Key Parameters in Modern Risk Assessment for Investors

Portfolio risk assessment employs a range of specialized parameters to quantify uncertainties, from everyday volatility to rare catastrophes, enabling wealth managers to tailor strategies for client needs like capital preservation or growth. These metrics, drawn from established financial practices, support informed decisions in diverse portfolios blending Indian equities, debt, and alternatives. I have explained part of this in my previous write-ups but were more focussed explaining terminologies; in this article I have covered them again from risk-aspects.

Standard Deviation

Standard deviation measures the dispersion of portfolio returns around their average, capturing total volatility regardless of direction. A value of 15% means returns typically vary by that amount annually, forming the basis of models like mean-variance optimization (MVO, not popular in India). It suits broad screening but equates upside gains with downside losses, overlooking skewed distributions common in emerging markets.

Beta

Beta evaluates systematic risk by comparing portfolio volatility to a benchmark, such as the Nifty 50. A beta of 1.1 indicates 10% higher sensitivity compare to market moves, helping isolate market-driven risks from unique holdings. While ideal for diversified exposures, it ignores idiosyncratic factors like company-specific events.

Maximum Drawdown

Maximum drawdown calculates the largest percentage drop from a peak value to a subsequent trough, such as a 28% decline during a market correction. It highlights real-world loss experiences and recovery periods, crucial for assessing drawdown tolerance in retirement portfolios. Path dependency makes it retrospective, varying with observation periods.

Conditional Drawdown-at-Risk (CDaR)

CDaR averages the worst portion of historical drawdowns, say the deepest 5%, providing a tail-focused view beyond single events. This forward-oriented metric excels in optimization, prioritizing severe declines over average volatility. Computation demands extensive data, limiting real-time use.

Upside Capture Ratio

Upside capture ratio divides portfolio gains by benchmark gains during positive periods, with values over 1.0 signaling strong rally participation. It reveals how well a strategy captures market upswings, useful for growth-oriented mandates. Results depend on chosen market phases and benchmarks.

Downside Capture Ratio

Downside capture ratio mirrors the upside version but for declines, where under 1.0 denotes relative protection. Essential for defensive positioning, it quantifies behavior in downturns like rate hikes. Like its counterpart, phase definitions introduce subjectivity.

Modified Duration

Modified duration estimates bond price sensitivity to yield changes, equating duration years to percentage price shifts per 1% rate move. It assumes parallel yield shifts and it neglects credit spreads or non-rate risks.

Convexity

Convexity adjusts modified duration for yield curve curvature, showing how duration itself changes with rates, often adding a positive buffer to price forecasts. A high convexity reduces loss estimates in falling rate scenarios for long bonds. It serves as a second-order refinement, not a standalone tool.

Value at Risk (VaR)

VaR forecasts the maximum potential loss over a horizon at a confidence level, like 2% daily loss at 95% confidence, using methods from historical data to simulations. Regulators rely on it for capital buffers, but it fails to detail losses beyond the threshold. Fat tails in Indian assets amplify this gap.

Conditional Value at Risk (CVaR)

CVaR, or Expected Shortfall, computes average losses in scenarios worse than VaR, capturing tail severity—for instance, 3.5% average in the bottom 5%. Superior for extreme event planning, it drives coherent optimizations. It requires more processing power than VaR.

Sortino Ratio

Sortino ratio refines the Sharpe ratio by dividing excess return solely by downside deviation, a threshold like 0% or inflation. A score of 1.5 flags strong risk-adjusted downside performance, favoring asymmetric strategies. Threshold selection affects comparability.

Tracking Error

Tracking error quantifies standard deviation of portfolio returns minus benchmark returns. It enforces mandate discipline, flagging unintended drifts. High values may reflect skill or slippage, demanding context.

