Methods of Stock Selection by Graham-Newman

Graham-Newman Corp. was an open-ended mutual fund that Benjamin Graham ran in partnership with Jerome Newman. Their portfolio averaged about 20% returns on millions of dollars of capital that they managed on behalf of their clients.

This article briefly discusses some of the specific methods they used for selecting stocks for their portfolio during the 30-year period from 1926 to 1956. Be warned that these techniques are not the ordinary ones and may not get much credo on Wall Street. But they worked for Graham-Newman. The methods were first described in their book The Intelligent Investor.


They made use of arbitrages between companies in situations such as reorganization, merger, or acquisition. An arbitrage in such a situation will involve purchasing a security and simultaneously selling one or more other securities into which the security purchased was to be exchanged into after the merger.


This involved buying shares of the companies that were to receive cash payments from the liquidation of a company. They followed strict criteria to select such companies. 1) the company should have 20% plus annual returns. 2) They should feel atleast 4/5 probability of a successful outcome.

Related Hedges

For example, they would buy the convertible bonds of a company and simultaneously sell the common stock of the same company into which the convertible bond will convert into. They would make a profit if the common stock fell more that the senior issue, and the position closed out in the market.

Net Current Asset Issues

They would also look for really cheap issues. The idea was to buy issues at a cost less than the net current assets value alone, not accounting for the plant value and any other assets. The purchases would be made at 2/3 of this low estimate of the asset value.

They also indulged in other methods but some of them were discontinued because of unsatisfactory results. For example, use of unrelated hedges did not provide dependable results.

Learn the skills required to excel in data science and data analytics covering R, Python, machine learning, and AI.

Free Guides - Getting Started with R and Python

Enter your name and email address below and we will email you the guides for R programming and Python.

Saylient AI Logo

Take the Next Step in Your Data Career

Join our membership for lifetime unlimited access to all our data analytics and data science learning content and resources.