

If you opened a Systematic Investment Plan three years ago promising yourself you would not touch it for ten years, and yet you stopped it in 22 months, the explanation is not weakness of character. It is a specific, measurable, replicable cognitive bias first formalised by Princeton economist Robert Strotz in 1956 and reformulated by Harvard’s David Laibson in 1997. The bias is called hyperbolic discounting, and it is the single most important reason why the average Indian retail investor never harvests long-term compounding — even when the mutual fund they own has demonstrably produced 18%+ CAGR. Today’s post deconstructs the bias, grounds it in NSE and AMFI holding-period data, walks through the counter-measures that great investors use, and closes with a detailed positive case study of how Titan Biotech Ltd (BSE: 524717) — through audited FY25 numbers — exhibits the corporate-behaviour version of anti-hyperbolic discounting in its capital allocation.
Table of Contents
ToggleHyperbolic discounting describes the empirically robust finding that humans value a reward today disproportionately more than a reward tomorrow, but value a reward 365 days from now almost the same as a reward 366 days from now. Mathematically, our true discount function is not exponential (δt, the rational-actor benchmark in classical economics) but quasi-hyperbolic of the form βδt, where β < 1 is the “present bias” parameter (Laibson, 1997). The practical consequence: I will plan today to wait five years for ₹2 lakh instead of taking ₹1 lakh tomorrow. But when “tomorrow” arrives, I will take the ₹1 lakh — because the immediate reward now carries the disproportionate β-discount, while the future reward does not. The plan reverses itself the moment present-tense temptation appears. Strotz called this “preference reversal,” and it is the architecture behind every broken SIP, every prematurely sold compounder, and every panic-trigger trade in the Indian markets.
Neuroeconomic experiments led by Samuel McClure, David Laibson, George Loewenstein and Jonathan Cohen (Science, 2004) used functional MRI to show that immediate rewards activate the limbic system — the dopaminergic, emotion-laden, reward-craving substrate — while delayed rewards activate the lateral prefrontal cortex, the slow, deliberative, planning circuit. The brain is, in effect, running two competing valuation engines on the same opportunity, and which engine “wins” depends on whether the option set contains an immediate payoff. The mathematics that capture this dual system is exactly the β–δ model:
When β = 1, the agent is dynamically consistent — a rational long-term investor. When β < 1, the agent is sophisticated if they know they are biased and pre-commit (Laibson’s “golden eggs”), or naïve if they keep believing they will follow through but never do. The empirical Indian SIP-quitter is, on Laibson’s taxonomy, a textbook naïve hyperbolic discounter. Critically, the bias is not about ignorance or low intelligence — it is a feature of human neurology that operates equally in PhDs and pavement vendors.
The numbers from India’s market regulators and industry bodies are devastating:

The behavioural-finance research group at IIM Bangalore (Prof. V. Ravi Anshuman and colleagues) has documented the same pattern in Indian individual demat-account data: investors plan ten-year horizons and execute eighteen-month horizons. Importantly, the post-mortem self-reports almost never blame patience deficits — investors rationalise the early exit as “market timing,” “tax reasons,” “rebalancing,” or “needed the money.” Strotz predicted precisely this rationalisation in 1956: “The individual will perceive his current action as optimal even after it has reversed his prior plan.” Hyperbolic discounting is invisible to the discounter.
Sophisticated hyperbolic discounters do not try to “will-power” their way out of the bias. They bind their future selves with commitment devices. The literature offers a clean set:
Benjamin Graham (1949) wrote in The Intelligent Investor: “The investor’s chief problem — and even his worst enemy — is likely to be himself.” Graham did not name the bias hyperbolic discounting, but his entire prescription — fixed allocation rules, dollar-cost-averaging, ten-year valuation tests — is a structural commitment device against the present-bias self.
Warren Buffett’s 2000 Chairman’s Letter: “Inactivity strikes us as intelligent behaviour.” Buffett’s documented average holding period (forever for his “Inevitables” — Coke, See’s, Geico) is the corporate manifestation of an exponential, not hyperbolic, discount function. He literally does not value tomorrow much less than today, year over year.
Charlie Munger (Daily Journal Meeting, 2017): “The big money is not in the buying and the selling, but in the waiting.” Munger framed patience as a learned discipline that overrides the limbic system’s hyperbolic preference for immediate action.
Seth Klarman (Margin of Safety, 1991): Klarman wrote that “the most successful investors I have known have one quality in common: they don’t need action; they don’t crave it.” Klarman’s prescription — wait for the fat pitch, then commit decisively — is a textbook anti-hyperbolic-discounting trading rule.

