13 sa_jan11_P3_forecasting

第1 条，共21 条标题: Does delaying servicefailure resolution ever make sense?作者: Zhou, YY (Zhou, Yuanyuan); Tsang, ASL (Tsang, Alex S. L.); Huang, MX (Huang, Minxue); Zhou, N (Zhou, Nan)来源出版物: JOURNAL OF BUSINESS RESEARCH 卷: 67 期: 2 页: 159166 DOI: 10.1016/j.jbusres.2012.10.009 出版年: FEB 2014文献类型: Article第2 条，共21 条标题: A Word Typebased Quantitative Study on the Lexical Change of American and British English作者: Lei, L (Lei, Lei); Liu, ZH (Liu, Zehua)来源出版物: JOURNAL OF QUANTITATIVE LINGUISTICS 卷: 21 期: 1 页: 3649 DOI: 10.1080/09296174.2013.856131 出版年: JAN 2 2014文献类型: Article第3 条，共21 条标题: Unintentional injuries among Chinese children with different types and severity of disability 作者: Zhu, HP (Zhu, Huiping); Xiang, HY (Xiang, Huiyun); Xia, X (Xia, Xin); Yang, X (Yang, Xia); Li, D (Li, Dan); Stallones, L (Stallones, Lorann); Du, YK (Du, Yukai)来源出版物: ANNALS OF EPIDEMIOLOGY 卷: 24 期: 1 页: 2328 DOI: 10.1016/j.annepidem.2013.10.015 出版年: JAN 2014文献类型: Article第4 条，共21 条标题: A simulationbased decision model for designing contract period in building energy performance contracting作者: Deng, QL (Deng, Qianli); Zhang, LM (Zhang, Limao); Cui, QB (Cui, Qingbin); Jiang, XL (Jiang, Xianglin)来源出版物: BUILDING AND ENVIRONMENT 卷: 71 页: 7180 DOI: 10.1016/j.buildenv.2013.09.010 出版年: JAN 2014文献类型: Article第5 条，共21 条标题: China's Rural Public Health System Performance: A CrossSectional Study作者: Tian, MM (Tian, Miaomiao); Feng, D (Feng, Da); Chen, X (Chen, Xi); Chen, YC (Chen, Yingchun); Sun, X (Sun, Xi); Xiang, YX (Xiang, Yuanxi); Yuan, F (Yuan, Fang); Feng, ZC (Feng, Zhanchun)来源出版物: PLOS ONE 卷: 8 期: 12 文献号: e83822 DOI: 10.1371/journal.pone.0083822 出版年: DEC 26 2013文献类型: Article第6 条，共21 条标题: Risk Identification and Prediction of Coal Workers' Pneumoconiosis in Kailuan Colliery Group in China: A Historical Cohort Study作者: Shen, FH (Shen, Fuhai); Yuan, JX (Yuan, Juxiang); Sun, ZQ (Sun, Zhiqian); Hua, ZB (Hua, Zhengbing); Qin, TB (Qin, Tianbang); Yao, SQ (Yao, Sanqiao); Fan, XY (Fan, Xueyun); Chen, WH (Chen, Weihong); Liu, HB (Liu, Hongbo); Chen, J (Chen, Jie)来源出版物: PLOS ONE 卷: 8 期: 12 文献号: e82181 DOI: 10.1371/journal.pone.0082181 出版年: DEC 23 2013文献类型: Article第7 条，共21 条标题: Risk management in liner ship fleet deployment: A joint chance constrained programmingmodel作者: Wang, TS (Wang, Tingsong); Meng, Q (Meng, Qiang); Wang, SI (Wang, Shuaian); Tan, ZJ (Tan, Zhijia)来源出版物: TRANSPORTATION RESEARCH PART ELOGISTICS AND TRANSPORTATION REVIEW 卷: 60 特刊: SI 页: 112 DOI: 10.1016/j.tre.2013.09.001 出版年: DEC 2013文献类型: Article第8 条，共21 条标题: Collocation: Applications and Implications作者: Lei, L (Lei, Lei)来源出版物: SYSTEM 卷: 41 期: 4 页: 10881090 DOI: 10.1016/j.system.2013.10.002 出版年: DEC 2013文献类型: Book Review第9 条，共21 条标题: Policy implementation of methadone maintenance treatment and HIV infection: evidence from Hubei province, China作者: Dai, JF (Dai, Jifang); Zhao, LY (Zhao, Lianyi); Liang, Y (Liang, Yuan)来源出版物: SUBSTANCE ABUSE TREATMENT PREVENTION AND POLICY 卷: 8 文献号: 38 DOI: 10.1186/1747597X838 出版年: NOV 5 2013文献类型: Article第10 条，共21 条标题: Do efforts on energy saving enhance firm values? Evidence from China's stock market作者: Ye, DZ (Ye, Dezhu); Liu, SS (Liu, Shasha); Kong, DM (Kong, Dongmin)来源出版物: ENERGY ECONOMICS 卷: 40 页: 360369 DOI: 10.1016/j.eneco.2013.07.017 出版年: NOV 2013文献类型: Article第11 条，共21 条标题: Beyond onestepahead forecasting: Evaluation of alternative multistepahead forecasting models for crude oil prices作者: Xiong, T (Xiong, Tao); Bao, YK (Bao, Yukun); Hu, ZY (Hu, Zhongyi)来源出版物: ENERGY ECONOMICS 卷: 40 页: 405415 DOI: 10.1016/j.eneco.2013.07.028 出版年: NOV 2013文献类型: Article第12 条，共21 条标题: Optimal density of radial major roads in a twodimensional monocentric city with endogenous residential distribution and housing prices作者: Li, ZC (Li, ZhiChun); Chen, YJ (Chen, YaJuan); Wang, YD (Wang, YaDong); Lam, WHK (Lam, William H. K.); Wong, SC (Wong, S. C.)来源出版物: REGIONAL SCIENCE AND URBAN ECONOMICS 卷: 43 期: 6 页: 927937 DOI: 10.1016/j.regsciurbeco.2013.09.010 出版年: NOV 2013文献类型: Article第13 条，共21 条标题: Optimal reinsurance strategies in regimeswitching jump diffusion models: Stochastic differential game formulation and numerical methods作者: Jin, Z (Jin, Zhuo); Yin, G (Yin, G.); Wu, F (Wu, Fuke)来源出版物: INSURANCE MATHEMATICS & ECONOMICS 卷: 53 期: 3 页: 733746 DOI: 10.1016/j.insmatheco.2013.09.015 出版年: NOV 2013文献类型: Article第14 条，共21 条标题: Child health security in China: A survey of child health insurance coverage in diverse areas of the country作者: Xiong, JY (Xiong, Juyang); Hipgrave, D (Hipgrave, David); Myklebust, K (Myklebust, Karoline); Guo, SF (Guo, Sufang); Scherpbier, RW (Scherpbier, Robert W.); Tong, XT (Tong, Xuetao); Yao, L (Yao, Lan); Moran, AE (Moran, Andrew E.)来源出版物: SOCIAL SCIENCE & MEDICINE 卷: 97 特刊: SI 页: 1519 DOI: 10.1016/j.socscimed.2013.08.006 出版年: NOV 2013文献类型: Article第15 条，共21 条标题: OSTEOPROTEGERIN INHIBITS CALCIFICATION OF VASCULAR SMOOTH MUSCLE CELL VIA DOWN REGULATION OF NOTCH1RBPJ kappa/MSX2 SIGNALING PATHWAY作者: Zhou, S (Zhou, S.); Li, W (Li, W.); Qiu, H (Qiu, H.); Guan, S (Guan, S.)来源出版物: GERONTOLOGIST 卷: 53 页: 271271 增刊: 1 出版年: NOV 2013文献类型: Meeting Abstract第16 条，共21 条标题: FOXP3 DEMETHYLATION SHOWS QUANTITATIVE DEFECT OF REGULATORY T CELLS IN OLD PATIENTS WITH ACUTE CORONARY SYNDROME作者: Lu, C (Lu, C.); Zhang, C (Zhang, C.)来源出版物: GERONTOLOGIST 卷: 53 页: 273273 增刊: 1 出版年: NOV 2013文献类型: Meeting Abstract第17 条，共21 条标题: Impact of decision sequence of pricing and quality investment in decentralized assemblysystem作者: Yu, JH (Yu, Jianhong); Ma, SH (Ma, Shihua)来源出版物: JOURNAL OF MANUFACTURING SYSTEMS 卷: 32 期: 4 页: 664679 DOI: 10.1016/j.jmsy.2013.02.004 出版年: OCT 2013文献类型: Article第18 条，共21 条标题: Exploring Corpus Linguistics: Language in Action作者: Lei, L (Lei, Lei)来源出版物: ELT JOURNAL 卷: 67 期: 4 页: 503505 DOI: 10.1093/elt/cct044 出版年: OCT 2013文献类型: Book Review第19 条，共21 条标题: Market Timing with Security Offering Regulations: Evidence from Private Placements of Chinese Listed Firms作者: Cao, LH (Cao, Lihong); Xia, XP (Xia, Xinping); Wang, YX (Wang, Yixia)来源出版物: EMERGING MARKETS FINANCE AND TRADE 卷: 49 页: 91106 DOI: 10.2753/REE1540496X4902S205 增刊: 2 出版年: MARAPR 2013文献类型: Article第20 条，共21 条标题: DOES CORPORATE SOCIAL RESPONSIBILITY AFFECT THE PARTICIPATION OF MINORITY SHAREHOLDERS IN CORPORATE GOVERNANCE?作者: Kong, DM (Kong, Dongmin)来源出版物: JOURNAL OF BUSINESS ECONOMICS AND MANAGEMENT 卷: 14 特刊: SI 页: S168S187 DOI: 10.3846/16111699.2012.711365 增刊: 1 出版年: 2013文献类型: Article第21 条，共21 条标题: An empirical investigation of mobile services' crosscategory promotions作者: Yang, SQ (Yang, Shuiqing); Lu, YB (Lu, Yaobin); Gupta, S (Gupta, Sumeet)来源出版物: INTERNATIONAL JOURNAL OF MOBILE COMMUNICATIONS 卷: 11 期: 6 页: 580596 DOI: 10.1504/IJMC.2013.057816 出版年: 2013文献类型: Article。

