2,784 research outputs found
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep
architectures for topic models to learn topic structures. Although several deep
models have been proposed to learn better topic proportions of documents, how
to leverage the benefits of deep structures for learning word distributions of
topics has not yet been rigorously studied. Here we propose a new multi-layer
generative process on word distributions of topics, where each layer consists
of a set of topics and each topic is drawn from a mixture of the topics of the
layer above. As the topics in all layers can be directly interpreted by words,
the proposed model is able to discover interpretable topic hierarchies. As a
self-contained module, our model can be flexibly adapted to different kinds of
topic models to improve their modelling accuracy and interpretability.
Extensive experiments on text corpora demonstrate the advantages of the
proposed model.Comment: accepted in NIPS 201
Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management
Financial portfolio management describes the task of distributing funds and
conducting trading operations on a set of financial assets, such as stocks,
index funds, foreign exchange or cryptocurrencies, aiming to maximize the
profit while minimizing the loss incurred by said operations. Deep Learning
(DL) methods have been consistently excelling at various tasks and automated
financial trading is one of the most complex one of those. This paper aims to
provide insight into various DL methods for financial trading, under both the
supervised and reinforcement learning schemes. At the same time, taking into
consideration sentiment information regarding the traded assets, we discuss and
demonstrate their usefulness through corresponding research studies. Finally,
we discuss commonly found problems in training such financial agents and equip
the reader with the necessary knowledge to avoid these problems and apply the
discussed methods in practice
Stock Market Prediction via Deep Learning Techniques: A Survey
The stock market prediction has been a traditional yet complex problem
researched within diverse research areas and application domains due to its
non-linear, highly volatile and complex nature. Existing surveys on stock
market prediction often focus on traditional machine learning methods instead
of deep learning methods. Deep learning has dominated many domains, gained much
success and popularity in recent years in stock market prediction. This
motivates us to provide a structured and comprehensive overview of the research
on stock market prediction focusing on deep learning techniques. We present
four elaborated subtasks of stock market prediction and propose a novel
taxonomy to summarize the state-of-the-art models based on deep neural networks
from 2011 to 2022. In addition, we also provide detailed statistics on the
datasets and evaluation metrics commonly used in the stock market. Finally, we
highlight some open issues and point out several future directions by sharing
some new perspectives on stock market prediction
A Systematic Study of Stock Markets Using Analytical and AI Techniques
Predicting stock market patterns is seen as a crucial and highly productive activity. Therefore, if investors make wise choices, stock prices will result in significant gains. Investors face a lot of difficulty making predictions about the stock market because of the noisy and stagnating data. As a result, making accurate stock market predictions is difficult for investors who want to put their money to work for them. Predictions of the stock market are made using mathematical techniques and study aids. Out of 30 research papers advocating approaches, this study offers a thorough analysis of each, including computational methodologies, AI algorithms( machine learning and deep learning), performance evaluation parameters, and chosen publications. Research questions are used to choose studies.
As a result, these chosen studies contribute to the discovery of ML methods and their corresponding data set for predicting security markets. The majority of Artificial Neural Network and Neural Network techniques are employed for producing precise stock market forecasts. The most recent stock market-related prediction system has significant limitations despite the substantial amount of work that has gone into it. In this survey, one may infer that the stock price forecasting procedure is a comprehensive affair and it is very necessary to look more closely at the typical parameters for the stock market prediction
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
Artificial Intelligence & Machine Learning in Finance: A literature review
In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market.
JEL Classification: C80
Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market.
JEL Classification: C80
Paper type: Theoretical Researc
Machine learning methods in finance: Recent applications and prospects
We study how researchers can apply machine learning (ML) methods in finance. We first establish that the two major categories of ML (supervised and unsupervised learning) address fundamentally different problems than traditional econometric approaches. Then, we review the current state of research on ML in finance and identify three archetypes of applications: (i) the construction of superior and novel measures, (ii) the reduction of prediction error, and (iii) the extension of the standard econometric toolset. With this taxonomy, we give an outlook on potential future directions for both researchers and practitioners. Our results suggest many benefits of ML methods compared to traditional approaches and indicate that ML holds great potential for future research in finance
- …