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Investment Decisions & Project Evaluation Biases

Corporate Investment Process

Stages:

  • Idea generation
  • Preliminary screening
  • Detailed analysis (NPV, IRR)
  • Capital budgeting approval
  • Implementation
  • Post-audit

Traditional Assumption: Rational NPV maximization at each stage.

Reality: Behavioral biases systematically distort every stage.

Biases in Project Selection

Overoptimism in Cash Flow Projections

The Problem: Revenue projections consistently exceed realized outcomes by 20-50%.

Behavioral Drivers:

  • Overconfidence: "We're better than competitors"
  • Planning fallacy: Underestimate time/cost, overestimate benefits
  • Anchoring: On best-case scenarios
  • Confirmation bias: Seek supporting data, ignore risks

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Reference Class Forecasting Failure

Error: Evaluating projects in isolation rather than comparing to similar past projects (reference class).

Example: New pharma R&D project

  • Isolated view: "This drug has 60% chance of FDA approval"
  • Reference class view: "Historical FDA approval rate for drugs in this class is 8%"

Why it matters: Anchoring on specific project details (inside view) vs statistical base rates (outside view).

Kahneman's Advice: Always check: "What happened to similar projects?" Use that base rate as starting point, then adjust for specifics.

Sunk Cost Fallacy in Project Continuation

Definition: Continuing projects because of past investments, even when future NPV is negative.

Example:

  • Spent ₹100 crore on project
  • Need ₹50 crore more to complete
  • Expected future value: ₹120 crore
  • Rational decision: NPV = ₹120cr - ₹50cr = ₹70cr positive → Continue
  • Biased decision: "We've already invested ₹100cr, can't waste it!" → Continue even if future NPV negative

Why it happens:

  • Loss aversion: Abandoning project feels like realizing loss
  • Escalation of commitment: "Just a bit more will make it work"
  • Self-justification: Don't want to admit initial decision was wrong

Indian Example: Kingfisher Airlines continued operating 2010-2012 despite mounting losses, pouring in funds to "recover" past investments, eventually losing everything. Sunk cost fallacy prevented timely exit.

Empire Building & Free Cash Flow

Jensen's Free Cash Flow Hypothesis: Managers with excess cash invest in value-destroying projects rather than returning to shareholders.

Behavioral Amplification:

  • Overpayment in acquisitions: Use cash for empire-building M&A
  • Pet projects: Fund low-NPV projects manager is passionate about
  • Avoiding difficult decisions: Easier to invest than downsize/return cash

Evidence: Firms with high free cash flow AND weak governance have ~30% lower ROI on new investments.

Indian Context: Several Indian conglomerates (Reliance ADAG, Sahara) diversified into unrelated businesses during cash-rich periods, destroying significant value. Core competency abandoned for empire expansion.

Capital Budgeting Process Biases

Hurdle Rate Manipulation

Managers gaming the system:

  • Strategic projects: Lower hurdle rate to approve pet projects
  • Unfavored projects: Raise hurdle rate to reject
  • Result: Capital allocation driven by politics, not economics

Earnings Management Distortion

Problem: Managers focused on quarterly earnings manipulate investment timing.

Example: Delay capex in Q4 to meet earnings target, even if NPV-positive.

Long-term cost: Underinvestment, deteriorating competitive position.

Prob

ability Weighting Errors

Overweight small probabilities: "Home run" projects with 1% chance of 100x return get funded over steady 20% IRR projects.

Underweight moderate probabilities: Steady projects with 70% success chance undervalued vs. "exciting" long-shot bets.

Result: Portfolio tilted toward lottery-like projects, underinvesting in boring but profitable opportunities.

Debiasing Techniques

Pre-Mortem Analysis

Process: Before approving project, imagine it's 3 years later and the project failed spectacularly. Team writes "failure report."

Benefits:

  • Surfaces risks that optimistic planning missed
  • Gives permission to voice doubts
  • Reduces overconfidence

Example: Amazon uses pre-mortems for major initiatives. Jeff Bezos: "This exercise is designed to call out risks in a productive way."

Outside View / Reference Class Forecasting

Process:

  • Identify class of similar projects (e.g., "retail store expansions in Tier-2 Indian cities")
  • Calculate historical outcomes (e.g., "60% achieved payback within 3 years")
  • Use that base rate as anchor, adjust for project-specific factors

Evidence: Reduces forecasting error by 30-40% for major infrastructure projects.

Independent Review Boards

Structure:

  • Separate team (no project involvement) reviews business case
  • Devil's advocate role: Explicitly argues against approval
  • External consultants challenge assumptions

Key: Independence prevents groupthink and confirmation bias.

Competitive Bidding for Internal Capital

Process: Business units compete for limited capital budget

Benefits:

  • Forces realistic projections (overpromise → career damage when missed)
  • Market discipline on internal allocations
  • Reduces empire building (can't fund everything)

Post-Investment Audits

Process: 3 years post-investment, compare actual vs. projected

Benefits:

  • Creates accountability
  • Reveals systematic optimism patterns
  • Improves future forecasts (learning)

Critical: Must be non-punitive for honest mistakes, only punish dishonest projections.


Key Takeaways

  • Overoptimism endemic: Cash flow projections exceed reality by 20-50% due to overconfidence, planning fallacy
  • Sunk cost fallacy: Continuing negative-NPV projects to "recover" past investments destroys value
  • Empire building: Free cash flow + weak governance → 30% lower ROI on new investments
  • Reference class neglect: Ignoring base rates of similar projects causes forecasting errors
  • Indian examples: Delhi Metro overruns, Kingfisher sunk costs, conglomerate diversification
  • Debiasing: Pre-mortems, outside view, independent review, competitive budgeting, post-audits
  • Process matters: Structure decision process to counter biases at each stage

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