Financial Market Prediction using Google Trends

Ahmed, Farrukh; Asif, Rameez; Hina, Saman; Muzammil, Muhammad

doi:10.14569/ijacsa.2017.080752

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…The combination of technical analysis and fundamental analysis, as proposed by Beyaz et al (2018), turned out to be a good idea. Features selection algorithms extracted many features based on Google Trends entries and this fact is consistent with the discovery of Ahmed et al (2017). It is worth noticing that singular variables recommended by literature e.g.…”

Section: Discussionsupporting

confidence: 66%

“…This paper is focussed on the second approach. Ahmed, Asif, Hina and Muzammil (2017) collected data from Google Trends to capture the relationship between online searches and political and business events. They used this knowledge to predict the ups and downs of the Pakistan Stock Exchange Index 100, quantifying the semantics of the international market.…”

Section: Literature Reviewmentioning

confidence: 99%

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Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Chlebuś

Dyczko

Woźniak

2021

Central European Economic Journal

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Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.

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Section: Discussionsupporting

confidence: 66%

Section: Literature Reviewmentioning

confidence: 99%

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Chlebuś

Dyczko

Woźniak

2021

Central European Economic Journal

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“…In the context of the Participatory Web or simply Web 2.0, social media provide an effective, sophisticated and powerful way to gather preferences and activities of groups of the population. For example, data that are produced on the social media services can generate a complex and adequate knowledge on a plethora of fields of application, such as economy (stock market analysis [26] and private consumption prediction [27]), politics (opinion polls [28] and predictions of political elections [29]), sports (predict football game results [30]), tourism (places to be visited by observing the most frequently attended places in a given location [31]), demographics (identifying gender and age of selected user groups [32]) and infotainment [33]. Approaches for exploiting social media data are already mature enough, going beyond research prototypes, to mature robust data analytics software products such as Sysomos, Keyhole, Agorapulse, and the Twitris platform.…”

Section: Related Workmentioning

confidence: 99%

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Kalatzis

Routis

Marinellis

et al. 2019

Sensors

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One of the main obstacles towards the promotion of IoT adoption and innovation is data interoperability. Facilitating cross-domain interoperability is expected to be the core element for the realisation of the next generation of the IoT computing paradigm that is already taking shape under the name of Internet of Everything (IoE). In this article, an analysis of the current status on IoT semantic interoperability is presented that leads to the identification of a set of generic requirements that act as fundamental design principles for the specification of interoperability enabling solutions. In addition, an extension of NGSIv2 data model and API (de-facto) standards is proposed aiming to bridge the gap among IoT and social media and hence to integrate user communities with cyber-physical systems. These specifications have been utilised for the implementation of the IoT2Edge interoperability enabling mechanism which is evaluated within the context of a catastrophic wildfire incident that took place in Greece on July 2018. Weather data, social media activity, video recordings from the fire, sensor measurements and satellite data, linked to the location and the time of this fire incident have been collected, modeled in a uniform manner and fed to an early fire detection decision support system. The findings of the experiment certify that achieving minimum data interoperability with light-weight, plug-n-play mechanisms can be realised with significant benefits for our society.

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“…Since trends present the volume and search for distinct keywords, researchers used this data to make stock market predictions. Various studies[28,29] and researches [24,25,26,27]show that Google trends help to make better decisions for the stock market and the authors proved a positive correlation between Google trends and market movements.…”

Section: A Predictive Power Of Google Trendsmentioning

confidence: 99%

Short Term Stock Movements with Big Data and Market Sentiments Analytics

Kalra¹,

Gupta²,

Prasad³

2019

IJITEE

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Machine Learning Techniques and Big Data analytics are two central points of data science. Big Data is important for organizations to gain insights into it and machine learning techniques are one of the substantial assets for analyzing a massive amount of data. In this paper, a framework has been proposed to improve the short term stock trend prediction accuracy using Logistic Regression model by means of qualitative and quantitative data. This paper makes a comprehensive survey of stock market trend prediction with the accumulation of various data sources by applying machine learning techniques and by using big data analytics approach. The model has been implemented in Big data Framework with Hadoop and Apache Spark. For qualitative data Tweets sentiments and news sentiments has been taken in to account and for quantitative data Google trends and historical data are considered. The proposed system has enhanced the prediction accuracy about 3-4 % in comparison to existing models by supplying Google trend as input data in addition to market sentiments and historical data. The implemented model can help the investors to take short term decisions to make money in the security market and the survey would help in finding the most effective resources which overly influence the stock prices.

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Financial Market Prediction using Google Trends

Abstract: Abstract-Financial

Cited by 12 publications

References 5 publications

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

Semantic Interoperability for IoT Platforms in Support of Decision Making: An Experiment on Early Wildfire Detection

Short Term Stock Movements with Big Data and Market Sentiments Analytics

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