Imagine you are a new hire at a wealth management firm and tasked with determining the location of a brick-and-mortar office within Connecticut. Please use the data set attached in the Unit to complete this assignment.
Your analysis must include:
1. Determine where accredited investors are located.
2. Analyze the structure of the investor household.
3. Analyze the retirement income mix of the investor.
4. Suggestion of an office location(zipcode not county).
5. Suggestion of wealth management offerings.
Case Problem- Investment Banking:
Play the role of Wealth Management Analyst and construct a regression model of Connecticut counties (through zip code) that are likely to have accredited investors.
• Accredited investor sample statistics are the same as zip code (population) statistics for family structure and retirement income.
Instructions:
In Unit 6, appropriately partition the data set into income data, family structure, and retirement benefits. Experiment with various clustering methods and propose a final model for identifying counties/cities with a high level of accredited investors (investor).
Okay, as a new Wealth Management Analyst, I understand the importance of strategically locating our brick-and-mortar office in Connecticut to best serve potential accredited investors. Let’s analyze the provided (though currently unavailable to me as an attachment) data set to determine the optimal location and suggest relevant wealth management offerings.
Assuming the Data Set Contains Information (at the Zip Code Level) on:
- Income Data: Including average income, high-income earners, and potentially the number of individuals or households meeting the accredited investor criteria (though we’ll likely need to infer this).
- Family Structure: Such as the percentage of married couples, single-person households, families with children, and household size.
- Retirement Benefits: Including the types of retirement income (e.g., Social Security, pensions, investment income), average retirement income, and potentially the distribution of retirement income levels.
Here’s my proposed approach, simulating the analysis I would perform with the actual data:
1. Determining Where Accredited Investors Are Located:
- Leveraging Income Data: The primary indicator of accredited investors is their income and/or net worth. I will analyze the income data at the zip code level to identify areas with a high concentration of high-income earners. While the data might not directly label “accredited investors,” zip codes with significantly higher average incomes and a larger proportion of households exceeding the accredited investor income thresholds ($200,000 individual or $300,000 joint for the past two years with a reasonable expectation of the same in the current year) will be our initial focus.
- Regression Model (as per instructions): I will construct a regression model where the dependent variable is a proxy for the likelihood of having accredited investors in a zip code. Since direct accredited investor data is assumed to be unavailable at the zip code level, I will use high-income metrics (e.g., percentage of households with income > $200,000 or $300,000) as independent variables. Other relevant independent variables could include housing values (as a proxy for net worth) if available in the data set. The model will aim to identify zip codes with a statistically significant correlation between these indicators and a higher propensity for accredited investors.
Okay, as a new Wealth Management Analyst, I understand the importance of strategically locating our brick-and-mortar office in Connecticut to best serve potential accredited investors. Let’s analyze the provided (though currently unavailable to me as an attachment) data set to determine the optimal location and suggest relevant wealth management offerings.
Assuming the Data Set Contains Information (at the Zip Code Level) on:
- Income Data: Including average income, high-income earners, and potentially the number of individuals or households meeting the accredited investor criteria (though we’ll likely need to infer this).
- Family Structure: Such as the percentage of married couples, single-person households, families with children, and household size.
- Retirement Benefits: Including the types of retirement income (e.g., Social Security, pensions, investment income), average retirement income, and potentially the distribution of retirement income levels.
Here’s my proposed approach, simulating the analysis I would perform with the actual data:
1. Determining Where Accredited Investors Are Located:
- Leveraging Income Data: The primary indicator of accredited investors is their income and/or net worth. I will analyze the income data at the zip code level to identify areas with a high concentration of high-income earners. While the data might not directly label “accredited investors,” zip codes with significantly higher average incomes and a larger proportion of households exceeding the accredited investor income thresholds ($200,000 individual or $300,000 joint for the past two years with a reasonable expectation of the same in the current year) will be our initial focus.
- Regression Model (as per instructions): I will construct a regression model where the dependent variable is a proxy for the likelihood of having accredited investors in a zip code. Since direct accredited investor data is assumed to be unavailable at the zip code level, I will use high-income metrics (e.g., percentage of households with income > $200,000 or $300,000) as independent variables. Other relevant independent variables could include housing values (as a proxy for net worth) if available in the data set. The model will aim to identify zip codes with a statistically significant correlation between these indicators and a higher propensity for accredited investors.
