Search
48 articles for “Stock prediction”
-
Using Elliot Wave Theory and Fibonacci Retracement and in Algorithmic Trading
Abstract: The analysis technique used in predicting stock prices consists of Fundamental analysis and Technical analysis. These analyses are complex in nature and usually unreliable. The algorithmic trading systems offered today consists of primitive technical analysis techniques such as, simple moving averages, exponential moving average, moving averages convergence and diversions and volume weighted average price. The primitive nature of this techniques makes them very unreliable and has a low success rate. …
Published in Trends in Machine design · Vol. 7, Issue 3, 2020 · pp. 22–29 Read article
-
Sentiment Analysis for Financial Predictions: A Review
Abstract: AbstractSentiment analysis is one of the popular research areas in computer science, as it finds its application in numerous areas like finance, election and product reviews. Behavioral economics suggests that decision of investing in financial markets is driven by feelings like greed, fear etc. With widespread adoption of technology and digitization of corporate disclosures, news and social media platforms, and information expressed through words can be mined and used to …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 6, Issue 1, 2019 · pp. 59–68 Read article
-
Visualizing And Forecasting Stocks Using Single Page Application
Abstract: In the fields of finance and economics, stock price forecasting is a vital and crucial subject. The stock market is not governed by any significant rules that may be used to anticipate or estimate the price of a stock. In an effort to forecast the price in the stock market, several techniques are employed, including technical analysis, fundamental analysis, time series analysis, statistical analysis, etc. However, none of these techniques …
Published in Journal of Electronic Design Technology · Vol. 13, Issue 2, 2022 · pp. 23–28 Read article
-
A Survey on Datamining Techniques and its Uses on Real-Time Applications
Abstract: AbstractData mining is one of the area gaining lot of sensible significance and is progressing at a brisk pace with new methods, methodologies and findings in diverse packages associated with remedy, computer science, bioinformatics and stock market prediction, climate forecast, textual content, audio and video processing to name a few. Data occurs to be the key difficulty in statistics mining. With the massive online facts generated from several sensors, Internet …
Published in Journal of Open Source Developments · Vol. 7, Issue 3, 2020 · pp. 12–17 Read article
-
Stock Market Forecasting Using Time Invariant Fuzzy Time Series Model
Abstract: Stock market volatility is an important for investment, option pricing and financial market regulation. In recent years a different types of models which apparently predict changes in stock market prices have been introduced. There are many methods in the literature to solve the problem of future prediction. The present study provides a foundation for the development and application of fuzzy time series model for short term investor as well as …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 104s–111s Read article
-
Stock Exchange Forecasting Using Sentiment Analysis and Machine Learning Approach
Abstract: Every penny traded in the market is a speculator, like millions of bucks transfer every day. System behavior dictates the trust of many organizations daily. Financial professionals no doubt endeavored for the foresee stock exchange from the beginning. Systems are beginning to use the sentiment analysis-based machine learning approach. For financial professionals, the ability to accurately predict changing trends is an attractive guarantee of wealth and influence. When things get …
Published in E-Commerce for Future & Trends · Vol. 9, Issue 3, 2022 · pp. 18–24 Read article
-
Implementation of K-Means clustering algorithm with technical indicators to identify Profitable stocks
Abstract: In the ever-changing world of stock market trading, accurately predicting price movements is key to maximizing profits. Technical analysis, which looks at past price data to predict future trends, provides valuable insights for investors. This paper delves into using machine learning methods, particularly the K-Means clustering algorithm, along with moving average data, to categorize daily trading patterns. By breaking down the market into clusters and examining the main patterns within …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 8–14 Read article
-
Cryptocurrency Price Prediction Using Python and ML
Abstract: Predicting the price of crypto currencies is one of the popular case research in the information technological know-how community. Over the last two years, geopolitical and economic problems have risen, global currency values have fallen, stock markets have slumped, and investors have lost their wealth. This has created a new interest in digital currencies. Cryptocurrencies, one of the most well-known digital currencies, are in the limelight as investors want some …
Published in Journal of Instrumentation Technology & Innovations · Vol. 12, Issue 2, 2022 · pp. 36–41 Read article
-
Artificial Intelligence for Sustainable Agriculture, Forestry and Rural Development: Recent Advances, Applications and Research Opportunities
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize agriculture, forestry, and rural development by enabling data-driven decision-making, resource optimization, and sustainable management practices. Rapid developments in computer vision (CV), machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive analytics have increased the use of AI in a variety of rural industries. In agriculture, AI-driven technologies support precision farming, crop yield prediction, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
-
Evaluating Advancements and Identifying Research Gaps in Automotive Spare Parts Demand Forecasting
