A company whose share price has fallen 60% is not automatically cheap. A company whose share price is near its all-time high is not automatically expensive. Yet “it has already fallen so much” remains one of the most common reasons people buy, or keep averaging down.
We studied the long-term monthly price history of companies that became multibaggers and compared it with companies that destroyed shareholder wealth. This post sets out what the data showed, the characteristics that differed between the groups, and what this kind of research cannot tell you.
What we studied
Examples of Indian listed companies were picked with hindsight and sorted into three groups:
- Multibaggers: businesses that compounded wealth many times over.
- Fallen angels: companies that were once strong compounders and later deteriorated.
- Wealth destroyers: companies that lost most of their value and stayed down.
The multibagger companies in the study were:
Aarti Industries, Abbott India, Ajanta Pharma, Asian Paints, Astral, Bajaj Finance, Bajaj Finserv, Bajaj Holdings, Balkrishna Industries, Berger Paints, Biocon, Britannia, CCL Products, Dabur India, Deepak Nitrite, Divi’s Laboratories, Dr Reddy’s, Eicher Motors, Gabriel India, Godfrey Phillips, Godrej Consumer, Havells, HCL Technologies, HDFC Bank, Hindustan Unilever, Info Edge, Infosys, ITC, JSW Steel, Kotak Mahindra Bank, Larsen & Toubro, Marico, MRF, Nilkamal, Page Industries, PI Industries, Pidilite, Procter & Gamble Hygiene, Radico Khaitan, Shree Cement, Siemens, Solar Industries, SRF, Sun Pharma, TCS, Tata Elxsi, Titan, Trent, TVS Motor, UltraTech Cement and Wipro.
The fallen-angel and wealth-destroyer companies are not named. This post is about patterns, not judgements on individual businesses. Some well-known former compounders do appear in the fallen-angel group, which is itself a finding: past greatness does not last by default.
Each company was studied using monthly price data (each month’s open, high, low and close) over whatever history was available, so histories differ in length. The study notes describe groups of 51, 29 and 37 companies. The exact historical membership has not been fully reconciled, though: one source file holds 38 wealth-destroyer sheets, and two companies appear in both the fallen-angel and wealth-destroyer files. The original selection rules, such as a minimum return or fixed dates, were not recorded. Treat the groups as historical examples, not a precisely defined sample. The labels are historical, not a verdict on any company today.
These groups were picked after the outcomes were known, so everything below describes the past. It is not a formula for finding the next multibagger, and it is not foolproof.
The trend test behind most of the measures
Most measures use one rule built on the 10-month moving average, the average of the last ten monthly closes. That is ten months, not ten days. A company is in a healthy trend in a given month when both of these hold:
- the month’s close is above its 10-month average, and
- that average is higher than it was two months earlier.
The share of months a company spends in this state is its trend share.
Example: a company has 200 months of usable history, counted from when the 10-month average first becomes available. It was in a healthy trend in 80 of them. Its trend share is 80 ÷ 200 = 40%, above the 35% cut-off used in the study. A trend share is only meaningful once there are at least 24 such months, which takes about three years of monthly data.
Finding 1: multibaggers spent far more time in healthy trends and near their highs
| What was measured (group average) | Multibaggers | Fallen angels | Wealth destroyers |
|---|---|---|---|
| Months in a healthy trend | 60.6% | 49.5% | 33.4% |
| Months with a positive return | 56.8% | 53.6% | 44.8% |
| Months making a higher monthly low | 61.2% | 58.3% | 46.5% |
| Months closing at a new high | 22.3% | 19.2% | 4.7% |
| Months closing within 20% of the highest monthly close so far | 66.0% | 55.0% | 14.4% |
| Overlapping 5-year periods with a positive price return | 95.5% | 76.3% | 38.8% |
| Months the company beat the Nifty when the Nifty fell more than 5% | 57.0% | 50.0% | 28.4% |
| Longest spell below a falling 10-month average (months) | 13.1 | 13.4 | 22.2 |
| Worst fall from a peak (monthly closes) | −69.0% | −59.8% | −94.8% |
The largest gap is time spent near the high: about two-thirds of the history for multibaggers against about one-seventh for wealth destroyers. Multibaggers repeatedly made new highs and higher lows; wealth destroyers mostly did not.
