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Artykuły w czasopismach na temat „Market price prediction opportunities”

Autor: Grafiati

Data publikacji: 3 czerwca 2025

Data aktualizacji: 31 lipca 2025

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Adithya, Vishnu. "Stock Market Analysis Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47888.

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Abstract— The stock market price prediction and classification is Stock market price prediction is a common problem in finance that involves using historical data to forecast future prices of stocks or other financial instruments. The goal of stock market price prediction is to identify profitable trading opportunities and make informed investment decisions, to resolve this issues different machine learning algorithm is implemented for predict the model accuracy of stock market price level The problem statement for stock market price prediction involves identifying the factors that influence s

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Purnama, Panji Satria Taqwa Putra. "Optimizing Bitcoin Price Prediction with LSTM: A Comprehensive Study on Feature Engineering and the April 2024 Halving Impact." Elinvo (Electronics, Informatics, and Vocational Education) 9, no. 1 (2024): 165–77. http://dx.doi.org/10.21831/elinvo.v9i1.72518.

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This research aims to develop a Bitcoin price prediction model using machine learning techniques, with a specific focus on Long Short-Term Memory (LSTM) neural networks. The Bitcoin market is characterized by unique features such as high volatility and the influence of various external factors, which differ significantly from traditional financial markets. As such, precise feature engineering is crucial for accurately modelling Bitcoin prices. Utilizing historical Bitcoin price data from 2014 to 2023, this study extensively evaluates LSTM models. The results indicate that LSTM models provide h

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Li, Jingyao. "Comparison of Different Machine Learning Approaches for Forecasting Stock Prices." Highlights in Science, Engineering and Technology 94 (April 26, 2024): 17–23. http://dx.doi.org/10.54097/2re5n809.

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Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a f

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Shah, Dev, Haruna Isah, and Farhana Zulkernine. "Stock Market Analysis: A Review and Taxonomy of Prediction Techniques." International Journal of Financial Studies 7, no. 2 (2019): 26. http://dx.doi.org/10.3390/ijfs7020026.

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Stock market prediction has always caught the attention of many analysts and researchers. Popular theories suggest that stock markets are essentially a random walk and it is a fool’s game to try and predict them. Predicting stock prices is a challenging problem in itself because of the number of variables which are involved. In the short term, the market behaves like a voting machine but in the longer term, it acts like a weighing machine and hence there is scope for predicting the market movements for a longer timeframe. Application of machine learning techniques and other algorithms for stoc

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Et. al., Ai Rosita ,. "Market Price Signal Prediction Based On Deep Learning Algorithm." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 11 (2021): 1051–57. http://dx.doi.org/10.17762/turcomat.v12i11.5995.

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Nowadays, many people are venturing into market trading and investment, thus producing many new traders and investors worldwide. The main goal is to gain profit and prevent loss. Most of them are researching global investment opportunities to learn about the market, especially to predict market prices in the future. However, it will become challenging as the financial indicators are very complicated, and it will require a lot of experience and knowledge. The price movement in the price chart also is hard to be predicted when using a fractal indicator. Recently, machine learning and deep learni

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Plastun, Alex, and Serhii Bashlai. "Volatility explosions and price prediction: case of oil market." Journal of Governance and Regulation 6, no. 2 (2017): 48–60. http://dx.doi.org/10.22495/jgr_v6_i2_p5.

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This paper explores behavior of oil market after volatility explosions (days with abnormally high price volatility). It examines possible price patterns and whether they create exploitable profit opportunities from trading. A number of statistical tests both parametrical (t-test, ANOVA, regression analysis with dummy variables) and non-parametrical (Mann–Whitney U test) confirm presence of price patterns after volatility explosions: the next day price changes in both directions are bigger than after “normal” days. Oil prices (case of Brent) for the period from January 2000 till the end of 2016

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Islam, Md Zahidul, Md Shahidul Islam, Md Abdullah Al Montaser, et al. "EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN PREDICTING CRYPTOCURRENCY PRICES UNDER MARKET VOLATILITY: A STUDY BASED ON THE USA FINANCIAL MARKET." American Journal of Management and Economics Innovations 06, no. 12 (2024): 15–38. https://doi.org/10.37547/tajmei/volume06issue12-03.

