor Intelligence; Consensus Inc.). We thus examine the relation between future returns and extreme sentiment. Examining that relation also allows us to test the hedging pressure theory, which we discuss in more detail below. To investigate the relation between extreme sentiment and returns, we sort sentiment by trader type into five equal-size groups, and compute the subsequent mean returns for the groups with extremely bullish (top 20%) and extremely bearish (bottom 20%) sentiments. We also calculate the mean excess return of the extremely bullish group over the extremely bearish group in subsequent periods.

2.4. Sources of futures return predictability

To test the validity of the EMH, we need to investigate whether potential profits to speculators result from superior forecasting ability or are solely compensation for bearing risk. Stoll (1979) and Hirshleifer (1988, 1990) present equilibrium models to show that futures risk premiums are determined by both market risk and hedging pressure. Carter et al. (1983), Bessembinder (1992), and De Roon et al. (2000) provide evidence on the combined role of market risk and hedging pressure in determining futures prices. In this study, we analyze the sources of speculative profits relative to the model of Hirshleifer (1990).

Based on Hirshleifer (1990), after accounting for market risk and hedging pres- sure, a positive return earned by speculators suggests that speculators possess superior timing ability. To determine whether potential profits to speculators result from futures risk premiums or superior forecasting ability, we adopt a two-stage procedure. In the first stage, we estimate expected returns for each futures market based on a conditional version of the CAPM. We then attempt to disentangle the profits to superior forecasting ability possessed by speculators from hedging pressure effects, with an assumption that the negative market risk-adjusted performance of hedgers represents hedging pressure effects.

Conditional versions of modern asset pricing theories imply a linear relation between expected return and systematic risk, stated as 9

where **, can be written as

We use the procedure outlined in Fama and MacBeth (1973) to estimate Eq. (5).

The conditional beta is obtained by a rolling regression of time series returns on the jth futures market against returns on the value-weighted CRSP index using data for the 52 weeks prior to week t. 11 Bessembinder (1992) also employs the CRSP index as a benchmark portfolio, despite Roll’s (1977) critique.

The Fama-MacBeth procedure allows the price of market risk to vary over time by estimating that price cross-sectionally for each period. To obtain a more accurate estimate, we use a broad cross-section of equity portfolios and futures contracts, based on evidence and procedures in Bessembinder (1992). 12 Specifically, for each year from January 1992 to March 2000, we sort all securities listed in the CRSP daily tape into 20 equal-size portfolios based on firms’ market capitalization at the end of the prior year. We then compute weekly (Tuesday close to Tuesday close) equal- weighted portfolio returns and estimate portfolio betas using data for the previous 52 weeks. The price of market risk is estimated with cross-sectional regressions of the following form:

where R**1 is the return on equity portfolio or futures contract p, and ** is the estimated conditional beta.

Eq. (6) is estimated each week across the 20 equity portfolios and five foreign currency futures contracts under study. 13 This procedure yields a time series of estimates for the price of risk. We estimate market risk premiums in week t as the product of the time varying price of risk and the conditional beta estimated using information up to week ***. After obtaining the expected return im- plied by the CAPM, we compute the abnormal return by subtracting the expected return from the raw return series.

Finally, we disentangle speculative profits to superior forecasting ability of spec- ulators from hedging pressure effects based on the model of Hirshleifer (1990). To do this, we follow the same procedure discussed previously and compute average market risk-adjusted returns associated with extreme investor sentiment. 14 Since hedging pressure effects must correlate with net hedging, the average market risk-adjusted return associated with extreme hedger sentiment measures hedging pressure effects. After controlling for market risk and hedging pressure effects, the profits to speculators measure superior forecasting ability of speculators. 3. Empirical results