CHAPTER 24: PORTFOLIO PERFORMANCE EVALUATIONPROBLEM SETS1. The dollar-weighted average will be the internal rate of return between the initial andfinal value of the account, including additions and withdrawals. Using Excel ’s XIRR function, utilizing the given dates and values, the dollar-weighted average return is as follows:26.99%=XIRR(C13:C20,B13:B20)Since the dates of additions and withdrawals are not equally spaced, there really is no way to solve this problem using a financial calculator. Excel can solve this very quickly.2.As established in the following result from the text, the Sharpe ratio depends on both alpha for the portfolio (P α) and the correlation between the portfolio and the market index (ρ):()αρσσP f PM PPE r r S -=+ Specifically, this result demonstrates that a lower correlation with the market index reduces the Sharpe ratio. Hence, if alpha is not sufficiently large, the portfolio is inferior to the index. Another way to think about this conclusion is to note that, even for a portfolio with a positive alpha, if its diversifiable risk is sufficiently large, thereby reducing the correlation with the market index, this can result in a lower Sharpe ratio. 3.The IRR (i.e., the dollar-weighted return) cannot be ranked relative to either the geometric average return (i.e., the time-weighted return) or the arithmetic average return. Under some conditions, the IRR is greater than each of the other two averages, and similarly, under other conditions, the IRR can also be less than each of the other averages. A number of scenarios can be developed to illustrate this conclusion. Forexample, consider a scenario where the rate of return each period consistentlyincreases over several time periods. If the amount invested also increases each period, and then all of the proceeds are withdrawn at the end of several periods, the IRR isgreater than either the geometric or the arithmetic average because more money isinvested at the higher rates than at the lower rates. On the other hand, if withdrawals gradually reduce the amount invested as the rate of return increases, then the IRR is less than each of the other averages. (Similar scenarios are illustrated with numerical examples in the text, where the IRR is shown to be less than the geometric average, and in Concept Check 1, where the IRR is greater than the geometric average.)4. It is not necessarily wise to shift resources to timing at the expense of securityselection. There is also tremendous potential value in security analysis. The decision as to whether to shift resources has to be made on the basis of the macro, compared to the micro, forecasting ability of the portfolio management team.5. a. Arithmetic average: r ABC = 10%; r XYZ = 10%b.Dispersion: σABC = 7.07%; σXYZ = 13.91%Stock XYZ has greater dispersion.(Note: We used 5 degrees of freedom in calculating standard deviations.)c. Geometric average:r ABC = (1.20 × 1.12 × 1.14 × 1.03 × 1.01)1/5– 1 = 0.0977 = 9.77%r XYZ = (1.30 × 1.12 × 1.18 × 1.00 × 0.90)1/5– 1 = 0.0911 = 9.11% Despite the fact that the two stocks have the same arithmetic average, thegeometric average for XYZ is less than the geometric average for ABC. Thereason for this result is the fact that the greater variance of XYZ drives thegeometric average further below the arithmetic average.d. In terms of ―forward-looking‖ statistics, the arithmetic average is thebetter estimate of expected rate of return. Therefore, if the data reflectthe probabilities of future returns, 10 percent is the expected rate ofreturn for both stocks.6. a. Time-weighted average returns are based on year-by-year rates of return:Year Return = (Capital gains + Dividend)/Price2013 − 2014 [($120 – $100) + $4]/$100 = 24.00%2014 − 2015 [($90 – $120) + $4]/$120 = –21.67%2015 − 2016 [($100 – $90) + $4]/$90 = 15.56%Arithmetic mean: (24% – 21.67% + 15.56%)/3 = 5.96%Geometric mean: (1.24 × 0.7833 × 1.1556)1/3– 1 = 0.0392 = 3.92%b.Date CashFlow Explanation1/1/13 –$300 Purchase of three shares at $100 each1/1/14 –$228 Purchase of two shares at $120 less dividend income on three shares held 1/1/15 $110 Dividends on five shares plus sale of one share at $901/1/16 $416 Dividends on four shares plus sale of four shares at $100 each-300Dollar-weighted return = Internal rate of return = –0.1607%(CF 0 = -$300; CF 1 = -$228; CF 2 = $110; CF 3 = $416; Solve for IRR = 16.07%.)7.Time Cash Flow Holding Period Return0 3×(–$90) = –$2701 $100 (100–90)/90 = 11.11%2 $100 0%3 $100 0%a. Time-weighted geometric average rate of return =(1.1111 × 1.0 × 1.0)1/3– 1 = 0.0357 = 3.57%b. Time-weighted arithmetic average rate of return = (11.11% + 0 + 0)/3 = 3.70%The arithmetic average is always greater than or equal to the geometric average;the greater the dispersion, the greater the difference.c. Dollar-weighted average rate of return = IRR = 5.46%[Using a financial calculator, enter: n = 3, PV = –270, FV = 0, PMT = 100. Thencompute the interest rate, or use the CF0=−300, CF1=100, F1=3, then computeIRR]. The IRR exceeds the other averages because the investment fund was thelargest when the highest return occurred.8. a.The alphas for the two portfolios are:αA = 12% – [5% + 0.7 × (13% – 5%)] = 1.4% αB = 16% – [5% + 1.4 × (13% – 5%)] = –0.2%Ideally, you would want to take a long position in Portfolio A and a short position in Portfolio B.b.If you will hold only one of the two portfolios, then the Sharpe measure is the appropriate criterion:.12.050.583.12A S -== .16.050.355.31B S -== Using the Sharpe criterion, Portfolio A is the preferred portfolio.9.a.Stock A Stock B (i) Alpha = regression intercept1.0%2.0% (ii)Information ratio = ασ(e )PP 0.0971 0.1047 (iii) *Sharpe measure = σP fP r r - 0.4907 0.3373(iv)†Treynor measure = βP fPr r - 8.83310.500* To compute the Sharpe measure, note that for each stock, (r P – r f ) can becomputed from the right-hand side of the regression equation, using the assumed parameters r M = 14% and r f = 6%. The standard deviation of each stock’s returns is given in the problem.† The beta to use for the Treynor measure is the slope coefficient of the regression equation presented in the problem.b.(i) If this is the only risky asset held by the investor, then Sharpe’s measure is the appropriate measure. Since the Sharpe measure is higher for Stock A, then A is the best choice.(ii) If the stock is mixed with the market index fund, then the contribution to the overall Sharpe measure is determined by the appraisal ratio; therefore, Stock B is preferred.(iii) If the stock is one of many stocks, then Treynor’s measure is the appropriate measure, and Stock B is preferred.10. We need to distinguish between market timing and security selection abilities. Theintercept of the scatter diagram is a measure of stock selection ability. If themanager tends to have a positive excess return even when the market’sperformance is merely ―neutral‖ (i.e., has zero excess return), then we concludethat the manager has on average made good stock picks. Stock selection must be the source of the positive excess returns.Timing ability is indicated by the curvature of the plotted line. Lines that become steeper as you move to the right along the horizontal axis show good timing ability.The steeper slope shows that the manager maintained higher portfolio sensitivity to market swings (i.e., a higher beta) in periods when the market performed well. This ability to choose more market-sensitive securities in anticipation of market upturns is the essence of good timing. In contrast, a declining slope as you move to the right means that the portfolio was more sensitive to the market when the market didpoorly and less sensitive when the market did well. This indicates poor timing.We can therefore classify performance for the four managers as follows:SelectionAbility Timing AbilityA. Bad GoodB. Good GoodC. Good BadD. Bad Bad11. a. Bogey: (0.60 × 2.5%) + (0.30 × 1.2%) + (0.10 × 0.5%) = 1.91%Actual: (0.70 × 2.0%) + (0.20 × 1.0%) + (0.10 × 0.5%) = 1.65Under performance: 0.26%b. Security Selection:(1) (2) (3) = (1) × (2)Market Differential Returnwithin Market(Manager – Index)Manager'sPortfolioWeightContribution toPerformanceEquity –0.5% 0.70 −0.35% Bonds –0.2 0.20 –0.04 Cash 0.0 0.10 0.00Contribution of security selection: −0.39%c. Asset Allocation:(1) (2) (3) = (1) × (2)MarketExcess Weight(Manager – Benchmark)IndexReturnContribution toPerformanceEquity 0.10% 2.5% 0.25%Bonds –0.10 1.2 –0.12Cash 0.00 0.5 0.00Contribution of asset allocation: 0.13%Summary:Security selection –0.39%Asset allocation 0.13Excess performance –0.26%12. a. Manager: (0.30 × 20%) + (0.10 × 15%) + (0.40 × 10%) + (0.20 × 5%) = 12.50%Bogey: (0.15 × 12%) + (0.30 × 15%) + (0.45 × 14%) + (0.10 × 12%) = 13.80Added value: –1.30%b. Added value from country allocation:(1) (2) (3) = (1) × (2)CountryExcess Weight(Manager – Benchmark)Index Returnminus BogeyContribution toPerformanceU.K. 0.15 −1.8% −0.27% Japan –0.20 1.2 –0.24U.S. −0.05 0.2 −0.01Germany 0.10 −1.8 −0.18Contribution of country allocation: −0.70%c. Added value from stock selection:(1) (2) (3) = (1) × (2)Country Differential Returnwithin Country(Manager – Index)Manager’sCountry weightContribution toPerformanceU.K. 0.08 0.30% 2.4% Japan 0.00 0.10 0.0 U.S. −0.04 0.40 −1.6 Germany −0.07 0.20 −1.4Contribution of stock selection: −0.6% Summary:Country allocation –0.70%Stock selection −0.60Excess performance –1.30%13. Support : A manager could be a better performer in one type of circumstance than inanother. For example, a manager who does no timing but simply maintains a high beta, will do better in up markets and worse in down markets. Therefore, we should observe performance over an entire cycle. Also, to the extent that observing amanager over an entire cycle increases the number of observations, it would improve the reliability of the measurement.Contradict : If we adequately control for exposure to the market (i.e., adjust for beta), then market performance should not affect the relative performance of individual managers. It is therefore not necessary to wait for an entire market cycle to pass before evaluating a manager.14. The use of universes of managers to evaluate relative investment performance does,to some extent, overcome statistical problems, as long as those manager groups can be made sufficiently homogeneous with respect to style.15. a. The manager’s alpha is 10% – [6% + 0.5 × (14% – 6%)] = 0b. From Black-Jensen-Scholes and others, we know that, on average, portfolioswith low beta have historically had positive alphas. (The slope of the empirical security market line is shallower than predicted by the CAPM.) Therefore, given the manager’s low beta, performance might actually be subpar despite the estimated alpha of zero.16. a. The most likely reason for a difference in ranking is due to the absence ofdiversification in Fund A. The Sharpe ratio measures excess return per unit of total risk, while the Treynor ratio measures excess return per unit of systematic risk. Since Fund A performed well on the Treynor measure and so poorly on the Sharpe Measure, it seems that the fund carries a greater amount of unsystematic risk, meaning it is not well-diversified and systematic risk is not the relevant risk measure.17. The within sector selection calculates the return according to security selection. This isdone by summing the weight of the security in the portfolio multiplied by the return of the security in the portfolio minus the return of the security in the benchmark:Large Cap Sector: 0.6(.17-.16)= 0.6%Mid Cap Sector: 0.15(.24-.26)-0.3%Small Cap Sector: 0.25(.20-.18)= 0.5%Total Within-Sector Selection = 0.6%-0.3%0.5%0.8%⨯⨯=⨯+=18. Primo Return 0.617%0.1524%0.2520%18.8%=⨯+⨯+⨯= Benchmark Return 0.516%0.426%0.118%20.2%=⨯+⨯+⨯=Primo – Benchmar k = 18.8% − 20.2% = -1.4% (Primo underperformed benchmark) To isolate the impact of Primo’s pure sector allocation decision relative to thebenchmark, multiply the weight difference between Primo and the benchmark portfolio in each sector by the benchmark sector returns:(0.60.5)(.16)(0.150.4)(.26)(0.250.1)(.18) 2.2%-⨯+-⨯+-⨯=-To isolate the impact of Primo’s pure security selection decisions relative to thebenchmark, multiply the return differences between Primo and the benchmark for each sector by Primo’s weightings :(.17.16)(.6)(.24.26)(.15)(.20.18)(.25)0.8%-⨯+-⨯+-⨯=19. Because the passively managed fund is mimicking the benchmark, the 2R of theregression should be very high (and thus probably higher than the actively managed fund).20. a. The euro appreciated while the pound depreciated. Primo had a greater stake inthe euro-denominated assets relative to the benchmark, resulting in a positive currency allocation effect. British stocks outperformed Dutch stocks resulting in a negative market allocation effect for Primo. Finally, within the Dutch and British investments, Primo outperformed with the Dutch investments and under-performed with the British investments. Since they had a greater