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The cryptocurrency market is one of the most dynamic and volatile markets in the world's financial ecosystem, and investment landscapes in the US financial market have changed so much. In slightly over a decade, cryptocurrencies have moved from niche digital assets to mainstream investment opportunities such as Bitcoin, Ethereum, and many others. The prime objective of this research project was to investigate the effectiveness of various machine learning algorithms in the prediction of cryptocurrency prices within the volatile US financial market. This research pinpointed which Machine Learnin

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Chen, Weihang. "Application of Market Cycle Analysis and LSTM in Prediction of Stock Price Movements." BCP Business & Management 38 (March 2, 2023): 856–61. http://dx.doi.org/10.54691/bcpbm.v38i.3787.

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The stock market prediction has been carried out by several ways in data science using deep learning approaches to capture profitable trading opportunities and making the trading plans. However, it is widely believed there are two main issues involved in it, i.e., efficient market hypothesis and low information noise ratio. Therefore, a prediction based model will be affected by noises thus hard to produce a prediction. In this paper, two methods will be presented for forecasting stock future performance. To be specific, LSTM (long-short time memory) and cycle analysis are implemented to predi

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Swamy, J. Kumara, and Navya V K. "Deep Learning Based Bitcoin Price Forecasing Using LSTM." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1094–99. http://dx.doi.org/10.22214/ijraset.2023.49601.

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Abstract: Bitcoin is one of the most popular and valuable cryptocurrency in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Bitcoin price prediction with different machine learning prediction algorithms. For the research: relevant features are taken from the dataset having strong correlation with Bitcoin prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes

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Mao, Jiayi, and Zhiyong Wang. "Deep Learning-Based Stock Price Prediction Using LSTM Model." Proceedings of Business and Economic Studies 7, no. 5 (2024): 176–85. http://dx.doi.org/10.26689/pbes.v7i5.8611.

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The stock market is a vital component of the broader financial system, with its dynamics closely linked to economic growth. The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets. By examining historical transaction data, latent opportunities for profit can be uncovered, providing valuable insights for both institutional and individual investors to make more informed decisions. This study focuses on analyzing historical transaction data from four banks to predict closing price trends. Various models, including decision tree

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Dutulescu, Andreea, Andy Catruna, Stefan Ruseti, et al. "Car Price Quotes Driven by Data-Comprehensive Predictions Grounded in Deep Learning Techniques." Electronics 12, no. 14 (2023): 3083. http://dx.doi.org/10.3390/electronics12143083.

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The used car market has a high global economic importance, with more than 35 million cars sold yearly. Accurately predicting prices is a crucial task for both buyers and sellers to facilitate informed decisions in terms of opportunities or potential problems. Although various machine learning techniques have been applied to create robust prediction models, a comprehensive approach has yet to be studied. This research introduced two datasets from different markets, one with over 300,000 entries from Germany to serve as a training basis for deep prediction models and a second dataset from Romani

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Ibrahim, S. K., and Pawan Singh. "Bitcoin Price Prediction Using Machine Learning Techniques." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 3, no. 1 (2022): 1–9. http://dx.doi.org/10.54060/jieee/003.01.005.

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This paper discusses, trying to accurately assess the price of Bitcoin by looking at different parameters affects the value of Bitcoin. In our work, we focus on understanding and seeing the evolution of Bitcoin daily market, a1 and gaining intuition in the most relevant aspects surrounding the Bitcoin price. In the meantime, market capitalization of publicly traded cryptocurrencies exceeds $ 230 billion. The most important cryptocurrency, Bitcoin, is used primarily as a digital value store, and its pricing opportunities have been extensively considered. These features are described in more det

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Xiao, Shuting. "A Study of Chinese Stock Price Prediction Based on LSTM and Time Series Linear Regression Model." Advances in Economics, Management and Political Sciences 98, no. 1 (2024): 242–53. http://dx.doi.org/10.54254/2754-1169/98/2024ox0186.

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With the advancement of technology and the increasing popularity of machine learning, new opportunities have emerged across various industries. In the financial sector, an increasing number of individuals are attempting to utilize machine learning to enhance the accuracy of stock price predictions. This paper will also endeavor to apply the LSTM model for predicting the stock prices of companies listed on the main boards of the Chinese stock market, while simultaneously comparing it with traditional time series linear regression model. Against the highly complex backdrop of the stock market, t

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Hassan, Abass. "BITCOIN PRICE PREDICTION BY USING ARIMA." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32489.

