An article published in the Notices of the American Mathematical Society looks into this investment practice and highlights the perils of performing a large volume of backtest on a historical data set that it would skew the results and favor the data set but would not be as practical or efficient in another out of sample set.
This is known as backtest overfitting. Since the model is based on past financial portfolios and tweaked to single out the best performing stocks, it may not be an efficient one and may just end up highlighting the extremes and vagaries in the financial data.
The article, "Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance" was written by David H. Bailey, Jonathan M. Borwein, Marcos Lopez de Prado, and Qiji Jim Zhu.
Founded in 1888 to further mathematical research and scholarship, today the more than 30,000 member American Mathematical Society fulfills its mission through programs and services that promote mathematical research and its uses, strengthen mathematical education, and foster awareness and appreciation of mathematics and its connections to other disciplines and to everyday life.
