Trust Based Stock Recommendation System – A Social Network Analysis Approach

Sankar, C. Prem; Vidyaraj, R.; Kumar, K. Satheesh

doi:10.1016/j.procs.2015.02.024

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…The trader plays the role of a weak learner in this method and provides the companies with slight information. Sankar et al (2015) have introduced an intelligent approach to predicting stock price. He has used ANNs, fuzzy logic and genetic algorithms to teach the data and feature selection.…”

Section: Candlestick Technical Analysismentioning

confidence: 99%

A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization

Mahmoodi

Hashemi

Jasemi

et al. 2022

AJEB

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PurposeIn this research, the main purpose is to use a suitable structure to predict the trading signals of the stock market with high accuracy. For this purpose, two models for the analysis of technical adaptation were used in this study.Design/methodology/approachIt can be seen that support vector machine (SVM) is used with particle swarm optimization (PSO) where PSO is used as a fast and accurate classification to search the problem-solving space and finally the results are compared with the neural network performance.FindingsBased on the result, the authors can say that both new models are trustworthy in 6 days, however, SVM-PSO is better than basic research. The hit rate of SVM-PSO is 77.5%, but the hit rate of neural networks (basic research) is 74.2.Originality/valueIn this research, two approaches (raw-based and signal-based) have been developed to generate input data for the model: raw-based and signal-based. For comparison, the hit rate is considered the percentage of correct predictions for 16 days.

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Section: Candlestick Technical Analysismentioning

confidence: 99%

A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization

Mahmoodi

Hashemi

Jasemi

et al. 2022

AJEB

View full text Add to dashboard Cite

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“…In Frikha et al [54] presented a solution that was able to avoid a cold-start problem, by using users' existing social network data to make initial recommendations. A combination of approaches is justified in another contribution, where authors argued that people tend to trust recommendations that are made by people or friends they know, rather than strangers [55]. Additionally, they presented an ontology that is related to recommendations, but now it represents the semantics of these items.…”

Section: Publications' Review and Research Findingsmentioning

confidence: 99%

Provenance and social network analysis for recommender systems: a literature review

Rrmoku

Selimi²,

Ahmedi³

2022

IJECE

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<span>Recommender systems (RS) and their scientific approach have become very important because they help scientists find suitable publications and approaches, customers find adequate items, tourists find their preferred points of interest, and many more recommendations on domains. This work will present a literature review of approaches and the influence that social network analysis (SNA) and data provenance has on RS. The aim is to analyze differences and similarities using several dimensions, public datasets for assessing their impacts and limitations, evaluations of methods and metrics along with their challenges by identifying the most efficient approaches, the most appropriate assessment data sets, and the most appropriate assessment methods and metrics. Hence, by correlating these three fields, the system will be able to improve the recommendation of certain items, by being able to choose the recommendations that are made from the most trusted nodes/resources within a social network. We have found that content-based filtering techniques, combined with term frequency-inverse document frequency (TF-IDF) features are the most feasible approaches when combined with provenance since our focus is to recommend the most trusted items, where trust, distrust, and ignorance are calculated as weight in terms of the relationship between nodes on a network.</span>

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“…The fundamental analysis is to study the reasons for price changes, including economic factors, non-economic factors, internal market factors, current industrial conditions, domestic and foreign economic conditions, etc [16], [17], [18]. The data to be collected for the fundamental analysis is huge, and not every relationship with the stock price is equally important.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

et al. 2021

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The sustainable development of the national economy depends on the continuous growth and growth of the capital market, and the stock market is an important factor of the capital market. The growth of the stock market can generate a huge positive force for the country's economic strength, and the steady growth of the stock market also plays a pivotal role in the overall economic pulsation and is very helpful to the country's high economic development. There are different views on whether the technical analysis of the stock market is efficient. This study aims to explore the feasibility and efficiency of using deep network and technical analysis indicators to estimate short-term price movements of stocks. The subject of this study is TWSE 0050, which is the most traded ETF in Taiwan's stock exchange, and the experimental transaction range is 2017/01 ~ 2019 Q3. A four layer Long Short-Term Memory (LSTM) model was constructed. This research uses well-known technical indicators such as the KD, RSI, BIAS, Williams% R, and MACD, combined with the opening price, closing price, daily high and low prices, etc., to predict the trend of stock prices. The results show that the combination of technical indicators and the LSTM deep network model can achieve 83.6% accuracy in the three categories of rise, fall, and flatness.

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Trust Based Stock Recommendation System – A Social Network Analysis Approach

Cited by 18 publications

References 11 publications

A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization

A novel approach for candlestick technical analysis using a combination of the support vector machine and particle swarm optimization

Provenance and social network analysis for recommender systems: a literature review

Exploring the effectiveness of deep neural networks with technical analysis applied to stock market prediction

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