proportion invested in Dutch stocks relative to the benchmark, we assume that they had a positive security allocation effect in total. However, this cannot be known for certain with this information. It is the best choice, however.21. a. Miranda S&P .102.02.225.02.2216.5568σ.37.44P f P r r S S ----→====b. To compute 2M measure, blend the Miranda Fund with a position in T-bills suchthat the adjusted portfolio has the same volatility as the market index. Using the data, the position in the Miranda Fund should be .44/.37 = 1.1892 and the position in T-bills should be 1 – 1.1892 = -0.1892 (assuming borrowing at the risk-free rate).The adjusted return is: *(1.1892)10.2%(.1892)2%.117511.75%P r =⨯-⨯==Calculate the difference in the adjusted Miranda Fund return and the benchmark: *211.75%(22.50%)34.25%M P M r r =-=--=[Note: The adjusted Miranda Fund is now 59.46% equity and 40.54% cash.] c.Miranda S&P .102.02.225.02.0745.245β 1.10 1.00P f P r r T T ----→====-d.22. This exercise is left to the student; answers will vary.CFA PROBLEMS1. a. Manager AStrength . Although Manager A’s one -year total return was somewhat below the international index return (–6.0 percent versus –5.0 percent), this manager apparently has some country/security return expertise. This large local market return advantage of 2.0 percent exceeds the 0.2 percent return for the international index.Weakness. Manager A has an obvious weakness in the currency management area. This manager experienced a marked currency return shortfall, with a return of –8.0 percent versus –5.2 percent for the index.Manager BStrength . Manager B’s total return exceeded that of the index, with a marke d positive increment apparent in the currency return. Manager B had a –1.0 percent currency return compared to a –5.2 percent currency return on the international index. Based on this outcome, Manager B’s strength appears to be expertise in the currency selection area.Weakness. Manager B had a marked shortfall in local market return. Therefore, Manager B appears to be weak in security/market selection ability.b.The following strategies would enable the fund to take advantage of the strengths of each of the two managers while minimizing their weaknesses. 1. Recommendation: One strategy would be to direct Manager A to make no currency bets relative to the international index and to direct Manager B to make only currency decisions, and no active country or security selection bets.Justification: This strategy would mitigate Manager A’s weakness by hedging all currency exposures into index-like weights. This would allow capture of Manager A’s country and stock selection skills while avoiding losses from poor currency management. This strategy would also mitigateα[β()]0.102[0.02 1.10(0.2250.02)].351535.15%P P f P M f r r r r =-+-=-+⨯--==Manager B’s weakness, leaving an index-like portfolio construct andcapitalizing on the apparent skill in currency management.2. Recommendation: Another strategy would be to combine the portfolios ofManager A and Manager B, with Manager A making country exposure andsecurity selection decisions and Manager B managing the currency exposurescreated by Manager A’s decisions (providing a ―currency overlay‖).Justification: This recommendation would capture the strengths of bothManager A and Manager B and would minimize their collective weaknesses.2. a. Indeed, the one year results were terrible, but one year is a poor statistical basefrom which to draw inferences. Moreover, the board of trustees had directed Karlto adopt a long-term horizon. The board specifically instructed the investmentmanager to give priority to long-term results.b. The sample of pension funds had a much larger share invested in equities thandid Alpine. Equities performed much better than bonds. Yet the trustees toldAlpine to hold down risk, investing not more than 25 percent of the plan’s assetsin common stocks. (Alpine’s beta was also somewhat defensive.) Alpine shouldnot be held responsible for an asset allocation policy dictated by the client.c. Alpine’s alpha measures its risk-adjusted performance compared to the market:α = 13.3% – [7.5% + 0.90 × (13.8% – 7.5%)] = 0.13% (actually above zero)d.Note that the last five years, and particularly the most recent year, have beenbad for bonds, the asset class that Alpine had been encouraged to hold. Withinthis asset class, however, Alpine did much better than the index fund.Moreover, despite the fact that the bond index underperformed both theactuarial return and T-bills, Alpine outperformed both. Alpine’s performancewithin each asset class has been superior on a risk-adjusted basis. Its overalldisappointing returns were due to a heavy asset allocation weighting towardsbonds which was the b oard’s, not Alpine’s, choice.e. A trustee may not care about the time-weighted return, but that return is moreindicative of the mana ger’s performance. After all, the manager has no controlover the cash inflows and outflows of the fund.3. a. Method I does nothing to separately identify the effects of market timing andsecurity selection decisions. It also uses a questionable ―neutral position,‖ thecomposition of the portfolio at the beginning of the year.b. Method II is not perfect but is the best of the three techniques. It at least attemptsto focus on market timing by examining the returns for portfolios constructedfrom bond market indexes using actual weights in various indexes versus year-average weights. The problem with this method is that the year-average weightsneed not correspond to a client’s ―neutral‖ weights . For example, what if the manager were optimistic over the entire year regarding long-term bonds? Her average weighting could reflect her optimism, and not a neutral position.c. Method III uses net purchases of bonds as a signal of bond manager optimism.But such net purchases can be motivated by withdrawals from or contributions to the fund rather than the manager’s decisions . (Note that this is an open-ended mutual fund.) Therefore, it is inappropriate to evaluate the manager based onwhether net purchases turn out to be reliable bullish or bearish signals.4. Treynor measure = 1788.1821.1-=5. Sharpe measure = (.24.08)0.888.18-=6. a. Treynor measures(106)(126)Portfolio X: 6.67S&P 500: 6.000.6 1.0--== Sharpe measures (.10.06)(.12.06)Portfolio X: 0.222S&P 500: 0.4620.18.13--== Portfolio X outperforms the market based on the Treynor measure, but underperforms based on the Sharpe measure.b. The two measures of performance are in conflict because they use differentmeasures of risk. Portfolio X has less systematic risk than the market, asmeasured by its lower beta, but more total risk (volatility), as measured by itshigher standard deviation. Therefore, the portfolio outperforms the market based on the Treynor measure but underperforms based on the Sharpe measure.7. Geometric average = (1.15 × 0.90)1/2 – 1 = 0.0173 = 1.73%8. Geometric average = (0.91 × 1.23 × 1.17)1/3 – 1 = 0.0941 = 9.41%9. Internal rate of return = 7.5% (CF 0 = -$2,000; CF 1 = $150; CF 2 = $2,150; Solve for IRR = 7.5%)10. d.11. Time-weighted average return = 1/2(1.15 1.1)112.47%⨯-=[The arithmetic mean is: 15%10%12.5%2+=]To compute dollar-weighted rate of return, cash flows are:CF0= −$500,000CF1= −$500,000CF2 = ($500,000 × 1.15 × 1.10) + ($500,000 × 1.10) = $1,182,500 Dollar-weighted rate of return = 11.71% (Solve for IRR in financial calculator).12. a. Each of these benchmarks has several deficiencies, as described below.Market index:∙A market index may exhibit survivorship bias. Firms that have gone out ofbusiness are removed from the index, resulting in a performance measure thatoverstates actual performance had the failed firms been included.∙A market index may exhibit double counting that arises because of companiesowning other companies and both being represented in the index.∙It is often difficult to exactly and continually replicate the holdings in themarket index without incurring substantial trading costs.∙The chosen index may not be an appropriate proxy for the management style ofthe managers.∙The chosen index may not represent the entire universe of securities. Forexample, the S&P 500 Index represents 65 to 70 percent of U.S. equity marketcapitalization.∙The chosen index (e.g., the S&P 500) may have a large capitalization bias.∙The chosen index may not be investable. There may be securities in the indexthat cannot be held in the portfolio.Benchmark normal portfolio:∙This is the most difficult performance measurement method to develop andcalculate.∙The normal portfolio must be continually updated, requiring substantial resources.∙Consultants and clients are concerned that managers who are involved indeveloping and calculating their benchmark portfolio may produce aneasily-beaten normal portfolio, making their performance appear better thanit actually is.Median of the manager universe:∙It can be difficult to identify a universe of managers appropriate for theinvestment style of the plan’s managers.∙Selection of a manager universe for comparison involves some, perhaps much,subjective judgment.∙Comparison with a manager universe does not take into account the risk takenin the portfolio.∙ The median of a manager universe does not represent an ―investable‖ portfolio; that is, a portfolio manager may not be able to invest in the median managerportfolio.∙ Such a benchmark may be ambiguous. The names and weights of the securities constituting the benchmark are not clearly delineated.∙ The benchmark is not constructed prior to the start of an evaluation period; it is not specified in advance.∙ A manager universe may exhibit survivorship bias; managers who have gone out of business are removed from the universe, resulting in a performance measure that overstates the actual performance had those managers been included.b. i. The Sharpe ratio is calculated by dividing the portfolio risk premium (i.e.,actual portfolio return minus the risk-free return) by the portfolio standarddeviation: Sharpe ratio = σP f Pr r - The Treynor measure is calculated by dividing the portfolio risk premium(i.e., actual portfolio return minus the risk-free return) by the portfolio beta: Treynor measure = βP fP r r -Jensen’s alpha is calculated by subtr acting the market risk premium, adjustedfor risk by the portfolio’s beta, from the actual portfolio excess return (riskpremium). It can be described as the difference in return earned by theportfolio compared to the return implied by the Capital Asset Pricing Modelor Security Market Line:α[β()]P P f P M f r r r r =-+-ii. The Sharpe ratio assumes that the relevant risk is total risk, and it measuresexcess return per unit of total risk. The Treynor measure assumes that therelevant risk is systematic risk, and it measures excess return per unit ofsystematic risk. Jensen’s alpha assumes that the relevant risk is systematicrisk, and it measures excess return at a given level of systematic risk.13. i. Incorrect. Valid benchmarks are unbiased. Median manager benchmarks,however, are subject to significant survivorship bias, which results in severaldrawbacks, including the following:∙ The performance of median manager benchmarks is biased upwards.∙ The upward bias increases with time.∙ Survivor bias introduces uncertainty with regard to manager rankings.∙ Survivor bias skews the shape of the distribution curve.ii.Incorrect. Valid benchmarks are unambiguous and can be replicated. The median manager benchmark is ambiguous because the weights of the individualsecurities in the benchmark are not known. The portfolio’s composition cannot be known before the conclusion of a measurement period because identification as a median manager can occur only after performance is measured.Valid benchmarks are also investable. The median manager benchmark is notinvestable —a manager using a median manager benchmark cannot forgo active management and simply hold the benchmark. This is a result of the fact that the weights of individual securities in the benchmark are not known.iii. The statement is correct. The median manager benchmark may be inappropriatebecause the median manager universe encompasses many investment styles and, therefore, may not be consistent with a given manager’s style.14. a. Sharpe ratio =σP f P r r - 22.1% 5.0%24.2% 5.0%: 1.02: 0.9516.8%20.2%Williamson Joyner S S --== Treynor measure =βP f P r r - 22.1% 5.0%24.2% 5.0%: 14.25: 24.001.20.8Williamson Joyner T T --== b. The difference in the rankings of Williamson and Joyner results directly from thedifference in diversification of the portfolios. Joyner has a higher Treynormeasure (24.00) and a lower Sharpe ratio (0.95) than does Williamson (14.25and 1.202, respectively), so Joyner must be less diversified than Williamson. The Treynor measure indicates that Joyner has a higher return per unit of systematic risk than does Williamson, while the Sharpe ratio indicates that Joyner has alower return per unit of total risk than does Williamson.。

