Treasury Management Roles

Financial Derivatives: A financial derivative is a financial contract whose value is derived from the value of an underlying asset, such as shares, bonds, currencies, commodities, interest rates, or market indices. Derivatives are used by investors, companies, and financial institutions to manage risk, earn profits, or protect against future price fluctuations. Features of Financial Derivatives Derived Value 1)The value of a derivative depends on the underlying asset. 2)Future Settlement Contracts are usually settled on a future date. 3)Risk Management Tool Helps reduce financial risk through hedging. 4)Leverage Small investment can control large amounts of assets. Transfer of Risk 5)Risk can be transferred from one party to another. Types of Financial Derivatives 1. Forward Contract A private agreement between two parties to buy or sell an asset at a future date at a predetermined price. Example: A farmer agrees to sell wheat after 3 months at a fixed price. 2. Futures Contract A standardized contract traded on stock exchanges to buy or sell assets at a future date. Example: An investor buys gold futures expecting gold prices to rise. 3. Options Contract Gives the buyer the right, but not the obligation, to buy or sell an asset. Call Option → Right to buy Put Option → Right to sell 4. Swaps An agreement between two parties to exchange cash flows or financial obligations. Example: Exchange of fixed interest payments with floating interest payments. Uses of Financial Derivatives Hedging Used to reduce risk from price changes. Speculation Used to earn profit from market movements. Arbitrage Used to take advantage of price differences in markets. Price Discovery Helps determine future market prices. Advantages of Financial Derivatives 1)Reduces financial risk 2)Improves market efficiency 3)Provides leverage 4)Enhances liquidity 5)Helps in portfolio management Disadvantages of Financial Derivatives 11)High risk due to market volatility Complex financial instruments Possibility of huge losses 2)Can lead to speculation and market instability Example of Financial Derivative Suppose a company expects the dollar price to increase after 2 months. 3)To avoid loss, the company enters into a currency futures contract today at a fixed exchange rate. Even if the dollar price rises later, the company can buy dollars at the agreed price. Conclusion Financial derivatives are important financial instruments used for hedging, speculation, and managing financial risks. They play a major role in modern financial markets, but they should be used carefully because they involve high risk and complexity.

NEW PAPER (forthcoming in a special issue on #riskmanagement in Journal of Portfolio Management) Basically, everyone seems to assume that AI will reduce information asymmetry in finance. I'm not so sure. The standard story is: AI reads more documents than humans can, extracts more information, and therefore investors make better decisions. But this assumes that documents stay the same once AI becomes the audience. But, it is unlikely. The AI may extract the numbers and tone perfectly. The problem is that they have been optimised for extraction. This is a different type of risk. It is not hallucination. It is not model risk. The model is doing exactly what it was designed to do. I call this technological epistemic risk. The implication is straightforward. Historical predictive power is no longer enough. Before trusting any AI-extracted signal, investors should ask two questions: • Has this signal predicted outcomes in the past? • How costly would it be for the reporting party to manipulate it? Only signals that score well on both deserve much weight. Ironically, AI may leave investors with more data, cleaner dashboards, faster due diligence... and less actual knowledge. The paper develops this argument using examples from private equity, but I suspect the mechanism applies much more broadly across asset management. Comments and criticisms are very welcome. #ArtificialIntelligence #AssetManagement #PrivateEquity #MachineLearning #Finance #RiskManagement

🚀 Demystifying Subordination Risk in Syndicated Loans & Private Credit Corporate debt structures are usually more complex especially in LBOs and leveraged recapitalizations. Understanding subordination risk is critical - whether you're a lender, investor, or a borrower. Let’s break it down with a real-world case study and hard data: $10Bn Financing for MegaCorp (Hypothetical LBO) Capital Structure: 1. $6Bn Senior Secured Loan (at an operating subsidiary, say OpCo, secured by charge on factories & IP) 2. $3Bn Unsecured Bonds (at the parent holding company, say HoldCo, no collateral) 3. $1Bn Subordinated Debt (at HoldCo, contractually junior in repayment) 1️⃣ Collateral Subordination: Risk: Only secured creditors can claim specific assets. What Happens in Default? - Banks (senior secured lenders) seize and sell MegaCorp’s factories/IP. - Unsecured bondholders get nothing until secured lenders are fully repaid. 💡 Data Point: Secured loans recover ~60-80% vs. ~30-50% for unsecured (S&P). 2️⃣ Contractual Subordination: Risk: Subordinated debt agreements explicitly rank repayment priority. What Happens in Default? - The $1Bn subordinated debt is contractually behind unsecured bonds at the HoldCo in repayment. - Even if HoldCo has $500million left after paying unsecured bonds, sub-debt may recover pennies on the dollar. 💡 Data Point: Subordinated debt recovers just ~20-30% on average (Moody’s). 3️⃣ Structural Subordination: Risk: HoldCo debt is structurally junior to OpCo debt because cash flows must service operating subsidiary debt first. What Happens in Default? 1. OpCo’s $6Bn loan is repaid first from subsidiary cash flows/assets. 2. HoldCo’s $3Bn bonds only get leftovers (if any). 3. Subordinated HoldCo debt? Near-total wipeout in a default scenario. 💡 Data Point: HoldCo debt recovers ~10-30% vs. ~60-80% for OpCo debt (Moody’s). Why Does This Matter: ✅ For Lenders: Pricing reflects subordination—HoldCo debt often yields 300-500bps more than OpCo debt. ✅ For PE Firms: They could exploit structural subordination by loading OpCo with assets and HoldCo with debt. ✅ For Investors: Recovery rates vary wildly — always important to check where you sit in the capital stack. In restructuring battles, OpCo lenders often block cash upstreaming to starve HoldCo lenders/creditors—a key risk in Leveraged Buyouts (LBOs). Krishank Parekh | LinkedIn