Wealth destroyers had good months too. The size of their average winning month relative to their average losing month (1.60×) was almost the same as for multibaggers (1.65×). The difference lay in how often they rose and whether the gains lasted.
Finding 2: multibaggers’ worst falls mostly came with the market; wealth destroyers’ did not
Multibaggers also suffered deep falls, around 70% at their worst. To see what else differed, we took each company’s single worst fall (peak to bottom, monthly closes) and looked at the Nifty 50 over the same months:
| During each company’s worst fall | Multibaggers | Fallen angels | Wealth destroyers |
|---|---|---|---|
| Median worst fall | −70% | −63% | −96% |
| Median change in the Nifty 50 from the company’s peak month to its bottom month | −24% | +5% | +59% |
| The Nifty was higher at the company’s bottom than at its peak | 8 of 36 (22%) | 13 of 22 (59%) | 21 of 25 (84%) |
| The bottom came while the Nifty was 20% or more below its own high | 30 of 41 (73%) | 8 of 22 (36%) | 14 of 25 (56%) |
| Median length of the fall | 21 months | 25 months | 97 months |
| Back above the old peak by the end of the data | 41 of 45 (91%) | 11 of 22 (50%) | 5 of 25 (20%) |
| Median time to regain the peak, for those that did | 22 months | 18 months | 96 months |
What the data shows:
- Multibaggers’ worst bottoms clustered in market-wide sell-offs: the 2000–03 dot-com bust (Infosys, Wipro, HCL Technologies), the 2008–09 global financial crisis (17 companies, including JSW Steel, UltraTech, Shree Cement, Siemens and HDFC Bank) and the March 2020 Covid crash (Page Industries, Godrej Consumer and others). By the end of the data, 41 of 45 had climbed back above their old peak, with a median of 22 months for the climb.
- For most wealth destroyers, the Nifty rose while the company fell. In 21 of 25 cases the index was higher at the company’s bottom than at its peak, with a median rise of 59%, while the company’s median fall was 96%. The median fall lasted 97 months, about eight years. Twelve of them made their final bottom in 2020, nine in the March 2020 crash, at the end of declines that had run for years. By the end of the data, 5 of 25 had regained their old peak.
- Fallen angels sat in between. In 13 of 22 cases the Nifty was higher at their bottom than at their peak.
A caution on reading this: a wealth destroyer’s fall lasted much longer, which gave the index more time to rise. And a bottom that coincides with a market crash does not prove the market caused the whole decline. The pattern describes what happened in this sample; it does not establish cause.
Finding 3: winners made their lows early; destroyers kept making new ones
A separate saved report covering the 51 multibaggers recorded when each made its lowest recorded price:
| Lowest recorded price occurred within | Companies (of 51) | Share |
|---|---|---|
| The first 12 months of recorded data | 11 | 21.6% |
| The first 24 months | 31 | 60.8% |
| The first 36 months | 35 | 68.6% |
The other side is late new lows: a company that is already five or ten years into its history and still closing below every monthly close it has recorded.
| Made a new lowest monthly close… | Multibaggers | Fallen angels | Wealth destroyers |
|---|---|---|---|
| after 5 years of data | 4 of 45 (9%) | 1 of 22 (5%) | 18 of 25 (72%) |
| after 10 years of data | 0 of 45 (0%) | 0 of 15 (0%) | 12 of 24 (50%) |
Most eventual winners had their worst moment early, then spent years making higher lows. That does not mean a young company that is falling will become a winner. But in this sample, a company still setting fresh lows after five or ten years of data was far more often a wealth destroyer than a multibagger.
Finding 4: the length of the damage differed more than the depth
A 70% fall needs a 233% rise just to get back to the old peak, so depth matters. But the bigger contrast was in duration: a median of 21 months for multibaggers’ worst falls against 97 months for wealth destroyers, whose recoveries kept failing and whose lows kept getting lower.
Finding 5: past winners are not protected forever
Fallen angels were once excellent compounders. On almost every measure in Finding 1 they sit between the two other groups, and in most cases their worst fall came while the Nifty was flat or rising. Business economics, competition and management decisions change, and the price history reflected it.
