Using four different online media sources to forecast the crude oil
price
- URL: http://arxiv.org/abs/2105.09154v1
- Date: Wed, 19 May 2021 14:19:18 GMT
- Title: Using four different online media sources to forecast the crude oil
price
- Authors: M. Elshendy, A. Fronzetti Colladon, E. Battistoni, P. A. Gloor
- Abstract summary: The study analyses, over a period of two years, the relationship between the West Texas Intermediate daily crude oil price and multiple predictors extracted from Twitter, Google Trends, Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT)
Results show that the combined analysis of the four media platforms carries valuable information in making financial forecasting.
This study also allows a comparison of the different fore-sighting abilities of each platform, in terms of how many days ahead a platform can predict a price movement before it happens.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This study looks for signals of economic awareness on online social media and
tests their significance in economic predictions. The study analyses, over a
period of two years, the relationship between the West Texas Intermediate daily
crude oil price and multiple predictors extracted from Twitter, Google Trends,
Wikipedia, and the Global Data on Events, Language, and Tone database (GDELT).
Semantic analysis is applied to study the sentiment, emotionality and
complexity of the language used. Autoregressive Integrated Moving Average with
Explanatory Variable (ARIMAX) models are used to make predictions and to
confirm the value of the study variables. Results show that the combined
analysis of the four media platforms carries valuable information in making
financial forecasting. Twitter language complexity, GDELT number of articles
and Wikipedia page reads have the highest predictive power. This study also
allows a comparison of the different fore-sighting abilities of each platform,
in terms of how many days ahead a platform can predict a price movement before
it happens. In comparison with previous work, more media sources and more
dimensions of the interaction and of the language used are combined in a joint
analysis.
Related papers
- Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
"Context is Key" (CiK) is a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.
We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.
Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings.
arXiv Detail & Related papers (2024-10-24T17:56:08Z) - AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework [48.3060010653088]
We release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data.
We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task.
arXiv Detail & Related papers (2024-03-19T09:45:33Z) - Natural Language Processing and Multimodal Stock Price Prediction [0.8702432681310401]
This paper utilizes stock percentage change as training data, in contrast to the traditional use of raw currency values.
The choice of percentage change aims to provide models with context regarding the significance of price fluctuations.
The study employs specialized BERT natural language processing models to predict stock price trends.
arXiv Detail & Related papers (2024-01-03T01:21:30Z) - Predicting Financial Market Trends using Time Series Analysis and
Natural Language Processing [0.0]
This study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple.
Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices.
arXiv Detail & Related papers (2023-08-31T21:20:58Z) - Testing the Predictions of Surprisal Theory in 11 Languages [77.45204595614]
We investigate the relationship between surprisal and reading times in eleven different languages.
By focusing on a more diverse set of languages, we argue that these results offer the most robust link to-date between information theory and incremental language processing across languages.
arXiv Detail & Related papers (2023-07-07T15:37:50Z) - ASPEST: Bridging the Gap Between Active Learning and Selective
Prediction [56.001808843574395]
Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain.
Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples.
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
arXiv Detail & Related papers (2023-04-07T23:51:07Z) - The Battle of Information Representations: Comparing Sentiment and
Semantic Features for Forecasting Market Trends [0.5249805590164902]
We study whether semantic features in the form of contextual embeddings are more valuable than sentiment attributes for forecasting market trends.
We consider the corpus of Twitter posts related to the largest companies by capitalization from NASDAQ and their close prices.
Our results show that in the substantially prevailing number of cases, the use of sentiment features leads to higher metrics.
arXiv Detail & Related papers (2023-03-24T18:30:15Z) - A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools
Stock Prediction [100.9772316028191]
In this paper, we experiment with a variety of adversarial attack configurations to fool three stock prediction victim models.
Our results show that the proposed attack method can achieve consistent success rates and cause significant monetary loss in trading simulation.
arXiv Detail & Related papers (2022-05-01T05:12:22Z) - Social Media Sentiment Analysis for Cryptocurrency Market Prediction [0.0]
We study how the different sentiment metrics are correlated with the price movements of Bitcoin.
One of the models outperforms more than 20 other public ones.
arXiv Detail & Related papers (2022-04-19T03:27:29Z) - Online Multi-Agent Forecasting with Interpretable Collaborative Graph
Neural Network [65.11999700562869]
We propose a novel collaborative prediction unit (CoPU), which aggregates predictions from multiple collaborative predictors according to a collaborative graph.
Our methods outperform state-of-the-art works on the three tasks by 28.6%, 17.4% and 21.0% on average.
arXiv Detail & Related papers (2021-07-02T08:20:06Z) - Macroeconomic forecasting through news, emotions and narrative [12.762298148425796]
This study expands the existing body of research by incorporating a wide array of emotions from newspapers around the world into macroeconomic forecasts.
We model industrial production and consumer prices across a diverse range of economies using an autoregressive framework.
We find that emotions associated with happiness and anger have the strongest predictive power for the variables we predict.
arXiv Detail & Related papers (2020-09-23T10:10:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.