Caply Wealth Management Program

What you'll learn

• Module 1: Portfolio Engineering and Compounding Simulations
• This module teaches students how to apply quantitative portfolio construction and forward-looking projection modeling using the Caply: Wealth Planner App [152].
• Lesson 1.1: Dynamic Projection Modeling and Compounding Projections
• Learning Objectives: Build multi-variable wealth projection engines that simulate capital accumulation and cash flow dynamics [152].
• Syllabus Content:
• Formulating continuous compounding equations and calculating net-worth trajectories over a 10-year horizon [152].
• Setting savings thresholds, modeling career earnings paths, and projecting inflation-adjusted terminal wealth [152].
• Using Monte Carlo simulations to assess the probability of achieving targeted financial milestones under various market regimes [152].
• Practical Lab: Programming a continuous savings and compounding projection engine in Python, incorporating different inflation and savings rates [152].
• Lesson 1.2: Modern Portfolio Theory and Asset Allocation Strategies
• Learning Objectives: Design diversified, multi-asset portfolios aligned with client risk and return parameters [153].
• Syllabus Content:
• Applying mean-variance optimization, calculating asset covariance matrices, and mapping the efficient frontier [153].
• Evaluating strategic and tactical asset allocation strategies to manage downside risk [153].
• Managing portfolio tracking error and automating rebalancing schedules [153].
• Practical Lab: Constructing an optimized, multi-asset portfolio with quarterly rebalancing protocols using historical asset returns [153].
• Lesson 1.3: Formulating the Investment Policy Statement (IPS)
• Learning Objectives: Draft and review legally resilient Investment Policy Statements for private clients [154].
• Syllabus Content:
• Defining return targets, risk parameters, liquidity needs, investment horizons, tax considerations, and legal constraints [154].
• Translating qualitative client interviews into measurable quantitative portfolio targets [154].
• Practical Lab: Case study analysis of a client scenario to draft, refine, and present a professional-grade Investment Policy Statement [154].
• Module 2: The Alternative Asset Class Landscape
• This module provides a detailed analysis of non-traditional investments, preparing students to evaluate private market and alternative opportunities [155].
• Lesson 2.1: Private Equity and Venture Capital Architecture
• Learning Objectives: Analyze private equity and venture capital fund structures, deal terms, and exit mechanisms [155].
• Syllabus Content:
• Evaluating fund agreements, management fee structures, hurdle rates, and waterfall distribution models [155].
• Applying valuation models for early-stage startups, including discounted cash flow modifications, comparable transactions, and capitalization tables [155].
• Practical Lab: Evaluating a venture capital offering memorandum, calculating waterfall distributions, and modeling liquidation preferences [155].
• Lesson 2.2: Private Credit Markets and Distressed Debt
• Learning Objectives: Conduct credit analysis and structure private debt instruments [156].
• Syllabus Content:
• Assessing private debt structures, performing corporate credit due diligence, and evaluating loan covenants [156].
• Analyzing cash-flow generation and collateral valuation within distressed asset-backed debt structures [156].
• Practical Lab: Constructing a credit underwriting model for a mid-market enterprise, including loan covenants and risk analysis [156].
• Lesson 2.3: Fractional Real Estate and Tokenization Mechanics
• Learning Objectives: Evaluate fractional real estate offerings and analyze property tokenization structures [157].
• Syllabus Content:
• Structuring Special Purpose Vehicles (SPVs) for fractional property ownership [157].
• Calculating net operating income (NOI), capitalization rates, and internal rates of return (IRR) for commercial assets [157].
• Evaluating the liquidity, compliance, and secondary trading mechanics of tokenized property assets [157].
• Practical Lab: Performing due diligence on a commercial property fractional offering, including cash flow and legal reviews [157].
• Module 3: Cross-Border Wealth and GIFT City IFSC Regulations
• This module teaches students how to navigate international regulations and structure cross-border wealth solutions within GIFT City IFSC [158].
• Lesson 3.1: GIFT City IFSC Regulatory Frameworks
• Learning Objectives: Apply the draft IFSC Financial Advisers Regulations and Fund Management Regulations to cross-border capital structures [158].
• Syllabus Content:
• Evaluating operational and net worth requirements for Fund Management Entities (FMEs) and IFSC Banking Units (IBUs) [158].
• Analyzing registration, disclosure, and compliance guidelines under the International Financial Services Centres Authority (IFSCA) [158].
• Practical Lab: Structuring a compliant Category II Alternative Investment Fund within GIFT City under IFSC guidelines [158].
