چکیده مقاله
Stock market prediction is one of the most important challenges that data analysts face in the finance sector Therefore, a plethora of research projects have been carried out to facilitate forecasting the future of stock market prices Recently, Hidden Markov Model HMM has been successfully utilized for this purpose In this paper, we elaborate on combining HMM and Term Frequency Inverse Document Frequency TF IDF term weights using online political news to predict next day’s stock prices for a few selected companies in Iranian stock market The HMM we use is based on Maximum a Posteriori MAP estimation instead of common Maximum Likelihood Estimation MLE approach Ourresults show that some surprisingly huge changes in stock prices could well be forecasted by our model We compare the results of this study to other well known machine learning algorithm, Artificial Neural Network ANN , by using Mean Absolute Percentage Error MAPE
کلیدواژهها
نویسندگان
Nasrin Shabani
Tamadon Investment Bank, Tehran, Iran
Ahmad Kuchaki
Tamadon Investment Bank, Tehran, Iran
شیوه ارجاع
Shabani, Nasrin and Kuchaki, Ahmad,1398,Stock Prediction Using Hidden Markov Model: A Case-Study of Iran s Stock Market Reacting to Political Events,Sixth National Congress on Electrical Engineering and Computer Engineering of Iran with a New Approach to New Energy,Tehran
ارائهشده در
مجموعه مقالات ششمین کنگره ملی تازه های مهندسی برق و کامپیوتر ایران با نگاه کاربردی بر انرژی های نو12 اردیبهشت 1398 · تهران