 

Risk Metrics Summary
Parameter	Primary Use Case	Strength	Limitation
Standard Deviation	Total volatility	Optimization baseline	Symmetric treatment
Beta	Market linkage	Systematic focus	Idiosyncratic blind spot
Max Drawdown	Peak loss	Client psychology	Historical focus
CDaR	Drawdown tails	Robust planning	Data heavy
Upside Capture	Rally capture	Growth assessment	Phase sensitivity
Downside Capture	Fall protection	Defense review	Phase sensitivity
Modified Duration	Rate impact	Bond forecasting	Narrow scope
Convexity	Rate refinement	Accuracy boost	Secondary metric
VaR	Loss threshold	Regulatory fit	Tail ignorance
CVaR	Extreme losses	Tail insight	Intensive calculation
Sortino Ratio	Downside efficiency	Asymmetry reward	Threshold choice
Tracking Error	Benchmark fidelity	Active control	Skill vs error ambiguity

Mastering these risk parameters enables a nuanced view of portfolio vulnerabilities, from symmetric volatility to extreme losses, fostering optimized allocations that balance growth with preservation. We as wealth professionals can integrate them into routine reporting, stress tests, and rebalancing to deliver superior client outcomes in volatile environments like India's dynamic markets. Ultimately, a multi-metric approach outperforms single measures, ensuring robustness across cycles.

Growth, Value, and Beyond: A Framework for Strategic Allocation

In the world of investing, success is rarely the result of random stock picking. Instead, it is often the product of a disciplined "style", a systematic framework used to group and allocate assets based on specific measurable characteristics. Whether an investor prioritizes rapid earnings expansion or seeks out undervalued gems, these styles serve as the navigational compass for portfolio construction. From the high-octane potential of Growth to the disciplined stability of Value, and the pragmatic middle ground of GARP, understanding these methodologies is essential for managing risk. This write-up explores the primary investment styles, their inherent trade-offs, and how to blend them to build a resilient, long-term strategy.

What Are Portfolio Investment Styles?

Portfolio investment “styles” refer to how managers group and allocate among stocks (or funds) based on measurable characteristics such as valuation, earnings growth, and quality metrics. The two most common styles are growth and value, with blended and hybrid strategies sitting in between. In practice, most long‑term portfolios are not purely one style; instead, they tilt toward one approach while using others to diversify risk and smooth returns.

Growth‑Style Portfolio Strategy

A growth‑style portfolio focuses on companies expected to expand earnings and revenues faster than the market average. These firms often operate in innovative or high‑beta sectors such as technology, select consumer discretionary, and certain healthcare segments, where future expectations drive valuations more than current book‑value‑based metrics.

Key Characteristics

• High recent and forecast earnings growth and revenue growth.
• Often trade at elevated multiples (e.g., high P/E ratio) because investors are paying for future expansion.
• Lower dividend yields, as profits are typically reinvested into the business rather than returned to shareholders.

Pros and Risks

Pros:

• Strong upside when growth narratives play out (e.g., successful product launches, market share gains, or regulatory tailwinds).
• Can compound wealth rapidly over long periods if the portfolio tilts into structurally growing industries.

Risks:

• High volatility and deep drawdowns when growth slows or expectations are not met.
• Valuation de‑rating risk: if interest rates rise or sentiment turns negative, multiples can compress sharply, even if fundamentals remain solid.

Value‑Style Portfolio Strategy

A value‑style portfolio targets companies that appear cheap relative to their earnings, book value, or cash‑flow generation. Value managers often screen for low P/E, high book‑value‑to‑price, strong dividend yields, and high free‑cash‑flow yields, betting that the market has underpriced known, stable earnings.

Key Characteristics

• Lower valuation multiples and higher dividend yields compared with the broader market.
• Often found in mature sectors such as banking, utilities, basic materials, and parts of consumer staples.

Pros and Risks

Pros:

• Generally lower volatility and drawdowns during market downturns, because expectations are already muted.
• Provides relatively steady income and can act as stabilizing agent when growth‑style stocks face de‑rating.

Risks:

• “Value traps”: companies that appear cheap but never recover due to structural issues, competitive erosion, or balance‑sheet stress.
• Periods of underperformance when growth‑style narratives dominate market sentiment (for example, in long bull markets in tech and quality stocks).

 Blended / Core‑Blend Portfolio Strategy

A blended or “core‑blend” portfolio deliberately combines growth and value stocks (or funds) within a single allocation, aiming to capture upside from growth while using value as a stabilizer. This is the default style for many balanced mutual funds and model portfolios, especially those marketed as “large‑cap blend” or “multi‑cap blend” products.

How It Works

• Typically allocates a portion of equity exposure to growth‑oriented names and a portion to value‑oriented names (for example, 60% value‑tilt and 40% growth‑tilt, or 50/50 “balanced hybrid”).
• May also overlay quality and momentum screens so that the blend is not random but tilted toward companies with strong fundamentals and positive price momentum.

