scholarly journals Redes neurais convolucionais aplicas à negociação de ativos no mercado financeiro

2021 ◽  
Author(s):  
◽  
D. G. Nascimento
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Financial Market ◽  
System Architecture ◽  
Statistical Evaluation ◽  
Signal Classification ◽  
Fixed Income ◽  
Stock Market Forecasting ◽  
Simulated Environment ◽  
Learning Techniques

Stock market forecasting has been a quite popular challenge in machine learning researches. Most investors want to make decisions based on criteria that will provide greater returns in their operations. Recently, studies have been using Deep Learning techniques, such as Convolutional Neural Networks (CNN), to perform price regression or trade signal classification in financial market. In this work, a system architecture that uses a CNN model is proposed to perform the indication of the best operation for each moment in the stock market, this system was called CNN Trading Classifier (CNN-TC). This system consists of data pre-processing, classification by the model and decision making in the market. It was evaluated based on data from the Brazilian and American stock market in three different periods. For this, statistical evaluation was performed, using the metrics of accuracy, precision, recall and F1 classification, and financial based on the classifications performed by the model. In addition, a test on a simulated environment using the MetaTrader software was evaluated in order to attest to the effectiveness of this approach. The results show that the system had better statistical and financial results in most evaluations compared to the use of other Deep Learning models and overcame the strategy Buy and Hold (BH) and fixed income returns

scholarly journals Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13099-13111
Author(s):  
Khaled A. Althelaya ◽  
Salahadin A. Mohammed ◽  
El-Sayed M. El-Alfy
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Multiresolution Analysis ◽  
Stock Market Forecasting

scholarly journals Review on Machine Learning Techniques for Stock-Market Forecasting

2020 ◽  
pp. 34-47
Author(s):  
Sushma Jaiswal ◽  
Tarun Jaiswal
Keyword(s):  
Stock Market ◽  
Soft Computing ◽  
Stock Prices ◽  
Machine Learning Techniques ◽  
Market Values ◽  
Stock Market Forecasting ◽  
Soft Computing Method ◽  
Learning Techniques ◽  
Stock Market Investors ◽  
Computing Method

Stock marketplace tradeoff is an endless investment implementation worldwide. It has capabilities to produce maximum profits on stockholders’venture. In the globe, the stock-market forecasting is a very puzzling job for the stock-market investors. The task is very challenging because of the ambiguity and precariousness of the stock market values. Due to commercialization and data mining modules the growth of stock marketplaces, it is essential to predict marketplace variations quick and easy way. Recently, ANN is very famous and attracted to investors for its easy-going process in the stock-market. ANN plays a very imperative part in today’s stock-market for decision making and prediction. The Multi-Layer-Perceptron methods are outperformed then other methods. Also, these approaches have countless likelihoods to envisage with high accuracy than other approaches. In this review paper, neural-based envisage implements are measured to foresee the imminent stock-prices and their enactment dimensions will be assessed. Here we deliver a broad impression of the soft computing based stock-market likelihood with emphasis on enabling technologies, issues and application issues. Soft computing is attracting a lot of researchers and industrial innovation. The purpose of this paper is to presents a survey of the existing soft computing method applied to stock market prediction, their comparison and possible solution. From the reviewed articles, it is obvious that investigators have resolutely intensive on the growth of fusion forecast representations and considerable effort has also been completed on the use of broadcasting data for stock marketplace forecast. It is also enlightening that most of the literature has focused on the forecast of stock prices in developing marketplace.

scholarly journals Formulation of a Prediction Index with the Help of WEKA Tool for Guiding the Stock Market Investors

2016 ◽  
Vol 9 (3) ◽  
pp. 212-225 ◽  
Author(s):  
Aseema Kulkarni ◽  
Ajit More
Keyword(s):  
Decision Making ◽  
Stock Market ◽  
Stock Prices ◽  
Machine Learning Techniques ◽  
Algorithmic Trading ◽  
Stock Market Forecasting ◽  
Learning Techniques ◽  
Stock Market Investors ◽  
Automated Decision Making ◽  
Radical Transformation