forcast指标-回复本文将以"forcast指标"为主题，详细介绍和解释这一概念，并逐步回答相关问题。

第一部分：介绍forcast指标（200字）Forecast指标是指用于预测未来发展趋势的一组量化指标。

这些指标可以在不同领域和行业中使用，包括经济、金融、市场营销等。

通过分析历史数据和当前趋势，使用合适的数学模型和统计工具，Forecast指标可以提供对未来发展的预测和预测。

这使得企业和决策者能够更好地规划和决策，减少风险并优化资源利用。

第二部分：Forecast指标的类型（500字）Forecast指标具有多种类型，根据需要和数据可用性的不同，可以选择不同的指标来进行预测。

1. 经济预测指标：这些指标用于预测整体经济发展趋势，包括国内生产总值（GDP）、失业率、通货膨胀率等。

经济预测指标通常基于历史数据和复杂的经济模型，可以提供对未来经济发展方向的预测。

这对政策制定者和企业家来说非常重要，可以帮助他们做出明智的决策。

2. 市场预测指标：这些指标用于预测市场需求和竞争趋势，帮助企业决策者制定产品定价、市场推广和库存策略等。

市场预测指标可以包括市场份额、销售量、消费者行为等。

通过分析这些指标，企业可以更好地了解市场需求并做出相应的调整。

3. 财务预测指标：这些指标用于预测公司的财务表现，包括收入、利润、现金流等。

财务预测指标可以帮助企业评估业务的健康程度，识别潜在的风险和机会，并做出相应的决策。

这对投资者和股东来说尤其重要，可以帮助他们评估投资回报率并做出投资决策。

第三部分：使用Forecast指标的挑战（500字）虽然Forecast指标在预测未来发展方向方面非常有用，但也面临一些挑战。

1. 数据质量：预测的准确性取决于所使用的数据质量。

如果历史数据不准确或缺乏可靠性，那么预测结果也可能不准确。

因此，确保数据质量成为预测过程中的关键挑战之一。

2. 复杂性：分析和解释Forecast指标可能需要使用复杂的数学模型和统计工具。

ForecastingWhy forecast?Features Common to all Forecasts∙Conditions in the past will continue in the future∙Rarely perfect∙Forecasts for groups tend to be more accurate than forecasts for individuals ∙Forecast accuracy declines as time horizon increasesElements of a Good Forecast∙Timely∙Accurate∙Reliable (should work consistently)∙Forecast expressed in meaningful units∙Communicated in writing∙Simple to understand and useSteps in Forecasting Process∙Determine purpose of the forecast∙Establish a time horizon∙Select forecasting technique∙Gather and analyze the appropriate data∙Prepare the forecast∙Monitor the forecastTypes of Forecasts∙Qualitativeo Judgment and opiniono Sales forceo Consumer surveyso Delphi technique∙Quantitativeo Regression and Correlation (associative)o Time seriesForecasts Based on Time Series Data∙What is Time Series?∙Components (behavior) of Time Series datao Trendo Cycleo Seasonalo Irregularo Random variationsNaïve MethodsNaïve Forecast – uses a single previous value of a time series as the basis of a forecast.Techniques for Averaging∙What is the purpose of averaging?∙Common Averaging Techniqueso Moving Averageso Exponential smoothingMoving AverageExponential SmoothingTechniques for TrendLinear Trend Equationline the of slope at of value pe riod time for fore cast from pe riods time of numbe r spe cifie d =====b ty a ty t t where t t 0:Curvilinear Trend Equationline the of slope at of value pe riod time for fore cast from pe riods time of numbe r spe cifie d =====b ty a ty t t where t t 0:Techniques for Seasonality∙ What is seasonality?∙ What are seasonal relatives or indexes?∙ How seasonal indexes are used:o Deseasonalizing datao Seasonalizing data∙ How indexes are computed (see Example 7 on page 109)Accuracy and Control of ForecastsMeasures of Accuracyo Mean Absolute Deviation (MAD)o Mean Squared Error (MSE)o Mean Absolute Percentage Error (MAPE) Forecast Control Measureo Tracking SignalMean Absolute Deviation (MAD)Mean Squared Error (or Deviation) (MSE)Mean Square Percentage Error (MAPE)Tracking SignalProblems:2 – Plot, Linear, MA, exponential Smoothing5 – Applying a linear trend to forecast15 – Computing seasonal relatives17 – Using indexes to deseasonalize values26 – Using MAD, MSE to measure forecast accuracyProblem 2 (110)National Mixer Inc., sells can openers. Monthly sales for a seven-month period were as follows:(a) Plot the monthly data on a sheet of graph paper.(b) Forecast September sales volume using each of the following:(1) A linear trend equation(2) A five-month moving average(3) Exponential smoothing with a smoothing constant equal to 0.20, assuming March forecast of19(000)(4) The Naïve Approach(5) A weighted average using 0.60 for August, 0.30 for July, and 0.10 for June(c) Which method seems least appropriate? Why?(d) What does use of the term sales rather than demand presume?EXCEL SOLUTION(a) Plot of the monthly dataHow to superimpose a trend line on the graph∙Click on the graph created above (note that when you do this an item called CHART will appear on the Excel menu bar)∙Click on Chart > Add Trend Line∙Click on the most appropriate Trend Regression Type∙Click OK(b) Forecast September sales volume using:(1) Linear Trend Equation∙Create a column for time period (t) codes (see column B)∙Click Tools > Data Analysis > Regression∙Fill in the appropriate information in the boxes in the Regression box that appearsCoded time periodSales dataCoded time period(2) Five-month moving average(3) Exponential Smoothing with a smoothing constant of 0.20, assuming March forecast of 19(000)∙Enter the smoothing factor in D1∙Enter “19” in D5 as forecast for March∙Create the exponential smoothing formula in D6, then copy it onto D7 to D11(4) The Naïve Approach(5) A weighted average using 0.60 for August, 0.30 for July, and 0.10 for JuneProblem 5 (110)A cosmetics manufactur er’s marketing department has developed a linear trend equation that can be used to predict annual sales of its popular Hand & Foot Cream.y t =80 + 15 twhere: y t = Annual sales (000 bottles) t0 = 1990(a) Are the annual sales increasing or decreasing? By how much?(b) Predict annual sales for the year 2006 using the equationProblem 15 (113)Obtain estimates of daily relatives for the number of customers at a restaurant for the evening meal, given the following data. (Hint: Use a seven-day moving average)Excel Solution∙Type a 7-day average formula in E6 ( =average(C3:c9) )∙In F6, type the formula =C6/E6∙Copy the formulas in E6 and F6 onto cells E7 to E27∙Compute the average ratio for Day 1 (see formula in E12)∙Copy and paste the formula in E12 onto E13 to E18 to complete the indexes for Days 2 to 7Problem 17 (113) – Using indexes to deseasonalize valuesNew car sales for a dealer in Cook County, Illinois, for the past year are shown in the following table, along with monthly (seasonal) relatives, which are supplied to the dealer by the regional distributor.(a) Plot the data. Does there seem to be a trend?(b) Deseasonalize car sales(c) Plot the deseasonalized data on the same graph as the original data. Comment on the two graphs.Excel Solution(a) Plot of original data (seasonalized car sales)(b) Deseasonalized Car Sales(c) Graph of seasonalized car sales versus deseasonalized car salesProblem 26 (115) – Using MAD, MSE, and MAPE to measure forecast accuracyTwo different forecasting techniques (F1 and F2) were used to forecast demand for cases of bottled water. Actual demand and the two sets of forecasts are as follows:(a) Compute MAD for each set of forecasts. Given your results, which forecast appears to be the mostaccurate? Explain.(b) Compute MSE for each set of forecasts. Given your results, which forecast appears to be the mostaccurate? Explain.(c) In practice, either MAD or MSE would be employed to compute forecast errors. What factors might leadyou to choose one rather than the other?(d) Compute MAPE for each data set. Which forecast appears to be more accurate?Excel Solution。