Pros and Risks

Pros:

• Diversification across styles lowers correlation and can improve the Sharpe ratio over time, as growth and value tend to outperform in different market environments.
• More stable long‑term compounding than a pure‑style portfolio, especially for investors with moderate risk tolerance.

Risks:

• Underperformance in single‑style bull markets; for example, in a pure‑tech‑style rally the blend may lag a dedicated growth‑style portfolio.
• Needs clear rules: without predefined allocation bands and rebalancing triggers, the blend can drift into an unintended style (e.g., overweight growth when valuations have already stretched).

Many practitioners recommend defining an explicit style split (for example, 60/40 value–growth) and rebalancing when the allocation drifts beyond a tolerance band (such as ±5%).

GARP (Growth at a Reasonable Price) and Hybrid Strategies

GARP sits conceptually between pure growth and pure value, seeking companies that offer meaningful earnings growth but at valuations that are not excessively stretched. In practice, GARP‑style portfolios often apply growth filters (e.g., reasonable earnings‑growth forecasts) together with valuation constraints.

Typical Features

• Focus on companies with sustainable competitive advantages, strong returns on invested capital, and moderate leverage.
• Use of multi‑metric screens: P/E vs. historical median, FCF yield, and PEG to ensure that growth is bought at a disciplined price.

Advantages:

• Reduces the risk of paying for speculative growth while still participating in structurally expanding businesses.
• Can be more tax‑efficient than pure‑style strategies, because position turnover is usually lower and purchases are anchored on valuation, not on style‑chasing.

Challenges:

• Requires more nuanced analysis and may be harder to replicate in a simple index‑based setup.
• In extreme risk‑on or risk‑off regimes, GARP portfolios may underperform more aggressive growth or more defensive value strategies.

Other Style‑Based Approaches Around the Core

Beyond growth, value, and blend, sophisticated portfolios often layer in additional style or factor dimensions:

• Momentum: Tilting toward stocks that have shown strong recent price performance, on the premise that trends can persist for a period.
• Quality: Focusing on high‑ROIC, low‑leverage, and stable‑cash‑flow businesses, regardless of whether they are classified as growth or value.
• Dividend/Income‑oriented: Allocating to high‑yield, often value‑like names that prioritize shareholder payouts.

Smart‑beta or factor‑tilted products can implement these ideas systematically, for example by blending growth, value, and quality factors to improve risk‑adjusted returns over time.

How to Choose the Right Style for Your Portfolio

There is no single “best” style; the right choice depends on individual circumstances.

• Aggressive, long‑term investors who can tolerate high volatility may accept a higher growth tilt, but should still cap single‑stock exposure and maintain some value or quality ballast.
• Conservative or income‑oriented investors often favour value or dividend‑oriented core sleeves, possibly with modest growth satellite positions.
• Most balanced investors benefit from a blended core (for example, 60/40 value–growth or quality‑weighted blend) with clearly defined allocation bands and rebalancing rules.

When I discuss this with my clients, it helps to frame the discussion around:

• Time horizon (years versus months),
• Drawdown tolerance (mostly perceived), and
• Whether the primary goal is capital appreciation, income, or a mix of both.

Practical Implementation Tips

• Define your style split (e.g., “60% value‑tilt, 40% growth‑tilt equity core”) and embed it in an investment framework.
• Use simple metrics such as P/E vs. 10‑year median, FCF yield for value, and PEG/DCF for growth, with clear thresholds for “attractive,” “fair,” and “expensive.”
• Rebalance periodically or when style drift exceeds a band (for example, ±5%), using cash flows before forced sales to manage tax and transaction‑cost drag.
• Prioritize quality across styles: demand ROIC >12% and stable cash‑flow conversion, and restrict high‑leverage names unless growth visibility is exceptional.

Investment Style	Primary Objective	Key Metrics	Primary Risk
Growth	Capital appreciation via rapid expansion.	High P/E, High EPS growth, Low dividends.	Volatility: Sensitive to interest rates and earnings misses.
Value	Capitalize on market mispricing/undervaluation.	Low P/E, High Dividends.	Value Traps: Stocks that stay cheap due to structural decline.
Blended (Core)	Balanced exposure and risk smoothing.	Mix of Growth and Value metrics.	Tracking Error: May lag behind "pure" styles during heavy bull runs.
GARP	Growth at a Reasonable Price.	PEG Ratio, ROIC, Moderate P/E.	Complexity: Harder to find; may underperform in extreme regimes.
Quality	Reliability and "safe" compounding.	High ROIC, Low Debt, Stable Cash Flow.	Opportunity Cost: May underperform during speculative bubbles.

