In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) by including market sentiment indicators based on Google search volume and Twitter sentiment. We have analysed 30 com- panies of the Dow Jones index for a period of 15 months. We have performed out-of-sample forecast and compiled a ranking of the extended models based on their relative performance. We have identified three relevant variables: daily negative tweets, daily Google search volume and weekly Google search volume. These variables improve forecast accuracy of the HAR model se- parately or in a Twitter-Google combination. Some specifications improve forecast accuracy by up to 22% for particular stocks, others impair forecast accuracy b...
Collective intelligence represented as sentiment extracted from social media mining found applicati...
We retrieve news stories and earnings announcements of the S&P 100 constituents from two profession...
The purpose of this thesis is to explore various possibilities of performing online sentiment analys...
In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) b...
We investigate whether day-ahead forecasts of individual stocks' volatility can be improved with Int...
This thesis aims to investigate the usability of Google Trends data for predicting stock market vola...
Study of the forecasting models using large scale microblog discussions and the search behavior data...
The thesis investigates relationship between daily stock return volatility of Dow Jones Industrial A...
Study of the forecasting models using large scale microblog discussions and the search behavior data...
Abstract—Financial market prediction on the basis of online sentiment tracking has drawn a lot of at...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The stock markets in the rece...
The stock market is well known for its volatility and many models are proposed to capture the volati...
The aim of this paper is to investigate the impact of public sentiment on tail risk forecasting. I...
In recent years, stock market trending analysis and prediction have become one of the more popular r...
This paper explores the use of Google trending data as a indicator for market sentiment. The Google ...
Collective intelligence represented as sentiment extracted from social media mining found applicati...
We retrieve news stories and earnings announcements of the S&P 100 constituents from two profession...
The purpose of this thesis is to explore various possibilities of performing online sentiment analys...
In this thesis, we aim to improve forecast accuracy of a heterogenous au- toregressive model (HAR) b...
We investigate whether day-ahead forecasts of individual stocks' volatility can be improved with Int...
This thesis aims to investigate the usability of Google Trends data for predicting stock market vola...
Study of the forecasting models using large scale microblog discussions and the search behavior data...
The thesis investigates relationship between daily stock return volatility of Dow Jones Industrial A...
Study of the forecasting models using large scale microblog discussions and the search behavior data...
Abstract—Financial market prediction on the basis of online sentiment tracking has drawn a lot of at...
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The stock markets in the rece...
The stock market is well known for its volatility and many models are proposed to capture the volati...
The aim of this paper is to investigate the impact of public sentiment on tail risk forecasting. I...
In recent years, stock market trending analysis and prediction have become one of the more popular r...
This paper explores the use of Google trending data as a indicator for market sentiment. The Google ...
Collective intelligence represented as sentiment extracted from social media mining found applicati...
We retrieve news stories and earnings announcements of the S&P 100 constituents from two profession...
The purpose of this thesis is to explore various possibilities of performing online sentiment analys...