In asset management, more data doesn't automatically lead to better investment decisions. Often, it simply creates more noise. However, we believe that when fundamental analysts partner with data specialists supported by AI capabilities, useful signals can be extracted from the ever-expanding universe of alternative data. In this article, Vinit Agrawal and Jason Nogueira detail how our research analysts and Investment Data Insights team work closely to: ● Determine the questions that matter most for a given company ● Identify where alternative data might reveal a gap between business fundamentals and market expectations ● Evaluate potential data sources, including their biases and blind spots, and monitor effectiveness over time They also highlight a compelling case study from Industrials Analyst Lee Sandquist, who paired fieldwork findings with alternative data to test and refine his thesis on interconnection companies underpinning AI infrastructure. Read more from Vinit and Jason as they explore how alternative data, when harnessed by the right people and processes, can help strengthen research in pursuit of a durable investment edge: https://trowe.com/4fAjYys

Evaluating Methodologies for Identifying Liquid Assets: A Strategic Approach Appraising the liquidity of an asset is fundamental in finance, affecting everything from day-to-day trading operations to long-term strategic planning. Identifying liquid assets accurately enables better risk management and optimises asset allocation. Several methodologies can assist in determining the liquidity of an asset, each with its distinct focus and applicability: 1. Volume Analysis: This involves examining the average volume of transactions over a specific period. High trading volumes generally indicate a higher liquidity level, as the asset can be bought or sold quickly without a substantial price impact. Volume analysis is straightforward and provides a real-time snapshot of market activity. 2. Bid-Ask Spread: The bid-ask spread is the difference between the highest price a buyer is willing to pay (bid) and the lowest price a seller is willing to accept (ask). Narrower spreads are typically indicative of more liquid assets, reflecting a healthy demand and supply balance. This method is particularly useful for assessing liquidity in real-time market conditions. 3. Market Depth: This method evaluates the size of orders at different price levels within an order book. Assets with deep market depth, where large orders can be accommodated with minimal impact on the asset's price, are considered highly liquid. Market depth provides a more nuanced insight into liquidity, beyond what volume and spread can reveal alone. 4. Time to Execution: Measuring the average time it takes for an order to be executed at a reasonable price also serves as an indicator of liquidity. Shorter execution times are characteristic of more liquid markets where buyers and sellers are readily available. 5. Resilience: This approach looks at how quickly prices return to equilibrium after a trade, indicating the market's ability to absorb shocks. A market that quickly recovers from large trades without large price fluctuations demonstrates high liquidity and resilience. Each of these methodologies has its advantages and limitations. For example, while volume analysis offers simplicity, it may not fully capture liquidity during off-peak hours or under unusual market conditions. Similarly, the bid-ask spread can quickly widen in volatile markets, temporarily misrepresenting an asset’s typical liquidity. It is therefore prudent to employ a combination of these methodologies to gain a comprehensive understanding of an asset's liquidity. This multifaceted approach not only enhances the accuracy of liquidity assessment but also provides a robust framework for managing financial risks more effectively. Understanding and applying these methodologies can significantly benefit portfolio management by ensuring that assets can be converted into cash quickly and efficiently when required, thereby maintaining financial stability and meeting operational needs without compromising on returns.

