scholarly journals Forecasting stock market return with nonlinearity: a genetic programming approach

2020 ◽  
Vol 11 (11) ◽  
pp. 4927-4939 ◽  
Author(s):  
Shusheng Ding ◽  
Tianxiang Cui ◽  
Xihan Xiong ◽  
Ruibin Bai
Keyword(s):  
Stock Market ◽  
Genetic Programming ◽  
Programming Approach ◽  
Market Return ◽  
Stock Market Return

In Search of Stock Market Proxy to Calculate Systematic Risk (Beta) of Stocks in Indonesia Stock Exchange

2020 ◽  
Vol 29 (2) ◽  
pp. 80-88
Author(s):  
Mochammad Chabachib
Keyword(s):  
Stock Market ◽  
Real World ◽  
Stock Price ◽  
Price Index ◽  
Stock Exchange ◽  
The Other ◽  
Stock Index ◽  
Market Return ◽  
Stock Market Return ◽  
Stock Index Returns

The calculation of beta stock in Indonesia is still debatable to this day. Though many researchers who have used sophisticated methods mathematically, the assumptions applied in developing the methods are impossible to happen in the real world, such as the ability of stock market return the day after (lead) affects the market return today. This study was conducted to assess the stock price index in Indonesia Stock Exchange that can be used as a proxy of stock market in Indonesia. The results of this study showed that there was a gap between beta stocks counted with JCI return as a market proxy with beta stocks counted with index returns of LQ-45, SRI-KEHATI, PEFINDO-25, BISNIS-27, IDX-30 and KOMPAS-100. This study has also found that the beta counted by using KOMPAS-100 return produced the smallest standard error of the estimate (SEE) that it was more applicable compared to the other stock index returns.

scholarly journals Capital Asset Pricing Model, Theory and Practice: Evidence from USA (2009-2016)

2017 ◽  
Vol 12 (8) ◽  
pp. 182 ◽  
Author(s):  
Mohammad AbdelMohsen Al-Afeef
Keyword(s):  
Stock Market ◽  
Multiple Regression Model ◽  
Theory And Practice ◽  
Pricing Model ◽  
Stock Market Index ◽  
Market Return ◽  
Market Index ◽  
Stock Market Return ◽  
The Us ◽  
The Impact

This study discussed the Capital Assets Pricing model (CAPM) and its ability to measure the required return, the researcher tested this model on Amazon Company listed in S&P 500 during the period (2009-2016), to measure the impact of beta stock and market index return on the required return. Multiple regression model was used to test the effect of independent variables (Beta stock, Market Index Return) on the dependent variable (Required return), it should be noted that there is a statistically significant impact of the US stock market Return (S&P500) and Amazon stock Beta factor on Amazon stock required return, and the study model explanatory was 20% , this means that 20% of the changes in the required return are due to beta and market return, and 80% of the changes due to other factors, also find that CAPM can be applied on efficiency markets and huge companies.The researcher recommends applying the variables of the study on a group of large companies in the S&P 500 index, and looking for other factors that may affect the required return.

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