3.1. Profitability of sentiment-based timing strategies

Table 2 reports the results of estimating Eq. (2) for the BP, CD, DM, JY, and SF futures markets. The slope coefficient estimates for speculators are uniformly positive and significant at conventional significance levels for all except the CD and SF futures in the periods of 2 and 8 weeks. Therefore, speculator sentiment is, on average, positively associated with subsequent returns. For example, in the JY futures market, an increase of 1% point in speculator sentiment is associated with 0.032% point (0.42% per annum) increase in the futures return over the subsequent 4 weeks. Given that the weekly standard deviation of investor sentiment ranges from 24% to 28% (see Table 1), the relation between sentiment and future returns appears to be economically significant. However, we note that classical and Bayesian hypothesis tests yield conflicting inferences. Classical t-statistics indicate that the slope co-efficients in Eq. (2) are significant in most cases. However, the posterior odds favor the alternative hypothesis in only 3 out of 15 cases. Therefore, classical hypothesis tests tend to lead to excessive rejection of a null hypothesis when the sample size is even moderately large.

In contrast to the results for speculators, the slope coefficients for hedgers are mostly negative and significant at conventional significance levels. For example, an increase of 1% point in hedger sentiment in the JY futures market is, on average, associated with 0.030% point (0.39% per annum) decrease in the return over the sub- sequent 4 weeks. 15 However, as was the case with speculators, the posterior odds favor the alternative hypothesis in many fewer cases (2 of 15) than classical t-tests suggest.

Although Bayesian posterior odds ratios in Table 2 favor the alternative hypoth- esis over the null in very few cases, we are interested in whether focusing on extreme investor sentiment increases the correlations with future returns. To investigate that hypothesis, we sort sentiments by trader type into two groups: H and L. H (L)

Mean returns for H and L for speculators are positive and negative respectively for all except the 4- and 8-week periods in the JY futures market. Mean returns for H and L for hedgers are negative and positive respectively except for H in the JY futures market. As expected, therefore, extreme investor sentiment is indeed associated with stronger future returns. This can be better understood by evaluating the profitability of HML. For extreme speculator sentiment in the 4-week prediction period, mean returns for HML are 0.67%, 0.62%, 0.91%, 1.33%, and 1.10% for the BP, CD, DM, JY, and SF futures markets respectively. Mean returns for hedgers are) 0.71%,) 0.57%,) 1.09%,) 1.04%, and) 1.18% respectively. Annualized HML returns to speculators range from 8.10% to 17.29%, and annualized returns to hedgers range from) 7.41% to) 15.34%. These profits to extreme sentiment- based timing strategies are nontrivial, and are unlikely to be explained by the low transaction cost of futures trading or the small serial correlation in futures returns.

Moreover, based on classical t-statistics, the mean returns for HML are significant for both speculators and hedgers for all horizons. The posterior odds ratios also favor the alternative hypothesis in 12 and 11 out of 15 cases for speculators and hedgers respectively. Thus, the profits from extreme sentiment strategies appear both economically and statistically significant.

3.2. What explains the speculative profits?

One possible explanation for the apparent profits to speculators in Table 3 is that they represent compensation for risk. Alternatively, they may result from the superior forecasting ability of speculators. To examine to what extent specula- tive profits are compensation for risk, or rewards to superior timing ability, we adopt the two-stage procedure described in Section 2. We first obtain estimated market risk premiums based on the CAPM. We then subtract estimated market risk premiums from the raw return series for each market to get market risk- adjusted returns.

Table 4 reports summary statistics for conditional betas, the price of market risk, and market risk premiums over the sample period. The time series mean of the betas is negative and significant for all except the CD futures market, which is positive and significant. Other researchers also report estimated betas for currency futures. For example, Bessembinder (1992) uses monthly observations and shows that the beta is positive for the CD, BP and DM futures, negative for the JY and SF futures, but only the beta for the CD futures is significant. De Roon et al. (2000) analyze semi-monthly data and finds the estimated beta is negative and insignificant for all except the CD futures market. The sign of estimated betas in this study is broadly consistent with that reported in the prior studies; however, the estimated betas are significantly different from zero in our regressions.

