Improving Polarity Classification for Financial News Using Semantic Similarity Techniques

This article discusses polarity classification for financial news articles. The proposed Semantic Sentiment Analyser makes use of semantic similarity techniques, sentiment composition rules, and the Positivity/Negativity (P/N) ratio in performing polarity classification. An experiment was conducted to compare the performance of three semantic similarity metrics namely HSO, LESK, and LIN to find the semantically similar pair of word as the input word. The best similarity technique (HSO) is incorporated into the sentiment analyser to find the possible polarity carrier from the analysed text before performing polarity classification. The performance of the proposed Semantic Sentiment Analyser was evaluated using a set of manually annotated financial news articles. The results obtained from the experiment showed that the proposed SSA was able to achieve an F-Score of 90.89% for all cases classification

Improving the Accuracy of Stock Price Prediction using Ensemble Neural Network.

This paper describes performance of different classifiers (established/combinations/new prediction methods) that are used in predicting stock price. Artificial Neural Network (ANN) was chosen as the target candidates for the forecasting model in this work because of its ability to solve complex problems such as the stock price prediction. We experimented three types of neural network namely Feed Forward Neural Network (FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network (JRNN) and compared their predictions’ accuracy. We then designed an ensemble neural network that combined FFNN, JRNN and ERNN using bagging method to build a more accurate predictive model. Based on the results obtained, our proposed ENN outperformed the other ANNs by achieving the highest prediction’s accuracy

Multi agent and artificial neural networks prediction framework development for stock investment strategy

In personal wealth management, it is necessary to have a plan before making investment in order to ensure a profitable return for the investors. The process of , generating an investment portfolio with good investment options is complex as it needs to consider a lot of parameters such as the track record of the companies, the company's revenue projection, the risk assessment, the political conditions and the nature of business. In this case, a multi-agent framework can be applied to solve the problem. This thesis focuses on the development of a multi-agent framework for wealth management particularly on stock market investment. The core objective is to develop an Intelligent Investment Planner which utilizes multiple agents that work together to plan, predict, assemble and generate a profitable investment portfolio for its investor. Kuala Lumpur Stock Exchange (KLSE) was selected as the targeted stock market. Four types of agents were developed, including the Web Mining Agent (WMA), the Wealth Forecasting Agent (WFA), the Strategy Agent (SA), and the Wealth Planning Agent (WPA). WMA comprises of an algorithm for web mining which enables it to mine and extract semi-structured information and create new structured information using ontology. The ontology developed is not limited to just a knowledge base to store data in structured format but it plays an important role as an inference sources in the decision making of the buying and selling of stock by performing fundamental analysis. WFA consists of a forecasting model to predict the stock price. This work involves the investigation of the performance of different classifiers (established/combinations/new prediction methods) that are used in stock market prediction. Artificial Neural Network (ANN) was chosen as the target candidates for the forecasting model in this work because of its ability to solve complex problems such as the stock price prediction. Feed Forward Neural Network (FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network (JRNN) and Ensemble Neural Network (ENN) were tested in the experiments. Based on the results, ENN outperformed the other ANNs and so it was used in the stock market prediction. SA is responsible to generate the buy-sell signal based on the predicted stock prices. WPA generates the investment portfolio based on the buy-sell signal and the fundamental analysis of stock. It selects potential stocks based on investor's preferences and passes these potential stock candidates to WFA for stock price prediction. In turn, WPA decides on a suitable trading strategy that gives the most profitable Investment returns and presents the investment portfolio to the investor. Several experiments were conducted to investigate the performance of the Intelligent Investment Planner in different environments using two trading strategies and the results obtained showed that the proposed planner was able to generate a profitable investment portfolio

Multi-agent and artificial neural networks prediction framework development for stock investment strategy

In personal wealth management, it is necessary to have a plan before making investment in order to ensure a profitable return for the investors. The process of generating an investment portfolio with good investment options is complex as it needs to consider a lot of parameters such as the track record of the companies, the company’s revenue projection, the risk assessment, the political conditions and the nature of business. In this case, a multi-agent framework can be applied to solve the problem. This thesis focuses on the development of a multi-agent framework for wealth management particularly on stock market investment. The core objective is to develop an Intelligent Investment Planner which utilizes multiple agents that work together to plan, predict, assemble and generate a profitable investment portfolio for its investor. Kuala Lumpur Stock Exchange (KLSE) was selected as the targeted stock market. Four types of agents were developed, including the Web Mining Agent (WMA), the Wealth Forecasting Agent (WFA), the Strategy Agent (SA), and the Wealth Planning Agent (WPA). WMA comprises of an algorithm for web mining which enables it to mine and extract semi-structured information and create new structured information using ontology. The ontology developed is not limited to just a knowledge base to store data in structured format but it plays an important role as an inference sources in the decision making of the buying and selling of stock by performing fundamental analysis. WFA consists of a forecasting model to predict the stock price. This work involves the investigation of the performance of different classifiers (established/combinations/new prediction methods) that are used in stock market prediction. Artificial Neural Network (ANN) was chosen as the target candidates for the forecasting model in this work because of its ability to solve complex problems such as the stock price prediction. Feed Forward Neural Network (FFNN), Elman Recurrent Neural Network (ERNN), Jordan Recurrent Neural Network (JRNN) and Ensemble Neural Network (ENN) were tested in the experiments. Based on the results, ENN outperformed the other ANNs and so it was used in the stock market prediction. SA is responsible to generate the buy-sell signal based on the predicted stock prices. WPA generates the investment portfolio based on the buy-sell signal and the fundamental analysis of stock. It selects potential stocks based on investor’s preferences and passes these potential stock candidates to WFA for stock price prediction. In turn, WPA decides on a suitable trading strategy that gives the most profitable investment returns and presents the investment portfolio to the investor. Several experiments were conducted to investigate the performance of the Intelligent Investment Planner in different environments using two trading strategies and the results obtained showed that the proposed planner was able to generate a profitable investment portfolio

Analysing market sentiment in financial news using lexical approach

Business and financial news bring us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Therefore, extracting sentiments and opinions from business and financial news is useful as it may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We use polarity lexicon to identify the positive or negative polarity of each term in the corpus. We conducted two sets of experiment using non-stemming tokens and stemming tokens by considering individual word found in the newspaper. The preliminary results are presented and discussed in this paper