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eLearning and Software for Education, 2020
E-learning, in most contexts, is becoming a more accessible and efficient method of training through which people could acquire new knowledge. Due the difference between epochs, reading and analysing the growing flow of information has become a very challenging task for financial professionals.
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E-learning, in most contexts, is becoming a more accessible and efficient method of training through which people could acquire new knowledge. Due the difference between epochs, reading and analysing the growing flow of information has become a very challenging task for financial professionals.
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International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24)
This study explores machine learning approaches for forecasting stock prices, addressing the complexities of high volatility and non linear market behaviour. Traditional forecasting methods, while useful, lack adaptability in dynamic markets. We employ three models Long Short Term Memory (LSTM) networks, Random Forest, and Linear Regression to analyze ...
Pushkala B +3 more
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This study explores machine learning approaches for forecasting stock prices, addressing the complexities of high volatility and non linear market behaviour. Traditional forecasting methods, while useful, lack adaptability in dynamic markets. We employ three models Long Short Term Memory (LSTM) networks, Random Forest, and Linear Regression to analyze ...
Pushkala B +3 more
+4 more sources
Stock Price Prediction Using AI
International Journal of Multidisciplinary Research and Growth Evaluation, 2023This study evaluates the use of Linear regression & Long Short-Term Memory Model for stock price prediction. The linear regression model is trained on historical stock data and used to predict future stock prices. The results show that linear regression can be an effective tool for stock price prediction when the stock market follows a predictable ...
Dr. Imtiyaz Khan +2 more
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Stock Price Analysis and Prediction
2021 International Conference on Communication information and Computing Technology (ICCICT), 2021Covid has taught a valuable lifelong lesson. During the pandemic, economies of countries collapsed and many nations had to undergo a complete lockdown. Individuals lost their sources of income and their savings dwindled trying to survive the lockdown. Many small-scale industries closed down for not being able to recover losses.
Chintan Vora +3 more
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Stock Price Prediction using LSTM
Indian Journal of Artificial Intelligence and Neural Networking, 2022The movement of stock prices is non-linear and complicated. In this study, we compared and analyzed various neural network forecasting methods based on real problems related to stock price demand forecasting. We ultimately selected the LSTM (Long Short-Term Memory) [1] neural network as traditional RNN’s long-term reliance is improved by LSTM, which ...
Sakshi Vora +3 more
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2020
Stock price prediction combines the Brownian motion theory of physics with the Monte Carlo theory of statistical mathematics. Put simply, Brownian motion just models the random motion of particles in gases and liquids. If we assumed that there was a general drift upward and to the right and we plotted the particles’ position in 2D over a period of ...
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Stock price prediction combines the Brownian motion theory of physics with the Monte Carlo theory of statistical mathematics. Put simply, Brownian motion just models the random motion of particles in gases and liquids. If we assumed that there was a general drift upward and to the right and we plotted the particles’ position in 2D over a period of ...
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Price–Dividend Ratios and Stock Price Predictability
Journal of Forecasting, 2011ABSTRACTA long‐standing puzzle to financial economists is the difficulty of outperforming the benchmark random walk model in out‐of‐sample contests. Using data from the USA over the period of 1872–2007, this paper re‐examines the out‐of‐sample predictability of real stock prices based on price–dividend (PD) ratios.
Jyh‐Lin Wu, Yu‐Hau Hu
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Genetic Programming Prediction of Stock Prices
Computational Economics, 2000zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Stock Prices, Inflation and Stock Returns Predictability
Finance, 2004Résumé Dans cet article, nous considérons la relation entre les cours boursiers et l’inflation aux États-Unis dans une nouvelle perspective. Nous estimons la tendance stochastique commune entre les cours boursiers réels, tels que reflétés par l’ Earning Price Ratio (EPR), et l’inflation anticipée et réalisée.
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Stock Price Prediction Using Mamba
2024 15th National Conference on Electrical and Electronics Engineering (ELECO)The prices in the stock market constantly fluctuate due to the influence of internal and external dynamics. For investors, predicting these movements can provide a significant advantage. Accurately forecasting stock prices is crucial for both reducing investment risks and increasing returns, while preventing financial losses.
Akgun, H.I., Özbayoğlu, Ahmet Murat
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