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.

翻译：由于技术指标等因素对股票价格预测的影响，特征选择对于选取最优指标至关重要。本研究采用技术指标及特征选择与回归方法来解决股票收盘价预测问题，旨在以最小误差预测股票市场价格。通过所提方法，将3天时间窗口生成的数据转换为适合回归方法的输入形式。本文对苹果公司过去13年的数据中10种回归器与123个技术指标进行了分析，并基于5种误差评估准则展开研究。结果表明：MLPSF相较于MLP性能提升56.47%，SVRSF比SVR改善67.42%，LRSF较LR提高76.7%，RISF方法使岭回归性能提升72.82%，DTRSB相比DTR改进24.23%，KNNSB较KNN回归提升15.52%，RFSB对RF改进6%，GBRSF相较GBR提升7%，ADASF与ADASB较ADA回归各自提升4%。此外，Ridge回归与LinearRegression在股票价格预测中表现最佳。最佳预测指标包括：Squeeze_pro、百分比价格振荡器、Thermo、Decay、Archer平衡交易量、布林带、Squeeze及一目均衡表。研究结果表明，采用建议指标与回归方法的优化组合可显著提升收盘价预测精度。