The relationships between sentiment,returns and volatilityYaw-Huei Wang a ,Aneel Keswani b ,Stephen J.Taylor c ,*aNational Central University,Taiwan bCass Business School,City University,UKcDepartment of Accounting and Finance,Lancaster University,Lancaster LA14YX,UKAbstractPrevious papers that test whether sentiment is useful for predicting volatility ignore whether lagged returns information might also be useful for this purpose.By doing so,these papers potentially overestimate the role of sentiment in predicting volatility.In this paper we test whether sentiment is useful for volatility forecasting purposes.We find that most of our sentiment measures are caused by returns and volatility rather than vice versa.In addition,we find that lagged returns cause volatility.All sentiment variables have extremely limited forecasting power once returns are included as a forecasting variable.D 2005International Institute of Forecasters.Published by Elsevier B.V .All rights reserved.JEL classification:G12;G14Keywords:Causality;Investor surveys;Market based sentiment measures;Realized volatility;Stock index returns1.IntroductionWhilst earlier papers have underplayed the impor-tance of noise traders,more recent analysis has dis-cussed how such traders acting on a noisy signal,such as sentiment,can induce systematic risk and affect asset prices in equilibrium.For example,De Long,Shleifer,Summers,and Waldmann (1990)demon-strate that if risk averse arbitrageurs know that prices may diverge further away from fundamentals before they converge closer,they may take smaller positionswhen betting against mis-pricing.Thus if such unin-formed noise traders base their trading decisions on sentiment,then measures of it may have predictive power for asset price behavior.Most papers that test whether sentiment can predict returns or volatility motivate the relationship through the role of noise traders who respond to changes in sentiment influencing subsequent returns and volatil-ity.If this is in fact what happens in practice,then it might be possible to use sentiment to forecast returns and volatility.10169-2070/$-see front matter D 2005International Institute of Forecasters.Published by Elsevier B.V .All rights reserved.doi:10.1016/j.ijforecast.2005.04.019*Corresponding author.Tel.:+441524593624;fax:+441524847321.E-mail addresses:yhwang@.tw (Y .-H.Wang),a.keswani@ (A.Keswani),s.taylor@ (S.J.Taylor).1Forecasting realized volatility is important for a number of reasons.Firstly,the future behavior of realized volatility has an impact on current derivatives prices.Secondly,it is a required input for many models that calculate value at risk.For example,Risk-metrics requires a volatility estimate to calculate value at risk.International Journal of Forecasting 22(2006)109–123/locate/ijforecastCausality must run from sentiment to market be-havior if we accept the noise trader explanation.If we step back from the noise trader framework,however, and ask how sentiment might be generated,it is quite natural to expect that market behavior should influ-ence sentiment.Evidence of this was found by Brown and Cliff(2004)and Solt and Statman(1988)who document the fact that returns cause sentiment rather than vice versa.If returns have a strong impact on sentiment then it is also possible that volatility influ-ences sentiment as well.If this is the case we might observe a much stronger link between sentiment and returns or volatility if we do not assume that sentiment is the causal variable.Thus it is clearly important to test for the direction of causality.A failure to recognize the impact of market behav-ior on sentiment may also explain why all previous studies that test the predictive power of sentiment fail to include lagged volatility when predicting returns and omit lagged returns as an additional variable when predicting volatility.However,if sentiment responds to lagged volatility or lagged returns then it makes sense to include these variables to supplement any forecasting tests of sentiment.Doing so is likely to avoid overestimating the true forecasting power of sentiment.We test these ideas at a market-wide level by first looking at whether aggregate sentiment measures cause the returns and the realized volatility of the S&P100index as predicted by the noise trader liter-ature or whether sentiment simply responds to market behavior.In addition we test whether returns cause volatility.2After deciding on the variables that cause returns and volatility,we use these variables for fore-casting.This allows us to determine the incremental contribution of sentiment for forecasting.The analysis is conducted on both a daily and weekly basis.In the daily analysis,the sentiment indicators used include the S&P100(OEX)put–call trading volume ratio(PCV),the OEX put–call open interest ratio(PCO),and the NYSE ARMS index.3In the weekly analysis,the sentiment indicators used consist of PCO,PCV and two sentiment ratios gath-ered through surveys by two different investment information providers.4As a number of papers have found a significant relationship between changes in sentiment and returns or volatility,we investigate both sentiment and its first differences.Overall it is found that all sentiment measures are Granger-caused by returns and that many measures of sentiment are also caused by realized volatility.We show that the one sentiment measure,the ARMS index,which appears to consistently Granger-cause volatility,has only limited predictive power once returns are included.This study makes two particular contributions. Firstly,it indicates that research that seeks to exploit the potential market impact of noise traders is unlikely to be successful for returns and volatility forecasting. Secondly,it clarifies the relationship between returns, sentiment and realized volatility.In particular our results show that it is returns rather than sentiment that contain useful information for volatility forecast-ing purposes.This paper is arranged as follows.Section2dis-cusses why sentiment,returns and realized volatility might be related and how this relationship might manifest itself.Section3presents the data and explains the choice of variables chosen to proxy for investor sentiment.The methods used to test whether market behavior causes investor sentiment or vice versa are presented in Section4with the results of these Granger-causality tests.Section5presents the results of the volatility forecasting analysis.Finally, conclusions are stated in Section6.2.Theory and literatureDe Long et al.(1990)construct a model that explains why noise trader risk in financial markets is priced.They argue that whilst prices will revert to their fundamental values in the long term,this process may not be smooth and may take a long time.As a result,arbitrageurs can lose out if prices diverge further away from fundamentals before they get closer.Their model makes predictions about the2Our paper focuses on sentiment measured at the aggregate levelrather than the security specific level.3The ARMS index is named after its creator Richard Arms and is defined in Section3.More details can be found in Arms(1989).4We were unable to use a weekly measure of the ARMS index as the data is not compiled.Y.-H.Wang et al./International Journal of Forecasting22(2006)109–123 110relationship between sentiment and price volatility at the level of individual securities:more noise trading is associated with increased price volatility. Furthermore,sentiment will affect returns via its impact on volatility.If the signal that drives noise trading is sentiment,then we would expect to see a link between measures of sentiment and returns and volatility.The precise form in which sentiment will affect returns or volatility is not clear ex ante.If noise traders are sensitive to sentiment changes,then sentiment changes should drive returns and volatility.Alterna-tively,if noise traders only trade if sentiment is ex-treme(either high or low)relative to previous levels, then it might be expected that it is sentiment levels that influence returns and volatility.The predictive power of sentiment for returns has been explored in a number of papers.The results that have been found are mixed.Neal and Wheatley(1998),Simon and Wiggins (2001)and Wang(2001)find that sentiment can predict returns.Neal and Wheatley(1998)find that two measures of individual investor sentiment predict equity returns,one compiled from the discounts on closed-end funds and the other redemptions of mutual funds.Wang(2001)uses the positions held by large traders in the futures markets as a proxy for sentiment and discovers that they are useful for predicting the returns on futures in a subsequent period.Simon and Wiggins(2001)also find that sentiment measures are able to predict returns on futures.However,not all papers that have studied the relationship between sentiment and returns have come to these conclusions.Fisher and Statman (2000)find that the causality between equity returns and sentiment can be significant in both directions. Brown and Cliff(2004)use a large number of senti-ment indicators to investigate the relationship between sentiment and equity returns and find much stronger evidence that sentiment is caused by returns.Solt and Statman(1988)also make similar findings.Both papers tell us that returns may be important for sen-timent determination.A few papers have also investigated the relation-ship between sentiment and volatility.Brown(1999) looks at whether investor sentiment levels are related to the volatility of closed-end fund returns.As mea-sures of sentiment he uses both investor survey data and closed-end fund discounts.His results show that deviations from the mean level of sentiment are pos-itively and significantly related to volatility during trading hours.Lee,Jiang,and Indro(2002)look at the rela-tionship between volatility,returns and sentiment. They estimate a GARCH-in-mean model which includes contemporaneous shifts in investor senti-ment in the mean equation and lagged shifts in sentiment in the conditional volatility equation. They use the survey indicator provided by Inves-tor’s Intelligence to examine the impact of changes in investor sentiment on the conditional volatilities of the DJIA,S&P500,and NASDAQ indices, which are estimated from the GJR-GARCH model.They find that bullish(bearish)changes in sentiment result in downward(upward)adjustments in volatility.In summary therefore the literature tells us that sentiment may be useful for forecasting volatility.It also tells us that this relationship may be influenced by the behavior of returns.In our empirical analy-sis we do two things.Firstly,we examine the causality relationship between returns,sentiment and volatility.Secondly,we examine whether sen-timent measures are useful for forecasting returns and volatility.In contrast to previous studies our analysis uses realized volatility rather than a latent volatility measure estimated using a time series model.3.DataThe sample period used for daily data is from1 February1990until31December2001.Results are obtained for the full period and also for two sub-samples given by dividing the sample period into two equal parts around11January1996.This allows us to assess whether the results are robust through time.The weekly data is for the slightly shorter period from6April1990to28December2001and it is also divided into two equal sub-samples.We study measures of realized volatility,returns and indicators of market participants’sentiment at the daily and weekly frequencies.The methods used to gather this data and the measures of sentiment used are explained in this section.Y.