Randomly walking through Wall Street

James Braselton, John Rafter, Patricia Humphrey and Martha Abell

Mathematics and Computers in Simulation (MATCOM), 1999, vol. 49, issue 4, 297-318

Abstract: The daily closing values of the S&P 500 Index from January 1, 1926 through June 11, 1993, a total of 17,610 values, were entered into Mathematica, and the day-to-day percent changes were calculated. Using the Standard Mathematica Package Statistics ‵ContinuousDistributions‵ and the built-in function NonLinearFit, procedures were developed to find the probability distribution that best models these daily changes. Although the log-normal distribution has been used traditionally, we found that a logistic distribution provides the best model, having a coefficient of determination 0.998. Using this model and Mathematica to simulate stock market performance we have found that, although the short-term changes in the stock market can often be explained by world events, longer-term behavior of the market can be modeled with accuracy. Simulations for time periods between 6 months and 10 years show that, although dollar-cost average investing has less volatility, the long-term investor can expect a higher return from a lump-sum investment.

Keywords: Dollar-cost averaging; Lump-sum averaging; Logistic distribution (search for similar items in EconPapers)
Date: 1999
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:49:y:1999:i:4:p:297-318

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