Nairobi Securities Exchange Prices 2008-2012 for 6 selected stocks

Stock market prediction remains active research in a quest to inform investors on how to trade (buy/sell) at the most opportune time. The prevalent methods used by stock market players in trying to predict the likely future trade prices are either technical, fundamental or time series analysis. This research wanted to try out machine learning methods, in contrast to the existing prevalent methods. Artificial neural networks (ANNs) tend to be the preferred machine learning method for this type of application. However, ANNs require some historical data to learn from, in order to do predictions. The research used an ANN model to test the hypothesis that the next day price (prediction) can be determined from the stock prices of the immediate last five days.

The final ANN model used for the tests was a feedforward multi-layer perceptron (MLP) with error backpropagation, using sigmoid activation function, with network configuration 5:21:21:1. The data period used was a 5-year dataset (2008 to 2012), with 80% of the data (4-year data) used for training and the balance 20% used for testing (last 1-year data).

The original raw data for Nairobi Securities Exchange (NSE) was scrapped from a publicly available and accessible website of a stock market analysis company in Kenya (Synergy, 2020). This daily prices data was first exported to a spreadsheet, then cleaned off headers and other redundant information, leaving only the data with stock name, date of trade and the related data such as volumes, low prices, high prices and adjusted prices. The data was further sorted by the stock names and then the trading dates. The data dimension was finally reduced to only what was needed for the research, which was the stock name, the date of trade and the adjusted price (average trade price). This final dataset was in CSV format, as hereby presented.

The research tested three NSE stocks with the mean absolute percentage error (MAPE) ranging between 0.77% to 1.91%, over the 3-month testing period, while the root mean squared error (RMSE) ranged between 1.83 and 3.07.

This raw data can be used to train and test any machine learning model that requires training and testing data. The data can also be used to validate and reproduce the results already presented in this research. There could be slight variance between what is obtained when reproducing the results, due to the differences in the final exact weights that the trained ANN model eventually achieves. However, these differences should not be significant.

List of data files on this dataset:
stock01_NSE_01jan2008_to_31dec2012_Kakuzi.csv
stock02_NSE_01jan2008_to_31dec2012_StandardBank.csv
stock03_NSE_01jan2008_to_31dec2012_KenyaAirways.csv
stock04_NSE_01jan2008_to_31dec2012_BamburiCement.csv
stock05_NSE_01jan2008_to_31dec2012_Kengen.csv
stock06_NSE_01jan2008_to_31dec2012_BAT.csv

References:
Synergy Systems Ltd. (2020). MyStocks. Retrieved March 9, 2020, from http://live.mystocks.co.ke/

股票市场预测研究持续活跃，旨在为投资者提供何时进行交易（买入/卖出）以获取最佳时机的信息。股票市场参与者用以预测未来交易价格的常用方法主要包括技术分析、基本面分析或时间序列分析。本研究旨在尝试运用机器学习方法，与现有的主流方法形成对比。人工神经网络（ANN）往往成为此类应用的首选机器学习方法。然而，ANN需要从历史数据中学习，以便进行预测。本研究采用ANN模型来验证假设，即次日股价（预测值）可以由最近五天的股票价格确定。最终用于测试的ANN模型为前馈多层感知器（MLP），采用误差反向传播算法，使用sigmoid激活函数，网络配置为5:21:21:1。所使用的数据周期为5年数据集（2008年至2012年），其中80%（4年数据）用于训练，剩余的20%（最后1年数据）用于测试。原始的纳伊罗比证券交易所（NSE）数据来自肯尼亚一家公开可访问的股票市场分析公司（Synergy，2020）的网站。首先将每日价格数据导出为电子表格，然后去除标题和其他冗余信息，仅保留包含股票名称、交易日期及相关数据（如成交量、最低价、最高价和调整价）的数据。随后按股票名称和交易日期对数据进行排序。最终将数据维度缩减至研究所需，即股票名称、交易日期和调整价（平均交易价）。最终的数据库以CSV格式呈现，如下所述。研究对三种NSE股票进行了测试，平均绝对百分比误差（MAPE）在0.77%至1.91%之间，测试周期为3个月，而均方根误差（RMSE）在1.83至3.07之间。原始数据可用于训练和测试任何需要训练和测试数据的机器学习模型。数据还可用于验证和重现本研究所呈现的结果。由于训练的ANN模型最终达到的精确权重可能存在细微差异，因此在重现结果时可能会出现轻微的偏差。然而，这些差异不应具有显著性。本数据集包含的数据文件清单如下：
stock01_NSE_01jan2008_to_31dec2012_Kakuzi.csv
stock02_NSE_01jan2008_to_31dec2012_StandardBank.csv
stock03_NSE_01jan2008_to_31dec2012_KenyaAirways.csv
stock04_NSE_01jan2008_to_31dec2012_BamburiCement.csv
stock05_NSE_01jan2008_to_31dec2012_Kengen.csv
stock06_NSE_01jan2008_to_31dec2012_BAT.csv

参考文献：Synergy Systems Ltd.（2020）.MyStocks.检索于2020年3月9日，来自http://live.mystocks.co.ke/。