979 research outputs found
Transforming Sentiment Analysis in the Financial Domain with ChatGPT
Financial sentiment analysis plays a crucial role in decoding market trends
and guiding strategic trading decisions. Despite the deployment of advanced
deep learning techniques and language models to refine sentiment analysis in
finance, this study breaks new ground by investigating the potential of large
language models, particularly ChatGPT 3.5, in financial sentiment analysis,
with a strong emphasis on the foreign exchange market (forex). Employing a
zero-shot prompting approach, we examine multiple ChatGPT prompts on a
meticulously curated dataset of forex-related news headlines, measuring
performance using metrics such as precision, recall, f1-score, and Mean
Absolute Error (MAE) of the sentiment class. Additionally, we probe the
correlation between predicted sentiment and market returns as an additional
evaluation approach. ChatGPT, compared to FinBERT, a well-established sentiment
analysis model for financial texts, exhibited approximately 35\% enhanced
performance in sentiment classification and a 36\% higher correlation with
market returns. By underlining the significance of prompt engineering,
particularly in zero-shot contexts, this study spotlights ChatGPT's potential
to substantially boost sentiment analysis in financial applications. By sharing
the utilized dataset, our intention is to stimulate further research and
advancements in the field of financial services.Comment: 10 pages, 8 figures, Preprint submitted to Machine Learning with
Application
Using Text Mining to Predicate Exchange Rates with Sentiment Indicators
Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Market Psych, which analyses social media in addition to newsfeeds to quantify various levels of individual’s emotions, as a predictor for financial time series returns of the Australian Dollar (AUD)-US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioral finance, combining technical and behavioral aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares Multivariate Linear Regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation
A Survey of Forex and Stock Price Prediction Using Deep Learning
The prediction of stock and foreign exchange (Forex) had always been a hot
and profitable area of study. Deep learning application had proven to yields
better accuracy and return in the field of financial prediction and
forecasting. In this survey we selected papers from the DBLP database for
comparison and analysis. We classified papers according to different deep
learning methods, which included: Convolutional neural network (CNN), Long
Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network
(RNN), Reinforcement Learning, and other deep learning methods such as HAN,
NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable,
model, and results of each article. The survey presented the results through
the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe
ratio, and return rate. We identified that recent models that combined LSTM
with other methods, for example, DNN, are widely researched. Reinforcement
learning and other deep learning method yielded great returns and performances.
We conclude that in recent years the trend of using deep-learning based method
for financial modeling is exponentially rising
Stock price change prediction using news text mining
Along with the advent of the Internet as a new way of propagating news in a digital format, came the need to understand and transform this data into information. This work presents a computational framework that aims to predict the changes of stock prices along the day, given the occurrence of news articles related to the companies listed in the Down Jones Index. For this task, an automated process that gathers, cleans, labels, classifies, and simulates investments was developed. This process integrates the existing data mining and text algorithms, with the proposal of new techniques of alignment between news articles and stock prices, pre-processing, and classifier ensemble. The result of experiments in terms of classification measures and the Cumulative Return obtained through investment simulation outperformed the other results found after an extensive review in the related literature. This work also argues that the classification measure of Accuracy and incorrect use of cross validation technique have too few to contribute in terms of investment recommendation for financial market. Altogether, the developed methodology and results contribute with the state of art in this emerging research field, demonstrating that the correct use of text mining techniques is an applicable alternative to predict stock price movements in the financial market.Com o advento da Internet como um meio de propagação de notícias em formato digital, veio a necessidade de entender e transformar esses dados em informação. Este trabalho tem como objetivo apresentar um processo computacional para predição de preços de ações ao longo do dia, dada a ocorrência de notícias relacionadas às companhias listadas no índice Down Jones. Para esta tarefa, um processo automatizado que coleta, limpa, rotula, classifica e simula investimentos foi desenvolvido. Este processo integra algoritmos de mineração de dados e textos já existentes, com novas técnicas de alinhamento entre notícias e preços de ações, pré-processamento, e assembleia de classificadores. Os resultados dos experimentos em termos de medidas de classificação e o retorno acumulado obtido através de simulação de investimentos foram maiores do que outros resultados encontrados após uma extensa revisão da literatura. Este trabalho também discute que a acurácia como medida de classificação, e a incorreta utilização da técnica de validação cruzada, têm muito pouco a contribuir em termos de recomendação de investimentos no mercado financeiro. Ao todo, a metodologia desenvolvida e resultados contribuem com o estado da arte nesta área de pesquisa emergente, demonstrando que o uso correto de técnicas de mineração de dados e texto é uma alternativa aplicável para a predição de movimentos no mercado financeiro
Machine Learning and Finance: A Review using Latent Dirichlet Allocation Technique (LDA)
The aim of this paper is provide a first comprehensive structuring of the literature applying machine learning to finance. We use a probabilistic topic modelling approach to make sense of this diverse body of research spanning across the disciplines of finance, economics, computer sciences, and decision sciences. Through the topic modelling approach, a Latent Dirichlet Allocation Technique (LDA), we can extract the 14 coherent research topics that are the focus of the 6,148 academic articles during the years 1990-2019 analysed. We first describe and structure these topics, and then further show how the topic focus has evolved over the last two decades. Our study thus provides a structured topography for finance researchers seeking to integrate machine learning research approaches in their exploration of finance phenomena. We also showcase the benefits to finance researchers of the method of probabilistic modelling of topics for deep comprehension of a body of literature, especially when that literature has diverse multi-disciplinary actors
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