Prediction of stock prices using various computer programs is on rise. Popularly known in the field of finance as algorithmic trading, a radical transformation has taken place in the field of stock markets for decision making through automated decision making agents. Machine learning techniques can be applied for predicting stock prices. This paper attempts to study the various stock market forecasting processes available in the forecasting plugin of the WEKA tool. Twenty experiments have been conducted on twenty different stocks to analyse the prediction capacity of the tool.

scholarly journals Stock Trading Classifier with Multichannel Convolutional Neural Network

2020 ◽  
Author(s):  
Davi Nascimento ◽  
Anna Costa ◽  
Reinaldo Bianchi
Keyword(s):  
Stock Market ◽  
Stock Trading ◽  
Financial Trading ◽  
Stock Market Forecasting ◽  
Novel Approach ◽  
Learning Techniques ◽  
Good Classification ◽  
Operation Indication ◽  
Using Data ◽  
Financial Result

Stock market forecasting has been a quite popular challenge in machine learning research. Recently, studies have been using deep learning techniques, such as Convolutional Neural Networks (CNN), to perform regression on the prices or classification on trading signal as an operation indication. However, they did not reach a satisfactory financial result. In this work we aim to design a financially profitable stock market method by proposing a novel approach called Multichannel CNN Trading Classifier (MCNN-TC). The model was evaluated using data from the Brazilian stock market. The results indicate a satisfactory financial trading performance compared to the Buy and Hold strategy and good classification metrics.

Evaluation of Pattern Based Customized Approach for Stock Market Trend Prediction With Big Data and Machine Learning Techniques

2019 ◽  
Vol 6 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Jai Prakash Verma ◽  
Sudeep Tanwar ◽  
Sanjay Garg ◽  
Ishit Gandhi ◽  
Nikita H. Bachani
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Prices ◽  
Time Window ◽  
Stock Exchange ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Trend Prediction ◽  
Stock Market Forecasting ◽  
Learning Techniques

The stock market is very volatile and non-stationary and generates huge volumes of data in every second. In this article, the existing machine learning algorithms are analyzed for stock market forecasting and also a new pattern-finding algorithm for forecasting stock trend is developed. Three approaches can be used to solve the problem: fundamental analysis, technical analysis, and the machine learning. Experimental analysis done in this article shows that the machine learning could be useful for investors to make profitable decisions. In order to conduct these processes, a real-time dataset has been obtained from the Indian stock market. This article learns the model from Indian National Stock Exchange (NSE) data obtained from Yahoo API to forecast stock prices and targets to make a profit over time. In this article, two separate algorithms and methodologies are analyzed to forecast stock market trends and iteratively improve the model to achieve higher accuracy. Results are showing that the proposed pattern-based customized algorithm is more accurate (10 to 15%) as compared to other two machine learning techniques, which are also increased as the time window increases.

scholarly journals Sailing through the COVID-19 Crisis by Using AI for Financial Market Predictions

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Gagan Deep Sharma ◽  
Burak Erkut ◽  
Mansi Jain ◽  
Tuğberk Kaya ◽  
Mandeep Mahendru ◽  
...  
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Future Research ◽  
Market Prices ◽  
Stock Market Returns ◽  
Extant Literature ◽  
Stock Market Forecasting ◽  
The Neural Network ◽  
Drying Up ◽  
The Times

The outbreak of COVID-19 has brought the world to an unprecedented position where financial and mental resources are drying up. Livelihoods are being lost, and it is becoming tough to save lives. These are the times to think of unprecedented solutions to the financial challenges being faced. Artificial intelligence (AI) has provided a fresh approach to finance through its implementation in the prediction of financial market prices by promising more generalizable results for stock market forecasting. Immense literature has attempted to apply AI and machine learning for predicting stock market returns and volatilities. The research on the applications of AI in finance lacks a consolidated overview of different research directions, findings, methodological approaches, and contributions. Therefore, there is a need to consolidate the extant literature in this upcoming field to consolidate the findings, identify the research gaps in the existing literature, and set a research agenda for future researchers. This paper addresses this need by synthesizing the extant literature in the form of a systematic review for addressing the use of AI in stock market predictions and interpreting the results in a narrative review. The gap formed through this article is the use of a combination of AI as a subject with the neural network as another area and stock market forecasting as another theme, and it will pave the way for future research studies. The analyses help highlight four important gaps in the existing literature on the subject.

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