The Enhanced Classification for the Stock Index Prediction

Kim, Hyeuk; Han, Sang Tae

doi:10.1016/j.procs.2016.07.077

Cited by 13 publications

(6 citation statements)

References 5 publications

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“…In this paper, we start with the assumption that predictions on capital markets are possible. A plethora of papers deal with prediction models in the context of capital markets and use traditional time series analyses or methods from AI for this purpose [23].…”

Section: Methodsmentioning

confidence: 99%

The Predictive Value of Data from Virtual Investment Communities

Abdel-Karim

Benlian

Hinz

2020

MAKE

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Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.

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Section: Methodsmentioning

confidence: 99%

The Predictive Value of Data from Virtual Investment Communities

Abdel-Karim

Benlian

Hinz

2020

MAKE

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“…The use of the first and second fuzzy types along with the neural network for classification and the PSO algorithm for extracting features from stock market data is considered. The presentation of a classification method is conducted with an observer and with the strengthening of input and training in [21], which uses a standard data set called KOSPI based on statistical arbitrage.…”

Section: Literature Reviewmentioning

confidence: 99%

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

et al. 2021

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The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.

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“…Then the grouping will give the output of the grouping of stock data such as "buy," "sell," "hold" [7], [12], [23], or "up" and "down" [24]. Several algorithms found will classify in this study, namely LSTM [10], [24]- [28], Random Forest used in research [9], [13], [29], and also random forest ensemble in research [12]. .…”

Section: Associationmentioning

confidence: 99%

“…[6], [15], [19], [27], [33], [34]. In addition, some use attributes in the form of technical data such as Moving Average, Bollinger Bands, Weighted Moving Average (WMA) and share other technical data attributes [7], [12], [23], [29], [35]- [39]. Then sentiment analysis is carried out by analyzing data or sentiment from an object in the form of news and social media to predict stock prices.…”

Section: The Most Type Of Analysis Used In Predicting Stocksmentioning

confidence: 99%

Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

Dopi¹,

Hartanto²,

Fauziati³

2021

Advances in Economics, Business and Management Research

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This research was conducted using a literature review method to analyze various studies that will identify the type of analysis with the attributes used, the methods used, the methods most often used, and the methods that have the best performance. This study collected research from 2016 -July 2021, selected based on predetermined criteria and then collected 40 papers. This review found that there are four research topics, namely estimation, classification, clustering, and association. The findings of this study are four research topics, namely fundamentals, technicals, sentiment analysis, and even a combination of analyzes that use their respective attributes and datasets. There are thirty-one different methods found to be used in predicting stock prices. LSTM, MLP, RF, and SVM are the most widely used methods. In addition, MLP is a method that gives the best performance of 71.63% and LSTM of 70%. The use of combined machine learning methods with ensemble techniques, deep learning, and selection of input attributes in pre-processing is recommended for better accuracy.

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The Enhanced Classification for the Stock Index Prediction

Cited by 13 publications

References 5 publications

The Predictive Value of Data from Virtual Investment Communities

The Predictive Value of Data from Virtual Investment Communities

Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

Systematic Literature Review: Stock Price Prediction Using Machine Learning and Deep Learning

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