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Cryptocurrency markets have emerged as a dynamic and intriguing domain, with Bitcoin at the forefront, captivating the attention of investors, researchers, and enthusiasts alike. The volatile nature of Bitcoin prices presents both opportunities and challenges for market participants seeking to understand and anticipate its movements. In this study, we delve into the realm of time series analysis to explore the feasibility of predicting The research journey begins with meticulous data preprocessing steps to ensure the quality and integrity of the input data. Leveraging Python libraries such as

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Dr., Chaitanya Kishore Reddy.M, Vyshnavi Devi G., Sai Sravani K., and Vasanth B. "Forecasting the Fluctuations in the Price of Cryptocurrency using LSTM in Machine Learning." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 624–29. https://doi.org/10.5281/zenodo.7950966.

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One of the most well-known and valuable cryptocurrencies in the present financial market is bitcoin, which attracts investors and thus creates new research opportunities for scientists. Numerous studies using various machine learning prediction methods have been conducted on predictingBitcoin prices. In order to conduct the study, important features from a dataset with a high connection to Bitcoin prices are gathered, and then random data chunks are chosen to train and test the model. The accuracy of price predictions may be lowered due to unfitting results caused by the random data that was c

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Brahmanapalli Kalyan, S Parameshwara Reddy, Krovvidi Krishna Kumari, and Manish Jain. "Comparative Analysis of Stock Price Prediction Accuracy: A Machine Learning Approach with ARIMA, LSTM, And Random Forest Models." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 05 (2024): 1141–51. http://dx.doi.org/10.47392/irjaeh.2024.0157.

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This research investigates the comparative effectiveness of three distinct predictive models – ARIMA (Auto Regressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Random Forest – in forecasting stock prices. Focusing on Tata Motors and Infosys stocks, historical data spanning a significant timeframe is collected using the finance library. These models are trained on a diverse set of features including open, close, high, and low prices to capture the underlying market dynamics. The evaluation of model performance is centred on their ability to forecast stock prices over varyin

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Yu, Xinpeng, and Dagang Li. "Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network." Applied Sciences 11, no. 9 (2021): 3984. http://dx.doi.org/10.3390/app11093984.

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Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading oppor

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Toka Sai Pallavi, Namoju Karthik, Namoju Karthik, et al. "Stock Market Price Predictions Using Machine Learning." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1819–28. https://doi.org/10.47392/irjaem.2025.0287.

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The stock market is a dynamic and complex system influenced by numerous factors, making the accurate prediction of stock prices a challenging task. This project focuses on developing a web-based platform that predicts and highlights stocks expected to increase in price. The primary goal is to assist investors by simplifying the decision-making process through real-time insights into market trends. To achieve this, we employ machine learning algorithms trained on historical stock market data, including features such as opening and closing prices, trading volume, market sentiment, and technical

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Kim, Jin. "A Study on the Forecasting of Real Estate Market Using Algorithms." Korea Real Estate Society 72 (June 30, 2024): 159–68. http://dx.doi.org/10.37407/kres.2024.42.2.159.

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Technology using algorithms has an impact on various aspects of the real estate market, such as real estate market price prediction, investment recommendation, and rent setting. First, it was confirmed that by improving the accuracy of real estate price prediction, real estate market price prediction using AI algorithm can derive more accurate results through machine learning and deep learning technologies based on past data. In addition, it is the development of an investment recommendation system. The investment recommendation system using AI can promote efficient fund management by discover

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Teck, Lim Yong, and Angelina Pramana Thenata. "Stock Price Prediction Using TCN-GAN Hybrid Model." sinkron 9, no. 1 (2025): 106–14. https://doi.org/10.33395/sinkron.v9i1.14246.

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The stock market plays a vital role in national economies, offering significant profit opportunities for investors while exposing them to substantial risks due to market uncertainties. Stock prices often experience significant fluctuations, making accurate prediction a challenging task. Temporal Convolutional Network (TCN) and Generative Adversarial Network (GAN) are the deep learning method proposed for this research. The purpose of this research is to analyze how well the TCN-GAN model predicts stock prices. Previous researches show both TCN and GAN perform well on time series data. TCN exce

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Nitesh Singh. "Crop Price Prediction Using Machine Learning." Journal of Electrical Systems 20, no. 7s (2024): 2258–69. http://dx.doi.org/10.52783/jes.3961.