-H.Wang et al./International Journal of Forecasting22(2006)109–1231113.1.Realized volatilityAndersen and Bollerslev (1998)show that the squared return can be a highly noisy measure of the realized variance of a financial asset’s return.Howev-er they also show that using the cumulative sum of high-frequency squared intraday returns can greatly mitigate the noisy component.5Five-minute S&P 100index returns are used to calculate a measure of daily realized volatility in this paper,as the 5-min frequency provides the best measure in Andersen and Bollerslev (1998).The latest observations available before 5-min marks from 09:30EST until 16:00EST are used to calculate 5-min returns.To construct the measure of daily realized variance,we sum the 78squared intra-day 5-min returns and the previous squared overnight return.For the weekly realized variance we average the daily realized variances by the number of trading days in the week.This procedure is used to avoid the bias induced by variations in the number of trading days in a week.3.2.Sentiment indicatorsThe sentiment indicators used are different for daily and weekly returns due to data availability.The daily sentiment indicators used consist of the OEX put–call trading volume ratio,the OEX put–call open interest ratio and the NYSE ARMS index.Whilst the put–call trading volume and open interest ratios are available on a weekly basis,6the ARMS index is not collated at the weekly frequency.As a result a weekly ARMS measure is not used in the analysis.This study does however use two additional sentiment indicators available on a weekly basis that are compiled from surveys by the American Associ-ation for Individual Investors (AAII)and Investor Intelligence (II).3.2.1.Put–call trading volume and open interest ratios The put–call trading volume ratio (PCV)is a mea-sure of market participants’sentiment derived from options and equals the trading volume of put options divided by the trading volume of call options.When market participants are bearish,they buy put options either to hedge their spot positions or to speculate bearishly.Therefore,when the trading volume of put options becomes large in relation to the trading vol-ume of call options,the ratio goes up,and vice versa.Another measure of the put–call volume ratio can be calculated using the open interest of options instead of trading volume.This ratio can be calculated on a daily basis using the open interest of options at the end of the day or on a weekly basis using the open interest of options at the end of the week.This might be a preferred measure of sentiment as it may be argued that the open interest of options is the final picture of sentiment at the end of the day or the week and is therefore likely to have better predictive power for volatility in subsequent periods.This measure of sentiment is therefore used as well.The put–call ratio calculated in this way is labeled the PCO ratio.3.2.2.ARMS indexThe ARMS index on day t is equal to the number of advancing issues scaled by the trading volume (shares)of advancing issues divided by the number of declining issues scaled by the trading volume (shares)of declining issues.It is calculated as:ARMS t ¼#Adv t =AdvVol t t t ¼DecVol t =#Dec tt twhere #Adv t ,#Dec t ,AdvVol t ,and DecVol t ,respective-ly,denote the number of advancing issues,the number of declining issues,the trading volume of advancing issues,and the trading volume of declining issues.ARMS can be interpreted as the ratio of volume per declining issue to the volume in each advancing issue.If the index is greater than one,more trading is taking place in declining issues,whilst if it is less than one more volume in advancing stocks outpaces the volume in each declining stock.Its creator,Richard Arms,argued that if the average volume in declining stocks far outweighs the average volume in rising stocks then the market is oversold and that this should be treated as a bullish sign.Likewise he argued that if the average volume in rising stocks far outweighs the average5Andersen and Bollerslev (1998)show that the more frequent theobservations,the more accurate the measure in theory.It is impos-sible in reality to obtain a continuous dataset because of the dis-continuities in the price process and the market microstructure effects such as bid-ask spreads and nonsynchronous trading effects.6Weekly PCV is calculated as the sum of daily put trading volume over the week divided by the sum of daily call trading volume over the week.Weekly PCO is the open interest calculated on the last trading day of the week.Y.-H.Wang et al./International Journal of Forecasting 22(2006)109–123112volume in falling stocks then the market is overbought and that this should be treated as a bearish sign.7 3.2.3.AAII and II ratiosSurveys of the bullishness or bearishness of inves-tors provide an alternative way to measure investor sentiment.The American Association for Individual Investors (AAII)has conducted a sentiment survey by polling a random sample of its members each week since1987. The respondents are asked whether they are bullish, bearish,or neutral about the future condition of the stock market in six months.Only subscribers to AAII are eligible to vote and they can only vote once during the survey period.8As the respondents to this survey are individuals,this can be interpreted as a measure of individual sentiment.The ratio of the bearish percent-age to the bullish percentage is used as a measure of investor sentiment in this paper.9Investor Intelligence(II)has compiled its sentiment data weekly by categorizing approximately150market newsletters since1964.Newsletters are read and marked starting on Friday each week.The results are reported as percent bullish,bearish,or neutral on the following Wednesday.10Since many of the writers of these newsletters are current or past market profes-sionals,the ratio of bullish to bearish responses com-piled by II can be considered as a proxy of institutional investors’sentiment.113.3.Summary statisticsTable1contains summary statistics of all the variables discussed in this section.12The statistics are presented for the full period and for two sub-periods of equal duration.The daily series of log realized volatility has high autocorrelations with a first-lag correlation of0.73for the full period.The weekly series of log realized volatility has a sim-ilar distribution to the daily series but has less kurtosis.Both daily and weekly returns display excess kurtosis,negative skewness and almost no serial correlation.The levels of all the sentiment indicators display a skewed and leptokurtic pattern,whilst the first differ-ences of all the indicators are also skewed and most are leptokurtic.All levels of sentiment indicators, except the ARMS index,have substantial positive autocorrelations,whilst the first differences have sig-nificant first-lag autocorrelations that are negative except for the II ratio.13Table2contains the contemporaneous correlations between the sentiment measures and the other vari-ables,namely returns and realized volatility.We find that ARMS has a substantial negative correlation with returns,betweenÀ0.7andÀ0.8for all periods considered.ARMS also has a small positive correla-tion with log realized volatility.The correlations are reduced when the first differences of ARMS are used.As regards the put–call volume ratios,we find that they are more correlated with returns than volatility.7The relationship between ARMS and whether the market is bearish or bullish may not be clear-cut.Let us suppose the market has been falling broadly across the majority of stocks and ARMS has risen.It is only if market participants perceive that the level of the market has reached a low enough point that a recovery will follow and only then can ARMS be treated as a bullish measure. Before that point is reached,high trading volume in declining stock may simply be treated as a sign that the market will continue to fall. 8The average response rate of the AAII survey is about50%with a standard deviation of15%.9AAII mails the questionnaires,and members fill them out and return them via US mail.Each week AAII collects responses from Friday to the following Thursday and reports the results on Thurs-day or Friday.10In the case of both the AAII and the II measures,there is a time lag between responses and reporting.If we want to look at the true relationship between sentiment in week t and subsequent market behavior it might be argued that we should actually work with the AAII or II measures reported1or2weeks ahead to overcome this reporting lag.Whilst these measures reported in week t+1or week t+2might more accurately reflect sentiment at week t,market participants would not have such information to hand in week t to predict subsequent market behavior.Hence in our analysis of the forecasting role of sentiment that follows we do not temporally adjust our AAII or II measures.11This point is made by Solt and Statman(1988).12In our analysis we work with the logarithm of realized volatility as in log form it is much closer to being normally distributed than the original variable(Andersen,Bollerslev,Diebold,&Ebens, 2001).13All the sentiment time series appear to be stationary,and all reject the unit root null hypothesis at the1%level(augmented Dickey Fuller test,with four lags).Interestingly the ARMS index has a low level of autocorrelation and so appears to be close to a white noise process.Thus it is not surprising that the first-lag autocorrelation of the first differences is close toÀ0.5.Y.-H.Wang et al./International Journal of Forecasting22(2006)109–123113Table 1Summary statistics VariableMeanStandard Skewness Kurtosis Autocorrelation deviationLag 12345Panel A:Daily data Log VFull sample À2.13630.20830.2936 3.36980.730.690.670.640.64Sub-sample 1À2.25650.15160.6928 4.04010.510.460.410.370.36Sub-sample 2À2.01600.1869À0.2175 5.72220.640.580.560.530.52ReturnsFull sample 4.44e À40.0103À0.1854 6.56750.01À0.03À0.040.02À0.04Sub-sample 1 4.17e À40.0075À0.0378 5.74710.00À0.02À0.060.03À0.01Sub-sample 2 4.71e À40.0125À0.2050 5.36080.01À0.04À0.040.02À0.05PCVFull sample 1.18200.2956 1.1053 5.65900.410.290.280.260.22Sub-sample 1 1.13370.24160.7595 3.95360.470.310.310.260.26Sub-sample 2 1.23030.3344 1.0355 5.22410.350.250.230.220.16D PCVFull sample 3.63e À40.3212À0.0378 6.0137À0.40À0.100.010.01À0.04Sub-sample 1 2.26e À40.24860.2293 4.7079À0.34À0.160.05À0.050.00Sub-sample 2 4.99e À40.3803À0.1103 5.2721À0.42À0.070.000.04À0.05PCOFull sample 1.22120.2609 1.1159 5.34530.900.850.810.770.74Sub-sample 1 1.19030.2271 1.3049 6.08210.860.810.770.730.70Sub-sample 2 1.25210.28750.9041 4.66440.930.880.830.790.75D PCOFull sample 9.61e À50.1146À2.778242.0705À0.25À0.03À0.01À0.04À0.01Sub-sample 1 2.75e À40.1207À2.994448.9912À0.33À0.04À0.01À0.01À0.02Sub-sample 2À8.25e À50.1081À2.451530.2010À0.14À0.03À0.02À0.08À0.01ARMSFull sample 0.99130.3524 2.035812.43000.060.060.010.040.05Sub-sample 10.97410.3324 1.781310.56520.010.02À0.010.020.07Sub-sample 2 1.00850.3706 2.191113.33340.100.090.020.050.02D ARMSFull sample 5.36e À40.4829À0.39938.7152À0.500.02À0.040.010.02Sub-sample 1 1.66e À40.4682À0.25027.9815À0.500.02À0.02À0.020.07Sub-sample 29.06e À40.4973À0.52359.2572À0.500.03À0.050.03À0.02VariableMeanStandard SkewnessKurtosisAutocorrelation deviationLag 12345Panel B:Weekly data Log VFull sample À1.76220.18780.4155 2.57810.820.750.720.700.69Sub-sample 1À1.88590.12570.7877 3.66710.600.520.490.460.46Sub-sample 2À1.63810.15500.2355 2.94760.730.600.530.480.45ReturnsFull sample 2.12e À30.0218À0.5237 5.2161À0.080.040.020.02À0.05Sub-sample 1 2.16e À30.0161À0.4397 5.8075À0.060.050.02À0.02À0.11Sub-sample 2 2.09e À30.0263À0.4956 4.1387À0.080.030.020.02À0.03AAIIFull sample 0.83500.5754 2.831415.61960.680.630.550.560.55Sub-sample 1 1.10120.7022 2.428511.20360.680.630.560.590.58Sub-sample 20.65920.32831.07433.96040.500.390.250.160.13Y.-H.Wang et al./International Journal of Forecasting 22(2006)109–123114The correlations between the volume ratio,PCV,and returns are more substantial for the daily frequency than the weekly frequency and they are similar for either the level or the change in PCV.The open interest ratio,PCO,has more substantial correlations for the weekly frequency than for the daily frequency;these are negative with returns and positive with volatility.There is evidence of non-negligible correlation between our survey based measures of sentiment and both returns and realized volatility.Small correla-tions are observed for both the levels and the first differences of the survey variables.4.Granger-causality tests4.1.MethodologyOn the way to investigating the predictive power of sentiment for returns and realized volatility,first we run Granger-causality tests to determine whether there exists any Granger-causality relationship among them.The results are given in this section. Then,in the next section,we try to discover if the sentiment measures that have a causal effect can be used for forecasting purposes.This requires us toTable1(continued)Variable Mean Standard Skewness Kurtosis Autocorrelationdeviation Lag12345 D AAIIFull sampleÀ7.28eÀ40.45990.206110.0813À0.420.03À0.120.040.07 Sub-sample1À1.46eÀ30.56240.25078.5421À0.430.03À0.150.080.10 Sub-sample28.35eÀ60.3274À0.1114 4.7991À0.390.02À0.04À0.06À0.02 IIFull sample0.82620.3265 1.5098 5.32710.950.870.800.730.66 Sub-sample10.97200.37320.9871 3.50650.940.860.790.720.65 Sub-sample20.67990.1770 1.2026 4.59970.890.740.570.400.26 D IIFull sampleÀ7.76eÀ40.1065À0.29428.27480.200.02À0.04À0.08À0.15 Sub-sample1À1.43eÀ30.1249À0.28487.58970.19À0.02À0.05À0.06À0.17 Sub-sample2À1.15eÀ40.0842À0.2360 5.86950.160.090.00À0.13À0.12 PCVFull sample 1.16410.20520.8466 4.98980.450.400.340.280.27 Sub-sample1 1.12380.18070.4290 2.80430.510.470.460.470.38 Sub-sample2 1.20450.22000.9626 5.45400.380.320.200.210.17 D PCVFull sample9.26eÀ40.21300.0298 4.2075À0.450.03À0.080.06À0.04 Sub-sample1 1.65eÀ40.17960.0281 3.4004À0.46À0.03À0.010.10À0.06 Sub-sample2 1.69eÀ30.24220.0250 4.0453À0.440.06À0.120.04À0.03 PCOFull sample 1.23630.2826 1.1681 5.12230.730.590.530.550.46 Sub-sample1 1.22040.2692 1.5662 6.81460.710.570.520.570.50 Sub-sample2 1.25220.29490.8455 4.01600.730.600.520.520.41 D PCOFull sample 4.89eÀ50.2090À0.9561 6.9610À0.26À0.13À0.160.21À0.01 Sub-sample1 4.33eÀ30.1902À0.9695 6.3796À0.28À0.12À0.190.30À0.06 Sub-sample2À4.25eÀ30.2266À0.9188 6.9846À0.23À0.11À0.100.17À0.04 This table shows summary statistics for the logarithm of realised volatility(Log V),returns and various sentiment measures.These are the put–call volume ratio(PCV),the put–call open interest ratio(PCO),the ARMS ratio and the survey based measures of the American Association for Individual Investors(AAII)and Investor Intelligence(II)defined in Section3.The full daily sample contains3005observations from1 February1990to31December2001,sub-sample1contains1503observations from1February1990to11January1996,and sub-sample2 contains1502observations from12January1996to31December2001.The symbol D represents the first difference.The full weekly sample contains613observations from6April1990to28December2001,sub-sample1contains307observations from6April1990to16February 1996,and sub-sample2contains306observations from23February1996to28December2001.Y.-H.Wang et al./International Journal of Forecasting22(2006)109–123115。

