Corroborate Benjamin Graham’s Approach of Valuing Equity with Special Reference to Indian Capital Market
Bibliographic record
Abstract
In this research we reconnoiter the effectiveness of Benjamin Graham’s formula for the Indian market and calculated the returns on BSE100 stocks for a tenure of a decade. Benjamin Graham devised a technique to calculate intrinsic value of stocks. His approach emphasized on buying the stocks with market value less than intrinsic value and selling the stocks with market value less than the intrinsic value. This strategy helped him to invest in stocks with less risk. The technique was originally developed by Graham in 1962 and reviewed by him in 1974. He offered a simple and effective formula to calculate the stock’s intrinsic value. Graham’s formula is used to measure an individual company’s intrinsic value. In this paper we wanted to study the effectiveness of Benjamin Graham’s formula on BSE100 stocks, to find out if the value investing method works. This method also helps investor to swiftly and precisely categorize underrated companies and expensive companies. We have conducted research based on past 10 years’ data to validate our findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".