The time series mean of the estimated price of risk is 0.21, indicating that an in- crease of one unit of market risk is associated with an increase of 0.21% point in the required rate of return. The average market risk premiums, consistent with the sign

of beta estimate in the respective futures market, are negative for all except the CD market. The time series means of estimated market risk premiums (per week) are) 0.041%, 0.013%,) 0.066%,) 0.058%, and) 0.083% for the BP, CD, DM, JY, and SF futures respectively. All these estimates are significant based on classical hypothesis tests, and all but the CD futures favor the alternative over the null based on Bayesian posterior odds ratios.

Despite the statistical significance of these estimates, their economic significance is doubtful. Therefore, we do not tabulate separate risk-adjustment results. In general, when we re-estimate Eq. (2) using risk-adjusted returns or recalculate profits to extreme sentiment trading strategies, the adjustment has almost no impact on the results presented in Tables 2 and 3. This finding is not surprising given the small estimate of the market price of risk in Table 4.

Given our inability to explain the predictive relationships between extreme investor sentiment and futures returns based on market risk, we now examine their relationship to hedging pressures. After accounting for the trivial economic effects of market risk, the negative performance associated with hedger sentiment captures hedging pressure effects, and the positive performance associated with speculator sentiment reflects the rewards to bearing nonmarketable risks and/or to superior forecasting ability. 16 Therefore, the difference between the return to a timing strategy following extreme speculator sentiment and the return to a strategy contrary to extreme hedger sentiment roughly measures the rewards to superior timing ability possessed by speculators.

Let SH (SL) represent the group with extremely bullish (bearish) speculator sentiment, HH (HL) represent the group with extremely bullish (bearish) hedger sentiment. Thus, the mean returns for HH and HL measure hedging pressure effects, or compensation for bearing nonmarketable risk (e.g., Hirshleifer, 1990). Let SHMHL (SLMHH) be a timing strategy following extremely bullish (bearish) speculator sentiment in excess of the return to a strategy contrary to extreme bearish (bullish) hedger sentiment. Thus, a positive return for SHMHL and a negative return for SLMHH suggest that speculators possess superior forecasting power.

Evidence on superior forecasting ability of speculators is presented in Table 5. The mean returns for SHMHL and SLMHH have mixed signs for the BP, CD, and SF futures, and are mostly insignificant based on classical t-statistics. The lone exception, for SLMHH in the SF futures over the period of 8 weeks (the mean return of 0.435% (t 1/4 1:79)), indicates that hedgers are paying more than what speculators earn. Thus, speculators appear to lack forecasting power in this instance. The mean returns for SHMHL (SLMHH) are positive (negative) for the DM and JY futures over the periods of 2, 4, and 8 weeks, but significant only for SHMHL in the DM futures based on conventional t-statistics. This suggests that speculators often initiate correct trades in buying and selling futures contracts in the DM and JY futures markets, but speculators appear to possess some forecasting ability when they are extremely bullish (and hedgers are extremely bearish) in the DM futures market. For example, using the 4-week projection period, the mean return for SHMHL is 0.44% (5.72% per annum). However, the posterior odds favor the null hypothesis (a zero mean return for SHMHL or SLMHH) for all the markets and holding horizons. The conflicting inferences from the two hypothesis-testing procedures are attributable to the moderately large sample size. Therefore, the significance level of classical hypothesis tests should be adjusted to avoid excessive rejection of a null hypothesis.
In summary, although speculator sentiment is positively correlated with future market movements, our results indicate that speculators are not associated with pri- vate information or superior forecasting power. Thus, risk premiums in these futures markets explain the negative performance of hedgers and the positive performance of speculators.

ASSIGNMENT -C

Q1: In regard to moving averages, it is considered to be a ____________ signal when market price breaks through the moving average from ____________.

A) bearish; below

B) bullish: below

C) bearish; above

D) bullish above

E) B and C

Q2: Two popular moving average periods are

A) 90-day and 52 week

B) 180-day and three year

C) 180-day two year

D) 200-day and 53 week

E) 200-day and two year

Q3: ____________ is a measure of the extent to which a movement in the market index is reflected in the price movements of all stocks in the market.