The characteristics side by side
These are the cut-offs used in the study to describe each pattern. They are design choices for describing price history, not proven optimal values or trading signals.
| Characteristic | Pattern more common among multibaggers | Pattern more common among wealth destroyers |
|---|---|---|
| Current trend | Monthly close above a rising 10-month average | Monthly close below a falling 10-month average |
| Trend share | Healthy trend in more than 35% of months | Healthy trend in 35% of months or fewer |
| Longest run below the 10-month average | Under 24 consecutive months | 24 consecutive months or more |
| Highs and lows | Repeated new highs and higher lows; much time near the high | Failed recoveries and lower lows; rarely near the high |
| Strength versus the Nifty (company price ÷ Nifty) | That ratio in a healthy trend more than 35% of the time | That ratio in a healthy trend less than 35% of the time |
| Worst fall versus the Nifty | The price-to-Nifty ratio held up | The ratio fell 90% or more from its peak (a 90% loss relative to the Nifty, not necessarily a 90% price fall) |
| Deep falls | Mostly during market-wide corrections, followed by recovery | Mostly while the market was rising, lasting years |
| New lows late in the history | Rare after 5 years; none after 10 years in this sample | Common after 5 and even 10 years |
One more pattern the study tracked as an early sign of deterioration: six consecutive monthly closes below the 10-month average, while that average is lower than it was two months earlier. It marks a change in behaviour. On its own it does not say whether the cause is a market-wide correction or a problem in the business.
You need only a few multibaggers
Wealth creation over a lifetime does not need dozens of multibaggers. A few, held with meaningful size, can do most of the work. Simple arithmetic shows why:
- ₹10 lakh split equally across 10 companies (₹1 lakh each). One becomes a 20-bagger and the other nine end flat. The portfolio is worth ₹20 lakh + ₹9 lakh = ₹29 lakh, 2.9 times the money.
- The same winner held at ₹2.5 lakh (a quarter of the portfolio), with the other ₹7.5 lakh flat: ₹50 lakh + ₹7.5 lakh = ₹57.5 lakh, 5.75 times the money.
Size works in both directions. Had that ₹2.5 lakh gone into a company that fell 96%, the median worst fall of the wealth destroyers in this study, it would have lost ₹2.4 lakh: almost a quarter of the portfolio. And holding a genuine multibagger meant sitting through falls of around 70% along the way. That is why identifying the business correctly matters more than anything else.
Not foolproof: price history plus fundamental analysis
Price history describes how investors behaved. It cannot show whether a company:
- has honest management that treats minority shareholders fairly;
- turns its profits into real cash;
- earns good returns on the money it reinvests;
- has a lasting competitive advantage and room to grow;
- is available at a sensible price.
These characteristics are not a stock-picking system on their own. Sound research combines both: fundamental analysis to judge whether the business is good and fairly priced, and price history to see whether the market has recognised it and whether something may be changing. A strong chart cannot make up for weak economics or a governance problem, and a weak chart alone does not prove that management is dishonest.
Data traps we found
- The first price is not always the IPO. Many older companies listed on the BSE or a regional exchange years before their NSE listing. A data provider’s first date can be decades late.
- Splits, bonuses and mergers can fake a crash. If a 5-for-1 split is not adjusted in the data, the chart shows an 80% “fall” that never happened.
- Missing history is unknown, not bad. A three-year-old company cannot pass a ten-year test. That is a gap, not a red flag.
That is why a company’s lowest price is described here as the lowest in the data held, not a confirmed lifetime low.
The limits of this research
- Hindsight bias. The winners and losers were chosen after the fact. The same cut-offs may not separate future winners from losers.
- Survivorship. Companies that disappeared or delisted are under-represented in most price data.
- Overlapping periods. Rolling five-year windows share most of their months, so they are not independent tests.
- Not yet re-validated. Before treating any figure here as a rule, it should be re-tested on companies and periods it was not built from.
The takeaway
In this sample, the useful distinction was between a temporary correction in a sound business, which mostly happened alongside the market and was followed by recovery, and persistent deterioration, which went on for years even while the market rose. A large fall alone did not tell the two apart, and being near a high was not a warning sign in itself. Monthly price history helps show which pattern a company resembles. Fundamental analysis shows which one it really is.
I am NOT registered with SEBI as a Research Analyst, an Investment Adviser or a Portfolio Manager. This is historical research shared for education only. It is not investment advice, not a recommendation to buy or sell any security, and not indicative of future results. Company names are used only to describe the study sample. The portfolio examples are simple arithmetic illustrations, not recommendations.