Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond

• URL: http://arxiv.org/abs/2403.12161v1
• Date: Mon, 18 Mar 2024 18:19:08 GMT
• Title: Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond
• Authors: Arijit Das, Tanmoy Nandi, Prasanta Saha, Suman Das, Saronyo Mukherjee, Sudip Kumar Naskar, Diganta Saha,
• Abstract summary: Deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future.
• Score: 1.6622844933418388
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter is the main objective of the present work. Substantial improvement is shown in the result. The main features of the present work are- a) proposing completely generalized algorithm which is able to generate models for any twitter handle and any financial component, b) predicting the time window for a tweets effect on a stock price c) analyzing the effect of multiple twitter handles for predicting the trend. A detailed survey is done to find out the latest work in recent years in the similar field, find the research gap, and collect the required data for analysis and prediction. State-of-the-art algorithm is proposed and complete implementation with environment is given. An insightful trend of the result improvement considering the NLP analysis of twitter data on financial market components is shown. The Indian and USA financial markets are explored in the present work where as other markets can be taken in future. The socio-economic impact of the present work is discussed in conclusion.

Related papers

• Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages [8.504685056067144]
  We propose a novel Targeted Aspect-Based Emotion Analysis (TABEA) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet. It is based on Natural Language Processing (NLP) techniques and Machine Learning streaming algorithms. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter.
  arXiv  Detail & Related papers  (2024-03-30T16:46:25Z)
• 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)
• FinBen: A Holistic Financial Benchmark for Large Language Models [75.09474986283394]
  FinBen is the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.
  arXiv  Detail & Related papers  (2024-02-20T02:16:16Z)
• Do Weibo platform experts perform better at predicting stock market? [0.8999666725996978]
  Weibo social networking platform is used as a sentiment data collection source. Weibo users are divided into Authorized Financial Advisor (AFA) and Unauthorized Financial Advisor (UFA) groups. The Hong Kong Hang Seng index is used to extract historical stock market change data.
  arXiv  Detail & Related papers  (2024-02-12T10:04:54Z)
• 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)
• Dynamic Datasets and Market Environments for Financial Reinforcement Learning [68.11692837240756]
  FinRL-Meta is a library that processes dynamic datasets from real-world markets into gym-style market environments. We provide examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance.
  arXiv  Detail & Related papers  (2023-04-25T22:17:31Z)
• 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)
• FinQA: A Dataset of Numerical Reasoning over Financial Data [52.7249610894623]
  We focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. We propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge.
  arXiv  Detail & Related papers  (2021-09-01T00:08:14Z)
• Stock Market Analysis with Text Data: A Review [7.789019365796933]
  Stock market movements are influenced by public and private information shared through news articles, company reports, and social media discussions. The majority of studies in the literature are based on traditional approaches that come short in analyzing unstructured, vast textual data. This study is to survey the main stock market analysis models, text representation techniques for financial market prediction, shortcomings of existing techniques, and propose promising directions for future research.
  arXiv  Detail & Related papers  (2021-06-23T04:31:56Z)
• Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective [58.353799280109904]
  We propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment. Specifically, we propose a Graph-based Market Environment (GME) model for predicting the fund-raising performance of the unpublished project by exploiting the market environment.
  arXiv  Detail & Related papers  (2021-05-27T02:39:30Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)

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.