A) put-call ratio

B) trin ratio

C) Breadth

D) confidence index

E) all of the above

Q4: The put/call ratio is computed as ____________ and higher values are considered ____________ signals.

A) the number of outstanding put options divided by outstanding call options; bullish or bearish

B) the number of outstanding put options divided by outstanding call options; bullish

C) the number of outstanding put options divided by outstanding call options; bearish

D) the number of outstanding call options divided by outstanding put options; bullish

E) the number of outstanding call options divided by outstanding put options; bullish

Q5: The efficient market hypothesis ____________.

A) implies that security prices properly reflect information available to investors

B) has little empirical validity

C) implies that active traders will find it difficult to outperform a buy-and-hold strategy

D) B and C

E) A and C

Q6: Tests of market efficiency have focused on ____________.

A) the mean-variance efficiency of the selected market proxy

B) strategies that would have provided superior risk-adjusted returns

C) results of actual investments of professional managers

D) B and C

E) A and B

Q7: The anomalies literature ____________.

A) provides a conclusive rejection of market efficiency

B) provides a conclusive support of market efficiency

C) suggests that several strategies would have provided superior returns

D) A and C

E) none of the above

Q8: A “random walk” occurs when:

A. Stock price changes are random but predictable

B. Stock prices respond slowly to both new and old information

C. Future price changes are uncorrelated with the past price changes

D. Past information is useful in predicting future prices

Q9: The Arbitrage Pricing Theory (APT) differs from the single-factor Capital Asset Pricing Model (CAPM) because the APT:

A. Place more emphasis on market risk

B. Minimize the importance of diversification

C. Recognize multiple unsystematic risk factors

D. Recognize multiple systematic risk factors

Q10: In contrast to Capital Asset Pricing Model (CAPM), Arbitrage Pricing Theory (APT):

A. Requires that markets be in equilibrium

B. Uses risk premiums based on micro-economic variables

C. Specifies the exact number of and identifies specific factors that determine expected returns

D. Does not require the restrictive assumption concerning the market portfolio.

Q11: If all securities are fairly priced (relative to the intrinsic, or true, value) an arbitrage opportunity __________ exist

A. Must

B. Might

C. Must not

D. Non of the above

Q12: Assume that a company is announcing an unexpectedly large dividend to its shareholders. In an efficient market without information leakage, one might expect:

A. An abnormal price change at the announcement

B. An abnormal price change before the announcement

C. An abnormal price change after the announcement

D. No abnormal price change before or after the announcement

Q13: According to the efficient market hypothesis:

A. High-beta stocks are consistently overpriced

B. Low-beta stocks are consistently overpriced

C. Positive alpha on stocks will quickly disappear

D. Negative alpha stocks consistently yield low return for arbitrageurs

Q14: According to the efficient markets view, value stocks earn higher expected return than growth stocks because:

A. Value stocks are riskier than growth stock

B. Value stocks are less risky than growth stock

C. Value stocks have higher expected future payoffs than growth stock

D. Value stocks have lower expected future payoffs than growth stock

Q15: An investor will take as large a position as possible when an equilibrium price relationship is violated. This is an example of _________.

A. A dominance argument

B. The mean-variance efficiency frontier

C. A risk-free arbitrage

D. The capital asset pricing model

E. None of the above

Q16: Arbel (1985) found that:

A. The January effect was highest for neglected firms

B. The book-to-market value ratio effect was highest in January

C. The liquidity effect was highest for small firms

D. The neglected firm effect was independent of the small firm effect

E. Small firms had higher book-to-market value ratios

Q17. Circle all true statements. According to the behavioral finance view of the financial market:

A. Investors sentiment may move stocks prices away from the fundamental values

B. Arbitrage forces cannot always correct the mis-valuations generated by investors sentiment

C. Arbitrage forces can never correct the mis-valuations generated by investors sentiment

D. Prices are not likely to be more inefficient for stocks with higher arbitrage costs.

Q18.____________ may be responsible for the prevalence of active versus passive investments management.

