Aathira J Maret

New York · NYU College of Arts and Sciences

Aathira J Maret

Economics major bridging academic theory and live market execution through valuation, financial modeling, and quantitative research.

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Education

Academic foundation

New York University — College of Arts and Sciences

Bachelor of Science; Economics Major (dual minor in Business and Psychology)

New York, NY · Aug 2023 – May 2027

  • Cumulative GPA: 3.5/4.0
  • CFA Institute: CFA Level I Candidate — awaiting results
  • Relevant coursework: Money and Banking, Intro to Econometrics, Management and Organizations, Urban Economics, Intermediate Macro/Microeconomics, Financial Accounting, Topics: Economic Research, Info Tech and Business

Corporate finance & projects

Selected work

M&A Financial Modeling & Valuation Case Studies

Personal Portfolio · Spring 2026

  • Built institutional-grade corporate finance models in Microsoft Excel to bridge academic theory and live market execution.
  • Constructed dynamic M&A accretion/dilution models to analyze pro forma financial impacts and structural synergies.
  • Evaluated transaction structuring variations, assessing optimal mixtures of debt and equity financing architectures.
  • Conducted standalone corporate valuations utilizing relative valuation and baseline financial modeling frameworks.

Macro Regional Credit Study — US Pacific Northwest

Personal Portfolio · Summer 2026

  • Engineered an automated financial analysis tool using Python, pandas, and yfinance to model solvency, interest coverage, and Altman Z-Scores across regional anchors (MSFT, NKE, INTC, BA); automated risk profiles into a visual report.

Product Lead & Co-Creator | Common Ground

Entrepreneurial Venture · Summer 2026

  • Spearheaded vision and prototyping for a privacy-first peer network (ages 18–25) that converts 1:1 sessions into an AI-anonymized video library; modeled core unit economics and defined a Phase 1 validation roadmap.

Professional & volunteer

Experience

Lines for Life

Youthline Worker (Presidential Volunteer Award Winner)

Portland, OR · Dec 2020 – May 2023

  • Provided critical, confidential crisis intervention and mental health support to adolescents across digital chat channels.
  • Deployed advanced active listening and rapid risk-assessment capabilities under highly stressful environments.
  • Collaborated with professional counselors to analyze intervention data and optimize crisis response frameworks.

Topgolf

Caddie / Front Desk

Hillsboro, OR · June 2023 – Aug 2023

  • Earned rapid promotion from Front Desk to Caddie based on rigorous service standards and operational reliability.
  • Managed facility workflows and floor logistics for 30 guest bays, coordinating up to 180 players concurrently.
  • Streamlined back-of-house hospitality logistics and team event operations to increase overall service efficiency.

Zara

Sales Associate

Portland, OR · Feb 2023 – June 2023

  • Optimized dressing room logistics and front-line queues to support a high-volume retail consumer operation.
  • Enforced meticulous visual merchandising compliance to drive floor performance and product visibility.

Valley Catholic School & SSMO Campus

Volleyball Coach

Beaverton, OR · Sept 2021 – Dec 2021

  • Structured and executed tactical training and practice regimens to accelerate skill acquisition and team synergy.
  • Handled stakeholder management as the primary liaison for family updates and organizational inquiries.

Capabilities

Skills & affiliations

Skills

Advanced Microsoft Excel, Corporate Valuation, Financial Modeling, Institutional Investment Frameworks, Quantitative Economics, High-Stress Crisis Management, High-Volume Workflow Logistics, Stakeholder Management & Leadership

Professional affiliations

CFA Society New York / CFA Institute

Certifications

Goldman Sachs Risk Job Simulation, Yale University Global Financial Crisis Course, Bloomberg Market Concepts (BMC)

Contact

Let’s connect

Contact details will be added here. Use the fields below once they are published.

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Location

Jupyter Lab · Summer 2026

Macro Regional Credit Study — US Pacific Northwest

Executed cells from credit-paw-evaluation.ipynb — interest coverage, net debt, Altman Z-Scores, and the plotted credit risk profile.

From the working notebook

Live excerpt of credit-paw-evaluation.ipynb — PNW anchors (MSFT, NKE, INTC, BA) with interest coverage, net debt, and Altman Z-Score credit profiles.

