The stock market is a key pivot in every growing and thriving economy, and every investment in the market is aimed at maximising profit and minimising associated risk. As a result, numerous studies have been conducted on the stock-market prediction using technical or fundamental analysis through various soft-computing techniques and algorithms. This study attempted to undertake a systematic and critical review of about one hundred and twenty-two (122) pertinent research works reported in academic journals over 11 years (2007–2018) in the area of stock market prediction using machine learning. The various techniques identified from these reports were clustered into three categories, namely technical, fundamental, and combined analyses. The grouping was done based on the following criteria: the nature of a dataset and the number of data sources used, the data timeframe, the machine learning algorithms used, machine learning task, used accuracy and error metrics and software packages used for modelling. The results revealed that 66% of documents reviewed were based on technical analysis; whiles 23% and 11% were based on fundamental analysis and combined analyses, respectively. Concerning the number of data source, 89.34% of documents reviewed, used single sources; whiles 8.2% and 2.46% used two and three sources respectively. Support vector machine and artificial neural network were found to be the most used machine learning algorithms for stock market prediction.

中文翻译：

---

对股市预测的基本面和技术分析的系统回顾

股票市场是每一个不断增长和繁荣的经济体的关键支点，市场上的每一项投资都旨在实现利润最大化和相关风险的最小化。因此，通过各种软计算技术和算法，使用技术或基本面分析对股市预测进行了大量研究。本研究试图对 11 年（2007-2018 年）在使用机器学习的股票市场预测领域的学术期刊上报道的大约一百二十二 (122) 项相关研究工作进行系统和批判性审查。从这些报告中确定的各种技术分为三类，即技术分析、基本分析和组合分析。分组是根据以下标准完成的：数据集的性质和使用的数据源数量，数据时间框架、使用的机器学习算法、机器学习任务、使用的准确度和误差指标以及用于建模的软件包。结果显示，66% 的审查文件是基于技术分析；而 23% 和 11% 分别基于基本面分析和综合分析。从数据来源的数量来看，89.34%的审查文件使用单一来源；而 8.2% 和 2.46% 分别使用了两个和三个来源。支持向量机和人工神经网络被发现是最常用的股票市场预测机器学习算法。结果显示，66% 的审查文件是基于技术分析；而 23% 和 11% 分别基于基本面分析和综合分析。从数据来源的数量来看，89.34%的审查文件使用单一来源；而 8.2% 和 2.46% 分别使用了两个和三个来源。支持向量机和人工神经网络被发现是最常用的股票市场预测机器学习算法。结果显示，66% 的审查文件是基于技术分析；而 23% 和 11% 分别基于基本面分析和综合分析。从数据来源的数量来看，89.34%的审查文件使用单一来源；而 8.2% 和 2.46% 分别使用了两个和三个来源。支持向量机和人工神经网络被发现是最常用的股票市场预测机器学习算法。