A. Forecasting errors

B. Overconfidence

C. Mental accounting

D. Conservatism

E. Regret avoidance

Q19. Assume the U.S. government was to decide to increase the budget deficit. This action will most likely cause __________ to increase.

A. interest rates

B. government borrowing

C. unemployment

D. both A and B

E. none of the above

Q20. Which of the following indexes of economic indicators are leading indicators:

A. Average weekly hours of production workers

B. Employees on non-agricultural payrolls

C. Personal income less transfer payments

D. Manufacturer’s new orders (consumer goods and materials industries)

E. Only b) and c)

F. Only a) and d)

21. Conventional theories presume 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

Conventional theories presume that investors are rational and behavioral finance presumes that they may not be rational.

22. The premise of behavioral finance is that

A. conventional financial theory ignores how real people make decisions and that people make a difference.

B. conventional financial theory considers how emotional people make decisions but the market is driven by rational utility maximizing investors.

C. conventional financial theory should ignore how the average person makes decisions because the market is driven by investors that are much more sophisticated than the average person.

D. B and C

E. none of the above

The premise of behavioral finance is that conventional financial theory ignores how real people make decisions and that people make a difference.

23. Some economists believe that the anomalies literature is consistent with investors ____________ and ____________.

A. ability to always process information correctly and therefore they infer correct probability distributions about future rates of return; given a probability distribution of returns, they always make consistent and optimal decisions.

B. inability to always process information correctly and therefore they infer incorrect probability distributions about future rates of return; given a probability distribution of returns, they always make consistent and optimal decisions.

C. ability to always process information correctly and therefore they infer correct probability distributions about future rates of return; given a probability distribution of returns, they often make inconsistent or suboptimal decisions

D. inability to always process information correctly and therefore they infer incorrect probability distributions about future rates of return; given a probability distribution of returns, they often make inconsistent or suboptimal decisions

E. none of the above

Some economists believe that the anomalies literature is consistent with investors inability to always process information correctly and therefore they infer incorrect probability distributions about future rates of return and given a probability distribution of returns, they often make inconsistent or suboptimal decisions.

24. Information processing errors consist of

I) forecasting errors

II) overconfidence

III) conservatism

IV) framing

A. I and II

B. I and III

C. III and IV

D. IV only

E. I, II and III

Information processing errors consist of forecasting errors, overconfidence, and conservatism.

25. Forecasting errors are potentially important because

A. research suggests that people underweight recent information.

B. research suggests that people overweight recent information.

C. research suggests that people correctly weight recent information.

D. either A or B depending on whether the information was good or bad.

E. none of the above.

Forecasting errors are potentially important because research suggests that people overweight recent information.

26. DeBondt and Thaler believe that high P/E result from investors

A. earnings expectations that are too extreme.

B. earnings expectations that are not extreme enough.

C. stock price expectations that are too extreme.

D. stock price expectations that are not extreme enough.

E. none of the above.

DeBondt and Thaler believe that high P/E result from investors earnings expectations that are too extreme.

Difficulty: Moderate

27. If a person gives too much weight to recent information compared to prior beliefs, they would make ________ errors.

A. framing

B. selection bias

C. overconfidence

D. conservatism

E. forecasting

If a person gives too much weight to recent information compared to prior beliefs, they would make forecasting errors.

28. Single men trade far more often than women. This is due to greater ________ among men.

A. framing

B. regret avoidance

C. overconfidence

D. Conservatism

E. none of the above

Single men trade far more often than women. This is due to greater overconfidence among men.

29. ____________ may be responsible for the prevalence of active versus passive investments management.

A. Forecasting errors

B. Overconfidence

C. Mental accounting

D. Conservatism

E. Regret avoidance

Overconfidence may be responsible for the prevalence of active versus passive investments management.

30. Barber and Odean (2000) ranked portfolios by turnover and report that the difference in return between the highest and lowest turnover portfolios is 7% per year. They attribute this to

A. overconfidence

B. framing

C. regret avoidance?

D.sample neglect

E. all of the above

They attribute this to overconfidence.

