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Relevant bibliographies by topics / Price directional forecasting

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Academic literature on the topic 'Price directional forecasting'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Price directional forecasting"

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Wang, Shiying, and Xinyu Yao. "The performance analysis of stock predication based on recurrent neural network." Applied and Computational Engineering 6, no. 1 (2023): 1276–82. http://dx.doi.org/10.54254/2755-2721/6/20230696.

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The stock exchange is unpredictable, and the stock price seems unpredictable. However, with the continuous development of the deep learning model's ability to deal with massive data, forecasting stock prices has become feasible and has reference value for investors. Many factors affect the stock price, and it is a great challenge to define these factors' influence on the price clearly. This paper selects multi-features stock price data sets of different companies. Because of the superiority of recurrent neural networks in dealing with time series problems, this paper compares and analyzes the

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Li, Jin. "Integrative forecasting and analysis of stock price using neural network and ARIMA model." Applied and Computational Engineering 6, no. 1 (2023): 969–81. http://dx.doi.org/10.54254/2755-2721/6/20230531.

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The volatilities of stock prices have a crucial effect on financial decision-making worldwide. With a reliable and accurate forecast model, investors could gain insights into stock price fluctuations and market trends, thus maximizing the opportunity to make profits. In this work, two models were proposed for stock price forecasting. A neural network based on exploiting the abilities of convolutional neural network and bi-directional long short-term memory is proposed and implemented for forecasting the Nasdaq-100 daily closing price. For long-term stock price forecast, we proposed a hybrid mo

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Balasubramanian, Dr Kannan. "Securing BitCoin Price Prediction using the LSTM Machine Learning Model." Indian Journal of Economics and Finance 4, no. 2 (2024): 68–72. http://dx.doi.org/10.54105/ijef.b1429.04021124.

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This research explores the application of Long Short Term Memory (LSTM) networks for short term Bitcoin price prediction, addressing the need for reliable models due to Bitcoins high volatility and trading volume. The study employs historical data from Kaggle to predict the direction and magnitude of price changes within a five minute interval. Implementation includes preprocessing the data, normalizing prices, and generating sequences for LSTM input. Two LSTM models were developed: one for directional prediction and another for magnitude. Training results showed a directional accuracy of appr

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Dr., Kannan Balasubramanian. "Securing BitCoin Price Prediction using the LSTM Machine Learning Model." Indian Journal of Economics and Finance (IJEF) 4, no. 2 (2024): 68–72. https://doi.org/10.54105/ijef.B1429.04021124.

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<strong>Abstract:</strong> This research explores the application of Long Short-Term Memory (LSTM) networks for short-term Bitcoin price prediction, addressing the need for reliable models due to Bitcoin's high volatility and trading volume. The study employs historical data from Kaggle to predict the direction and magnitude of price changes within a five-minute interval. Implementation includes preprocessing the data, normalizing prices, and generating sequences for LSTM input. Two LSTM models were developed: one for directional prediction and another for magnitude. Training results showed a

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Irlapale, Pranav Kishor. "Elevating Cryptocurrency Predictions: Bidirectional LSTM Methodology." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34330.

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The system proposed in this paper aims to predict cryptocurrency prices using Bi-Directional Long Short- Term Memory (LSTM), leveraging historical data obtained from Yahoo Finance and CoinGecko APIs. The goal is to assess LSTM models effectiveness in forecasting cryptocurrency prices and offer an interactive interface for users to visualize historical and forecasted prices. Several research works have been conducted on the prediction of cryptocurrency prices through various Deep Learning (DL) based algorithms. This project comprises two main approaches : one involves data analysis, LSTM modeli

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Baumeister, Christiane, Lutz Kilian, and Xiaoqing Zhou. "ARE PRODUCT SPREADS USEFUL FOR FORECASTING OIL PRICES? AN EMPIRICAL EVALUATION OF THE VERLEGER HYPOTHESIS." Macroeconomic Dynamics 22, no. 3 (2017): 562–80. http://dx.doi.org/10.1017/s1365100516000237.

