Prediction of Stocks and Stock Price using Artificial Intelligence : A Bibliometric Study using Scopus Database

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms like – SVMs, CNNs, LSTMs, RNNs , etc. This bibliometric study focusses on the study based primarily on the Scopus database. We have considered important keywords, authors, citations along with the correlations between the co-appearing authors, source titles and keywords with the use of network diagrams for visualisation. On the basis of this paper, we conclude that there is ample opportunity for research in the domain of financial market

Modelling risk for commodities in Brazil: an application to live cattle spot and futures prices

This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity’s behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transactions’ results, investors must analyse fluctuations in assets’ value for longer periods. Bibliographic research revealed that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2017, this sector moved 523.25 billion Brazilian reals (about 130.5 billion United States dollars). In that year, agribusiness contributed 22% of Brazil’s total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors’ reach produce more effective risk management. The methodology was based on Holt-Winters exponential smoothing algorithm, autoregressive integrated moving average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving average (GARMA) models. More specifically, 5 different methods were applied that allowed a comparison of 12 different models as ways to portray and predict the BGI commodity’s behaviour. The results show that GARMA with order c(2,1) and without intercept is the best model..info:eu-repo/semantics/publishedVersio

Modelling risk for commodities in Brazil: an application to live cattle spot and futures prices

This study analysed a series of live cattle spot and futures prices from the Boi Gordo Index (BGI) in Brazil. The objective was to develop a model that best portrays this commodity’s behaviour to estimate futures prices more accurately. The database created contained 2,010 daily entries in which trade in futures contracts occurred, as well as BGI spot sales in the market, from 1 December 2006 to 30 April 2015. One of the most important reasons why this type of risk needs to be measured is to set loss limits. To identify patterns in price behaviour in order to improve future transactions’ results, investors must analyse fluctuations in assets’ value for longer periods. Bibliographic research revealed that no other study has conducted a comprehensive analysis of this commodity using this approach. Cattle ranching is big business in Brazil given that in 2017, this sector moved 523.25 billion Brazilian reals (about 130.5 billion United States dollars). In that year, agribusiness contributed 22% of Brazil’s total gross domestic product. Using the proposed risk modelling technique, economic agents can make the best decision about which options within these investors’ reach produce more effective risk management. The methodology was based on Holt-Winters exponential smoothing algorithm, autoregressive integrated moving average (ARIMA), ARIMA with exogenous inputs, generalised autoregressive conditionally heteroskedastic and generalised autoregressive moving average (GARMA) models. More specifically, 5 different methods were applied that allowed a comparison of 12 different models as ways to portray and predict the BGI commodity’s behaviour. The results show that GARMA with order c(2,1) and without intercept is the best model..info:eu-repo/semantics/publishedVersio

Analisis Price Earning Ratio Dan Unusual Market Activity Terhadap Stock Price Movement Dengan Stock Investment Risk Sebagai Pemoderasi

Penelitian ini adalah penelitian tentang pengaruh price earning ratio dan unusual market activity terhadap stock price movement yang dimoderasi stock investment risk. Di dalam investasi ada 3 fase penting dalam pergerakan harga saham, yang pertama fase akumulasi, kedua fase partisipasi publik dan yang terakhir fase distribusi. Pergerakan harga saham yang tidak wajar dapat diindikasi dengan diterbitkannya unusual market activity, yang bertujuan sebagai pemberitahuan bahwa harga saham tersebut sedang mengalami fluktuasi yang diindikasikan tidak wajar. Stock price movement pada dasarnya sangat sensitif terhadap sentiment pasar baik positif maupun negatif, dalam hal ini price earning ratio merupakan salah satu rasio keuangan yang digunakan oleh investor terutama investor para penganut analisis fundamental, dimana price earning ratio mencerminkan harga untuk setiap rupiah laba

A combined machine learning algorithms and DEA method for measuring and predicting the efficiency of Chinese manufacturing listed companies

