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      代寫BUSANA 7003、代做Python/Java語言編程
      代寫BUSANA 7003、代做Python/Java語言編程

      時間:2024-09-08  來源:合肥網hfw.cc  作者:hfw.cc 我要糾錯



      BUSANA 7003 – Business Analytics Project – Semester 2, 2024 

      Final Project 
      Background. 
      You are starting a new job as a Business Analyst at AQR Asset Management, a global investment 
      management firm focused on quantitative investment strategies. Your first task is to analyse the 
      performance of US-listed securities to help the firm manage their portfolio. You have several datasets 
      on US securities (stocks and ETFs), and you should help your employer with the following tasks: 
       
      (1) Exploratory data analysis, visualisations, descriptive statistics 
      Examine the dataset Stock_data_part1.xlsx. Characterise the performance of US securities between 
      February 14, 2020, and March 20, 2020, and compare their performance to a similar period not affected 
      by the COVID-19 pandemic. Justify how you choose the non-COVID period to make a fair comparison. 
      Based on your analysis, comment on the effect of the COVID-19 pandemic on securities’ performance. 
      In your analysis, you should consider the following: 
      • Which variables to analyse? Consider the following: returns, volatility, Sharpe ratio, bid-ask 
      spread, dollar volume, number of trades. You should do your own research and also use the 
      course content to understand these variables, and potentially add others, which could be helpful 
      for understanding the market dynamics during the COVID-19 pandemic and beyond it. 
      • What is the data structure (unit of observation) that you need for this analysis? (Cross-sectional? 
      Time series? Panel?) How to transform your data into the units of observation that you need? 
      • How can you add value with your analysis? Can you offer relevant comparisons of returns or 
      other variables across different groups (e.g., COVID vs non-COVID period, by industry, by 
      company size, ETFs vs stocks etc.)? 
       
      Use your own judgement to select the relevant descriptive statistics and visualisations. However, at a 
      minimum, you should consider the following: 
      • A table with descriptive statistics (mean, median, 25
      th
       percentile, 75
      th
       percentile, standard 
      deviation, min, max) of the key variables you choose to analyse. (Add comparisons by group if 
      relevant). 
      • The time series of the key variables of interest (Add comparisons by group if relevant). Make 
      sure you cover the entire time period of the sample. You can add analysis by sub-period (e.g., 
      COVID vs non-COVID periods) as an additional point. 
       
       
      (2) Supervised Machine Learning – OLS regressions 
      Transform your data into a cross-section: for each security, compute returns between February 14, 2020, 
      and March 20, 2020, and run regression analysis to help explain returns. Then, do the same for returns 
      between December 14, 2019, and January 20, 2020. You may use any variables in the existing dataset, 
      or variables outside the provided datasets (from external sources). For example, you may consider the 
      following external sources: BUSANA 7003 – Business Analytics Project – Semester 2, 2024 
       
      - Robinhood stock popularity history: https://www.kaggle.com/datasets/cprimozi/robinhoodstock-popularity-history?resource=download
      (historical data about the number of users that 
      hold each stock available on the Robinhood stock brokerage), 
      - Stock exposure to the market, and to Fama-French factors. The time series data on Fama-French 
      factors is available here: 
      - https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html NOTE: you need to 
      first run OLS regressions of stock returns on 3 Factors (Mkt, HML, SMB), and compute betas 
      (factor exposures) for each stock. Then, you can use those betas as explanatory variables in 
      your SML analysis. 
       
      Please, make sure to split the dataset you use for analysis into training and testing samples, and comment 
      on the model accuracy. Please, analyse the following: 
      - Are you better able to predict returns between February 14, 2020, and March 20, 2020 or returns 
      between December 14, 2019, and January 20, 2020? Why? 
      - Are you better able to predict returns between February 14, 2020, and March 20, 2020 for stocks 
      or for ETFs? Why? 
       
      In your analysis, you should consider the following: 
      • Which explanatory variables to use? 
      • Over which period of time to compute the explanatory variables? Imagine it is February 14, 
      2020, and you know nothing about the future returns, market caps, volumes, investor interest 
      etc. You are tasked to develop a SML model to help predict returns between February 14, 2020, 
      and March 20, 2020. How can you use the data from the past to predict the future? 
      • Consider running separate models for stocks vs ETFs. Think about which explanatory variables 
      are more relevant for each group. 
       
      • What is the data structure (unit of observation) that you need for this analysis? (Cross-sectional? 
      Time series? Panel?) How to transform your data into the units of observation that you need? 
      • How well does your model perform? Can you offer relevant comparisons of your model 
      accuracy across different groups (e.g., COVID vs non-COVID period, by industry, by company 
      size, ETFs vs stocks etc.)? 
       
      In your analysis, at a minimum, you should present the following output: 
      • Regression coefficients from multiple models (at least five). 
      • Model evaluation metrics: MSE, RMSE, MAE for each model. 
      • Your assessment of which factors are most important for explaining stock returns between 
      February 14, 2020, and March 20, 2020, and between December 14, 2019, and January 20, 
      2020. 
      • Your assessment of which factors are most important for explaining ETF returns between 
      February 14, 2020, and March 20, 2020, and between December 14, 2019, and January 20, 
      2020. 
       BUSANA 7003 – Business Analytics Project – Semester 2, 2024 
       
      (3) Supervised Machine Learning – Logistic regressions 
      Use the same dataset as in part (2), and introduce a dummy variable for whether a given security 
      increased in price between February 14, 2020, and March 20, 2020. Model the probability of a price 
      increase using any continuous or categorical variables you find relevant. You may use any variables in 
      the existing dataset, or variables outside the provided datasets (from external sources). 
      Please, make sure to split the dataset you use for analysis into training and testing samples, and comment 
      on the model accuracy. 
       