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Many oil industry analysts believe that there is predictive power in the product spread, defined as the difference between suitably weighted refined product market prices and the price of crude oil. We derive a number of alternative forecasting model specifications based on product spreads and compare the implied forecasts to the no-change forecast of the real price of oil. We show that not all product spread models are useful for out-of-sample forecasting, but some models are, even at horizons between one and two years. The most accurate model is a time-varying parameter model of gasoline and

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Liu, Juan, Wei Huang, and Pingping Kong. "Deep Learning and Variational Modal Decomposition in Stock Price Prediction." Scientific Journal of Economics and Management Research 6, no. 12 (2024): 211–20. https://doi.org/10.54691/wf3sbh45.

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This study explores stock price forecasting, a critical topic for economic stability and investor decision-making. Traditional models like ARIMA struggle with stock market complexity due to their linear assumptions. To address this, the study examines advanced methods, focusing on deep learning techniques such as CNNs and LSTMs for their predictive strengths. It proposes a hybrid model combining Variational Mode Decomposition (VMD) and Bi-directional Long Short-Term Memory Networks (BiLSTM). VMD reduces time series non-stationarity, while BiLSTM captures sequence features via bi-directional pr

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MacKinnon, Douglas, and Martin Pavlovič. "A Bayesian analysis of hop price fluctuations." Agricultural Economics (Zemědělská ekonomika) 66, No. 12 (2020): 519–26. http://dx.doi.org/10.17221/239/2020-agricecon.

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This paper quantifies the correlation between U.S. season average prices for hops with U.S. hop stocks and U.S. hop hectarage. The Hop Equilibrium Ratio, a measure of the supply/demand relationship for U.S. hops, was introduced. Through the Bayesian inference method, the authors used these data to calculate the effect an incremental change to one metric had on the probability of directional changes of future U.S. season average prices (SAP). Between 2010 and 2020, the dominance of proprietary varieties created unprecedented cartel-like powers offering opportunities for supply- and price-manage

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Wang, Xinyu, Kegui Chen, and Xueping Tan. "Forecasting the Direction of Short-Term Crude Oil Price Changes with Genetic-Fuzzy Information Distribution." Mathematical Problems in Engineering 2018 (December 5, 2018): 1–12. http://dx.doi.org/10.1155/2018/3868923.

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This paper proposes a novel approach to the directional forecasting problem of short-term oil price changes. In this approach, the short-term oil price series is associated with incomplete fuzzy information, and a new fused genetic-fuzzy information distribution method is developed to process such a fuzzy incomplete information set; then a feasible coding method of multidimensional information controlling points is adopted to fit genetic-fuzzy information distribution to time series forecasting. Using the crude oil spot prices of West Texas Intermediate (WTI) and Brent as sample data, the empi

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Raut, Supriya. "Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30115.

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The objective of this research is to develop a Deep Learning model to forecast the stock price, by using the variant of Long Short-Term Memory. This model predicts the close price of the stock for the future selected date, choosing as inputs the following data: open, high, low, adj close and close prices. This model shows a comparative analysis between three different LSTM networks: Long Short-Term Memory (LSTM), Stacked Long Short-Term Memory (Stacked LSTM), and Stacked Bi-directional Long Short-Term Memory (Stacked Bidirectional LSTM) concluding which one is the best and implementing the mod

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Books on the topic "Price directional forecasting"

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Christoffersen, Peter F. Financial asset returns, direction-of-change forecasting, and volatility dynamics. National Bureau of Economic Research, 2003.

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Makridakis, Spyros G. Forecasting, planning, and strategy for the 21st century. Free Press, 1990.

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Book chapters on the topic "Price directional forecasting"

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Anitha, A., Bidyanand Mishra, Rahul Kumar Sahu, Nikhil Raj, S. Srinath, and Balakrishnan Kamaraj. "Hybridized Deep Learning Models and Federated Learning Techniques to Forecast Stock Market Movement." In Advances in Logistics, Operations, and Management Science. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-5912-9.ch007.