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Crop price prediction is a pivotal aspect of agricultural economics, impacting stakeholders across the industry, from farmers to policymakers to consumers. Traditional methodologies often struggle to provide accurate and efficient predictions, primarily due to the intricate and ever-changing nature of agricultural markets. However, in recent years, the emergence of machine learning techniques has offered promising solutions to enhance crop price prediction. This paper conducts an extensive review of various machine learning approaches utilized for this purpose, covering regression-based method

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You, Zixuan. "Evaluation of two models for predicting Amazon stock based on machine learning." BCP Business & Management 34 (December 14, 2022): 39–47. http://dx.doi.org/10.54691/bcpbm.v34i.2862.

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With the fast growth of artificial intelligence and technology, the use of machine learning techniques in financial markets is gaining popularity. As a result, many opportunities arise, such as predicting future stock movements. Financial markets are complex and constantly evolving environments, so analyzing them can be challenging and interesting. There are no specific rules to predict or estimate the value of a stock in the stock market, so one can do stock price prediction by various methods. In this project, the stock price data of Amazon for the past five years, ‘Date’, ‘Starting price’,

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Geerts, Margot, Seppe vanden Broucke, and Jochen De Weerdt. "A Survey of Methods and Input Data Types for House Price Prediction." ISPRS International Journal of Geo-Information 12, no. 5 (2023): 200. http://dx.doi.org/10.3390/ijgi12050200.

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Predicting house prices is a challenging task that many researchers have attempted to address. As accurate house prices allow better informing parties in the real estate market, improving housing policies and real estate appraisal, a comprehensive overview of house price prediction strategies is valuable for both research and society. In this work, we present a systematic literature review in order to provide insights with regard to the data types and modeling approaches that have been utilized in the current body of research. As such, we identified 93 articles published between 1992 and 2021

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Chandu, Venkateswarlu, Archi Agarwal, Tummala Likitha, Bindu Sri Datla, and Rubi Shagufta. "Prediction ARIMA Model-based Investor’s Perception on Stock Market Apps." Journal of Sensors, IoT & Health Sciences 2, no. 4 (2024): 56–68. https://doi.org/10.69996/jsihs.2024021.

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The advent of stock trading apps has revolutionized the landscape of stock market participation, particularly among retail investors. This study investigates the impact of stock trading apps on the behaviour of retail investors in the stock market, the impact of social media In growing importance of online trading app and the opportunities for budding investors that the growth of stock market apps has brought with itself. Our findings indicate the changes brought in the economy through mobile trading apps and how it has significantly contributed to FinTech not just economically but also financ

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Sameer, Gupta, and Bhardwaj Sunil. "Price Discovery in Indian Spot and Future markets of Gold and Silver." RESEARCH REVIEW International Journal of Multidisciplinary 03, no. 08 (2018): 41–49. https://doi.org/10.5281/zenodo.1341288.

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Last twenty years have exposed the corporate world to many financial risks due to policy of liberalization and globalization policy across the world. In today"s dynamic business environment risk management has become very critical for the survival of MNCs. Therefore the emergence of derivative markets in India is attributed to the need of effective and less costly risk management tools for predicting the price of underlying assets. To reduce the extent of financial risks by providing commitment of price of an asset at future date is the basic feature of these financial instruments which had ma

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VAN DER LAAN, GERARD, and HAROLD HOUBA. "ONE-SELLER/TWO-BUYER MARKETS WITH BUYER EXTERNALITIES AND (IM)PERFECT COMPETITION." International Game Theory Review 04, no. 02 (2002): 141–64. http://dx.doi.org/10.1142/s0219198902000616.

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In this paper the one-seller/two-buyer problem with buyer externalities is investigated under the assumption that the two buyers have legal opportunities to cooperate. It is shown that the Competitive equilibrium and the Core are robust with respect to negligible externalities and that the range of market prices in the Core belongs to range of Competitive equilibrium prices. However, these concepts yield no prediction for relatively severe externalities. Therefore, in order to provide a prediction the Bargaining set and the Multilateral Nash (MN) solution are also investigated. Surprisingly, i

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Shafeeq, Ur Rahaman, Sudheer Patchipulusu, and Jabeen Abdul Mahe. "Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches." International Journal of Current Science Research and Review 07, no. 10 (2024): 7597–609. https://doi.org/10.5281/zenodo.13889480.

