Using Twitter to Predict the Stock Market

Nofer, Michael; Hinz, Oliver

doi:10.1007/s12599-015-0390-4

Cited by 126 publications

(49 citation statements)

References 94 publications

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“…Some work on textual analysis has been done in the area of big data and markets (Loughran and McDonald ; Luo et al. ; Nofer and Hinz ). Algorithms purporting to predict stock market moves such as ‘I know first’ (2014) use a combination of machine learning via genetic algorithms (survival of the fittest) (Sharma et al.…”

Section: Research Directionsmentioning

confidence: 99%

Big Data Opportunities for Accounting and Finance Practice and Research

Cockcroft

Russell

2018

Australian Accounting Review

134

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We examine the research opportunities for the use of 'big data' in accounting and finance. The purpose of the study is to present a snapshot of big data academic research in information systems, accounting and finance, and to highlight areas for further research in accounting and finance. The research question addressed in this work is: What are the major themes in existing research in big data and where are the resulting gaps in the accounting and finance literature? An analysis is presented of 47 accounting, finance and information systems journals from 2007-2016. We identify and sample the relevant literature to derive a taxonomy of themes. These themes are presented as a conceptual matrix in which the themes from the taxonomy are used as concepts, and the matrix identifies where they appear, and where there are potential areas for further research. Prior research in big data in accounting, finance and information systems falls into six themes. The six under-researched areas of big data in accounting and finance are risk and security, data visualisation and predictive analytics, data management and data quality. Increased research in these areas will lead to improvements in industry practices, and opportunities for cross-disciplinary research.

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Section: Research Directionsmentioning

confidence: 99%

Big Data Opportunities for Accounting and Finance Practice and Research

Cockcroft

Russell

2018

Australian Accounting Review

134

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“…Additionally, [2] reviews many stock market trading techniques, variables, and statistical tests. There have been several studies concerning the German stock markets, e.g., relations between beta and realized returns [23], liquidity [31], volatility and short selling constraints [6], or behavioural finance [43].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

Fister¹,

Mun²,

Jagrić³

et al. 2019

NNW

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Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading. A mechanical trading system is used to evaluate its performance. The proposed solution is compared to traditional trading strategies, i.e., passive and rule-based trading strategies, as well as machine learning classifiers. We have discovered that the deep-learning long short-term memory network has outperformed other trading strategies for the German blue-chip stock, BMW, during the 2010-2018 period. system (MTS) to sequentially (day-by-day) execute trading signals (decisions), and thus trade with real stocks [66] in virtual time. Consequently, the quality and effectiveness of a trading system over a period of a few years can be evaluated in a matter of seconds.The MTS works by giving three common trading signals, i.e., buy, hold, and sell, for each of the stocks. It is given an initial amount of cash to buy stocks that can be held in the portfolio or be sold at a later date. Buying and selling stocks can generate profits if trading signals are given rationally or generate loss if they are not. Profits and losses can be monitored and reviewed at the end of the trading period to realize strengths and weaknesses of the trading decisions [47]. Trading decisions are typically given by trading strategies, i.e., automated algorithms that constantly monitor market behaviour and react accordingly. Multiple trading strategies are usually incorporated within the trading system, by each giving unique trading decisions. The latter can be evaluated quickly and inexpensively either in realworld time or using the MTS.In this paper we are proposing a concept of an automated, single stock, trading system using the MTS, examining its potential by five different trading strategies, realizing their strengths and weaknesses, and proving the correctness of an optimal strategy. Based on this, we encourage further analysis and the design of an automated portfolio trading system for many similarly treated stocks in parallel. Here we employ three different trading strategies: (1) passive, (2) rule-based -relative strength index (RSI) and moving average convergence/divergence (MACD) technical indicators, and (3) surrogate model trading strategies using machine learning classifiers (MLC) and long short-term memory network (LSTM). Each of them can provide three trading signals: buy, hold, or sell.Since deep learning (DL) [37] is primarily intended for engineering applications, such as image and sound processing, we have not found many examples of DL in the fields of finance, banking, or insurance. In line with this, we would like to apply the DL to the area of finance, particularly mechanical trading systems as an alternative, and thus test whether the DL can be successfully deployed into this area.The structure of the paper is as follows: Section 2 presents the literature review, MLC application...

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“…It turned out that different emotional tendencies correspond to different learning styles. Nofer, et al [36] predicted stock returns by extracting user sentiment levels from Twitter. Tumasjan, et al [37] predicted sentiment trends in political events (government, elections, security and defense, and health insurance) by processing and classifying the data in Twitter.…”

Section: Emotional Analysismentioning

confidence: 99%

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

Gao

et al. 2020

Sustainability

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(1) Background Genetic Modification (GM) refers to the transfer of genes with known functional traits into the target organism, and ultimately the acquisition of individuals with specific genetic traits. GM technology in China has developed rapidly. However, the process is controversial; thus, future development may be hindered. China has become the world’s largest importer of GM products. Research on the attitudes towards GM food in China will help the government achieve sustainable development by better understanding and applications of the technology. (2) Methods This research utilizes data from Sina Weibo (microblog), one of the biggest social network services (SNS) in China. By using the self-created Python crawler program, comments related to the genetically modified food in the People’s Daily account are analyzed. Sentiment classifications are analyzed via multivariate logistic regression. (3) Results Based on the factor analysis, theme type characteristics, the propagation characteristics, the body information characteristics, and the comment characteristics have different degrees of influence on the user’s emotional distribution. (4) Conclusion Practical implications and conclusions are provided based on the results at the end.

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Using Twitter to Predict the Stock Market

Cited by 126 publications

References 94 publications

Big Data Opportunities for Accounting and Finance Practice and Research

Big Data Opportunities for Accounting and Finance Practice and Research

Deep Learning for Stock Market Trading: A Superior Trading Strategy?

What Causes Different Sentiment Classification on Social Network Services? Evidence from Weibo with Genetically Modified Food in China

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