 

By thoughtfully combining growth, value, and blended or hybrid approaches, and by anchoring decisions to transparent, repeatable rules; investors can build strategies that are aligned with their goals while controlling style risk and emotional bias.

 

Understanding the RSI Indicator

Technical analysis offers a variety of tools to help traders navigate the complexities of financial markets. Among the most widely used is the Relative Strength Index (RSI). This momentum oscillator provides a window into the speed and magnitude of price changes, helping market participants determine whether an asset is being over-extended or is ripe for a reversal.

What is the RSI?

Developed by J. Welles Wilder Jr., the RSI is a momentum indicator that oscillates on a scale of 0 to 100. Unlike trend-following indicators like moving averages, the RSI measures the internal strength of a security by comparing the magnitude of its recent gains to its recent losses.

By default, the indicator uses a 14-period look-back window. The core logic is expressed through the following formula:

 

Interpreting Key RSI Levels

The power of the RSI lies in its ability to highlight extreme market conditions. Traders typically focus on three critical thresholds:

• Overbought (70+): When the RSI rises above 70, it suggests the asset has experienced significant upward pressure and may be due for a pullback or consolidation.
• Oversold (30-): Conversely, a reading below 30 indicates that selling pressure has been intense, potentially leaving the asset undervalued and ready for a bounce.
• The 50 Centerline: This serves as the "trend barometer." A reading consistently above 50 suggests a bullish environment, while staying below 50 indicates bearish dominance.

 

Strategic Applications of RSI

Traders utilize the RSI in several ways to refine their entry and exit points:

1. Overbought and Oversold Reversals

The most straightforward strategy involves watching for "mean reversion." A trader might wait for the RSI to dip below 30 and then look for a buy signal as the line crosses back above the 30 threshold. Similarly, a cross below 70 after a peak is often viewed as a signal to take profits or go short.

2. Identifying Divergence

Divergence is a powerful "early warning" signal that occurs when the RSI stops moving in sync with the price.

• Bullish Divergence: Price makes a lower low, but RSI makes a higher low. This suggests downward momentum is weakening.
• Bearish Divergence: Price makes a higher high, but RSI makes a lower high. This suggests upward momentum is fading.

3. RSI Failure Swings

A failure swing is a specific pattern that ignores price action and looks only at the RSI line. A bullish failure swing occurs when the RSI drops into the oversold zone, bounces, pulls back (staying above the 30 line), and then breaks its previous peak. This is often considered a strong confirmation of a trend shift.

Limitations and Best Practices

While the RSI is a versatile tool, it is not infallible. In a strong trending market, the RSI can remain in overbought or oversold territory for extended periods. Selling a stock simply because the RSI hit 70 during a massive bull run can result in missing out on substantial gains.

To increase accuracy, professionals often pair the RSI with other technical tools such as Moving Average Convergence Divergence (MACD), volume analysis, or simple support and resistance levels.

Conclusion

The Relative Strength Index remains a cornerstone of technical analysis because of its simplicity and effectiveness in gauging market momentum. By identifying overextended conditions and spotting divergences, it allows traders to look beneath the surface of price action. However, like any individual indicator, its true value is unlocked when used as part of a broader, disciplined trading strategy.

Rising Bond Yields: A Tactical Guide

Rising bond yields (the interest rates paid on government debt) are often a double-edged sword. While they signal a higher inflation, they create immediate downward pressure on bond prices and high-growth equity valuations.

As of April 2026, with the India 10-year G-Sec yield climbing toward 7.1% and the US 10-year Treasury hovering around 4.4%, (though I find it transitory) a tactical shift could be a good idea. Here is a comprehensive guide on how to position your portfolio during this cycle.

Here is a unique, restructured guide to managing your assets in a rising-rate environment.

1. Fixed Income: Shortening the Leash

In a rising-yield environment, long-term bonds act like a weight. Since bond prices fall when yields rise, the longer the bond's maturity, the harder it gets hit.