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Abstract : The rapidly evolving landscape of cryptocurrency markets presents unique challenges and opportunities. The significant daily variations in cryptocurrency exchange rates lead to substantial risks associated with investments in crypto assets. This study aims to forecast the prices of cryptocurrencies using advanced machine learning models. Among seven models that were tested for their prediction and validation efficiency, Neutral Networks performed the best with minimum error. Thus, Long Short-Term Memory (LSTM) neural networks were used for predicting future trends. LSTM model is wel

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Browell, Jethro, and Ciaran Gilbert. "Predicting Electricity Imbalance Prices and Volumes: Capabilities and Opportunities." Energies 15, no. 10 (2022): 3645. http://dx.doi.org/10.3390/en15103645.

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Electricity imbalance pricing provides the ultimate incentive for generators and suppliers to contract with one another ahead of time and deliver against their obligations. As delivery time approaches, traders must judge whether to trade-out a position or settle it in the balancing market at the as-yet-unknown imbalance price. Forecasting the imbalance price (and related volumes) is therefore a necessity in short-term markets. However, this topic has received surprisingly little attention in the academic literature despite clear need by practitioners. Furthermore, the emergence of algorithmic

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Yuan, Shilong. "Nasdaq and Shanghai Composite Index Forecast Based on ARIMA and ETS Models." Advances in Economics, Management and Political Sciences 141, no. 1 (2024): 100–105. https://doi.org/10.54254/2754-1169/2024.ga18852.

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Accurately predicting asset prices is crucial for investors, as it not only helps them avoid risks and seize market opportunities, but also influences economic trends, optimizes resource allocation, and ensures the scientific and effective nature of investment decisions. In this paper, ARIMA model and ETS model are used to predict the time series of NASDAQ index and Shanghai Composite Index respectively. By comparing the prediction performance indexes of the two models, such as mean square error (MSE) and root mean square error (RMSE), it is found that ARIMA model has higher prediction accurac

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Dutta, Pushan Kumar, Sayed M. El-kenawy, Mostafa Abotaleb, and Marwa M. Eid. "AI-Driven Marketplaces and Price Prediction Tools for Rag Pickers: Enhancing Economic Opportunities in Africa's Circular Economy." Babylonian Journal of Artificial Intelligence 2023 (July 24, 2023): 33–42. http://dx.doi.org/10.58496/bjai/2023/007.

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This paper aims at identifying and analyzing the use of the AI solutions in enhancing the economic status of rag pickers within Africa’s circular economy sector and their access to markets. In this study, we explore how effectively the employment of AI technologies advances the welfare of the weaver people by identifying new potential methods of income generation and skill development in waste management techniques. Our research adopts a case study approach, examining the potential of two key AI applications: The resources include a marketplace that helps rag pickers find buyers for recyclable

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Baetens, Jens, Jeroen D. M. De Kooning, Greet Van Eetvelde, and Lieven Vandevelde. "A Two-Stage Stochastic Optimisation Methodology for the Operation of a Chlor-Alkali Electrolyser under Variable DAM and FCR Market Prices." Energies 13, no. 21 (2020): 5675. http://dx.doi.org/10.3390/en13215675.

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The increased penetration of renewable energy sources in the electrical grid raises the need for more power system flexibility. One of the high potential groups to provide such flexibility is the industry. Incentives to do so are provided by variable pricing and remuneration of supplied ancillary services. The operational flexibility of a chlor-alkali electrolysis process shows opportunities in the current energy and ancillary services markets. A co-optimisation of operating the chlor-alkali process under an hourly variable priced electricity sourcing strategy and the delivery of Frequency Con

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Rodrigues, Fátima, and Miguel Machado. "High-Frequency Cryptocurrency Price Forecasting Using Machine Learning Models: A Comparative Study." Information 16, no. 4 (2025): 300. https://doi.org/10.3390/info16040300.

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The cryptocurrency market presents immense opportunities and significant risks due to its high volatility. Accurate price forecasting is crucial for informed investment decisions, enabling investors to optimize portfolio allocation, mitigate risk, and potentially maximize returns. Existing forecasting methods often struggle with the inherent non-stationarity and complexity of cryptocurrency price dynamics. This study addresses this challenge by developing a system for high-frequency forecasting of the closing prices of ten leading cryptocurrencies. We compare various machine learning models, i

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Yang, Zhenhao, and Zhiyang Wang. "The Research of NVIDIA Stock Price Prediction Based on LSTM And ARIMA Model." Highlights in Business, Economics and Management 24 (January 22, 2024): 896–902. http://dx.doi.org/10.54097/dndygw34.

