  Due to the influence of many factors, including technical indicators on stock
price prediction, feature selection is important to choose the best indicators.
This study uses technical indicators and features selection and regression
methods to solve the problem of closing the stock market price. The aim of this
research is to predict the stock market price with the least error. By the
proposed method, the data created by the 3-day time window were converted to
the appropriate input for regression methods. In this paper, 10 regressor and
123 technical indicators have been examined on data of the last 13 years of
Apple Company. The results have been investigated by 5 error-based evaluation
criteria. Based on results of the proposed method, MLPSF has 56/47% better
performance than MLP. Also, SVRSF has 67/42% improved compared to SVR. LRSF was
76.7 % improved compared to LR. The RISF method also improved 72.82 % of Ridge
regression. The DTRSB method had 24.23 % improvement over DTR. KNNSB had 15.52
% improvement over KNN regression. RFSB had a 6 % improvement over RF. GBRSF
also improved at 7% over GBR. Finally, ADASF and ADASB also had a 4%
improvement over the ADA regression. Also, Ridge and LinearRegression had the
best results for stock price prediction. Based on results, the best indicators
to predict stock price are: the Squeeze_pro, Percentage Price Oscillator,
Thermo, Decay, Archer On-Balance Volume, Bollinger Bands, Squeeze and Ichimoku
indicator. According to the results, the use of suitable combination of
suggested indicators along with regression methods has resulted in high
accuracy in predicting the closing price.