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Traditional stock market prediction methods often rely on assumptions and conventional approaches, but the emergence of deep learning techniques offers a transformative opportunity. This study evaluates the effectiveness of several deep learning architectures, including LSTM networks, CNNs, GRU, and Bi-directional GRU and Federated Learning in forecasting stock prices. Through rigorous experimentation and assessment using key metrics such as Loss, MAE, MSE, MAPE, and RMSE, deep learning models, particularly LSTM networks and CNNs, demonstrate notable improvements in accuracy compared to tradit

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Sarakam, Bhuvan Chandra. "Predicting Stock Price Directions using Support Vector Machines: A Data Science Approach." In International Conference in Emerging Trends in Computer Science. Scientific Explore Publications, 2025. https://doi.org/10.34293/icetcs2024.ch004.

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Predicting stock price movements remains a formidable challenge due to the inherent volatility and complexity of financial markets. This paper investigates the application of Support Vector Machines (SVM) for forecasting stock price directions in the technology sector, leveraging historical data from 34 mature technology companies over the period 2007 to 2014. The study employs four key features: price volatility and momentum for individual stocks and sector-wide metrics, with the goal of uncovering patterns in historical data that can inform future price movements. Results indicate that short

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Mumini, Omisore Olatunji, Fayemiwo Michael Adebisi, Ofoegbu Osita Edward, and Adeniyi Shukurat Abidemi. "Simulation of Stock Prediction System using Artificial Neural Networks." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch029.

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Stock trading, used to predict the direction of future stock prices, is a dynamic business primarily based on human intuition. This involves analyzing some non-linear fundamental and technical stock variables which are recorded periodically. This study presents the development of an ANN-based prediction model for forecasting closing price in the stock markets. The major steps taken are identification of technical variables used for prediction of stock prices, collection and pre-processing of stock data, and formulation of the ANN-based predictive model. Stock data of periods between 2010 and 2

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El Youssefi, Ahmed, Abdelaaziz Hessane, Imad Zeroual, and Yousef Farhaoui. "Machine Learning for Bitcoin Price Forecasting Using Kline and Averaged Bars Candlesticks." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-1220-0.ch010.

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This paper investigates the predictive performance of three machine learning regressors—Random Forest, Light Gradient Boosting Machine, and k-Nearest Neighbors—for forecasting Bitcoin's next-step log returns across multiple intervals (5, 10, 15, 30 mins, and 1h). By leveraging Japanese and averaged bars (Heikin-Ashi) candlestick data in conjunction with traditional OHLC features, the study highlights the influence of candlestick smoothing techniques on predictive accuracy. Empirical results indicate that Random Forest and LightGBM outperform the kNN regressor, underscoring the advantages of en

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Diebold, Francis X., and Glenn D. Rudebusch. "Macro-Finance." In Yield Curve Modeling and Forecasting. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691146805.003.0005.

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This chapter discusses a variety of arbitrage-free Nelson–Siegel (AFNS) macro-finance yield curve approaches. The AFNS factor structure provides a very useful framework for examining various macro-finance questions given the computational difficulties in extending finance-only affine arbitrage-free models. One application of the AFNS model, in Christensen et al. (2010c), produces estimates of the inflation expectations of financial market participants from prices of nominal and real bonds. A second macro-finance application of the AFNS model, provided in Christensen et al. (2009), investigates

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Özdemir, Enes, and Burhan Uluyol. "Determining the Best Machine Learning Model by Predicting the Participation Index of the Borsa Istanbul Stock Exchange With Artificial Intelligence." In Advances in Business Strategy and Competitive Advantage. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-9586-8.ch006.

