Sentiment Analysis

Financial News-Driven LLM Reinforcement Learning for Portfolio Management ArXiv ID: 2411.11059 “View on arXiv” Authors: Unknown Abstract Reinforcement learning (RL) has emerged as a transformative approach for financial trading, enabling dynamic strategy optimization in complex markets. This study explores the integration of sentiment analysis, derived from large language models (LLMs), into RL frameworks to enhance trading performance. Experiments were conducted on single-stock trading with Apple Inc. (AAPL) and portfolio trading with the ING Corporate Leaders Trust Series B (LEXCX). The sentiment-enhanced RL models demonstrated superior net worth and cumulative profit compared to RL models without sentiment and, in the portfolio experiment, outperformed the actual LEXCX portfolio’s buy-and-hold strategy. These results highlight the potential of incorporating qualitative market signals to improve decision-making, bridging the gap between quantitative and qualitative approaches in financial trading. ...

Analyst Reports and Stock Performance: Evidence from the Chinese Market ArXiv ID: 2411.08726 “View on arXiv” Authors: Unknown Abstract This article applies natural language processing (NLP) to extract and quantify textual information to predict stock performance. Using an extensive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess returns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature on sentiment analysis and the response of the stock market to news in the Chinese stock market. ...

Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS) ArXiv ID: 2411.03035 “View on arXiv” Authors: Unknown Abstract With the increasing maturity and expansion of the cryptocurrency market, understanding and predicting its price fluctuations has become an important issue in the field of financial engineering. This article introduces an innovative Genetic Algorithm-generated Alpha Sentiment (GAS) blending ensemble model specifically designed to predict Bitcoin market trends. The model integrates advanced ensemble learning methods, feature selection algorithms, and in-depth sentiment analysis to effectively capture the complexity and variability of daily Bitcoin trading data. The GAS framework combines 34 Alpha factors with 8 news economic sentiment factors to provide deep insights into Bitcoin price fluctuations by accurately analyzing market sentiment and technical indicators. The core of this study is using a stacked model (including LightGBM, XGBoost, and Random Forest Classifier) for trend prediction which demonstrates excellent performance in traditional buy-and-hold strategies. In addition, this article also explores the effectiveness of using genetic algorithms to automate alpha factor construction as well as enhancing predictive models through sentiment analysis. Experimental results show that the GAS model performs competitively in daily Bitcoin trend prediction especially when analyzing highly volatile financial assets with rich data. ...

FinBERT-BiLSTM: A Deep Learning Model for Predicting Volatile Cryptocurrency Market Prices Using Market Sentiment Dynamics ArXiv ID: 2411.12748 “View on arXiv” Authors: Unknown Abstract Time series forecasting is a key tool in financial markets, helping to predict asset prices and guide investment decisions. In highly volatile markets, such as cryptocurrencies like Bitcoin (BTC) and Ethereum (ETH), forecasting becomes more difficult due to extreme price fluctuations driven by market sentiment, technological changes, and regulatory shifts. Traditionally, forecasting relied on statistical methods, but as markets became more complex, deep learning models like LSTM, Bi-LSTM, and the newer FinBERT-LSTM emerged to capture intricate patterns. Building upon recent advancements and addressing the volatility inherent in cryptocurrency markets, we propose a hybrid model that combines Bidirectional Long Short-Term Memory (Bi-LSTM) networks with FinBERT to enhance forecasting accuracy for these assets. This approach fills a key gap in forecasting volatile financial markets by blending advanced time series models with sentiment analysis, offering valuable insights for investors and analysts navigating unpredictable markets. ...

News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models ArXiv ID: 2411.05788 “View on arXiv” Authors: Unknown Abstract Forecasting stock market prices remains a complex challenge for traders, analysts, and engineers due to the multitude of factors that influence price movements. Recent advancements in artificial intelligence (AI) and natural language processing (NLP) have significantly enhanced stock price prediction capabilities. AI’s ability to process vast and intricate data sets has led to more sophisticated forecasts. However, achieving consistently high accuracy in stock price forecasting remains elusive. In this paper, we leverage 30 years of historical data from national banks in India, sourced from the National Stock Exchange, to forecast stock prices. Our approach utilizes state-of-the-art deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM optimized through Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). We further integrate sentiment analysis from tweets and reliable financial sources such as Business Standard and Reuters, acknowledging their crucial influence on stock price fluctuations. ...

