Instructions• This assignment accounts for 15% of your fifinal result for the course. It will be marked out of a maximum total of 45 points. • This is an individual assignment. You are not allowed to copy a classmate’s assignment (or to borrow the bulk of the material from a classmate’s assignment). You are required to perform the full assignment on your own and hand in independently. • This assignment is due by 5pm on Friday November 6, 2020. Upload your assignment through Canvas. • Upload a single compressed (ZIP format) fifile containing your R scripts, your data, and your report (as a PDF document). • When decompressed (unzipped) all documents should be in the one folder. The PDF report, and the scripts must all include your student ID. • The fifile name for the single compressed fifile must also include your student ID. The single compressed fifile that you upload to the Canvas assignment must have a “.zip” fifile extension. • Be concise in your report (limit it to 1,500 words). Use tables and graphs where appropriate. • The assignment involves preparing a report for a hypothetical “client” that is a large fifinancial institution with the ability to borrow and lend at or very near to interbank interest rates. If you have questions to put to the “client”, raise them on the Assignment topic of the Ed discussion forum. For fairness, questions for the “client” will not be answered in any other forum (tutorials/consultation hours/emails etc.). 1Background You have been asked to assess an equity trading algorithm. Your client has dismissed the quant. responsible for designing the algorithm because the documentation was inadequate. The client now wants to determine whether they should, adopt, modify and then adopt, or write-offff the cost of developing the trading algorithm. The trading algorithm has been implemented in a set of R scripts. You will need to be able to run these (and modify them in places) to gather the information required for your report. The scripts are documented in more detail below. Each month, the algorithm rebalances the weights on a selection of Australian equities to form a fully-invested portfolio. Your report must assess: 1. the suitability of the input data; [15/45 points] 2. the trading algorithm itself; and [10/45 points] 3. the performance evaluation of the trading algorithm when used to manage a portfolio with an initial value of $10,000,000. [10/45 points] The report should also suggest ways in which the algorithm could be improved if it is adopted and reasons for not using it if the recommendation is to not use the trading algorithm. [5/45 points] Conclude your report with a recommendation for your client about how to proceed with the trading algorithm. [5/45 points] In evaluating the input data: • State the stocks that the trading algorithm is allowed to invest in. • Identify any flflaws in or problems with the data sourced from Yahoo Finance and the Reserve Bank of Australia. • Describe the properties of the data (using graphs where appropriate) and their implications for the trading algorithm. In your analysis of the data: 1. Rank the individual stocks by their average monthly excess returns over the risk free rate. 2. Rank the individual stocks by their monthly excess return standard deviations. 3. Rank the individual stocks by their Sharpe ratios. 4. Examine the correlations between the excess returns on the stocks. 5. Assess the appropriateness of assuming that excess returns are normally distributed. 26. Address the stability over time of the distribution of excess returns. • Identify any extreme outliers and assess whether they have been suitably handled in the data preparation process. • Assess the adequacy of the way in which monthly returns have been computed. • Assess the adequacy of the way in which missing values have been handled. In evaluating the trading algorithm: • Describe the features of the trading algorithm and their impact on the portfolio weights. • Indicate any features of the trading algorithm or its development process that are likely to compromise the informativeness of its performance assessment. In evaluating the performance assessment for the trading algorithm: • State how likely it is that the predicted profifitability of the trading algorithm will be informative about performance into the future and explain your view. • Include in you report, a 1-month-ahead 99th percentile Value-at-Risk measure computed for the portfolio that would be held at the end of the available data if the trading algorithm were to be adopted for an initial investment of $10,000,000. Explain and justify your decisions when deciding how to compute the VaR. If you suggest improvements to the algorithm, describe and motivate the recommended changes in your report.
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