      In your analysis, you should consider the following: 
      • How does the industry variable affect whether a given security increased in price between 
      February 14, 2020, and March 20, 2020? 
      • How does being a stock or an ETF affect whether a given security increased in price between 
      February 14, 2020, and March 20, 2020? 
      • Imagine we are facing a replay of the COVID-19 pandemic now, and you are asked to predict 
      whether your testing sample securities will increase or decrease in price, based on what you 
      learnt from your analysis over February 14, 2020, and March 20, 2020. Comment on which 
      factors are affecting this. 
       
      In your analysis, at a minimum, you should present the following output: 
       
      • Regression coefficients from multiple models (at least five). 
      • Model evaluation metrics: accuracy, precision, recall for each model. 
      • Your assessment of which factors are most important for explaining whether a given security 
      increased in price between February 14, 2020, and March 20, 2020? 
       
       
      (4) Unsupervised Machine Learning – K-means clustering 
      Examine the dataset Stock_data_part2.xlsx to perform the k-means clustering. You may combine 
      Stock_data_part2.xlsx dataset with any other data (e.g., returns) from the previous exercise. The key 
      part of this analysis is finding clusters if stocks with similar characteristics (financial ratios), and 
      showing whether their returns during between February 14, 2020, and March 20, 2020 were 
      significantly different. How about their returns during December 14, 2019 - January 20, 2020? 
      In your analysis, you should consider the following: 
      • Variable Selection: Which two financial ratios did you select as the most important variables 
      for clustering, and why? 
      • Cluster Characteristics: What are the average values of the selected financial ratios for each 
      cluster? Present a table showing the average values of the selected financial ratios for each 
      identified cluster. 
      - Cluster Visualization: How can we visualize the distribution of the clusters in a twodimensional
       space using the selected financial ratios? 
      - Provide a scatter plot where each point represents a stock, coloured by cluster, using the two 
      selected financial ratios on the axes. BUSANA 7003 – Business Analytics Project – Semester 2, 2024 
       
      In your analysis, at a minimum, you should present the following output: 
       
      • Elbow plot and cluster visualisation in two-dimensional space. 
      • How returns differ across cluster, with comparison of two periods: February 14, 2020 - March 
      20, 2020, vs December 14, 2019 - January 20, 2020. 
      • Your assessment of investment implications. Which stocks should AQR invest in, if market 
      conditions are similar to those on February 14, 2020, vs those on December 14, 2019? 
       
      (5) Unsupervised Machine Learning – Principal Component Analysis 
      The lead asset manager asks you to distil a variety of market-wide factors to the core Principal 
      Components. The Principal components will be used to explain average monthly returns on S&P500 
      index. You may combine Stock_data_part3.xlsx dataset with any other data (e.g., valuation ratios, 
      market returns, Fama-French factors (external), interest rates (external) etc.), and analyse the principal 
      components to understand the factors driving market returns. 
      In your analysis, you should present the following output: 
      • A detailed summary of the principal components extracted from the PCA, including the amount 
      of variance explained by each component. This should include a scree plot or a table 
      summarizing the eigenvalues and the percentage of variance explained by each principal 
      component, helping to identify the most significant factors affecting monthly returns. 
      • Factor Loadings: For each principal component identified as significant, provide the factor 
      loadings of the original variables (e.g., valuation ratios, market returns, Fama-French factors, 
      interest rates). This will show how each original variable contributes to the principal 
      components, indicating which factors are most influential in driving returns. 
      • Factor Interpretation: An interpretation of what each significant principal component represents 
      in the context of market-wide and company-specific factors. For example, the first principal 
      component might be interpreted as overall market risk, while the next components could 
      represent size and value factors, sector exposures, or interest rate sensitivity. This section 
      should bridge the mathematical output of PCA with intuitive financial concepts. 
      • Implications for Monthly Returns: Discuss how each principal component influences monthly 
      returns on S&P500 index. This could involve analyzing how movements in the principal 
      components are associated with changes in market returns, providing insights into the 
      underlying risk factors or market conditions that impact asset prices. 
       
       (6) Analysing experimental evidence using Difference-in-Difference regressions 
      AQR wants to analyse the effects of the SEC Tick Size Pilot program, and find out how the securities 
      in test groups were affected in terms of relative bid-ask spread. Use monthly data from 
      Stock_data_part3.xlsx and the list of treatment and control securities (https://www.finra.org/rulesguidance/key-topics/tick-size-pilot-program)
      to investigate this question. 
      In your analysis, you should present the following output: 
      • A regression specification with detailed explanation of the null hypothesis and alternative 
      hypothesis. 
      • Regression output and interpretation of coefficients. BUSANA 7003 – Business Analytics Project – Semester 2, 2024 
       
      • Implications for AQR trading strategy. How should AQR adjust their trading in treated and 
      control stocks to minimise their transaction costs? 
       
      (7) Summary 
      Based on your earlier analysis, draw conclusions for AQR that tackle the following overarching 
      questions: 
      - What are your recommendations to AQR about their stock and ETF selection strategy, if they 
      found themselves in market conditions similar to pre-COVID (e.g., February 14, 2020)? 
      - How accurate are your ML models in predicting the price changes, and how confident are you 
      in providing recommendations based on those models? 
      - What does your analysis reveal about the market dynamics around the COVID-19 period, and 
      how it differs across different groups of securities? 
      - Which variables from the dataset are most important in their contribution to the variation in 
      S&P500 returns? 
      - What did you learn about the way the effect on liquidity, as a result of the Tick Size Pilot? 

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