A Framework for the Construction of a Sentiment-Driven Performance Index: The Case of DAX40 ArXiv ID: 2409.20397 “View on arXiv” Authors: Unknown Abstract We extract the sentiment from german and english news articles on companies in the DAX40 stock market index and use it to create a sentiment-powered pendant. Comparing it to existing products which adjust their weights at pre-defined dates once per month, we show that our index is able to react more swiftly to sentiment information mined from online news. Over the nearly 6 years we considered, the sentiment index manages to create an annualized return of 7.51% compared to the 2.13% of the DAX40, while taking transaction costs into account. In this work, we present the framework we employed to develop this sentiment index. ...

A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting ArXiv ID: 2410.12807 “View on arXiv” Authors: Unknown Abstract The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising. ...

SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest ArXiv ID: 2410.07143 “View on arXiv” Authors: Unknown Abstract Stock trend forecasting, a challenging problem in the financial domain, involves ex-tensive data and related indicators. Relying solely on empirical analysis often yields unsustainable and ineffective results. Machine learning researchers have demonstrated that the application of random forest algorithm can enhance predictions in this context, playing a crucial auxiliary role in forecasting stock trends. This study introduces a new approach to stock market prediction by integrating sentiment analysis using FinGPT generative AI model with the traditional Random Forest model. The proposed technique aims to optimize the accuracy of stock price forecasts by leveraging the nuanced understanding of financial sentiments provided by FinGPT. We present a new methodology called “Sentiment-Augmented Random Forest” (SARF), which in-corporates sentiment features into the Random Forest framework. Our experiments demonstrate that SARF outperforms conventional Random Forest and LSTM models with an average accuracy improvement of 9.23% and lower prediction errors in pre-dicting stock market movements. ...

Cross-Lingual News Event Correlation for Stock Market Trend Prediction ArXiv ID: 2410.00024 “View on arXiv” Authors: Unknown Abstract In the modern economic landscape, integrating financial services with Financial Technology (FinTech) has become essential, particularly in stock trend analysis. This study addresses the gap in comprehending financial dynamics across diverse global economies by creating a structured financial dataset and proposing a cross-lingual Natural Language-based Financial Forecasting (NLFF) pipeline for comprehensive financial analysis. Utilizing sentiment analysis, Named Entity Recognition (NER), and semantic textual similarity, we conducted an analytical examination of news articles to extract, map, and visualize financial event timelines, uncovering the correlation between news events and stock market trends. Our method demonstrated a meaningful correlation between stock price movements and cross-linguistic news sentiments, validated by processing two-year cross-lingual news data on two prominent sectors of the Pakistan Stock Exchange. This study offers significant insights into key events, ensuring a substantial decision margin for investors through effective visualization and providing optimal investment opportunities. ...

Global Public Sentiment on Decentralized Finance: A Spatiotemporal Analysis of Geo-tagged Tweets from 150 Countries ArXiv ID: 2409.00843 “View on arXiv” Authors: Unknown Abstract Blockchain technology and decentralized finance (DeFi) are reshaping global financial systems. Despite their impact, the spatial distribution of public sentiment and its economic and geopolitical determinants are often overlooked. This study analyzes over 150 million geo-tagged, DeFi-related tweets from 2012 to 2022, sourced from a larger dataset of 7.4 billion tweets. Using sentiment scores from a BERT-based multilingual classification model, we integrated these tweets with economic and geopolitical data to create a multimodal dataset. Employing techniques like sentiment analysis, spatial econometrics, clustering, and topic modeling, we uncovered significant global variations in DeFi engagement and sentiment. Our findings indicate that economic development significantly influences DeFi engagement, particularly after 2015. Geographically weighted regression analysis revealed GDP per capita as a key predictor of DeFi tweet proportions, with its impact growing following major increases in cryptocurrency values such as bitcoin. While wealthier nations are more actively engaged in DeFi discourse, the lowest-income countries often discuss DeFi in terms of financial security and sudden wealth. Conversely, middle-income countries relate DeFi to social and religious themes, whereas high-income countries view it mainly as a speculative instrument or entertainment. This research advances interdisciplinary studies in computational social science and finance and supports open science by making our dataset and code available on GitHub, and providing a non-code workflow on the KNIME platform. These contributions enable a broad range of scholars to explore DeFi adoption and sentiment, aiding policymakers, regulators, and developers in promoting financial inclusion and responsible DeFi engagement globally. ...