Price Trend Prediction Research Articles

Sentiment Analysis of Stock Blog Network Communities for Prediction of Stock Price Trends

It has been a challenge to develop a successful model for accurate stock price trend prediction. This paper aimed to develop an accurate model based on semantic analysis of social network communities for predicting stock day end closing prices using the wisdom of crowds ; www.mmb.moneycontrol.com, a financial blog covers all the companies listed on the National Stock Exchange of India. Influential and accurate opinions expressed in the blogs lead to community formation. The study focused on detection of such communities using betweenness centrality measure and performed a semantic analysis of their content to develop a prediction model based on correlation between blog sentiments and stock day end closing prices for predicting the stock trends. Thirty nine Indian banks were selected for the study during the period from October 1, 2017 to December 31, 2017 and the experimental results of the number of correct predictions of upside and downside movement of day end stock price were validated against the actual values. The model achieved a prediction accuracy of 84% and correlation of the model was within the significant limits of Z - test and Pearson's coefficient of 0.8.

A new graphic kernel method of stock price trend prediction based on financial news semantic and structural similarity

Lots of researches try to predict the stock price movement using financial news based on machine learning represented by SVM (Support Vector Machine). But almost all of them focus on the news contents while very few consider the information hiding in the relationship between different news. In this paper, we proposed a new kernel based on SVM concerning not only the contents themselves but also the information structures among them. As both the news contents and the information structures are imported into our kernel, this kernel is named as semantic and structural kernel, referred to S&S kernel. Medical industry financial news is used to illustrate the efficiency of our kernel. By comparing the predicting accuracy of S&S kernel with other kernels, such as linear kernel, we find our method outperforms the others by at least 5% on accuracy, which is a quite meaningful promotion. The result also confirms the information structure contained in daily financial news can offer extra information helping to predict the trend of stock price.

빅데이터를 활용한 다구간(多區間) 항공 경로 최적화 시스템

This paper proposes a multi-city flight route optimization system using big-data analytics. When one plans a travel for visiting a set of cities, the sequence of visiting should be determined so the total travel cost is minimized. Such problem is known as the travelling salesman problem (TSP) in the literature. We present a variant of TSP considering the dynamic travel costs between two cities and a total travel duration. We present a mixed integer programming formulation for solving the problem, with developing a flight price trend prediction model. The goal of the flight price trend prediction model is to reduce time and cost that are required to construct the route optimization problem. We employed big-data analytics to build the appropriate price trend prediction model that involves massive web crawling, statistical analysis, and regression. A web-based system was, then, built on the developed models for demonstrating the validity of the proposed approach.

A novel data-driven stock price trend prediction system

This paper proposes a novel stock price trend prediction system that can predict both stock price movement and its interval of growth (or decline) rate within the predefined prediction durations. It utilizes an unsupervised heuristic algorithm to cut raw transaction data of each stock into multiple clips with the predefined fixed length and classifies them into four main classes (Up, Down, Flat, and Unknown) according to the shapes of their close prices. The clips in Up and Down can be further classified into different levels reflecting the extents of their growth (or decline) rates with respect to both close price and relative return rate. The features of clips include their prices and technical indices. The prediction models are trained from these clips by a combination of random forests, imbalance learning and feature selection. Evaluations on the seven-year Shenzhen Growth Enterprise Market (China) transaction data show that the proposed system can make effective predictions, is robust to the market volatility, and outperforms some existing methods in terms of accuracy and return per trade.

Hybrid multiple structural break model for stock price trend prediction

Because of the noises from the internal and external factors, the uncertainty increases in the financial market. The challenges of nonlinearities, volatility clusters, and multiple structural breaks which entail risk. Due to the risk, the prediction task becomes more complex. First, this work proposes a hybrid model to predict the one-day future price for the stocks; MSFT, Apple, Goldman Sachs and JP Morgan use the Markov switching model coupled with radial basis function network for prediction. Second, this paper forecasts the buy/sell trading strategy using the proposed hybrid method. Also, this paper explores the risk of investment decisions and the trading performance based on different value at risk (VaR) methods. Finally, by comparing the proposed model results with the pure linear and non-linear models, the prediction efficiency is evaluated. The experimental results indicate the investment risk, and the investment trading strategy provides a better accuracy with the best investment decision for the selected stocks.