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Forcasting the future direction of stock indinces has been received significant attention by researchers and investors. Due to the complexcity of information, it is very difficult to predict future stock market price behavior. In this paper, we determine the best machine learning model by forecasting the Borsa Istanbul Stock Exchange participation index with Artificial Intelligence (AI). Six different machine learning algorithms are used to predict the prices of a participation index such as Linear Regression, LSTM, KNN, Auto-ARIMA, Gradient Boosting and Random Forest. Models were built by usi

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Averchenko, Dmitry, and Artem Aldyrev. "Applying Neural Networks for Modeling of Financial Assets." In Advances in Finance, Accounting, and Economics. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3767-0.ch010.

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The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key p

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Averchenko, Dmitry, and Artem Aldyrev. "Applying Neural Networks for Modeling of Financial Assets." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch067.

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The purpose of this chapter is to develop an analytical system for forecasting prices of financial assets with the use of artificial neural networks technology. Proposed by the authors, the analytical system consists of several neural networks, each of which makes the forecast of financial assets prices. The system includes recurrence (with feedback) neural networks with sigmoidal activation formula. This allows the networks to “remember” a sequence of reactions to the same stimulus. The learning process of neural networks is performed using an algorithm of back propagation of error. The key p

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Fidan, Neslihan, and Beyza Ahlatcioglu Ozkok. "A Review on Applied Data Mining Techniques to Stock Market Prediction." In Enterprise Business Modeling, Optimization Techniques, and Flexible Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-3946-1.ch009.

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A portfolio manager considers forecasting the asset prices and measurement of the market risk of an underlying asset. Financial institutions produce datasets to handle their problems by using data mining tools. Recently new technologies have been developed for tracking, collecting, and processing financial data. From a data analysis point of view, this chapter reviews the published articles based upon predictive data mining applications to stock market index. It is observed that hybrid models that combine data mining techniques or integrate an algorithm to a method work efficiently. Finally, t

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Raavi, Tarun Sai, Ramanchi Radhika, and C. Shree Charan. "AI-Powered Insights." In Advances in Finance, Accounting, and Economics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8507-4.ch009.

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Stock market forecasting remains a complex and highly interested field. The transition from conventional statistical techniques to sophisticated machine learning (AI) technology has brought about a significant change in the approach to financial predictive analysis. With an emphasis on machine learning (ML) and deep learning (DL), this chapter attempts to provide a thorough synthesis of the literature on the application of AI techniques in stock market price prediction. Furthermore, this chapter delineates deficiencies in existing knowledge, domains of agreement, and directions for future inve

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Conference papers on the topic "Price directional forecasting"

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Shynkevich, Yauheniya, T. M. McGinnity, Sonya Coleman, Yuhua Li, and Ammar Belatreche. "Forecasting stock price directional movements using technical indicators: Investigating window size effects on one-step-ahead forecasting." In 2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). IEEE, 2014. http://dx.doi.org/10.1109/cifer.2014.6924093.

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Stankovic, Marko, Nebojsa Bacanin, Nebojsa Budimirovic, Miodrag Zivkovic, Marko Sarac, and Ivana Strumberger. "Bi-Directional Long Short-Term Memory Optimization by Improved Teaching-Learning Based Algorithm for Univariate Gold Price Forecasting." In 2023 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2023. http://dx.doi.org/10.1109/icict57646.2023.10134131.

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Hu, T., C. Chen, and H. Wei. "A Novel Methodology for Forecasting Petrochemical Product Prices in East China Market by Applying ARIMAX Time Series and Machine Learning Models." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-23114-ms.

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Abstract Forecasting petrochemical product prices is essential for economic decision making in the petrochemical industry. However, it is a challenging task to achieve accurate forecasts, given the price volatility in East China market, and the fact that the petrochemical product prices can be affected by various factors relevant in the industry. Therefore, we proposed a novel methodology which applied ARIMAX time series and machine learning models, combined with feature selection, for the price forecasting. This paper proposes a novel approach, which involves four steps of data gathering, fac

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Kılıç, Süleyman Bilgin, and Salih Çam. "Estimation of Direction of Exchange Rate, Gold Price and Stock Market Returns with High Order Markov Chain Models." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01736.

