10.2019年6月CFA2级Mock Exam AM（试题版）

2019 Level II Mock Exam AM The morning session of the 2019 Level II Chartered Financial Analyst Mock Examination has 60 questions. To best simulate the exam day experience, candidates are advised to allocate an average of 18 minutes per item set (vignette and 6 multiple choice questions) for a total of 180 minutes (3 hours) for this session of the exam. QuestionsTopicMinutes 16Ethical and Professional Standards18 712Quantitative Methods18 1318Financial Reporting and Analysis18 1924Corporate Finance18 2530Equity18 3136Fixed Income18 3742Derivatives18 4348Alternative Investments18 4954Portfolio Management18 5560Portfolio Management18 Total:180 By accessing this mock exam, you agree to the following terms of use: This mock exam is provided to currently registered CFA candidates. Candidates may view and print the exam for personal exam prepara- tion only. The following activities are strictly prohibited and may result in disciplinary and/or legal action: accessing or permitting access by anyone other than currently- registered CFA candidates; copying, posting to any website, emailing, distributing and/or reprinting the mock exam for any purpose 2018 CFA Institute. All rights reserved. 22019 Level II Mock Exam AM 2019 LEVEL II MOCK EXAM AM Lucas Thorpe Case Scenario One month ago, Lucas Thorpe, a portfolio manager for an investment management firm and a CFA Program Level II candidate, received a letter from Keiko Okada, CFA, the designated officer for the CFA Institute Professional Conduct Program (PCP). The letter explained that the PCP had received a complaint, accusing him of violating the CFA Institute Code of Ethics and Standards of Professional Conduct. Okada requested Thorpes cooperation, asking him to explain why he sold publicly traded Savanna Honey Products (Savanna) shares for both his personal and client accounts. Okada noted the anonymous complaint she received indicated that sales were executed one day after his research visit to Savanna and the day before Savanna released an earnings warning due to an expected significant drop in profit margins. In his defense, Thorpe responded in a letter to Okada as follows: “I arranged the research visit to Savanna as part of my routine review of the company. Were a small firm, so the portfolio managers do their own analysis. The earnings warning infor- mation I received from the chief financial officer (CFO) of Savanna was freely given; I didnt ask for it. The CFO even stated he had been giving the same information to any analyst who had visited in the last two days. My clients would have been harmed if I had not sold, because other managers would be selling before me. Besides, what I did is not illegal in my market. I treated my clients fairly; I sold Savanna shares for all my clients before I sold my own.” Concerned about the strength of his defense and to avoid any additional violations, Thorpe consulted with the firms compliance officer. Consequently, to support his claim that he did not violate Standard III (B): Fair Dealing and without violating his firms policies or any applicable local laws, Thorpe provided Okada copies of documents for all the trades executed for his clients, including contact details and the percentage of assets under management (AUM) the trades represented. Thorpe further stated in his letter to Okada that he received from the CFO six very large gift baskets full of high- end honey products worth USD100 per basket. He explained his firm has a very strict policy about accepting gifts valued at more than USD100 per gift. Thorpe accepted and distributed the gift baskets on behalf of himself and his five colleagues. However, he noted that the gifts in no way influenced his investment decision. Following Thorpes submission to the CFA Institute Professional Conduct Program, Okada informs him he has been found in violation of the CFA Institute Standards of Professional Conduct and will be publicly sanctioned and prohibited from future participation in the CFA Program exams. Thorpe contests the sanction and asks to present his case to a Disciplinary Review Committee Hearing Panel. While presenting his case, Thorpe mentions he regularly collects information he finds in the public domain when determining investment recommendations for his clients portfolios. He states that for “fast- moving consumer goods” (FMCG), he collects data by talking to industry experts who are former consultants of competing firms, making his own observations of the number of times grocery store shelves are restocked as well as gathering information from open specialty social media sites. Thorpe continues by informing the Hearing Panel that he worked with his firms compliance officer after the firm adopted the CFA Institute Code and Standards to enhance its policies regarding the handling of material non- public information. Currently the firm restricts proprietary and personal trading when portfolio managers 32019 Level II Mock Exam AM are in possession of material non- public information. Thorpe shares with the Hearing Panel the following draft policies being considered for adoption to ensure compliance with Standard II(A): Material Nonpublic Information: Policy 1 Portfolio managers are required to submit to the compliance officer all research reports distributed to clients. Policy 2 Heightened review of all trading when the firm is in possession of material non- public information is required. Policy 3 Receipt of potential material non- public information should be reported at the next earliest compliance meeting. Shortly after Thorpes presentation to the Hearing Panel, he states on his social media page, “Im desperate! Im so afraid Ill be permanently kicked out of the CFA Program. But Ive taken the following actions to protect myself no matter what the outcome: Ive written to all my clients to reconfirm my commitment to continuing educa- tion, but I left out the part about the potential sanction; I complained to my compliance officer about how unfair I thought the Hearing Panel process is in case my boss wants to fire me; and I advertised in the CFA Society newsletter to promote my new consulting prac- tice to help people going through a disciplinary review.” 