Abstract: The automotive industry, a key driver of global economic activity, relies heavily on the effective management of spare parts to ensure vehicle longevity and reliability. Accurate prediction of demand for these components is imperative to uphold ideal stock levels, minimize expenditures, and elevate customer contentment. This review of literature assesses recent progressions in demand prediction methodologies for automotive spare parts, with a specific emphasis on conventional statistical methods and contemporary …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 47–58 Read article
-
Financial Forecast: Systematic Analysis Using Principal Component Analysis
Abstract: The most popular technique for data exploration and analysis across all scientific disciplines that can influence the classification model's accuracy is principal component analysis (PCA). In this study, the high dimensionality of the stock market is examined in order to forecast market movements using PCA in conjunction with linear regression. While reducing data redundancy, PCA can aid in enhancing the predictive performance of machine learning techniques. The New York Stock …
Published in E-Commerce for Future & Trends · Vol. 9, Issue 2, 2022 · pp. 18–24 Read article
-
Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration
Abstract: Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which …
Published in International Journal of Industrial and Product Design Engineering · Vol. 3, Issue 2, 2025 · pp. 8–17 Read article
-
Comparative Performance Study: Deterministic vs. Probabilistic Models in Retail Chains
Abstract: The finished goods, raw materials, and product stock that a business has on hand for sale are referred to as inventory. They enable the companies to achieve their sales levels and are a chance to cost control and decision making. It is a huge asset to a manufacturing firm. Inventory model permits forecasting of quantities of raw material, inventory and spare parts of the equipment to a very high level …
Published in Recent Trends in Mathematics · Vol. 2, Issue 1, 2025 · pp. 1–6 Read article
-
Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
-
Soil Erosion Assessment and Its Effect on Sustainability for a Mountainous Watershed using Remote Sensing and GIS
Abstract: Normal 0 false false false EN-US X-NONE AR-SA Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Normal 0 false false false EN-US X-NONE AR-SA MicrosoftInternetExplorer4 Soil erosion is an important social and economic problem, and an essential factor in assessing health and function of ecosystem. Soil-erosion control requires a quantitative evaluation of potential soil erosion on a specific location. The revised universal soil loss equation (RUSLE), remote sensing, and …
Published in Journal of Remote Sensing & GIS · Vol. 4, Issue 3, 2013 · pp. 68–80 Read article
-
The Elliott Wave Principle and its Applications in Security Analysis
Abstract: The study is determined on an analysis and pattern recognition of equity or share price, which are fractal in nature. Elliott waves are considered as the crowded psychological effect. The wave principle used by trader or investor to evaluate market cycle and forecast the future trend of market by identifying the waves with price movement of market. The share price fluctuations are due to several economic and non-economic factors. Among …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 1–6 Read article
-
Analysis of Gold Price Trend Using the Hidden Markov Model
Abstract: This study aims to analyze the behavior of gold prices in India through a two-state Hidden Markov Model (HMM). We first formulated crucial parameters, such as the Transition Probability Matrix (TPM), Initial Probability Vector (IPV), and Emission Probability Matrix (EPM). Subsequently, we constructed a hidden Markov probability distribution and evaluated Pearson’s coefficients to gauge the correlations separately for each state. The goodness of fit of the developed model was assessed …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 11, Issue 2, 2024 · pp. 7–16 Read article
-
Time Series Forecasting Based on PyAF and fbProphet
Abstract: Time series forecasting is the technique of predicting future events using previous data. Time series data includes information that is collected and recorded at regular intervals, such as daily stock prices, monthly sales figures, or hourly temperature readings. The purpose of time series forecasting is to use previous data to create accurate forecasts about the future values of a given variable. This can be beneficial for a range of applications, …
Published in International Journal of Information Security Engineering · Vol. 1, Issue 1, 2023 · pp. 32–36 Read article
-
Exploring the Efficiency of Leading and Lagging Indicators in Algorithmic Trading
Abstract: This paper details a comparison of the overall performance of leading and lagging technical indicators used in algorithmic trading over an extended period. While much of the prior research focuses on index price forecasting and some on statistical arbitrage derived from these predictive techniques, there is a scarcity of studies that assess and evaluate trading strategies. The strategies considered for the study were tested on historical data of the 50 …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 2, 2024 · pp. 8–18 Read article
-
Enhancing Mutual Fund Investment Decision-making Using Machine Learning: A Survey
Abstract: In India, a significant portion of individuals save a part of their income for a secure future. Government and various public sector financial companies also provide some saving schemes through banks, post offices, and Life Insurance Corporation (LICs) such as Recurring Deposit (RD), Public Provident Fund (PPF), Sukanya Samridhhi Account (SSA) fixed deposits, etc. Over the past decade, many individuals have shifted their saving schemes to vigorously searching for investment …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 43–51 Read article