Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction

Pei‐Chann Chang · Chin-Yuan Fan · Chen-Hao Liu

135 citations0/0 files storedIEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Stock Market Forecasting Methods Neural Networks and Applications Time Series Analysis and Forecasting

Abstract

Recently, the piecewise linear representation (PLR) method has been applied to the stock market for pattern matching. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. Thus, it further increases the profitability of the model. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 135 citations exist upstream.

References

2003Support vector machine with adaptive parameters in financial time series forecasting 2000Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index 1990Stock market prediction system with modular neural networks 2002Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index 1996Introduction to financial forecasting 2006A TSK type fuzzy rule based system for stock price prediction 2002Predicting stock price performance: a neural network approach 2001Hybrid Intelligent Systems for Stock Market Analysis

Cited by

2011A novel model by evolving partially connected neural network for stock price trend forecasting 2016Stock trend prediction based on a new status box method and AdaBoost probabilistic support vector machine 2012Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction 2015A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting 2011

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.