1 As a result of Thorpes admission he traded in Savanna shares, which CFA Institute Standard of Practice will Okada least likely investigate for a possible violation? A Professionalism B Duties to Clients C Integrity of Capital Markets 2 Should Thorpe most likely revise how he submitted his Fair Dealing defense to avoid violating Standard III(E): Preservation of Confidentiality? A No. B Yes, he must delete the contact details. C Yes, he must remove the AUM percentage details. 3 Did Thorpe most likely violate CFA Institute Standard I: Professionalism by accepting the CFOs six gift packages? A No. B Yes, he violated Standard I(C): Misconduct. C Yes, he violated Standard I(B): Independence and Objectivity. 4 Which of Thorpes information- gathering techniques described to the Hearing Panel most likely requires him to exercise more care to avoid violating the CFA Institute Standards of Professional Conduct? A Data from social media B Use of industry consultants C Grocery turnover observations 5 Which of the draft policies concerning Standard II(A): Material Nonpublic Information should Thorpes firm most likely adopt? A Policy 1 B Policy 2 C Policy 3 42019 Level II Mock Exam AM 6 Which of Thorpes actions after the Hearing Panel presentation most likely vio- lated CFA Institute Standards? A His letter to his clients B His complaint to the compliance officer C His new disciplinary review consulting practice Litvenko Consulting Case Scenario Yuri Litvenko is the founder and primary analyst for Litvenko Consulting, a firm that specializes in analysis and modeling for investment advisories. Litvenko has recently taken on a new client, Linda Epstein. Epstein manages an equity fund and is seeking new strategies that will help her excel in picking companies for the fund that will out- perform the market. Litvenko suggests a quantitative approach to selecting securities. According to his research, a multiple regression can provide a useful screen for new stocks. He proposes the following model: Rt+1 = b0 + b1 (Rmt Rft) + b2 SMBt + b3 HMLt + b4 Dt + b5 RIt, where Rt+1 = the expected return on the security in the next period Rmt = the return on the relevant market index Rft = the risk- free rate SMBt = the excess return of the smallest- decile market- cap stocks over the biggest- decile ones HMLt = the excess return of the highest- decile book- to- market stocks over the lowest- decile ones Dt = the current dividend yield RIt = the companys earnings reinvestment rate Litvenko tells Epstein that he will use five years of quarterly historical data to esti- mate the model and advises her to select those securities with returns above her target threshold for further analysis. As an example, he estimates the model for Storcon, Inc., a building construction firm, and provides the results shown in Exhibit 1. Exhibit 1 Selected Regression Data for Storcon, Inc. CoefficientInput Value Intercept (b0)0.040 Market index (Rmt) Risk- free rate (Rft)0.780(0.161 0.034) Small minus big (SMBt; from FamaFrench Model)0.0252.96 High minus low (HMLt; from FamaFrench Model)0.1320.18 Dividend yield (Dt ) 0.1200.054 Reinvestment rate (RIt)0.0500.586 Epstein remarks that the model is fine, but it doesnt seem particularly unique. Additionally, she is concerned as to whether it matters that some of the variables, such as dividend yield and reinvestment rate, appear to be related to each other. 52019 Level II Mock Exam AM Epstein asks Litvenko whether it is possible to identify companies that are likely to outperform the market in the next period rather than just trying to predict the return for a company. He replies that his model as stated would not be a suitable approach for identifying outperformers, but there are ways to conduct such an analysis. Litvenko tells Epstein that he can use a dataset harvested from social media to develop better predictive models based on behavioral factors. While he has limited computing power and has not yet worked with the data, he believes that it offers a unique opportunity to implement innovative strategies if the right tools are used. Epstein agrees to a trial with the new data, and Litvenko considers how best to approach the problem using machine learning. He begins by creating the data description table shown in Exhibit 2 for those variables he would like to include, with the goal of creating a model to predict which mid- cap stocks will outperform the index. All variables will be measured continuously over a four- year period. Exhibit 2 List of Variables to Be Used in Machine Learning Trial Russell Midcap Index % change Pro- government posts, likes, shares/retweets Anti- government posts, likes, shares/retweets Russell Midcap Index individual component stock positive tweets, nega- tive tweets Selected mid- cap company stock returns List of mid- cap companies labeled as to whether they outperformed the index or not Before he can view meaningful results, Litvenko realizes he will have to train the machine learning model so that it follows the correct path. Because he is new to the machine learning approach, he begins by identifying the principles of model speci- fication and model training. He makes the following list of the steps he believes are involved in the machine learning model training process: 1 Find the appropriate underlying economic theory. 2 Establish training and validation samples. 3 Improve the classification accuracy of the model. Litvenko later studies the model created by the computer. While he is generally satisfied with its fit, he is concerned that the large number of variables used may mean that he is “overfitting” his model, with some variables adding little to its explanatory power. He is also convinced that the relationships he is observing between the binary outcome and the explanatory variables are non- linear. He considers alternatives to his modeling approach to address these concerns. 