Stock Prices Prediction using the Title of Newspaper Articles with Korean Natural Language Processing

2019 ◽  
Author(s):  
Hyungbin Yun ◽  
Ghudae Sim ◽  
Junhee Seok
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Newspaper Articles

scholarly journals Forecasting Net Income Estimate and Stock Price Using Text Mining from Economic Reports

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 292 ◽  
Author(s):  
Masahiro Suzuki ◽  
Hiroki Sakaji ◽  
Kiyoshi Izumi ◽  
Hiroyasu Matsushima ◽  
Yasushi Ishikawa
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Net Income ◽  
The Difference

This paper proposes and analyzes a methodology of forecasting movements of the analysts’ net income estimates and those of stock prices. We achieve this by applying natural language processing and neural networks in the context of analyst reports. In the pre-experiment, we applied our method to extract opinion sentences from the analyst report while classifying the remaining parts as non-opinion sentences. Then, we performed two additional experiments. First, we employed our proposed method for forecasting the movements of analysts’ net income estimates by inputting the opinion and non-opinion sentences into separate neural networks. Besides the reports, we inputted the trend of the net income estimate to the networks. Second, we employed our proposed method for forecasting the movements of stock prices. Consequently, we found differences between security firms, which depend on whether analysts’ net income estimates tend to be forecasted by opinions or facts in the context of analyst reports. Furthermore, the trend of the net income estimate was found to be effective for the forecast as well as an analyst report. However, in experiments of forecasting movements of stock prices, the difference between opinion sentences and non-opinion sentences was not effective.

scholarly journals A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Predictive Model ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Price Movement ◽  
Closing Price

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.

scholarly journals A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing

2021 ◽  
Author(s):  
Jaydip Sen ◽  
Sidra Mehtab
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Predictive Model ◽  
Language Processing ◽  
Stock Prices ◽  
Stock Price ◽  
Price Movement ◽  
Closing Price

Prediction of future movement of stock prices has been a subject matter of many research work. There is a gamut of literature of technical analysis of stock prices where the objective is to identify patterns in stock price movements and derive profit from it. Improving the prediction accuracy remains the single most challenge in this area of research. We propose a hybrid approach for stock price movement prediction using machine learning, deep learning, and natural language processing. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, and collect its daily price movement over a period of three years (2015 – 2017). Based on the data of 2015 – 2017, we build various predictive models using machine learning, and then use those models to predict the closing value of NIFTY 50 for the period January 2018 till June 2019 with a prediction horizon of one week. For predicting the price movement patterns, we use a number of classification techniques, while for predicting the actual closing price of the stock, various regression models have been used. We also build a Long and Short-Term Memory (LSTM)-based deep learning network for predicting the closing price of the stocks and compare the prediction accuracies of the machine learning models with the LSTM model. We further augment the predictive model by integrating a sentiment analysis module on Twitter data to correlate the public sentiment of stock prices with the market sentiment. This has been done using Twitter sentiment and previous week closing values to predict stock price movement for the next week. We tested our proposed scheme using a cross validation method based on Self Organizing Fuzzy Neural Networks (SOFNN) and found extremely interesting results.

scholarly journals Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing

2019 ◽  
Vol 9 (1) ◽  
pp. 2870-2875
Keyword(s):  
Natural Language Processing ◽  
Stock Market ◽  
Natural Language ◽  
Language Processing ◽  
Stock Prices ◽  
Market Price ◽  
Majority Voting ◽  
Svm Model ◽  
Market Trends ◽  
The Right

In the financial world, stock trading is one of the most crucial activities. Investors make educated guesses to predict stock market trends by analyzing news, studying the company history, industrial history and a lot of other data. successfully predicting the stock market trends and investing in the right shares at the right time can maximize the investor’s profit or at least minimize the losses. Stock market price data is generated in huge volumes and is affected by various diverse factors. This work proposes two models to predict the stock market prices. The first model is an LSTM model that employs a backpropagation optimized LSTM network to forecast future stock prices. The second model is a hybrid model that combines an SVM model, KNN model and a Random Forest classifier using the Majority Voting algorithm to predict stock market trends. Both models have a sentiment analyzer to factor the news influencing the stock market using Natural Language Processing (NLP). The project aims to help investors who are new to the stock market and don’t possess sufficient knowledge to make share investments as well the experienced investors by predicting stock market trends.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

1243-P: Novel Use of Natural Language Processing to Identify Reasons for Insulin Discontinuation in Patients with T2DM: A Real-World Evidence Study

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1243-P
Author(s):  
JIANMIN WU ◽  
FRITHA J. MORRISON ◽  
ZHENXIANG ZHAO ◽  
XUANYAO HE ◽  
MARIA SHUBINA ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Real World ◽  
Real World Evidence

WHO DO WE THINK WE ARE? COMPARING INTERSECTIONAL IDENTITY TRENDS IN ASEE AND CEEA-ACEG USING NATURAL LANGUAGE PROCESSING AND REVIEW OF PROCEEDINGS

2019 ◽  
Author(s):  
Pamela Rogalski ◽  
Eric Mikulin ◽  
Deborah Tihanyi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Activity Theory ◽  
Language Processing ◽  
Division Of Labour ◽  
Cultural Historical Activity Theory ◽  
Original Question ◽  
Micro Level ◽  
Historical Activity ◽  
Cultural Historical Activity

In 2018, we overheard many CEEA-AGEC members stating that they have "found their people"; this led us to wonder what makes this evolving community unique. Using cultural historical activity theory to view the proceedings of CEEA-ACEG 2004-2018 in comparison with the geographically and intellectually adjacent ASEE, we used both machine-driven (Natural Language Processing, NLP) and human-driven (literature review of the proceedings) methods. Here, we hoped to build on surveys—most recently by Nelson and Brennan (2018)—to understand, beyond what members say about themselves, what makes the CEEA-AGEC community distinct, where it has come from, and where it is going. Engaging in the two methods of data collection quickly diverted our focus from an analysis of the data themselves to the characteristics of the data in terms of cultural historical activity theory. Our preliminary findings point to some unique characteristics of machine- and human-driven results, with the former, as might be expected, focusing on the micro-level (words and language patterns) and the latter on the macro-level (ideas and concepts). NLP generated data within the realms of "community" and "division of labour" while the review of proceedings centred on "subject" and "object"; both found "instruments," although NLP with greater granularity. With this new understanding of the relative strengths of each method, we have a revised framework for addressing our original question.  

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