• Embrace "Agile" Debt: Pivot toward Short-Duration funds or T-Bills. By keeping your maturity profile between 1 and 3 years, you minimize capital loss and can quickly reinvest into newer, higher-yielding bonds as they become available.
• Ride the Float: Floating-rate instruments are your best friend when yields are elevated for longer period. Unlike fixed-rate bonds that lose value, Floating Rate Bonds see their interest payouts increase as market benchmarks (like the National Savings Certificate in case of RBI Floater) move up.
• The Accrual Engine: Focus on "Yield-to-Maturity" (YTM) rather than price appreciation. In 2026, high-quality Corporate Bonds (AAA or AA) and Banking/PSU funds offer a "carry" that provides a steady cushion against market volatility.

2. Equities: Valuation over Vision

Rising yields increase the "discount rate," which shrinks the present value of future earnings. This hit is hardest for companies that promise big profits a decade from now but earn very little today.

Where to Lean In

• The Banking Advantage: Banks often thrive in long term, when rates rise because they can expand their Net Interest Margins (NIMs). They tend to hike lending rates for borrowers faster than they raise interest for depositors.
• The "Old Economy" Winners: Look at sectors with tangible assets and immediate cash flows; Energy, Commodities, and Industrials. These "Value" stocks are often less sensitive to interest rate spikes than their "Growth" counterparts.
• Cash-Rich Balance Sheets: Prioritize companies with zero or low debt. As the cost of borrowing rises, firms that self-fund their growth will significantly outperform those burdened by high-interest repayments.

Where to Step Back

• Speculative Tech: Avoid companies with high "burn rates" and no path to immediate profitability.
• High-Leverage Utilities: Companies that rely on massive debt to build infrastructure will see their margins squeezed by rising interest expenses.

3. Other Assets: Diversifying the Defense

• Gold as a Counter-Weight: While gold doesn't pay interest, it serves as a crucial hedge if yields are rising due to geopolitical friction or currency devaluation (may or may not reflect this exact relationship in short run).
• Strategic Liquidity: Keeping 10% to 15% in Liquid Funds is not a lack of conviction; it is tactical flexibility. It allows you to "buy the dip" if the equity market overreacts to a sudden yield spike.

Strategic Summary Table
Asset Class	Tactical Shift	Core Objective
Debt	Move to Ultra-Short/Liquid	Minimize price erosion; maximize flexibility.
Debt	Floating Rate Bonds	Capture rising interest income in real-time.
Equity	Heavy on Value & Banks	Benefit from credit cycles and real earnings.
Equity	Light on High-Debt Growth	Avoid valuation "de-rating" and interest stress.
Other Assets	Gold & Cash Reserves	Provide a safety net and "buy-the-dip" capital.

 

Conclusion: In a higher interest period, prudent approach is playing defensive, in 2026, the goal is not just to find the highest return, but to find the return that is most likely to survive volatility.

How Wealth Shapes Investor Risk Attitudes- Risk Theories

 

Traditional economic models assume investors always act rationally, maximizing returns while minimizing risk through steady preferences. However, real-world decisions often defy this, blending caution with gambles; this article explores three landmark theories that reveal why.

A- Friedman-Savage hypothesis (1948 by Milton Friedman and Leonard Savage)

The Friedman-Savage hypothesis is a theory in behavioral economics and utility theory designed to explain a specific contradiction in human behavior: why the same person might simultaneously buy insurance (to avoid risk) and lottery tickets (to seek risk).

Proposed by Milton Friedman and Leonard Savage in 1948, it suggests that an individual’s attitude toward risk is not constant but changes depending on their level of wealth.

The Paradox: Insurance vs. Gambling

Standard economic theory (the Bernoulli hypothesis) originally suggested that most people are "risk-averse" because they experience diminishing marginal utility—each extra dollar provides less satisfaction than the one before.

• Insurance: A risk-averse person pays a premium to avoid a large potential loss.
• Gambling: A risk-seeking person pays for a small chance at a large gain, even if the "expected value" is negative.

Friedman and Savage argued that because people often do both, a simple concave utility curve isn't enough to describe human behavior.

The "S-Shaped" Utility Function

To resolve this, they proposed a utility function with a unique, alternating shape:

Segment	Wealth Level	Marginal Utility	Behavior	Example
Lower	Low Income	Diminishing	Risk-Averse	Buying insurance to protect small assets.
Middle	Transition	Increasing	Risk-Seeking	Buying lottery tickets to "jump" to a higher social class.
Upper	High Income	Diminishing	Risk-Averse	Protecting newly acquired wealth through safe investments.