In our latest IFM Investors Insights paper we turn our attention to strategic role of private debt within institutional portfolios, with a particular focus on its contribution to portfolio defensiveness, diversification and robustness. Our analysis is motivated by the growing institutional interest in the asset class, driven by its potential to deliver stable income, downside protection, and diversification benefits in an increasingly uncertain macroeconomic environment. Key findings include: • Private debt plays a foundational role in defensive portfolios, offering superior risk-adjusted returns compared to traditional fixed income and other private market assets. • Diversification benefits are most pronounced for risk-averse investors, with diminishing marginal utility as risk appetite increases and allocations shift toward higher-growth alternatives. • Portfolio construction within private debt matters—the choice of strategy and underlying exposures significantly influences the magnitude of performance enhancement. • The results reinforce the case for a more prominent strategic allocation to private debt, particularly for investors seeking to enhance portfolio resilience amid evolving, highly uncertain, market dynamics. With IFM Economics Frans van den Bogaerde, CFA & Christopher Skondreas and Hiran Wanigasekera #privatedebt #assetallocation #investment #riskadjustedreturns #IFM

As an investor, you’re probably familiar with risk tolerance, or how much risk you can “stomach” in your portfolio, but what about your risk capacity? Do you know how much money you may need soon from your portfolio, and will that cash be there for you when you need it? We believe that a personalized approach to portfolio allocation that considers risk capacity - along with risk tolerance - can be more beneficial than a general, age-based guideline. Once you know your risk capacity, build your allocation from the bottom up, starting with the dollar amount you need for short-term income and liquidity first. Then consider investing the rest for growth. In the latest edition of Wealth Management Insights, I offer insights to help you determine your risk capacity and to understand why doing so is key to helping you meet your short- and long-term financial goals. #wealthmanagement #portfoliomanagement

𝐓𝐢𝐦𝐞 𝐢𝐬 𝐧𝐨𝐭 𝐧𝐞𝐮𝐭𝐫𝐚𝐥—𝐢𝐭 𝐢𝐬 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞. 𝐖𝐡𝐞𝐫𝐞 𝐛𝐚𝐥𝐚𝐧𝐜𝐞 𝐬𝐡𝐞𝐞𝐭 𝐢𝐬 𝐝𝐨𝐢𝐧𝐠 𝐦𝐨𝐫𝐞 𝐰𝐨𝐫𝐤 𝐭𝐡𝐚𝐧 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 and 𝐩𝐫𝐨𝐟𝐢𝐭𝐬 𝐜𝐚𝐧 𝐜𝐡𝐚𝐧𝐠𝐞 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐭𝐬 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐃𝐄𝐂𝐎𝐃𝐈𝐍𝐆 Hero Fincorp's DRHP The biggest driver of reported numbers here is not growth. It’s structure. Capital instrument design explains more volatility than operating behaviour. That is where attention should start. An instrument that changes profits without changing cash Inside Hero FinCorp, a large block of compulsorily convertible #preference shares plays an outsized role. 𝐋𝐞𝐠𝐚𝐥𝐥𝐲 --->> they sit within share capital. 𝐄𝐜𝐨𝐧𝐨𝐦𝐢𝐜𝐚𝐥𝐥𝐲--->> they behave like a liability. 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠-𝐰𝐢𝐬𝐞--->>they are measured at fair value through profit and loss. #FVTPL That combination matters. 𝐓𝐡𝐞 𝐃𝐑𝐇𝐏 𝐢𝐭𝐬𝐞𝐥𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐟𝐢𝐞𝐬 𝐭𝐡𝐢𝐬 𝐬𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐢𝐭𝐲: if these instruments were treated as #equity instead of a financial #liability, reported #profits and #networth would look materially different — without any change in borrowers, collections, or credit costs. Only the accounting lens shifts. Time is explicitly priced into this capital The cost of this capital is not flat. For a defined period, the effective return is modest — around 3%. If a specified #capitalmarkets milestone is not completed within that window, the return steps up sharply — to approximately 16% until conversion. This is the part worth slowing down for. The structure does not merely encourage a timeline. It prices delay non-linearly. At that point, time is no longer strategic preference. It becomes an economic variable. Why this matters analytically Nothing about this mechanism: improves operating cash flows, or changes borrower behavior, or alters credit underwriting. But it does change incentives. When reported performance is sensitive to: instrument classification, and time-linked cost escalation, the first analytical task is separation. Separate what improved because the business improved from what moved because the structure was engineered to move. Balance sheets don’t just report outcomes. Sometimes, they shape them. #IndiaIPO #IPODecoded #UpcomingIPOs #IPOAlert #CPA #Finance #Economics #Nifty #IndiaGrowthStory #NSE #BSE