7 Based on the information provided in Exhibit 1, Epsteins estimate of the next period return on Storcon, Inc., is closest to: A 18.5%. B 22.5%. C 27.3%. 8 Epsteins concern regarding the relationship between the dividend yield and the reinvestment rate variables is most appropriately addressed by evaluating the: 62019 Level II Mock Exam AM A R2 and t-statistics. B DurbinWatson statistic. C BreuschPagan test results. 9 The most appropriate way to address the outperformance issue discussed by Epstein and Litvenko is to: A use a probit model. B add a dummy variable to the regression. C replace one or more of the independent variables with its logarithmic transformation. 10 Given Litvenkos resources and experience and using the variables shown in Exhibit 2, the type of machine learning he should use is best described as: A deep learning. B supervised learning. C unsupervised learning. 11 The item from Litvenkos list that best describes a step in the machine learning model training process is: A Item 1. B Item 2. C Item 3. 12 The concerns Litvenko has with his machine learning model can best be addressed using which of the following alternative modeling approaches? A CART approach B Clustering algorithm C Penalized regression technique Trana Case Scenario Marcus Eriksson, chief financial officer of Trana AB, and Katrina Lars, director of financial reporting, are preparing the companys 2015 annual report. Todays meeting is to discuss the transactions and disclosures related to Tranas foreign operations. Trana, which reports under International Financial Reporting Standards (IFRS), is a Sweden- based retailer operating stores in three geographic locations: Sweden, the eurozone (with a current presence only in France, Germany, and Italy), and the United States. The stores in the eurozone and the United States are operated through a wholly owned subsidiary in each region. Consistent with Swedish accounting prac- tice, the annual report includes separate financial statements for the parent company (Trana) and consolidated, or group, financial statements. The income statements are presented in Exhibit 1. Exhibit 1 Trana AB Income Statements for the Years Ended 31 December Consolidated Income Statement (SEK millions) Parent Income Statement (SEK millions) 2015201420152014 Sales30,20026,8924,7004,653 Cost of goods sold13,59012,6392,6002,650 Gross profit16,61014,2532,1002,003 72019 Level II Mock Exam AM Consolidated Income Statement (SEK millions) Parent Income Statement (SEK millions) 2015201420152014 Selling expenses10,8729,1431,5001,525 Admin. expenses1,5101,076260200 Operating profit4,2284,034340278 Net other costs/losses101523 Earnings before taxes4,2184,019338275 Income taxes1,1221,0717461 Net profit3,0962,948264214 Eriksson and Lars start the meeting by reviewing some of the relevant currency exchange rates, shown in Exhibit 2. The functional currency for the eurozone and US subsidiaries is the local currency (EUR and USD, respectively), thus the financial statements of both are translated using the current rate method. Both subsidiaries are consistently profitable. Exhibit 2 Exchange Rates SEK per EURSEK per USD Beginning 20148.436.32 Average 20148.5556.35 End of 20148.886.38 Average 20159.1257.595 End of 20159.318.81 Next, they review the performance and related disclosures by region. The number of stores operated in each region is shown in Exhibit 3. Exhibit 3 Number of Stores by Region YearEurozoneSwedenUnited StatesTotal 20143409980519 201540010080580 In preparation for the meeting, Lars looked at the US region and calculated the effect of the change in the SEK/USD exchange rate on the increase in sales from 2014 to 2015. Her notes include the following: In 2014, the sales per store, in SEK, were the same for both US and Swedish stores. The sales per US store in USD remained constant in 2015. Exhibit 1 (Continued) 82019 Level II Mock Exam AM Eriksson reminds Lars that Trana defines organic growth in retail as coming from two factors: 1 increasing the number of stores, and 2 increasing the sales per store in the local currency. He says that he wants to provide disclosures related to the organic growth rate in domestic sales per store, by region, and asks Lars to calculate it for the eurozone region where the sales figures (in millions) were SEK18,394 in 2014 and SEK21,640 in 2015. In 2012, at the start of Tranas expansion into North American markets, the com- pany established a subsidiary, Anart Inc., in a South American country to benefit from lower labor and shipping costs. The details of the Anart investment are as follows: Anart is 80% owned by Trana with 20% local investment. It sells all of its production to Trana and Tranas other subsidiaries and deter- mines the transfer price as full cost plus 5%. In 2015, sales (in millions) from Anart to Trana companies were SEK4,485 with net profit of SEK204. The corporate tax rate in the country is 10%. Throughout 2013, the South American country experienced high rates of infla- tion, approaching 30% per year. Trana had originally assumed that the high inflation rate was temporary, but it has shown no signs of decreasing and is now a concern. Eriksson and Lars discuss the impact of Anart on Tranas financial statements and Eriksson asks Lars: “Is the same accounting method being used this year to account for Anart in the consolidated financial statements as in prior years?” Eriksson reminds Lars that there is a proposal in Sweden to reduce the corporate tax rate from the current 22% to 16.5%. He would like to provide pro- forma disclosures related to the potential change in net income this change could provide for Trana. He reminds Lars that the average tax rate for the eurozone countri