Stock Price Trend Prediction and Recommendation using Cognitive Process

The paper emphasizes on stock price trend prediction based on the online textual news. Cognitive process uses existing knowledge and generates new knowledge. Contextual features (CF) from news sites are extracted & recommendations based on the interpretations are generated. A Naïve bays classification algorithm is used to classify the news sentiments. A News Sentiment Index (NSI) is calculated and effect of the news on particular stock is calculated to predict the trend. Along with news sentiment index, technical quality of the same stock is calculated by various statistical technical indicators which are called as Stock Technical Index (STI). The weighted index of NSI and STI is used to predict the trend of stock price. In the previous recommendation systems, the context of the recommendation is not considered. However, it is shown in this research that if the authors consider the news context while recommendation, the performance of the recommendation system will drastically improve. The results are compared with traditional systems and it shows significant improvement.

Forecasting Oil Price Trends with Sentiment of Online News Articles

With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logistic regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.

Indicator selection with committee decision of filter methods for stock market price trend in ISE

Prediction of the stock market price direction is a challenging and important task of the financial time series. This study presents the prediction of the next day stock price direction by the optimal subset indicators selected with ensemble feature selection approach. The main focus is to obtain the final best feature subset which also yields good prediction of the next day price trend by removing irrelevant and redundant indicators from the dataset. For this purpose, filter methods are combined, support vector machines (SVM) has been carried out and finally voting scheme is applied. In order to conduct these processes, a real dataset obtained from Istanbul Stock Exchange (ISE) is used with technical and macroeconomic indicators. The result of this study shows that the prediction of the next day direction with reduced dataset has an improvement over the prediction of it with full dataset.

Forecasting Oil Price Trends with Sentiment of Online News Articles

With the rapid development of the Internet and big data technologies, a rich of online data (including news releases) can helpfully facilitate forecasting oil price trends. Accordingly, this study introduces sentiment analysis, a useful big data analysis tool, to understand the relevant information of online news articles and formulate an oil price trend prediction method with sentiment. Three main steps are included in the proposed method, i.e., sentiment analysis, relationship investigation and trend prediction. In sentiment analysis, the sentiment (or tone) is extracted based on a dictionary-based approach to capture the relevant online information concerning oil markets and the driving factors. In relationship investigation, the Granger causality analysis is conducted to explore whether and how the sentiment impacts oil price. In trend prediction, the sentiment is used as an important independent variable, and some popular forecasting models, e.g., logit regression, support vector machine, decision tree and back propagation neural network, are performed. With crude oil futures prices of the West Texas Intermediate (WTI) and news articles of the Thomson Reuters as studying samples, the empirical results statistically support the powerful predictive power of sentiment for oil price trends and hence the effectiveness of the proposed method.

Supervised classification of share price trends

Share price trends can be recognized by using data clustering methods. However, the accuracy of these methods may be rather low. This paper presents a novel supervised classification scheme for the recognition and prediction of share price trends. We first produce a smooth time series using zero-phase filtering and singular spectrum analysis from the original share price data. We train pattern classifiers using the classification results of both original and filtered time series and then use these classifiers to predict the future share price trends. Experiment results obtained from both synthetic data and real share prices show that the proposed method is effective and outperforms the well-known K-means clustering algorithm.

Intelligent stock trading system with price trend prediction and reversal recognition using dual-module neural networks

This article presents an intelligent stock trading system that can generate timely stock trading suggestions according to the prediction of short-term trends of price movement using dual-module neural networks(dual net). Retrospective technical indicators extracted from raw price and volume time series data gathered from the market are used as independent variables for neural modeling. Both neural network modules of thedual net learn the correlation between the trends of price movement and the retrospective technical indicators by use of a modified back-propagation learning algorithm. Reinforcing the temporary correlation between the neural weights and the training patterns, dual modules of neural networks are respectively trained on a short-term and a long-term moving-window of training patterns. An adaptive reversal recognition mechanism that can self-tune thresholds for identification of the timing for buying or selling stocks has also been developed in our system. It is shown that the proposeddual net architecture generalizes better than one single-module neural network. According to the features of acceptable rate of returns and consistent quality of trading suggestions shown in the performance evaluation, an intelligent stock trading system with price trend prediction and reversal recognition can be realized using the proposed dual-module neural networks.