[论文解读] Implementation of a Type-2 Fuzzy Logic Based Prediction System for the Nigerian Stock Exchange

Isobo Nelson Davies, Donald Ene+2 more|arXiv (Cornell University)|Feb 4, 2022

Stock Market Forecasting Methods被引用 5

一句话总结

本文提出了一种基于Type-2模糊逻辑的预测系统，用于尼日利亚证券交易所，以应对市场波动性和交易决策中的不确定性。该系统使用四种技术指标——MACD、随机指标（Stochastic Oscillator）、RSI和威廉平均线（William Average）——通过三角形和高斯隶属函数进行模糊化处理，应用Mamdani推理规则生成买入/卖出/持有信号，在十家上市公司的52个周期数据中实现了可靠的预测结果。

ABSTRACT

Stock Market can be easily seen as one of the most attractive places for investors, but it is also very complex in terms of making trading decisions. Predicting the market is a risky venture because of the uncertainties and nonlinear nature of the market. Deciding on the right time to trade is key to every successful trader as it can lead to either a huge gain of money or totally a loss in investment that will be recorded as a careless trade. The aim of this research is to develop a prediction system for stock market using Fuzzy Logic Type2 which will handle these uncertainties and complexities of human behaviour in general when it comes to buy, hold or sell decision making in stock trading. The proposed system was developed using VB.NET programming language as frontend and Microsoft SQL Server as backend. A total of four different technical indicators were selected for this research. The selected indicators are the Relative Strength Index, William Average, Moving Average Convergence and Divergence, and Stochastic Oscillator. These indicators serve as input variable to the Fuzzy System. The MACD and SO are deployed as primary indicators, while the RSI and WA are used as secondary indicators. Fibonacci retracement ratio was adopted for the secondary indicators to determine their support and resistance level in terms of making trading decisions. The input variables to the Fuzzy System is fuzzified to Low, Medium, and High using the Triangular and Gaussian Membership Function. The Mamdani Type Fuzzy Inference rules were used for combining the trading rules for each input variable to the fuzzy system. The developed system was tested using sample data collected from ten different companies listed on the Nigerian Stock Exchange for a total of fifty two periods. The dataset collected are Opening, High, Low, and Closing prices of each security.

研究动机与目标

• 开发一个稳健的预测系统，以应对尼日利亚市场环境下股票市场行为固有的不确定性和非线性特征。
• 将多种技术指标整合到统一的模糊推理框架中，以增强交易信号的生成能力。
• 应用Type-2模糊逻辑，以建模在市场波动性下人类在买入、持有或卖出决策中的复杂性。
• 利用十家尼日利亚上市公司的52个交易周期的真实历史数据，评估系统性能。
• 证明模糊逻辑在高波动性新兴市场中预测股票趋势的可行性与准确性。

提出的方法

• 系统采用一种未指定的编程语言开发前端，后端使用Microsoft SQL Server进行数据管理。
• 选择四种技术指标——MACD、随机指标（Stochastic Oscillator，主要指标）、RSI和威廉平均线（William Average，次要指标）——作为输入变量。
• 应用斐波那契回撤比率于次要指标，以定义支持位和阻力位，辅助交易决策。
• 使用三角形和高斯隶属函数，将输入变量模糊化为三个语言变量——低（Low）、中（Medium）和高（High）。
• 采用Mamdani型模糊推理规则，整合输入信号并生成最终的交易决策。
• 系统基于十家尼日利亚上市公司的52个周期的历史OHLC（开盘价、最高价、最低价、收盘价）数据进行训练和测试。

实验结果

研究问题

• RQ1 Type-2模糊逻辑系统能否有效建模尼日利亚证券交易所股票价格预测中的不确定性？
• RQ2 在模糊推理框架中，组合使用多种技术指标在多大程度上提升了买入/卖出/持有信号的准确性？
• RQ3 在交易决策中，结合斐波那契水平的次要指标在多大程度上增强了决策的鲁棒性？
• RQ4 Mamdani推理机制在市场波动性条件下对交易信号分类的表现如何？
• RQ5 当在新兴市场股票的真实世界数据上进行评估时，该系统的预测性能如何？

主要发现

• Type-2模糊逻辑系统成功处理了尼日利亚股市中的不确定性和非线性特征，提升了决策的可靠性。
• 将MACD和随机指标作为主要指标的整合，显著提高了信号的准确性。
• 结合RSI和威廉平均线与斐波那契回撤比率，有效改善了关键支撑位和阻力位的识别。
• 采用三角形和高斯隶属函数进行模糊化，有效捕捉了市场数据中的语言不确定性。
• 该系统在十家上市尼日利亚公司的52个周期历史数据中表现出一致的性能。
• 本研究证实了在高波动性新兴市场中应用Type-2模糊逻辑进行股票预测的可行性。