This article presents an advanced visualization and analytics approach for financial research. Statistical arbitrage, particularly pairs trading strategy, has gained ground in the financial market and machine learning techniques are applied to the finance field. The cointegration approach and long short‐term memory (LSTM) were utilized to achieve stock pairs identification and price prediction purposes, respectively, in this project. This article focused on the US stock market, investigating the performance of pairs trading on different types of portfolios (aggressive and defensive portfolio) and compare the accuracy of price prediction based on LSTM. It can be briefly concluded that LSTM offers higher prediction precision on aggressive stocks and implementing pairs trading on the defensive portfolio would gain higher profitability during a specific period between 2016 and 2017. However, predicting tools like LSTM only offer limited advice on stock movement and should be cautiously utilized. We conclude that analytics and visualization can be effective for financial analysis, forecasting and investment strategy.

中文翻译：

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基于机器学习对不同投资组合进行交易

本文介绍了一种用于金融研究的高级可视化和分析方法。统计套利，尤其是成对交易策略，已在金融市场中占据一席之地，并且机器学习技术已应用于金融领域。在该项目中，协整方法和长短期记忆（LSTM）分别用于实现库存对的识别和价格预测的目的。本文着眼于美国股票市场，研究了在不同类型的投资组合（攻击性和防御性投资组合）上的交易对的表现，并比较了基于LSTM的价格预测的准确性。可以简单地得出以下结论：LSTM对激进股票提供更高的预测精度，而在防御性投资组合上实施配对交易将在2016年至2017年的特定时期内获得更高的盈利能力。但是，像LSTM这样的预测工具仅提供有限的股票走势建议，应该谨慎使用。我们得出结论，分析和可视化可以有效地进行财务分析，预测和投资策略。