The Visual Curve

If you were to graph this, the curve looks like an elongated 'S':

1. It starts concave (curving down), representing risk aversion at low income.
2. It hits an inflection point and becomes convex (curving up), representing a "love of risk" as people try to escape their current socioeconomic status.
3. It hits a second inflection point and becomes concave again at very high wealth levels.

Socioeconomic Significance

The hypothesis is often used to explain social mobility.

• People in the lower-middle class might gamble because a small loss won't change their life, but a massive win (the lottery) could fundamentally move them into a higher socioeconomic bracket.
• The "pain" of losing a few dollars is outweighed by the "utility" of the potential life-changing jump in status.

B- Harry Markowitz’s Utility Theory (1952 by Harry Markowitz)

While Friedman and Savage focused on absolute levels of wealth; Harry Markowitz (the father of Modern Portfolio Theory, who won a Nobel Prize for Modern Portfolio Theory) argued that this was unrealistic. Markowitz pointed out that the Friedman-Savage model implied that people's risk preferences were tied to absolute wealth levels. He argued instead that people care about changes in wealth relative to their "current" position. In other words, Markowitz indicated that if utility depended on absolute wealth, a person’s behavior would change drastically every time their bank balance moved. Instead, he proposed that utility is determined by changes in wealth relative to a person's current position (a reference point).

The Markowitz Utility Function

Markowitz suggested a "doubly S-shaped" utility function. Unlike the single S-curve of Friedman-Savage, Markowitz’s curve centered around the individual's current wealth.

 

• Gains: People are risk-averse for small gains but risk-seeking for large gains (lottery behavior).
• Losses: People are risk-seeking for small losses but risk-averse for large losses (insurance behavior).

C- Prospect Theory (1979, by Daniel Kahneman and Amos Tversky)

 Kahneman and Tversky took Markowitz’s ideas and added psychological depth, creating Prospect Theory, which is now the foundation of modern behavioral finance. It describes how people actually make decisions under risk, rather than how "rational" people should make them.

 

 

Key Pillars of Prospect Theory

1. Reference Dependence: People do not evaluate outcomes in isolation. We evaluate them as gains or losses relative to a reference point (usually the status quo).
2. Loss Aversion: This is the most famous finding. Psychologically, the pain of losing is twice as powerful as the joy of gaining. Losing Rs 1,00,000 hurts much more than winning Rs 1,00,000 feels good.
3. Diminishing Sensitivity: The difference between Rs 0 and Rs 1000 feels huge, but the difference between Rs1,00,000 and Rs 1,01,000 feels marginal. This leads to the "S" shape: concave for gains (risk aversion) and convex for losses (risk seeking).
4. Probability Weighting: People tend to "overweight" small probabilities (which explains why we buy lottery tickets) and "underweight" large, certain probabilities.

The Value Function

The Prospect Theory curve is asymmetrical. It is steeper for losses than for gains, visually representing that losses makes larger impact than gains.

 

 Conclusion-

In finance, these theories explain why investors often "ride" losing stocks for too long (risk-seeking in the face of loss) while selling winning stocks too early (risk-aversion in the face of gain). This is known as the Disposition Effect (I have explained different kind of behavioral biases in my previous write-up titled “Behavioral Biases in Investing: How Psychology Shapes Financial Decisions”).

Comparison of the Three Theories
Feature	Friedman-Savage	Markowitz Theory	Prospect Theory
Driver of Utility	Absolute Wealth	Change in Wealth	Gains and Losses
Reference Point	None	Current Wealth	Subjective Reference Point
Key Behavioral Insight	Explains lottery/insurance paradox.	Utility depends on your "starting point."	Loss Aversion (Losses > Gains).
Shape of Curve	Concave-Convex-Concave	S-shaped on both sides of zero	Steeper for losses than gains

 

Sources

1-    https://www.journals.uchicago.edu/doi/abs/10.1086/256692 (The Utility Analysis of Choices Involving Risk- by Milton Friedman and L. J. Savage)

2-    https://www.journals.uchicago.edu/doi/abs/10.1086/257177  (The Utility of Wealth by Harry Markowitz)

3-    https://www.econometricsociety.org/publications/econometrica/1979/03/01/prospect-theory-analysis-decision-under-risk (An Analysis of Decision under Risk by Daniel Kahneman and Amos Tversky)