Stock-Market-Analysis-With-Python
Performing the Financial Analysis on Historic Stock Market Data such as calculating various risks, returns,etc.
- Simple_n_Log_returns : It predicts the daily and annual returns(simple and logarithmic) based on historic data.
Note : Simple returns are taken into consideration when we are considering multiple stocks while Logarithmmic return for individual stock consideration.
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Portfolio_returns : Predicting the combined returns of stocks in a portfolio.
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Var_cov_correlation : The variance refers to the spread of the data set—how far apart the numbers are in relation to the mean, for instance. Variance is particularly useful when calculating the probability of future events or performance. A covariance refers to the measure of how two random variables will change together and is used to calculate the correlation between variables. Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together.
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Standard Deviation : Standard deviation is a number used to tell how measurements for a group are spread out from the average (mean), or expected value i.e, what is the percentage that inverstors will be surprised by the result.
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Div_non_Div_risk : It calculates diversifiable risk and non-diversifiable risk i.e, risk that are beyond our control and risk that are organizational and can be controled.
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Regression : The relation between house prices and house area is taken and checked how close is the relation with lots of characteristics to see from.
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Markowitz_Algorithm : The algorithm that determines the best combination of weights in a pportfolio to get the minimum risk and maximum returns.
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BETA and Beta_for_PG : Beta is a variable that tells about how much a stock moves with respect to the market. If BETA < 1 then stock is defensive , if BETA > 1 then stock is aggressive, if BETA = 0 then there is no relationship.