Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio

Abstract

Using the topological structure of financial networks to build a portfolio has attracted a wide range of research interests. A similarity matrix based on the technical indicators (TIs), and a correlation matrix based on the stock returns, are used to construct the financial networks. Hybrid topological measures are calculated for both networks to select the stocks that are further used to build portfolios. The random matrix theory (RMT) tool is used to filter the risk measurement in the Markowitz optimization process. A general result is presented in this study, which indicates that the portfolios composed of peripheral stocks consistently outperform those composed of central stocks. As shown in the empirical results, the RMT method can improve the Markowitz optimization process and further improve the performance of portfolios. By comparing the different portfolios established under different networks, the results reveal the advantage of the topological measure based on TIs. This study provides an important approach for constructing financial networks from the time series data, and it explores the impact of different similarity measurements on the network-based models for portfolio selection.

A Hybrid Data Analytics Framework with Sentiment Convergence and Multi-Feature Fusion for Stock Trend Prediction

“…However, since stock prices are inherently unpredictable in the short term, using historical trading data to analyze stock prices has its limitations and cannot further improve the prediction results. Behavioral economics theory states that investors are susceptible to personal and social emotions in complex and uncertain decision problems [18]. The main cause of stock price changes is the reaction to new information, and news in the media can be useful as exogenous sources of information for short-term stock price prediction [13,19].…”

“…To solve this problem, the long short-term memory (LSTM) model has been proposed as an improved version of RNN. Recent studies have shown that the LSTM outperforms the RNN and conventional machine learning algorithms such as random forest (RF), support vector machine (SVM), and decision tree (DT) in addressing stock prediction problems based on time series data [4,[18][19][20][21][22].…”

Portfolio Optimization for Several Industries among the U.S. Stock Market

“…They also concluded the result that the optimal portfolio is related to its holding period. Mo and Chen used the topological structure of the financial network to select stocks to build the portfolio and then used the random matrix theory to improve the performance of the portfolio by reducing its risk [7]. However, few studies aim to concentrate on industries like Online E-Commerce, Commercial Banks, Motor Vehicles, Mobile Communication Production, and Telecommunications.…”