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- W4297129223 abstract "The normal distribution is one of the most important models in economics, biology and statistics. While many economists assume stock return is normally distributed, in this paper I will use code-generated programs to test that statement and see if the conclusion is true. In this paper, I employed mostly the Kolmogorov-Smirnov test to test the stock market at different lengths: from one-year period, three-year period to ten-year period. While I used samples from AAPL at first, I later used a more macro sample which is SPX500. Not only have I mentioned different methods for normality, but I have also suggested my one distinctive way of two-sample t-test to see if this test could be an accurate method. It seems that my sample sizes are generally large so I used the Kolmogorov-Smirnov test most of the time to ensure there is less bias. Using this test, I get the result that suggests most of the time stock return is not normally distributed. Using a two-sample t-test, I also get a similar result and it helps me to conclude when the sample size increase, using a two-sample t-test is indeed becoming a more reliable method." @default.
- W4297129223 created "2022-09-27" @default.
- W4297129223 creator A5082557305 @default.
- W4297129223 date "2022-01-01" @default.
- W4297129223 modified "2023-10-05" @default.
- W4297129223 title "Testing for Normality for Stock Returns" @default.
- W4297129223 cites W2162020807 @default.
- W4297129223 doi "https://doi.org/10.1007/978-981-19-5727-7_7" @default.
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