Stock market one-day ahead movement prediction using disparate data sources

Weng, Bin; Ahmed, Mohamed A.; Megahed, Fadel M.

doi:10.1016/j.eswa.2017.02.041

Cited by 177 publications

(109 citation statements)

References 37 publications

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“…Daily stock data from index report in each company are collected by Yahoo Finance in the same period during stock data and financial news headlines. Daily trading data, which are common predictors of stock price [23,33], and technical indicator features were used in our model. There are opening price, closing price, high price, low price, and volume and three technical indicators.…”

Section: Methodsmentioning

confidence: 99%

“…The main feature extraction in the previous studies [28,29] is sentiment analysis, which neglected the event characteristics in the text. Furthermore, the existing literature [23,29] had proved the positive effect of technical indicators on stock market prediction. In summary, our research highlights syntax analysis in financial news, which also incorporates with other features extraction (stock data, technical indicators, and bagof-words).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Stocks that have had more or stronger positive changes have a higher RSI than do those that have had more or stronger negative changes. Strength indicators fall between 0 and 100; investors sell if this value is ≥ 80 and buy if it is ≤ 20 [23,36].…”

Section: Datamentioning

confidence: 99%

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Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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An increasing number of the renowned company's investors are turning attention to stock prediction in the search for new efficient ways of hypothesizing about markets through the application of behavioral finance. Accordingly, research on stock prediction is becoming a popular direction in academia and industry. In this study, the goal is to establish a model for predicting stock price movement through knowledge graph from the financial news of the renowned companies. In contrast to traditional methods of stock prediction, our approach considers the effects of event tuple characteristics on stocks on the basis of knowledge graph and deep learning. The proposed model and other feature selection models were used to perform feature extraction on the websites of Thomson Reuters and Cable News Network. Numerous experiments were conducted to derive evidence of the effectiveness of knowledge graph embedding for classification tasks in stock prediction. A comparison of the average accuracy with which the same feature combinations were extracted over six stocks indicated that the proposed method achieves better performance than that exhibited by an approach that uses only stock data, a bag-of-words method, and convolutional neural network. Our work highlights the usefulness of knowledge graph in implementing business activities and helping practitioners and managers make business decisions.Hindawi Complexity Volume 2019, Article ID 9202457, 15 pages https://doi.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

et al. 2019

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“…have provided evidence that adding such features can improve return prediction and aid in the design of a trading strategy across several asset classes (Feuerriegel & Prendinger, 2016;Chen et al, 2014;Andersen et al, 2007;Chen & Gau, 2010;El Ouadghiri et al, 2016). Taking an even larger information set, (Weng et al, 2017) searches for disparate data sources (Google, Wikipedia, and so on) to increase the knowledge base that an algorithm can tap into for making predictions. Their inference engine used three modelling techniques (decision trees, NN and SVM) and compared favourably to other reported results in the literature.…”

Section: Cash Instruments Strategiesmentioning

confidence: 99%

A derivatives trading recommendation system: The mid‐curve calendar spread case

Koshiyama

Firoozye

Treleaven

2019

Intell Sys Acc Fin Mgmt

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Summary Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their job. Hence, this work is aimed to develop a trading recommendation system, and to apply this system to the so‐called Mid‐Curve Calendar Spread (MCCS) trade. To suggest that such approach is feasible, we used a list of 35 different types of MCCSs; a total of 11 predictive and 4 benchmark models. Our results suggest that linear regression with l1‐regularisation (Lasso) compared favourably to other approaches from a predictive and interpretability point of views.

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“…Bin Weng, et al [13] developed a method combining data which is collected from online, with traditional time series and technical analysis for stack that can provide more efficient and intelligent daily trading expect system. The three machine learning methods such as decision tree, Support vector machine and neural networks used for "inference engine".…”

Section: V Kamal D Vasumathimentioning

confidence: 99%

Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method

2019

IJRTE

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Stock market and its prices prediction are considered as one of the challenging task in financial forecasting. In my research, the framework is created based on the support vector regression (SVR) and Monte Carlo method to predict the stock price. The radial basis function (RBF) has high capacity, simpler design, and adopted for kernel function in SVR. The stock price of four companies Microsoft, Facebook, Amazon and Google is used to analyze the efficiency of the proposed method. The different parameters like mean square error (MSE), mean absolute error (MAE) measured to estimate the outcome of the proposed method. The experimental result showed the efficiency of the SVR-Monte Carlo in terms of error value. The MSE for the SVR-Monte Carlo in Google stock obtained as 0.2162 and MAE for the predicted value is 0.0164.

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Stock market one-day ahead movement prediction using disparate data sources

Cited by 177 publications

References 37 publications

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

Anticipating Stock Market of the Renowned Companies: A Knowledge Graph Approach

A derivatives trading recommendation system: The mid‐curve calendar spread case

Stock Price Forecasting Framework based on the Support Vector Regression and Monte Carlo Method

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