• Lesson 3.2: Compliance, KYC, and Anti-Money Laundering Architecture
• Learning Objectives: Design onboarding and transaction monitoring systems that comply with global FATF and IFSC standards [159].
• Syllabus Content:
• Verifying Ultimate Beneficial Ownership (UBO) under the updated 10% threshold [159].
• Structuring secure cross-border data transfer protocols and integrating IFSC KYC Registration Agencies [159].
• Practical Lab: Managing a mock client onboarding file for an offshore corporate client, resolving complex ownership and tax documentation [159].
• Lesson 3.3: Global Tax Planning and Wealth Preservation
• Learning Objectives: Design tax-efficient asset holding structures utilizing IFSC tax exemptions and Double Taxation Avoidance Agreements DTAA [160].
• Syllabus Content:
• Analyzing tax incentives: zero withholding taxes, pass-through status for Category I and II AIFs, and capital gains exemptions [160].
• Managing general anti-avoidance rules (GAAR) and cross-border estate tax planning [160].
• Practical Lab: Structuring an offshore family trust to hold Indian equities, optimized for capital gains tax exemptions [160].
• Module 4: Quantitative Portfolio Management and Systematic Trading
• This module covers systematic asset management, teaching students how to design, backtest, and deploy algorithmic trading strategies [161].
• Lesson 4.1: Python for Computational Finance
• Learning Objectives: Use Python to acquire, clean, and analyze high-frequency financial data [161].
• Syllabus Content:
• Using computational libraries, including Pandas, NumPy, and Matplotlib, to analyze financial datasets [161].
• Formulating and modeling quantitative market indicators, such as moving averages, volatility windows, and momentum [161].
• Practical Lab: Developing a systematic script to parse historic Nifty 50 equity data and compute trailing Sharpe and Sortino ratios [161].
• Lesson 4.2: Algorithmic Strategy Construction and Backtesting
• Learning Objectives: Build, backtest, and optimize systematic trading strategies while managing transaction costs and slippage [162].
• Syllabus Content:
• Designing systematic strategies, including trend-following, mean-reversion, pair trading, and index arbitrage [162].
• Implementing options strategies (such as butterfly spreads, credit spreads, and conversion-reversal structures) [162].
• Analyzing backtesting results to identify risk factors, including curve-fitting, survivorship bias, and transaction drag [162].
• Practical Lab: Modeling and backtesting an options butterfly strategy in Moneytrail’s simulated environment [162].
• Lesson 4.3: Quantitative Risk Management and Execution Systems
• Learning Objectives: Implement real-time risk controls and design trading workflows that align with exchange compliance guidelines [163].
• Syllabus Content:
• Managing trading system infrastructure, including market data feeds, order routing systems, and colocation mechanics [163].
• Designing algorithmic risk parameters, pre-trade risk controls, size limits, and automated emergency "kill switches" [163].
• Practical Lab: Stress-testing an algorithmic execution system in a mock live market under conditions of high volatility and latency [163].
• Module 5: Client Psychology and Family Governance
• This module teaches students how to address behavioral biases and coordinate family wealth transitions and estate plans [164].
• Lesson 5.1: Applied Behavioral Finance and Relationship Building
• Learning Objectives: Identify cognitive biases in client behavior and implement structured communication strategies to mitigate them [164].
• Syllabus Content:
• Analyzing behavioral concepts: loss aversion, overconfidence, herd behavior, anchoring, and mental accounting [164].
• Using behavioral questionnaires and scenario analysis to assess a client's actual risk tolerance [164].
• Practical Lab: Conducting a role-play exercise to manage a client’s panic-driven sell requests during a market correction [164].
• Lesson 5.2: Family Governance and Succession Planning
• Learning Objectives: Structure family governance frameworks to manage intergenerational wealth transitions [165].
• Syllabus Content:
• Drafting family constitutions, establishing family assemblies, and defining the role of the family office [165].
• Designing business succession plans to transition leadership from founders to next-generation members [165].
• Practical Lab: Drafting a family constitution and business succession plan for a multi-generational family enterprise [165].
• Lesson 5.3: Trusts, Wills, and Estate Architecture
• Learning Objectives: Structure comprehensive estate plans to protect assets and facilitate wealth transfer [166].
• Syllabus Content:
• Analyzing legal structures for estate planning: private trusts, public trusts, asset titling, and wills [166].
• Managing generation-skipping transfer taxes, inheritance regulations, and multi-jurisdictional estate laws [166].
• Practical Lab: Case study analysis of a client scenario to structure a multi-tier private trust with specific distribution criteria [166].

Curriculum