In [1]:

%pip install yfinance pandas openpyxl matplotlib
Requirement already satisfied: yfinance in /opt/anaconda3/lib/python3.13/site-packages (1.7.0)
Requirement already satisfied: pandas in /opt/anaconda3/lib/python3.13/site-packages (2.3.3)
Requirement already satisfied: openpyxl in /opt/anaconda3/lib/python3.13/site-packages (3.1.5)
Requirement already satisfied: matplotlib in /opt/anaconda3/lib/python3.13/site-packages (3.10.6)
Note: you may need to restart the kernel to use updated packages.

In [2]:

import yfinance as yf
import pandas as pd

In [3]:

pnw_portfolio = {
    "MSFT": "Microsoft Corp. (Redmond, WA)",
    "NKE": "Nike Inc. (Beaverton, OR)",
    "INTC": "Intel Corp. (Hillsboro, OR)",
    "BA": "Boeing Co. (Puget Sound, WA)"
}

In [4]:

print("Environment setup successful! Tickers loaded:")
for ticker, location in pnw_portfolio.items():
    print(f"- {ticker}: {location}")
Environment setup successful! Tickers loaded:
- MSFT: Microsoft Corp. (Redmond, WA)
- NKE: Nike Inc. (Beaverton, OR)
- INTC: Intel Corp. (Hillsboro, OR)
- BA: Boeing Co. (Puget Sound, WA)

In [5]:

def analyze_pnw_credit(ticker):
    company = yf.Ticker(ticker)
    bs = company.balance_sheet
    inc = company.financials
    
    def get_val(df, keys):
        for k in keys:
            if k in df.index:
                return df.loc[k].iloc[0]
        return 0.0

    assets = get_val(bs, ['Total Assets'])
    liab = get_val(bs, ['Total Liabilities Net Minority Interest', 'Total Liabilities'])
    eq = get_val(bs, ['Stockholders Equity', 'Total Equity Gross Minority Interest'])
    wc = get_val(bs, ['Working Capital'])
    re = get_val(bs, ['Retained Earnings'])
    cash = get_val(bs, ['Cash And Cash Equivalents', 'Cash Cash Equivalents And Short Term Investments'])
    debt = get_val(bs, ['Total Debt'])
    
    ebit = get_val(inc, ['EBIT', 'Operating Income'])
    interest = abs(get_val(inc, ['Interest Expense', 'Interest Expense Non Operating']))
    rev = get_val(inc, ['Total Revenue'])

    coverage = round(ebit / interest, 2) if interest != 0 else 999.0
    net_debt_m = round((debt - cash) / 1e6, 2)
    
    a = assets if assets != 0 else 1.0
    x1, x2, x3, x4, x5 = wc/a, re/a, ebit/a, eq/(liab if liab != 0 else 1.0), rev/a
    z = round((1.2 * x1) + (1.4 * x2) + (3.3 * x3) + (0.6 * x4) + (0.999 * x5), 2)
    
    status = "Safe / Low Risk" if z > 2.99 else ("Grey Zone" if z > 1.81 else "Distress / High Risk")

    return {
        "Ticker": ticker,
        "Interest Coverage": coverage,
        "Net Debt ($M)": net_debt_m,
        "Altman Z-Score": z,
        "Credit Profile": status
    }

# Run loop across PNW Portfolio
results = [analyze_pnw_credit(t) for t in pnw_portfolio.keys()]
df_output = pd.DataFrame(results)

In [6]:

display(df_output)
Ticker Interest Coverage Net Debt ($M) Altman Z-Score Credit Profile
0 MSFT 55.39 35891.0 2.68 Grey Zone
1 NKE 999.00 3470.0 2.28 Grey Zone
2 INTC 2.43 32320.0 1.60 Distress / High Risk
3 BA 1.95 43512.0 0.95 Distress / High Risk

In [7]:

import matplotlib.pyplot as plt

# Plot Altman Z-Scores
plt.figure(figsize=(8, 4))
plt.bar(df_output['Ticker'], df_output['Altman Z-Score'], color=['green', 'blue', 'orange', 'red'])
plt.axhline(y=2.99, color='green', linestyle='--', label='Safe Threshold (2.99)')
plt.axhline(y=1.81, color='red', linestyle='--', label='Distress Threshold (1.81)')
plt.title('Pacific Northwest Corporate Credit Risk Profile (Altman Z-Score)')
plt.ylabel('Z-Score')
plt.legend()
plt.show()
Bar chart of Altman Z-Scores for MSFT, NKE, INTC, and BA from the notebook
Notebook figure: Pacific Northwest Corporate Credit Risk Profile (Altman Z-Score).

Source notebook: credit-paw-evaluation.ipynb — code and outputs shown as executed in Jupyter Lab.