Paper Details: Group Assignment With A Minimum Group

Paper Detailsthis Is A Group Assignment With A Minimum Group Size Of T

This is a group assignment with a minimum group size of two and a maximum group size of three. All group members must be enrolled in the same tutorial. There will be two parts to this assignment: Part A: Group Work (A Business Report) and Part B: Individual Reflective Piece (A Ministerial Brief). The allocation of marks: Part A 30 (Statistical Analysis 22, Professional Report 8) and Part B 10 (Total 40). Group members will receive a common mark for Part A and an individual mark for Part B. The response to Part A must be provided as a business report no longer than 10 pages, including cover page, structured with a Title, Executive Summary, Introduction, and Conclusions. Part B must be a ministerial brief no more than 200 words, containing no more than three graphs, with student ID and name clearly indicated. Additionally, you must complete a Micro-credential on cross-cultural communication and include the badge copy in your submission. Submit an electronic copy combining group response to Part A and individual responses to Part B via Canvas. Only one submission per group is required; hard copies are not accepted. Show your work for calculations, using Microsoft Excel, and, if using Windows, employ the Data Analysis ToolPak; if using Mac with Excel 2011, use StatPlus:MAC LE. The assignment involves evaluating Bitcoin’s recent prices to determine whether individuals should invest, including graphical presentation, statistical calculations, hypothesis testing, and confidence intervals, along with comparative analysis with selected Australian securities.

Paper For Above instruction

The advent of blockchain technology has heralded transformative shifts in financial markets, notably through cryptocurrencies like Bitcoin. As an innovative decentralized ledger, blockchain enables secure peer-to-peer transactions without the necessity of a central authority, positioning it as a groundbreaking solution that promises enhanced market efficiency in terms of speed, cost, and security (Nakamoto, 2008). The question of whether individual investors should take an interest in Bitcoin depends on an in-depth analysis of its recent performance, statistical properties, and comparative value against traditional securities.

Part A of this assignment requires a comprehensive statistical analysis of Bitcoin’s weekly closing prices, as well as of selected Australian securities—National Australia Bank (NAB), Wesfarmers Limited, and Woodside Energy. The first step involves graphically presenting Bitcoin's weekly closing prices, with line graphs being the most appropriate to illustrate trends over time. Such plots facilitate visual identification of upward or downward movements, volatility, and potential cyclical patterns. An initial visual examination often reveals whether there is a clear trend, seasonality, or irregular fluctuations (Shmueli & Lichtendahl, 2020).

Next, calculating weekly returns offers insight into the relative changes from week to week. The formula \(\frac{\text{Current Price} - \text{Previous Price}}{\text{Previous Price}} \times 100\) quantifies percentage gains or losses, critical in assessing risk and profitability. Constructing a histogram of these returns allows further evaluation of their distribution, specifically whether they approximate normality—a common assumption in financial modeling (Mandelbrot & Hudson, 2004). Outliers, if present, can significantly influence the analysis and may indicate market anomalies or data errors, necessitating careful investigation.

Subsequently, descriptive analysis involves examining the location, shape, and spread of the weekly return data. Measures such as mean (average return), skewness (asymmetry), kurtosis (tail heaviness), and standard deviation (volatility) provide a comprehensive picture of the distribution's characteristics (Lütkepohl, 2005). Together, these metrics help interpret the risk-return profile of Bitcoin relative to traditional investments.

Calculating the empirical probability of a loss—i.e., the proportion of weeks with negative returns—serves as an indicator of downside risk (Bodie, Kane, & Marcus, 2014). A higher probability suggests greater volatility and risk for investors considering Bitcoin as part of their portfolio.

The analysis then extends to evaluating the same metrics for Australian securities, namely NAB, Wesfarmers, and Woodside. Comparing their weekly returns, distributions, and risk profiles enables an informed discussion about diversification benefits and risk exposure across asset classes.

Constructing 95% confidence intervals (CIs) for the mean returns provides statistical estimates of expected profitability, incorporating sampling variability. Changes in confidence level (90%, 99%) affect the interval's width, with higher confidence producing wider intervals. These intervals are essential for assessing whether the mean return is statistically different from zero, informing whether an investment is statistically significant or potentially due to chance (Lehmann & Casella, 1998).

Hypothesis testing evaluates the claim that Bitcoin’s return is 4%, versus the null hypothesis that the true return differs from this figure. Using a two-tailed test at the 5% significance level involves calculating the test statistic and comparing it to critical values (Walpole et al., 2012). Rejection or failure to reject the null hypothesis guides whether the observed data supports or contradicts the investment advisor’s assertion.

Part B requires writing a succinct ministerial brief of no more than 200 words, synthesizing the findings from Part A. The brief should articulate whether confident investment recommendations can be made for Bitcoin compared to one of the Australian shares, supported by evidence such as statistical measures, confidence intervals, and hypothesis test results. It should also incorporate relevant contextual information not explicitly covered in Part A, such as recent market developments or macroeconomic factors, to provide a comprehensive and credible assessment. The inclusion of up to three graphs aids visual communication of key findings, and each student’s individual brief should clearly state their name and ID to ensure clarity in group collaboration.

References

• Bodie, Z., Kane, A., & Marcus, A. J. (2014). Investments. McGraw-Hill Education.
• Lehmann, E. L., & Casella, G. (1998). Theory of Point Estimation. Springer.
• Lütkepohl, H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
• Mandelbrot, B., & Hudson, R. L. (2004). The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward. Basic Books.
• Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. https://Bitcoin.org/bitcoin.pdf
• Shmueli, G., & Lichtendahl Jr, K. C. (2020). Practical Time Series Forecasting with R: A Hands-On Guide. CRC Press.
• Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & Statistics for Engineering and the Sciences. Pearson.