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This study uses a hybrid high order Markov Chains Model to predict direction of exchange rate, gold price and stock market returns with the Artificial Neural Network Algorithm as an estimator of transition probability matrix. Many forecasting techniques are used to examine the direction of returns forecasting in the literature such as Markov Chains Model and Artificial Neural Network Algorithm. In this study, it is aimed to combine these two techniques and to utilize the predict values of the Artificial Neural Network Algorithm for calculate transition probabilities matrix. Calculations show t

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Thakur, Anita, Aishwarya Tiwari, Saswat Kumar, Aditya Jain, and Jagjot Singh. "NARX based forecasting of petrol prices." In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2016. http://dx.doi.org/10.1109/icrito.2016.7785027.

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Inani Jindal, Sarveshwar Kumar, Gaurav Kabra, Arun Balodi, and Vaishali Pagaria. "A Bibliometric Review of Gold Price Forecasting Techniques: Trends and Future Directions." In 2023 International Conference on Sustainable Emerging Innovations in Engineering and Technology (ICSEIET). IEEE, 2023. http://dx.doi.org/10.1109/icseiet58677.2023.10303581.

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"THE POTENTIAL OF HYBRID LSTM-GENERATIVE AI ECO-MODEL IN FORECASTING FINANCIAL AND ECONOMIC INDICATORS." In XIII TRADITIONAL SCIENTIFIC CONFERENCE NEW ECONOMY 2025. Oikos Institute – Research Center, Bijeljina, Bosnia and Herzegovina, 2025. https://doi.org/10.61432/cpne0301171i.

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This study presents the development and evaluation of The Hybrid LSTMGenerative AI ECO-Model for forecasting financial and economic indicators such as EUR/USD exchange rate, utilizing a combination of Long Short-Term Memory (LSTM) networks and generative AI models (GPT-2 and Llama- 3.2-1B). The primary objective was to achieve high prediction accuracy while minimizing computational resource consumption and ensuring ease of use of the model on various devices. The model was trained and tested on historical financial and economic data, including exchange rates, macroeconomic indicators, commodit

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Takbiradzani, K., and Z. Rustam. "Forecasting the direction of Indonesia’s consumer goods sector stock price movement using Fuzzy Kernel Robust C-Means." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132482.

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Stalovinaitė, Ilona, Nijolė Maknickienė, and Raimonda Martinkutė-Kaulienė. "Investigation of decision making support in digital trading." In 11th International Scientific Conference „Business and Management 2020“. VGTU Technika, 2020. http://dx.doi.org/10.3846/bm.2020.510.

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In order to trade successfully investors are looking for the best method to determine possible directions of the price changes of financial means. The main objective of this paper is to evaluate the results of digital trading using different decision-making techniques. The paper examines deep learning technique known as Long Short – Term Memory (LSTM) neural network and parabolic stop and reverse (SAR) technical indicator as possible means for decision-making support. Based on an investigation of theoretical and practical aspects of digital trading and its support possibilities, investment por

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Mokiy, Vladimir, and Tatiana Lukyanova. "Modern Transdisciplinarity: Results of the Development of the Prime Cause and Initial Ideas." In InSITE 2022: Informing Science + IT Education Conferences InSITE 2022. Informing Science Institute, 2022. http://dx.doi.org/10.28945/4931.

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Aim/Purpose This paper focuses on systematizing and rethinking the conformity of modern transdisciplinarity with its prime cause and initial ideas. Background The difficulties of implementing transdisciplinarity into science and education are connected with the fact that its generally accepted definition, identification characteristics, and methodological features are still missing. In or-der to eliminate these disadvantages of transdisciplinarity, its prime cause and initial ideas had to be detected. It is also important to analyze the correspondence of the existing opinions about transdiscip

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Reports on the topic "Price directional forecasting"

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressi

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