Data Envelopment Analysis (DEA) is a linear programming methodology for measuring the efficiency of Decision Making Units (DMUs) to improve organizational performance in the private and public sectors. However, if a new DMU needs to be known its efficiency score, the DEA analysis would have to be re-conducted, especially nowadays, datasets from many fields have been growing rapidly in the real world, which will need a huge amount of computation. Following the previous studies, this paper aims to establish a linkage between the DEA method and machine learning (ML) algorithms, and proposes an alternative way that combines DEA with ML (ML-DEA) algorithms to measure and predict the DEA efficiency of DMUs. Four ML-DEA algorithms are discussed, namely DEA-CCR model combined with back-propagation neural network (BPNN-DEA), with genetic algorithm (GA) integrated with back-propagation neural network (GANN-DEA), with support vector machines (SVM-DEA), and with improved support vector machines (ISVM-DEA), respectively. To illustrate the applicability of above models, the performance of Chinese manufacturing listed companies in 2016 is measured, predicted and compared with the DEA efficiency scores obtained by the DEA-CCR model. The empirical results show that the average accuracy of the predicted efficiency of DMUs is about 94%, and the comprehensive performance order of four ML-DEA algorithms ranked from good to poor is GANN-DEA, BPNN-DEA, ISVM-DEA, and SVM-DEA

Training Multilayer Perceptron with Genetic Algorithms and Particle Swarm Optimization for Modeling Stock Price Index Prediction

publishedVersio

Desafios legais relacionados a inteligência artificial e gestão empresarial

Orientador : Márcia Ramos MayArtigo (especialização) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas. Curso de Especialização MBA em Inteligência de NegóciosInclui referênciasResumo : A gestão empresarial tem enfrentado tecnologias inovadoras, frente ao novo paradigma tecnológico, concebido a partir de máquinas e dispositivos inteligentes, com competências digitais cada vez mais avançadas, fruto de sistemas baseados em inteligência artificial (IA), os quais analisam opiniões, decisões e comportamentos das pessoas, adquirindo e aprendendo conhecimentos do usuário. Nesse aspecto, sua crescente aplicação nos negócios torna necessária uma avaliação de estratégias, benefícios e dificuldades enfrentadas inclusive na perspectiva jurídica. O principal objetivo deste artigo é apresentar o uso da inteligência artificial na gestão de empresarial, inclusive pelo viés da análise jurídiconormativa do assunto, apresentando conceitos sobre o tema e levantando possíveis desafios, ao combinar tecnologias inovadoras aos seus recursos principais construindo meio eficaz dentro da inteligência de negócios

The Stock Exchange Prediction using Machine Learning Techniques: A Comprehensive and Systematic Literature Review

This literature review identifies and analyzes research topic trends, types of data sets, learning algorithm, methods improvements, and frameworks used in stock exchange prediction. A total of 81 studies were investigated, which were published regarding stock predictions in the period January 2015 to June 2020 which took into account the inclusion and exclusion criteria. The literature review methodology is carried out in three major phases: review planning, implementation, and report preparation, in nine steps from defining systematic review requirements to presentation of results. Estimation or regression, clustering, association, classification, and preprocessing analysis of data sets are the five main focuses revealed in the main study of stock prediction research. The classification method gets a share of 35.80% from related studies, the estimation method is 56.79%, data analytics is 4.94%, the rest is clustering and association is 1.23%. Furthermore, the use of the technical indicator data set is 74.07%, the rest are combinations of datasets. To develop a stock prediction model 48 different methods have been applied, 9 of the most widely applied methods were identified. The best method in terms of accuracy and also small error rate such as SVM, DNN, CNN, RNN, LSTM, bagging ensembles such as RF, boosting ensembles such as XGBoost, ensemble majority vote and the meta-learner approach is ensemble Stacking. Several techniques are proposed to improve prediction accuracy by combining several methods, using boosting algorithms, adding feature selection and using parameter and hyper-parameter optimization

Decision Support Using Machine Learning Indication for Financial Investment

To support the decision-making process of new investors, this paper aims to implement Machine Learning algorithms to generate investment indications, considering the Brazilian scenario. Three artificial intelligence techniqueswere implemented, namely: Multilayer Perceptron, Logistic Regression and Decision Tree, which performed the classification of investments. The database used was the one provided by the website Oceans14, containing the history of Fundamental Indicators and the history of Quotations, considering BOVESPA (São Paulo State Stock Exchange). The results of the different algorithms were compared to each other using the following metrics: accuracy, precision, recall, and F1-score. The Decision Tree was the algorithm that obtained the best classification metrics and an accuracy of 77%.N/