31. ________ bias means that investors are too slow in updating their beliefs in response to evidence.

A. framing

B. regret avoidance

C. overconfidence

D. conservatism

E. none of the above

Conservatism bias means that investors are too slow in updating their beliefs in response to evidence.

32. Psychologists have found that people who make decisions that turn out badly blame themselves more when that decision was unconventional. The name for this phenomenon is

A. regret avoidance

B. framing

C. mental accounting

D. overconfidence

E. obnoxicity

An investments example given in the text is buying the stock of a star-up firm that shows subsequent poor performance, versus buying blue chip stocks that perform poorly. Investors tend to have more regret if they chose the less conventional start-up stock. DeBondt and Thaler say that such regret theory is consistent with the size effect and the book-to-market effect.

33. An example of ________ is that a person may reject an investment when it is posed in terms of risk surrounding potential gains but may accept the same investment if it is posed in terms of risk surrounding potential losses.

A. framing

B. regret avoidance

C. overconfidence

D. conservatism

E. none of the above

An example of framing is that a person may reject an investment when it is posed in terms of risk surrounding potential gains but may accept the same investment if it is posed in terms of risk surrounding potential losses.

34. Statman (1977) argues that ________ is consistent with some investors’ irrational preference for stocks with high cash dividends and with a tendency to hold losing positions too long.

A. mental accounting

B. regret avoidance

C. overconfidence

D. conservatism

E. none of the above

Statman (1977) argues that mental accounting is consistent with some investors’ irrational preference for stocks with high cash dividends and with a tendency to hold losing positions too long

35. Arbitrageurs may be unable to exploit behavioral biases due to ____________.

I) fundamental risk

II) implementation costs

III) model risk

IV) conservatism

V) regret avoidance

A. I and II only

B. I, II, and III

C. I, II, III, and V

D. II, III, and IV

E. IV and V

Arbitrageurs may be unable to exploit behavioral biases due to fundamental risk, implementation costs, and model risk.

36. ____________ are good examples of the limits to arbitrage because they show that the law of one price is violated.

I) Siamese Twin Companies

II) Unit trusts

III) Closed end funds

IV) Open end funds

V) Equity carve outs

A. I and II

B. I, II, and III

C. I, III, and V

D. IV and V E. V

Siamese Twin Companies, closed end funds, and equity carve outs are good examples of the limits to arbitrage because they show that the law of one price is violated.

37. __________ was the grandfather of technical analysis.

A. Harry Markowitz

B. William Sharpe

C. Charles Dow

D. Benjamin Graham

E. none of the above

Charles Dow, the originator of the Dow Theory, was the grandfather of technical analysis. Benjamin Graham might be considered the grandfather of fundamental analysis. Harry Markowitz and William Sharpe might be considered the grandfathers of modern portfolio theory.

38. The goal of the Dow theory is to

A. identify head and shoulder patterns.

B. identify breakaway points.

C. identify resistance levels.

D.Identify support levels.

E. identify long-term trends.

The Dow theory uses the Dow Jones Industrial Average as an indicator of long-term trends in market prices.

39. The Dow theory posits that the three forces that simultaneously affect stock prices are ____________.

I) primary trend

II) intermediate trend

III) momentum trend

IV) minor trend

V) contrarian trend

A. I, II, and III

B. II, III, and IV

C. III, IV and V

D. I, II, and IV

E. I, III, and V

The Dow Theory posits that the three forces that simultaneously affect stock prices are primary trend, intermediate trend, and minor trend.

40. The Elliot Wave Theory ____________.

A. is a recent variation of the Dow Theory

B. suggests that stock prices can be described by a set of wave patterns

C. is similar to the Kondratieff Wave theory

D.A and B

E. A, B, and C

Both the Elliot Wave Theory and the Kondratieff Wave Theory are recent variations on the Dow Theory, which suggests that stock prices move in identifiable wave patterns.

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