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The paper explores stock forecasting methods using NVIDIA as the research object. It contrasts how well LSTM and ARIMA models forecast NVIDIA's stock return. According to the study, LSTM surpasses ARIMA in terms of prediction accuracy. However, both models capture the overall trend of the stock. The results suggest that LSTM is better suited for forecasting stock movements due to its ability to handle time series data. The non-stationary nature of the stock market adds complexity to predictions. The significance of stock forecasting is that informed investment decisions can be made to maximise

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Kaur, Arvinder, and Kavita Chavali. "Deciphering the Risk–Return Dynamics of Pharmaceutical Companies Using the GARCH-M Model." Risks 13, no. 5 (2025): 87. https://doi.org/10.3390/risks13050087.

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This study focuses on the precise forecasting of stock price movement to determine returns, diversify risk, and demystify existing opportunities. It also aims to gauge the difference in terms of the stock volatility of various pharma companies before and during the pandemic era. The prediction of stock market volatility and associated risks is demonstrated by using the GARCH-M model. A sample is collected by clustering daily closing and opening prices from the official websites of the top ten pharmaceutical companies listed on the Bombay Stock Exchange for ten years, from 2012 to 2023. It is e

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Sonkavde, Gaurang, Deepak Sudhakar Dharrao, Anupkumar M. Bongale, Sarika T. Deokate, Deepak Doreswamy, and Subraya Krishna Bhat. "Forecasting Stock Market Prices Using Machine Learning and Deep Learning Models: A Systematic Review, Performance Analysis and Discussion of Implications." International Journal of Financial Studies 11, no. 3 (2023): 94. http://dx.doi.org/10.3390/ijfs11030094.

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The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widesprea

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Saputra, Moch Panji Agung, Riza Andrian Ibrahim, and Renda Sandi Saputra. "Comparative Analysis of LSTM and GRU Models for Ethereum (ETH) Price Prediction." International Journal of Business, Economics, and Social Development 6, no. 1 (2025): 132–38. https://doi.org/10.46336/ijbesd.v6i1.887.

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The increasing use of cryptocurrencies has changed the dynamics of investment, presenting both opportunities and challenges for investors. Although various studies have compared the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) in predicting financial asset prices, there are still differences in results regarding which model is superior. Therefore, this study aims to compare the performance of LSTM and GRU in predicting Ethereum prices using a hyperparameter tuning approach. The data used is historical data of Ethereum (ETH) shares from 2020 to 2025. The research

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Stádník, Bohumil. "Market Price Forecasting and Profitability – How to Tame Mrandom Walk?" Business: Theory and Practice 14, no. (2) (2013): 166–76. https://doi.org/10.3846/btp.2013.18.

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Directional forecasting of a future market price development of liquid investment instruments is the focus of interest of investment companies, individual investors, banks and other financial market participants. This problematic has still not been fully answered because the market price development is a process which is very close to a random walk and appropriate models are still under the discussion. The opportunities can be used for the better prediction, their usage for profit making, quantification and also their discussion according to the current financial market models (models with the

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Bijesh, Dhyani, Taneja Sanjay, Prakash Chandra, Tiwari Rajesh, and Özen Ercan. "BRAIN. Broad Research in Artificial Intelligence and Neuroscience - Deep Learning for Financial News Analysis and Stock Price Prediction: A Case Study of TCS." BRAIN. Broad Research in Artificial Intelligence and Neuroscience 15, no. 3 (2024): 153–66. https://doi.org/10.70594/brain/15.3/11.

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Finance has been the most important aspect of people’s lives for making small to large decisions in life. Finances affect our ability to invest in opportunities, save for uncertain future events, and purchase necessities. In addition, money has a significant impact on reducing poverty, creating jobs, and advancing society. On the other side, deep learning is a growing field as it is transforming and revolutionizing various areas and industries. While realising the importance of both finance and deep learning in our lives for better decision-making, our study aims to find the connectednes

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Ciuvercă, Alexandra-Cristina-Daniela. "Price Prediction Models for Construction Sector – Efficiency and Interpretability." Economic Insights – Trends and Challenges 2024, no. 4 (2024): 93–115. https://doi.org/10.51865/eitc.2024.04.08.

