Author(s): Mohammad Sayrani, Jalal Sadeghi sharif
Forecasting financial data is one of the most important areas in financial markets. Forecasting is the process of making predictions using historical data and with the help of mathematical models. Predicting and reviewing financial time series data has always been one of the key areas of interest for capital market participants, including investors and analysts. Machine learning algorithms such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) have been widely used to predict financial time series and have been shown to outperform traditional linear models such as the Auto Regressive Integrated Moving Average (ARIMA). The goal is to forecast stock trading signals and establish a system that predicts when to buy and sell stocks to maximize profit. This paper integrates the piecewise linear representation (PLR) and the Zig Zag method into the Weighted Support Vector Machine (WSVM) to forecast stock Turning Points (TPs). The Relative Strength Index (RSI) is also used to determine whether the predicted TP is a buying point or selling point. 40 companies listed on the Tehran Stock Exchange (TSE) are examined with a daily unit of measurement between 2016 and 2019, of which 20 are top companies in terms of stock market index and 20 are randomly selected from non-top companies. The results indicate the poor performance of PLR in forecasting stock TPs in the TSE, although it is slightly more accurate than Zig Zag.