Predicting Indian stock market using the psycho-linguistic features of financial news
Financial forecasting using news articles is an emerging field. In this paper, we proposed hybrid intelligent models for stock market prediction using the psycholinguistic variables (LIWC and TAALES) extracted from news articles as predictor variables. For prediction purpose, we employed various intelligent techniques such as Multilayer Perceptron (MLP), Group Method of Data Handling (GMDH), General Regression Neural Network (GRNN), Random Forest (RF), Quantile Regression Random Forest (QRRF), Classification and regression tree (CART) and Support Vector Regression (SVR). We experimented on the data of 12 companies stocks, which are listed in the Bombay Stock Exchange (BSE). We employed chi-squared and maximum relevance and minimum redundancy (MRMR) feature selection techniques on the psycho-linguistic features obtained from the new articles etc. After extensive experimentation, using the Diebold-Mariano test, we conclude that GMDH and GRNN are statistically the best techniques in that order with respect to the MAPE and NRMSE values.
Code (0)
등록된 구현이 없습니다.
Tasks
Articlesfeature selectionquantile regressionregressionStock Market PredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Is the Indian Stock Market efficient - A comprehensive study of Bombay Stock Exchange Indices
How an investor invests in the market is largely influenced by the market efficiency because if a market is efficient, it is extremely difficult to make excessive returns because in an efficient market there will be no u…
Time SeriesTime Series AnalysisA Study on the Efficiency of the Indian Stock Market
The efficiency of the stock market has a significant impact on the potential return on investment. An efficient market eliminates the possibility of arbitrage and unexploited profit opportunities. This study analyzes the…
FormEffect of Demonetisation of on Indian High Denomination Currencies on Indian Stock Market and its Relationship with Foreign Exchange Rate
This study examines the impact of the foreign exchange rate, i.e., US Dollar to Indian Rupee (USD/INR) on the Indian Stock Market Index (Nifty 50) during the demonetization of high denomination Indian currencies. A daily…
Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach
Our research presents a new approach for forecasting the synchronization of stock prices using machine learning and non-linear time-series analysis. To capture the complex non-linear relationships between stock prices, w…
ManagementTime SeriesTime Series AnalysisPredicting Stock Prices using Permutation Decision Trees and Strategic Trailing
In this paper, we explore the application of Permutation Decision Trees (PDT) and strategic trailing for predicting stock market movements and executing profitable trades in the Indian stock market. We focus on high-freq…