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The construction sector, essential for the global economy, is characterized by high price volatility, influenced by economic factors such as material costs, labor market fluctuations, and oil price variations. At the same time, advances in technology and artificial intelligence (AI) have opened new opportunities for improving price predictability through the application of advanced machine learning models. This research aims to compare the performance of several price prediction models, such as Ridge Regression, Random Forest, XGBoost, and Neural Networks, to identify the most efficient model

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Singh, Abhiraj, Krishan Chand, Susheel Singh, and Kamal Soni. "Dream House Price Predictor." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1441–46. http://dx.doi.org/10.22214/ijraset.2023.50307.

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Abstract: The dream house price predictor project aims to build a machine learning model that can predict the selling price of a house based on various features such as location, number of bedrooms, square footage, and other relevant factors. The model will be trained on a dataset of historical housing prices and features, and will use regression techniques to make predictions on new, unseen data. The project will also explore the impact of different features on house prices and provide insights into which factors are the most important in determining the value of a property. The goal of the p

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Hadi Abdullah, Aamna Tariq, Ijaz khan, Rizwan Iqbal, Faisal Khan, and Arshad iqbal. "<b>Recurrent Neural Networks in Time-Series Forecasting: A Deep Learning Approach to Stock Market Prediction</b>." Annual Methodological Archive Research Review 3, no. 6 (2025): 72–101. https://doi.org/10.63075/24bjb734.

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Stock market prediction has been a grand challenge due to dynamic nature, non-linearity and volatility of the financial markets. Traditional statistical models have proved useful historically, but are less likely to successfully model the complex temporal dependencies in stock price data. In recent years there was a breakthrough in deep learning, namely Recurrent Neural Networks (RNNs), which opens up new opportunities in time-series forecasting. The purpose of the work is to investigate how three variations of RNN-based models, such as Simple RNN, Long Short-Term Memory (LSTM), and Gated Recu

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Gokalani, Lata Bai, Bhagwan Das, Dilip Kumar Ramnani, Mahender Kumar, and Mazhar Ali Shah. "House Price Prediction of Real Time Data (DHA Defence) Karachi Using Machine Learning." Sir Syed University Research Journal of Engineering & Technology 12, no. 2 (2022): 75–80. http://dx.doi.org/10.33317/ssurj.504.

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Pakistan’s real estate market has a large impact in GDP growth. Investment in real estate sector in Pakistan is encumbered with lucrative opportunities. The market demand for housing is ever increasing year by year. House sales prices keep on changing and increasing frequently, so there is a need for a system to forecast house sales prices in the future. Several factors that influence house sales price includes; location, physical attributes, number of bedrooms as well as several other economic factors. One of the main motivation of choosing Karachi for the house prediction is that Karachi is

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Anirban, Chakraborty. "Determining House Price Using Regression." International Organization of Research & Development (IORD) 8, no. 1 (2020): 5. https://doi.org/10.5281/zenodo.3956571.

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The purpose of this article is to estimate the purchasing and sale opportunities of houses on the market by Machine learning techniques. For financial stability, the housing sector is quite critical. People seeking to purchase a new house appear to be more cautious in their expectations and sales tactics analyzing historical industry patterns and pricing levels, as well as potential changes. The index of our method consists of the price of the house and its efficiency metrics, such as the amount of renovation, the distance from the city center, the construction programs, the height of the prop

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Yang, Zhengjie. "Grid Resilience and Energy Storage Leveraging Machine Learning for Grid Services and Ancillary." International Journal of Computer Science and Information Technology 3, no. 2 (2024): 232–41. http://dx.doi.org/10.62051/ijcsit.v3n2.27.

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This paper discusses the challenges and opportunities of optimizing the economic benefits of energy storage systems in the electricity market, focusing on the key role of machine learning in energy storage bidding optimization. By analyzing complex market data and applying different machine learning models, such as regression models, time series prediction models, and deep learning models, energy storage systems are able to accurately predict market price fluctuations, optimize charge and discharge strategies, and maximize their economic returns in the energy market, ancillary services market,

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Dalimunthe, Dzakiyyatul Kirom, and Raden Bagus Fajriya Hakim. "APPLICATION OF RANDOM FOREST ALGORITHM ON WATCH PRICE PREDICTION SYSTEM USING FRAMEWORK FLASK." BAREKENG: Jurnal Ilmu Matematika dan Terapan 17, no. 1 (2023): 0171–84. http://dx.doi.org/10.30598/barekengvol17iss1pp0171-0184.

