Stock price prediction using data analytics

Tiwari, Shashank; Bharadwaj, Akshay; Gupta, Sandeep K.

doi:10.1109/icac3.2017.8318783

Cited by 24 publications

(15 citation statements)

References 3 publications

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“…Pujari et al [12] selected the XAMPP platform as the display surface to implement a web scraping scene application. Tiwari et al [1] used the web scraping technique to catch stock price data and predicted the stock price trend; in addition, they compared the predicted results with traditional statistics and neural network forecast method usage. Zhao et al [2] considered and discussed the relationship between stock news and stock price trends.…”

Section: Related Workmentioning

confidence: 99%

“…It has a 4 h operating time if it contains board lot time after 13:00 p.m. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, such as tape-reading users, a concept similar to that of Tiwari et al [1]. However, it is the stock user's experience.…”

Section: Introductionmentioning

confidence: 99%

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Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Lin

Yang

2022

ASI

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Stock tape reading involves surveilling stock prices once in a while and recording stock prices. The method of observing stock prices may be television or stock exchange. The time step for recoding stock prices is every stock user’s experience and their theory, perhaps 3 min or 2 h and so on. As an example, the Taiwan stock market starts at 9:00 a.m. to 13:30 p.m. It will have a 4 h operating time. Splitting the 4 h operating time for tape reading is the skill of stock users. The stock price sequence generated by tape reading can be predicted by stock users, but finally, it is the stock user’s experience. Therefore, the meaning of tape reading is to record the stock price, but its concept should have no prediction purpose. This study used thread technology and proposed a tape-reading method with web scraping. This method can periodically scrape stock prices and generate a stock price sequence to Excel file. This application can satisfy the demand of these stock users, who are called day trading users. Because these day trading users want to gain stock price sequences minute by minute, rather than the stock exchange format day by day, and also ones which are better than the those provided by the stock website service, because its stock sequence format is limited and not normalized, these day trading users think that minute-by-minute stock price sequences are very clear to forecast. This study implemented the prior scheme and designed the GUI to query a company’s stock price and its stock news, even per second, etc., and how long it took to update the stock price, and the GUI also included a time-up feature to stop scraping stock prices if users just wanted to scrape stock prices periodically.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Lin

Yang

2022

ASI

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“…Shashank Tiwari [7] work done on this paper utilizing the information investigation and anticipate the stock cost. This paper proposes utilization of Data examination to be utilized in help with speculators for making right money related expectation so right choice on venture can be taken by Investors.…”

Section: Related Workmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques

Reshma¹,

S²,

Sathiyavathi³

et al. 2021

Advances in Parallel Computing Technologies and Applications

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Predicting the future in all the areas using machine learning techniques was the recent research in the current scenario. Stock market is one among them which needs the prediction future market to invest in the new enterprise or to sell their existing shares to get profit. This need the efficient prediction technique which studies the previous exchanges of stock market and gives the future prediction based on that. This article proposed the prediction system of stock market price based on the exchange takes place in previous scenario. The system studies the diversing effect of market price of product in a particular time gap and analyze its future trend whether it’s loss or gain. During the system of thinking about diverse strategies and variables that should be taken into account, we observed out that strategies like random forest, Support vector machine and regression algorithm. Support vector regression is a beneficial and effective gadget gaining knowledge of approach to apprehend sample of time collection dataset. The data collected for the four years duration which was accumulated to get the expecting prices of the share of the firm. It can produce true prediction end result if the fee of essential parameters may be decided properly. It has been located that the guide vector regression version with RBF kernel indicates higher overall performance while in comparison with different models.

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“…we tend to tend to formulate prophetical model exploitation the foremost important choices to predict the credit goodness of a given shopper. [5] to formulate banks' risk automatic system.…”

Section: Literature Surveymentioning

confidence: 99%

Anticipating the Creditworthiness of an Organization using R

R.*,

R.,

J. M.

et al. 2020

IJITEE

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Numerous start-ups are being created day-by-day. Government also welcomes those by providing funds and loans. As India’s economy grows at tremendous pace there is a need for analytical models to help investors track down and predict the performance of industry. Thus, predictive models help us to find and make an informed decision about the financial markets in the future. It allows investors to predict the right shares to obtain profitable investments, banks to invest on repayable customers, mutual funds providers to predict the credit worthiness and shares in order to obtain accuracy about investments and outcomes etc. while there are many models that have been created and perfected by numerous banks and credit rating agencies with their own software tool and data analytics processing there are no such models and systems exists for common retail stock mutual fund investors. This paper mainly focuses on building an open source user friendly model that predict the future performance of concern industry based on the historical records of financial data that is available in BSE/NSE market for various stake holders by focusing on different performance parameters of the concerned company. This prediction is done using R. The Descriptive and predictive models have been created using the financial data collected for more than 3000 companies and tested on accuracy with various statistical methods like ROC.

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Stock price prediction using data analytics

Cited by 24 publications

References 3 publications

Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Taiwan Stock Tape Reading Periodically Using Web Scraping Technology with GUI

Stock Market Prediction Using Machine Learning Techniques

Anticipating the Creditworthiness of an Organization using R

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