FX & Interest Rate Risk Management Cheat Sheet! 2 critical financial risks treasury teams manage are FX risk and Interest Rate Risk (IRR). If not properly managed, both can erode margins, distort earnings, and create instability in cashflow planning. Learn more: https://lnkd.in/gwSMHnRG Here is a concise framework you can use: 1. Foreign Exchange (FX) Risk Key FX Risk Types • Transactional FX Risk – Exposure from future contractual cashflows such as imports, exports, accounts receivable, and accounts payable. Impact: Margin volatility and cashflow uncertainty. • Translational FX Risk – FX impact when consolidating financial statements of foreign subsidiaries. Impact: Earnings volatility in the balance sheet and income statement. • Economic FX Risk – Long-term impact of exchange rate movements on competitiveness and pricing strategy. Impact: Potential market share erosion. Measurement & Monitoring You can track exposure using tools such as: • Net Open Position (NOP) – aggregate currency mismatch across inflows and outflows. • FX Sensitivity Analysis – EBITDA impact from ±5–10% currency movements. • Scenario Modeling – base, worst, and best exchange rate scenarios. Operational Mitigation (Natural Hedging) Before using derivatives, you can reduce exposure through: • Currency matching of receivables and payables • FX budget rates for pricing and procurement planning • Local currency settlement strategies • Procurement timing adjustments based on FX trend Financial Hedging Instruments When natural hedges are insufficient, you may use: • FX Forwards – lock in exchange rates for future obligations • FX Options – downside protection with upside participation • Cross-Currency Swaps – exchanging one currency for another Strong governance is essential, including hedge ratio policies, counterparty monitoring, hedge effectiveness testing, and board-approved FX policies. 2. Interest Rate Risk (IRR) Interest rate volatility affects borrowing costs and investment returns. Key IRR Types • Repricing Risk – mismatch between asset and liability maturities • Yield Curve Risk – changes in short- vs long-term rates affecting refinancing costs • Basis Risk – mismatch between benchmark indices (e.g., SOFR vs Prime) • Optionality Risk – early repayment or prepayment risk affecting expected cashflows Measurement Tools Treasury teams typically use: • Interest Rate Gap Analysis • Duration Analysis • Stress testing using ±100–200 bps scenarios IRR Hedging Instruments Common tools include: • Interest Rate Swaps – convert floating debt into fixed rates • Interest Rate Caps – set maximum borrowing cost • Interest Rate Floors – protect minimum investment returns • Collars – combine cap and floor for cost-controlled protection Treasury is really about protecting enterprise value from financial market volatility while maintaining stable margins and predictable cashflows. 📌 Repost & Share!

1. What Are Derivatives? Derivatives are financial instruments whose value is derived from an underlying asset such as equity, index, interest rate, FX, credit, commodity, or volatility. Their pricing is model-driven, unlike equities or bonds, which are mostly market-driven. This immediately creates complexity in pricing, reference data, valuation controls, and risk distribution. ⸻ 2. Major Types of Derivatives (with pricing impact) A) Futures Examples: Equity index futures (NIFTY), Bond futures, Currency futures, Commodity futures Key reference fields: contract month, expiry date, lot size, underlying index, multiplier, settlement type. Impact on Pricing: • Price validity depends on expiry and daily settlement. • Requires mark-to-market (MTM) daily settlement price, not last traded price. • Reference data must hold contract specifications; otherwise valuations and margin calculations fail. ⸻ B) Options (Equity, Index, FX, Commodity) Types: Call, Put, American, European Key reference attributes: strike price, expiry, option type, underlying, contract size, style. Impact on Pricing: • Options need models like Black-Scholes, binomial, local volatility, etc. • Need many market inputs: underlying price, volatility, risk-free rate, dividends, FX curves. • Implied volatility feeds from vendors must be mapped correctly; if reference data is wrong, model price becomes invalid. • Corporate actions (splits, dividends) drastically alter pricing. ⸻ C) Swaps Types: • Interest Rate Swaps (IRS) • Currency Swaps • Credit Default Swaps (CDS) • Total Return Swaps (TRS) Reference attributes: legs, reset frequency, curves used, index (LIBOR/SOFR/MIBOR), day count, notional. Impact on Pricing: • Pricing requires yield curves, credit curves, discount factors, and reference indices. • Wrong or stale curves → wrong swap NPVs → wrong NAV and risk. • TRS uses underlying asset price + financing leg → depends on accuracy of underlying equity/bond pricing. ⸻ D) Forwards Examples: FX forwards, equity forwards, commodity forwards Reference attributes: forward points, tenor, settlement type, contract size. Impact: • Forward valuation relies on spot price + interest rate differential. • Incorrect reference data (currency pair, quote direction, holiday calendars) causes valuation breaks. ⸻ E) Structured Products Examples: Barrier options, autocallables, range accruals, convertible bonds Reference attributes: payoff formula, barrier levels, coupon schedule, callable triggers. Impact: • Highly model dependent (Monte Carlo, stochastic volatility models). • Requires clean reference rules; any missing payoff variable invalidates valuation. • Corporate actions on underlying → full re-strike or adjustment. #Derivaties #RefData #Pricing #Nav