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In the modern era like today, watches not only function as timepieces, but have become a fashion trend for the community, especially teenagers. The increasing market demand for watches opens up opportunities for counterfeit watch sellers to sell their products by claiming that the watches they sell are genuine watches by offering relatively cheaper prices compared to genuine watches. This is very detrimental to consumers and also the watch industry. To minimize fraud committed by fake watch sellers, it is necessary to know the price of the original watch in advance, before buying the desired w

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McNeil, D. L., and M. K. Felgate. "Analysis of UK walnut market import purchase behaviour." British Food Journal 116, no. 3 (2014): 405–18. http://dx.doi.org/10.1108/bfj-03-2012-0071.

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Purpose – This paper aims to use readily available data to validate several predictions on UK walnut import and consumer use behaviours. It then seeks to hypothesise how this information can be used to determine whether and how additional value can be created in the chain. Design/methodology/approach – The chain used was that from sourcing to retail sale of walnuts in the UK. The data used were: UK import price/quantity/origin and total consumption data, global comparisons for import price/quantity/origin and total consumption data. Findings – This paper validates predictions for walnut qualit

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Chen, Xinran. "Stock Price Prediction Using Machine Learning Strategies." BCP Business & Management 36 (January 13, 2023): 488–97. http://dx.doi.org/10.54691/bcpbm.v36i.3507.

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Being able to foresee the potential opportunities or crisis in stock market has always been desirable among investors. Especially during the Covid-19 global pandemic, the skill of risk management is of great importance to sustain in such an unstable environment. Apart from various kinds of strategies in traditional business analysis, a robust intelligent system that can correctly predict stock price is desired to determine investment strategies. At present, much related research involve with predicting the stock price trend, and most of them use deep learning methods. Although these research m

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Aníbal, Tomás López, and Rabiu Okanlawon. "Stock Price Prediction of ReconAfrica (RECAF) Using Gated Recurrent Unit (GRU): Analysis and Implications for Investment Decisions." International Journal Artificial Intelligent and Informatics 2, no. 2 (2025): 41–46. https://doi.org/10.33292/ijarlit.v2i2.35.

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This study develops a stock price prediction model for ReconAfrica (RECAF) using Gated Recurrent Unit (GRU), an effective deep learning method for capturing temporal and non-linear patterns in stock price data. The model was trained and tested using five years of historical RECAF stock price data and evaluated with metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results show that the GRU model achieved an MAE of 0.0992, MSE of 0.0397, RMSE of 0.1993, and MAPE of 4.27, indicating a hig

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Srushti Dongrey. "Study of Market Indicators used for Technical Analysis." International Journal of Engineering and Management Research 12, no. 2 (2022): 64–83. http://dx.doi.org/10.31033/ijemr.12.2.11.

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Anticipating and analyzing the stock market and the price movement is a challenging task as the nature of the stock prices is quite complicated, non-linear and dynamic. Examining the financial time series data and making decisions is the toughest job in stock market. These correct decisions help in improving the returns on investment and minimize the loss and risks incurred. Technical analysis has been a trading tool since the 18th century which is used by investors and traders to evaluate the investments, identify the trading opportunities and forecast the future stock prices movement by anal

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Millikan, Evan, Preethi Subramanian, and Minnu Helen Joseph. "Bitcoin Vision: Using Machine Learning and Data Mining to Predict the Short-Term and Long-Term Price of Bitcoin." Webology 18, SI05 (2021): 751–60. http://dx.doi.org/10.14704/web/v18si05/web18259.

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Cryptocurrencies are non-physical currency that solely exist as represented by 0s and 1s within the world of computers. One of the most popular cryptocurrencies in the market right now being Bitcoin, was first invented to solve the inherent problem with using traditional currency when purchasing online. However, unexpectedly Bitcoin soon found itself to be one of the most profitable investment opportunities to be hedged on with its yearly growth unrivaled by any traditional investment product such as stocks, bonds, or real-estate. However, unlike the stock market which has been the subject of

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