Howard Marks (Memo “On the Couch”, January 2016): “The biggest investing errors come not from factors that are informational or analytical, but from those that are psychological.” Marks’s “second-level thinking” is, in part, a forced delay between stimulus and response — exactly the kind of executive-function intervention behavioural neuroscience prescribes.
Important framing: What follows is an educational case study of management process and capital-allocation discipline. It is NOT a buy/sell/hold recommendation, NOT a valuation call, and contains NO price target. We are using audited FY25 numbers to illustrate the corporate-behaviour analog of the day’s bias, nothing more.
Most listed Indian small-caps exhibit textbook hyperbolic-discounting behaviour at the corporate level: they over-borrow to chase the immediate revenue print, expense fast to show a near-term EBITDA margin, defer maintenance capex, and pay out cash to placate short-horizon shareholders even when reinvestment economics are attractive. Titan Biotech Ltd (a Delhi-headquartered biotech company manufacturing bio-products, peptones, yeast extracts, microbial culture media, and gelatin), through its audited FY25 consolidated financials, exhibits the mirror image: a quiet, exponentially-discounted capital plan that builds capacity over a decade rather than a quarter.
Consider the following marker → audited FY25 number → behavioural-process interpretation map:
| Marker | Audited FY25 Number | Behavioural-Process Interpretation |
|---|---|---|
| Gross fixed-asset build | ₹57 Cr (FY25) vs ₹11 Cr (FY15) — ~5.2x in 10 yrs | Capacity built over a decade, not a quarter; capex spread reflects a long-horizon (low-β) discount function, not present-bias spending bursts. |
| Borrowings trajectory | ₹3 Cr (FY25) vs ₹16 Cr (FY21) — 81% decline | Refusal to borrow against future cash flows for present growth — a corporate “no-leverage” commitment device that protects the long-run plan. |
| 10-yr Sales CAGR / 10-yr PAT CAGR | 15% / 29% | Profits compounded 2x faster than sales over a decade — fruit of patient operating-leverage harvesting, not quarterly maximisation. |
| CFO / Operating Profit | 103% (FY25); 85% (FY24); 97% (FY23) | Reported profits convert to actual cash >100% in FY25; management does not pull forward revenue or defer expenses to make the next quarter — an exponential, not hyperbolic, reporting culture. |
| CWIP trajectory | ₹4 Cr (Sep 2025), peaked ₹13 Cr (FY23) | Capex pipeline disciplined — projects move from CWIP to gross block on schedule rather than being abandoned for whatever is “hot now.” |
| Depreciation ratio (FY25) | ~7.0% of gross block (peers ~4–5%) | Management depreciates conservatively — they accept lower reported short-run EBITDA so the long-run asset base is honestly stated. |
| Contingent liabilities | ₹7.78 Cr (FY25) vs ₹12.90 Cr (FY24), −39.7% YoY; 5.08% of net worth | Off-balance-sheet risk actively retired rather than rolled forward — long-time-horizon risk hygiene. |
| FY26 quarterly revenue cadence | ₹46.50 Cr → ₹54 Cr → ₹56 Cr (Q1→Q2→Q3) | Three consecutive QoQ increases without leverage spikes — proof the patient capex laid in FY15-FY24 is now generating operating leverage. |
| Board structure | 11 directors; 4 independent (36.4%); 2 women (18.2%); independent chair; 14 board meetings in FY25 | Governance design itself is a Laibson “golden egg” — pre-commitment that prevents the management team from making any single high-β decision in isolation. |
What does this collection of numbers not tell you? It tells you nothing about whether Titan Biotech is “cheap” or “expensive” at ₹430. It tells you nothing about whether the stock will be higher or lower in 12 months. It is not a price call. What it does illustrate is a corporate process that behaves as if its decision-makers run an exponential discount function — they look at the 10-year asset base, the 10-year revenue and profit CAGR, the multi-year retirement of debt, and the 14 board meetings a year — rather than a hyperbolic one optimising for the next print. That, and only that, is the behavioural lesson of this section.
— Manish Goel / SEBI Registered Investment Advisor (INA100007736)
