Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System

Shen, Jingyi

doi:10.22215/etd/2019-13721

Cited by 2 publications

(3 citation statements)

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“…This research is supported by Carleton University, in Ottawa, ON, Canada. This research paper has been built based on the thesis [36] of Jingyi Shen, supervised by M. Omair Shafiq at Carleton University, Canada, available at https ://curve .carle ton.ca/52e91 87a-7f71-48ce-bdfe-e3f6a 420e3 1a.…”

Section: Acknowledgementsmentioning

confidence: 99%

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Short-term stock market price trend prediction using a comprehensive deep learning system

2020

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Stock market is one of the major fields that investors are dedicated to, thus stock market price trend prediction is always a hot topic for researchers from both financial and technical domains. In this research, our objective is to build a state-of-art prediction model for price trend prediction, which focuses on short-term price trend prediction. As concluded by Fama in [26], financial time series prediction is known to be a notoriously difficult task due to the generally accepted, semi-strong form of market efficiency and the high level of noise. Back in 2003, Wang et al. in [44] already applied artificial neural networks on stock market price prediction and focused on volume, as a specific feature of stock market. One of the key findings by them was that the volume was not found to be effective in improving the forecasting performance on the datasets they used, which was S&P 500 and DJI. Ince and Trafalis in [15] targeted short-term forecasting and applied support vector machine (SVM) model on the stock price prediction. Their main contribution is performing a comparison between multi-layer perceptron (MLP) and SVM then found that most of the scenarios SVM outperformed MLP, while

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Section: Acknowledgementsmentioning

confidence: 99%

“…"Conclusion" section presents the conclusion. This research paper has been built based on Shen [36].…”

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Short-term stock market price trend prediction using a comprehensive deep learning system

2020

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“…The upward and downward fluctuation of the stock price is referred to as volatility. Stocks with higher volatility have a price that fluctuates considerably [17] in either direction over a short period of time [18]. Stocks with low price volatility are less likely to rise or fall dramatically on any given day.…”

Section: Introductionmentioning

confidence: 99%

Time Series Stock Market Predictions Using Time Interval Triggered Flag Attribute Model Using Deep Learning

Kalva

Naganjaneyulu

2022

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Despite being one of the most widely used techniques of financial management, stocks have drawn increasing numbers of investors during recent years. A substantial degree of risk is involved in buying stocks. The foremost concern for investors is how to minimise risks and maximise returns. One of the most common issues in the stock market is predicting a company's stock value. Volatility in individual profits and the health of the economy are negatively impacted by fluctuations in stock prices. One of the most widely held beliefs among humans is that investing in stock markets, which are supposed to produce excellent outcomes, is one of the finest ways to generate money. Volatility in stock market prices can occur for a variety of causes. It fosters an environment of uncertainty, which discourages constructive investment. Stock markets are notorious for their volatility. Those who are directly or indirectly involved in stock markets should be aware of it. It is necessary to create an intelligent system that can make forecasts based on a variety of indications such as fundamental, statistical, and technical trends. However, no single good predictive model has ever been able to consistently outperform market patterns. Traditionally, predictions for time series data have been made based on previous data and market trends, as well as historical correlation data and projections. Above all, there is no system that calculates projections based on a user's choice of investment type and risk tolerance. The main focus of this research work is on predicting stock market price changes. Instead of looking at daily changes, this research examines the price trend over specific time intervals by identifying turning points. To determine the increasing trend of price change, deep neural network model is used for accurate predictions. In this research work, an Efficient Time Series Stock Market Predictions using Time Interval Triggered Flag Attribute Model (ETSSMP-TITFA) using deep learning is proposed that predicts the lower bound and upper bound of stock market price predictions of multiple companies. The proposed model is contrasted with the traditional models and the results represent that the proposed model performance is better.

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Short-term Stock Market Price Trend Prediction Using a Customized Deep Learning System

Cited by 2 publications

References 42 publications

Short-term stock market price trend prediction using a comprehensive deep learning system

Short-term stock market price trend prediction using a comprehensive deep learning system

Time Series Stock Market Predictions Using Time Interval Triggered Flag Attribute Model Using Deep Learning

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