大学英语六级改革适用（阅读）模拟题2019年(26)(总分710,考试时间130分钟)Part III Reading ComprehensionSection ADrought, tsunami, violent crime, financial meltdown—the world is full of risks. The poor are often most【C1】______to their effects. Instead of【C2】______responding to crises, aid workers and policymakers should anticipate and help to guard against such rare and【C3】______disastrous events.After the world suffered major crises in 2008, the concept of risk management has gained 【C4】______in international development. The links between risk, livelihoods and poverty are all too clear. Mounting evidence shows that【C5】______shocks—above all, health and weather shocks and economic crises—play a major role in pushing households below the poverty line and keeping them there.But forward-thinking interventions can help【C6】______the costs of future shocks. Bangladesh offers a good example. In 1970, a large typhoon caused 300,000 deaths in Bangladesh. In 2007, a typhoon of the same【C7】______and strength caused only 4,000 deaths. The reason for the change was that the country had built a number of shelters. It went from having only 12 shelters in 1970 to having 2,500 in 2007. It also had a system of warning the population and a system of【C8】______these events.But risk management isn't just about lessening the effects of crises; it can also help people get ahead. Farmers in Ghana and India who had access to rainfall insurance were more likely to【C9】______in fertilizer, seeds, and other farming inputs, the report said, instead of sitting on their money to guard against potential future shocks.Several recent studies have predicted that extreme events will become **mon. If we fail to anticipate and plan for those events, then we could【C10】______giving up many of the development gains made over the past few decades.A)forecasting B)prominence C)optimum D)vulnerableE)guidelines F)motivate G)simply H)riskI)adverse J)invest K)offset L)paralyzingM)potentially N)primarily O)characteristics1. 【C1】2. 【C2】3. 【C3】4. 【C4】5. 【C5】6. 【C6】7. 【C7】8. 【C8】9. 【C9】10. 【C10】Section BSecret E-Scores[A]Americans are obsessed with their scores. Credit scores, G.P.A.'s, SAT's, blood pressure and cholesterol(胆固醇)levels—you name it. So here's a new score to obsess about: the e-score, an online calculation that is assuming an increasingly important, and controversial, role in e-commerce.[B]These digital scores, known broadly as consumer valuation or buying-power scores, measure our potential value as customers. What's your e-score? You'll probably never know. That's because they are largely invisible to the public. But they are highly valuable to companies that want—or in some cases, don't want—to have you as their customer.[C]Online consumer scores are calculated by a handful of start-ups, as well as a few financial services, that specialize in the flourishing field of predictive consumer analytics. It is a Google like business, one fueled by almost unimaginable amounts of data and powered by **puter algorithms(算法). The result is a private, digital ranking of American society unlike anything that **e before. A company, called eBureau, develops eScores—its name for custom scoring algorithms—to predict whether someone is likely to become a customer. Gordy Meyer, the founder and chief executive, says his system needs less than a second to size up a consumer and to transmit his or her score to an eBureau client.[D]It's true that credit scores, based on personal credit reports, have been around for decades. And direct **panies have long ranked consumers by their socioeconomic status. But e-scores go further. They can take into account facts like occupation, salary and home value to spending on luxury goods or pet food, and do it all with algorithms that their creators say accurately predict spending.[E]A growing number of companies, including banks, credit and debit card(借记卡)providers, insurers and online educational institutions are using these scores to choose whom to persuade on the Web. These scores can determine whether someone deserves a super credit card or a plain one, a full-service cable plan or none at all. They can determine whether a customer is routed promptly to an attentive service agent or moved to an overflow call center.[F]Federal regulators and consumer advocates worry that these scores could eventually put some consumers at a disadvantage, particularly those under financial stress. In effect, they say, the scores could create a new subprime class: people who are bypassed by companies online without even knowing it. Financial institutions, in particular, might avoid people with low scores, reducing those people's access to home loans, credit cards and insurance.[G]"The scoring is a tool to enable financial institutions to make decisions about financing based on unconventional methods," says David Vladeck, the director of the bureau of consumer protection at the Federal Trade Commission. "We are troubled by these practices."[H]Federal law governs the use of old-fashioned credit scores. Companies must have a legally permissible purpose before checking consumers' credit reports and must alert them if they are denied credit or insurance based on information in those reports. But the law does not extend to the new valuation scores because they are derived from nontraditional data and promoted for marketing. Ed Mierzwinski, consumer program director at the United States Public Interest Research Group in Washington, worries that federal laws haven't kept pace with change in the digital age.[I]"There's a nontransparent scoring system that collects information about you to generate a score—and what your score is results in the offers you get on the Internet," he says. "In most cases, you don't know who is collecting the information, you don't know what predictions they have made about you, or the potential for being denied choice or paying too much."[J]Here's how e-scores work: A client submits a data set containing names of tens of thousands of sales leads(线索)it has already bought, along with the names of leads who went on to become customers. EBureau then adds several thousand details—like age, income, occupation, property value, length of residence and retail history—from its databases to each customer profile. From those raw data points, the system calculates up to 50,000 additional variables per person. Then it searches thoroughly all that data for the **mon factors among the existing customer base. The result scores prospective customers based on their resemblance to previous customers.[K]E-scores might range from 0 to 99, with 99 indicating a consumer who is a likely return on investment and 0 indicating an unprofitable one. But in some industries, "knowing the bottom is more important than knowing the top," Mr. Meyer says. In online education, for instance, e-scores help schools distinguish prospective students who are not worth the investment of expensive course catalogs or attentive follow-up calls—like people who use fake names or adopt the identities of relatives. "If we can find 25 percent who have zero chance of enrolling, we can say 'don't waste your money on them,'" he says. EBureau charges clients 3 to 75 cents a score, depending on the industry and the volume of leads. Such scores increase the accuracy and speed with **panies can identify potential customers, says Mr. Weintraub of the LeadsCon conference. "Scores tell you 'this person might actually qualify, so let's focus on them,' " he says. "This way you are not focusing on people who really can't qualify."[L]Most people never see their value scores. But some services openly discuss how their measurements work. A case study on the eBureau site, for example, describes how **pany ranked prospective customers for a national prepaid debit card issuer, assigning each a score of 0 to 998. People who scored above 950 were considered likely to become highly profitable customers, generating revenue over six months of an estimated $213 per card. Those who scored less than 550 were predicted to be unprofitable clients, with estimated revenue of $74 or less. With eBureau's system, the card issuer could identify and court the high scorers while avoiding low scorers.[M]**panies, this kind of scoring clearly increases the speed and reduces the cost of acquiring customers. But consumers are paying a heavy price for that increased corporate efficiency, public interests advocates say. The digital scores create a two-tiered system that invisibly prioritizes some online users for credit and insurance offers while denying the sameopportunities to others, says Mr. Mierzwinski of the Public Interest Research Group.[N]Mr. Meyer and other eBureau executives disagree, saying the concerns are misplaced. EBureau, Mr. Meyer says, went to great lengths to build a system with both regulatory requirements and consumer privacy in mind. **pany, he says, has put firewalls in place to separate databases containing federally regulated data, like credit or debt information used for purposes like risk management, from databases about consumers used to generate scores for marketing purposes.[O]He adds that eBureau's clients use the scores only to narrow their field of prospective customers— not for the purposes of approving people for credit, loans or insurance. Moreover, he says, **pany does not sell consumer data to others, nor does it retain the scores it transmits to clients. "We are an evaluator," Mr. Meyer says. "We are trying to stay away from being intrusive to the consumer."[P]It's just another sign of the rise of what might be called the Scored Society. Google ranks our search results by our location and search history. Facebook scores us based on our online activities. Klout scores us by how many followers we have on Twitter, among other things. And now e-scores rank our potential value to companies.11. An executive of eBureau claims that **pany keeps the federally regulated data apart from those used to produce e-scores for marketing purposes.12. Federal regulators and consumer advocates share concerns that e-scores may negatively affect some consumers who are deliberately neglected by **panies.13. The e-score is a type of digital score which measures a consumer's buying power.14. The amount of fees eBureau asks for ranges from three to seventy-five cents per score.15. The e-score is just another indication of the rising Scored Society.16. E-scores do much more than evaluate consumers' socioeconomic status.17. According to a staff member of eBureau, **pany neither sells consumer data nor keeps the e-scores sent to its clients.18. EBureau cites the example of scoring potential customers for a prepaid debit card issuer to prove that its e-score measurement works.19. The calculation of the e-score involves a large quantity of data and relies on computers.20. There is no existing federal law that governs the use of e-scores.Section COffering a gift can be a mutual pleasure; some might say it should be a pleasure for giver and recipient. A problem with a **mercial Christmas, however, is that buying gifts can become a chore. Often it is a stress ridden chore in the dying days before Christmas Day, as everything gets left to the last minute.Why not make this next Christmas a time to make the choosing of individual gifts a pleasure for yourself, and for the recipient? Often in the last minute haste to buy gifts in time for Christmas Day, people become detached from not only the purpose, but the person to whom they are giving. Bought hastily in a crowded stress filled store, scarcely a thought may pass for the individual on the receiving end, however close they may be to you.Most of the year, if not all, can be filled with work, commuting, rushing here and there, stress,and self focus. How about time and attention for those who really matter in your life, whether spouse, offspring, other relatives, friends or colleagues? The choosing of a gift, and presentation of it, can be a silent way of giving each of them special attention, and then culminating with their pleasure at the receipt of the gift.Behind every good present there is a person who worked hard to make the best choice. The secret to buying the perfect gift is to think about the message you want to send out, when the receiver opens it. If you think about his or her hobbies, to his or her vacation plans etc., it means you have really studied that person and you bought the present precisely for them, for that occasion; in this case, Christmas.Friendship and caring are themselves a gift, so you can see that if you put some real selfless effort into choosing gifts, the value of the gift is magnified. That is something which will shine through the wrapping paper, and in the moment of giving the pleasure that you feel in making the gesture will radiate in the warmth of your expression. The choosing and the giving of a gift are inseparable.21. What makes buying gifts for Christmas become a chore?A. The pressure of the holiday.B. The lack of time for shopping.C. The increasing variety of gifts.D. **mercialization of the holiday.22. When buying gifts in the last minute, people tend to be concerned about______.A. choosing individual gifts for the recipientsB. what is the more suitable gift for a holidayC. getting a gift for everyoneD. to whom they send the gift23. A perfect gift differs from other gifts in their______.A. usefulnessB. meaningfulnessC. exquisitenessD. artistry24. The "gesture"(Line 3, Para. 5)most probably refers to______.A. giving the gift to the recipientB. choosing a gift with selfless effortC. wrapping the gift before sending itD. showing your concern to the recipient25. In this passage, the author is most likely to point out______.A. the best way of choosing and giving the right giftsB. the importance of thoughtfulness in choosing a giftC. that one's love for others can best be demonstrated by giftsD. that offering a gift benefits both the giver and the receiverDuring the next several weeks I **pletely to the wolves. I took a tiny tent and set it up on the shore of bay. The big telescope was set up in the mouth of the tent in such a way that I could observe the wolves by day or night.Quite by accident I had pitched my tent within ten yards of one of the major paths used by the wolves. Shortly after I had taken up residence, one of the wolves came back and discoveredme and my tent, but he did not stop or hesitate in his pace. Later, one or more wolves used the track past my tent and never did they show the slightest interest in me. I felt uncomfortable at being so totally ignored. The next day I noticed a male wolf make boundary markers by passing water on the rounds of his family lands.Once I had become aware of the strong feeling of property rights which existed among the wolves, I decided to use this knowledge to make them at least recognize my existence. One evening, after they had gone off for their regular nightly hunt, I staked out a property claim of my own, including a long section of the wolves' path. In order to ensure that my claim would not be overlooked, I made a property mark on stones, dumps of moss, and patches of vegetation with a lot of tea. Before the hunters came back, task was done, and I retired, somewhat exhausted, to observe results. A few minutes later the leading male appeared. As usual he did not bother to glance at the tent, but when he reached the point where my property line intersected the trail, he stopped as abruptly as if he had run into an invisible wall.Cautiously he extended his nose and sniffed at one of my marked bushes. After a minute of hesitation he backed away a few yards and sat down. Then, he looked directly at the tent and at me.His glare seemed to become fiercer as I attempted to stare him down. The situation was becoming intolerable. To break the impasse I turned my back on the wolf. Then briskly, and with an air of decision, he turned his attention away from me and began a systematic tour of the area.I had staked out as my own. As he came to each boundary marker he sniffed it once or twice, then carefully placed his mark on the outside of mine.26. The author is most probably a ______.A. hunterB. biologistC. journalistD. traveler27. Why did the wolves ignore the author's presence?A. Because his tent was out of the wolves' estate boundaries.B. Because the author has just arrived at the spot.C. Because the wolves were too busy to notice him.D. Because the wolves were afraid of strangers.28. Which of the following is TRUE about the wolves according to the passage?A. The wolves do not start attacking humans unless irritated by humans.B. The wolves go hunting at night and go back to the dens at dawn.C. The wolves do not rely on their sight to recognize boundaries.D. The wolves mark their family boundaries by means of water.29. The phrase "break the impasse"(Line 2, Para. 5)is closest to the meaning of "______".A. avoid being attackedB. break the iceC. trigger hostilityD. defeat the enemy30. Which of the following best explains the reason why the author stakes out an area of his own?A. He thought it better to be stared at than to be ignored.B. He didn't want the wolves to use the track past his tent.C. He wanted the wolves to take notice of his existence.D. He wanted to find out how fierce the wolf's glare was.。

注：文章仅用于英语学习，禁止用于其他用途！第一篇Shown in the artist’s rendering is the Mars Helicopter, a small, autonomous rotorcraft, will travel with NASA’s Mars Perseverance rover, currently scheduled to launch in July 2020, to demonstrate the viability and potential of heavier-than-air vehicles on the Red Planet.Destined to become the first aircraft to attempt powered flight on another planet, NASA’s Mars Helicopter officially has received a new name: Ingenuity. Vaneeza Rupani, a junior at Tuscaloosa County High School in Northport, Alabama, came up with the name and the motivation behind it during NASA’s “Name the Rover”essay contest（征文比赛）.Image Credit: NASA/JPL-Caltech（翻译）艺术家的渲染图上显示了火星直升机，这是一种小型的自动旋翼飞机，将与NASA 的毅力号火星车一起登陆火星。

毅力号火星车目前计划于2020年7月发射，以展示火星上比空气重的飞行器的可行性和潜力。

NASA的火星直升机将成为尝试在另一个星球上进行动力飞行的第一架飞机，现在它正式获得了一个新名字：Ingenuity。

专利名称：Weather forecast data distributing systemand method发明人：Masaya Mine申请号：US11117971申请日：20050429公开号：US20050246103A1公开日：20051103专利内容由知识产权出版社提供专利附图：摘要：Broadcast signal c is transmitted from a broadcast transmitter to an artificial satellite for predetermined signal conversion therein, whereby broadcast signals ato an are transmitted to the entire area of Japan. On-earth stations -to -are installed forweather forecast data. These on-earth stations receive the broadcast signals ato a, respectively, transmitted from the artificial satellite and transmit receiving state data bto b, respectively, based on receiving level data to the artificial satellite The receiving state signals are changed with variations of their radio wave attenuation according to whether it is cloudy, the cloud density, whether it rains or snows, etc. The receiving state data are collectively sent back to a weather forecast center Weather forecast data e obtained by analysis in the weather forecast center is transmitted to the artificial satellite which in turn sends weather forecast data f back to the full area in Japan.申请人：Masaya Mine地址：Kanagawa JP国籍：JP更多信息请下载全文后查看。