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Journal articles on the topic 'Price directional forecasting'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

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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Seabe, Phumudzo Lloyd, Claude Rodrigue Bambe Moutsinga, and Edson Pindza. "Forecasting Cryptocurrency Prices Using LSTM, GRU, and Bi-Directional LSTM: A Deep Learning Approach." Fractal and Fractional 7, no. 2 (2023): 203. http://dx.doi.org/10.3390/fractalfract7020203.

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Highly accurate cryptocurrency price predictions are of paramount interest to investors and researchers. However, owing to the nonlinearity of the cryptocurrency market, it is difficult to assess the distinct nature of time-series data, resulting in challenges in generating appropriate price predictions. Numerous studies have been conducted on cryptocurrency price prediction using different Deep Learning (DL) based algorithms. This study proposes three types of Recurrent Neural Networks (RNNs): namely, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bi-Directional LSTM (Bi-LSTM)

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Livieris, Ioannis E., Emmanuel Pintelas, Stavros Stavroyiannis, and Panagiotis Pintelas. "Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series." Algorithms 13, no. 5 (2020): 121. http://dx.doi.org/10.3390/a13050121.

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Nowadays, cryptocurrency has infiltrated almost all financial transactions; thus, it is generally recognized as an alternative method for paying and exchanging currency. Cryptocurrency trade constitutes a constantly increasing financial market and a promising type of profitable investment; however, it is characterized by high volatility and strong fluctuations of prices over time. Therefore, the development of an intelligent forecasting model is considered essential for portfolio optimization and decision making. The main contribution of this research is the combination of three of the most wi

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Li, Taiyong, Zhenda Hu, Yanchi Jia, Jiang Wu, and Yingrui Zhou. "Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning." Energies 11, no. 7 (2018): 1882. http://dx.doi.org/10.3390/en11071882.

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Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addit

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Chen, Zhiyang. "Stock Price Prediction with Denoising Autoencoder and Transformers." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 803–10. http://dx.doi.org/10.54097/1skct023.

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Predicting stock market price movement has been a challenging problem for time series forecasting due to its inherent volatility. The introduction of machine learning techniques, such as the use of a recurrent neural network (RNN), have since dominated the field for stock trend forecasting and stock price prediction. RNNs have inherent model limitations that are solved with the introduction of the transformer model, which has since been used in many sequential classification and generative tasks. This study demonstrates the viability of a transformer-based model on the field of stock price pre

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Majid, Muhammad Althaf, Prilyandari Dina Saputri, and Soehardjoepri Soehardjoepri. "Stock Market Index Prediction using Bi-directional Long Short-Term Memory." Journal of Applied Informatics and Computing 8, no. 1 (2024): 55–61. http://dx.doi.org/10.30871/jaic.v8i1.7195.

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The IHSG (Indonesia Stock Exchange Composite Index) is a stock price index in the Indonesia Stock Exchange (BEI) that serves as an indicator reflecting the performance of company stocks through stock price movements. Therefore, IHSG becomes a reference for investors in making investment decisions. Advanced stock exchanges generally have a strong influence on other stock exchanges. Several studies have proven the influence of one global index on another. Global index is a term that refers to each country's index to represent the movement of its country's stock performance. Forecasting IHSG can

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Dhivya, R., M. Prahadeeswaran, R. Parimalaragan, C. Thangamani, and S. Kavitha. "Commodity Future Trading and Cointegration of Turmeric Markets in India." Asian Journal of Agricultural Extension, Economics & Sociology 41, no. 9 (2023): 190–99. http://dx.doi.org/10.9734/ajaees/2023/v41i92031.

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The government has reduced its direct market intervention in order to promote private sector engagement based on market forces, Farmers in an agriculture-dominated economy like India suffer not only yield risk but also pricing risk. As a result, agricultural products are now more vulnerable to market risks related to pricing and other factors. The futures market has to decide the prices of a commodity on the basis of demand and supply. It is important to know about the bi-directional and unidirectional relationship between different market’s the prices and future and Spot markets in India, pri

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Vallarino, Diego. "An AI-Enhanced Forecasting Framework: Integrating LSTM and Transformer-Based Sentiment for Stock Price Prediction." Journal of Economic Analysis 4, no. 3 (2025): 1–15. https://doi.org/10.58567/jea04030001.

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Forecasting stock prices remains a fundamental yet complex challenge in financial economics due to the nonlinearity, volatility, and exogenous shocks characterizing market behavior. This paper proposes a hybrid deep learning framework that integrates Long Short-Term Memory (LSTM) networks for time-series modeling with Transformer-based architectures for textual sentiment extraction from financial news. The goal is to enhance predictive accuracy by combining structured historical data with unstructured semantic signals. Using three years of daily data from Apple Inc. (AAPL), the model captures

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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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Ocran, Matthew. "South Africa and United States stock prices and the Rand/Dollar exchange rate." South African Journal of Economic and Management Sciences 13, no. 3 (2010): 362–75. http://dx.doi.org/10.4102/sajems.v13i3.106.

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This paper seeks to examine the dynamic causal relations between the two major financial assets, stock prices of the US and South Africa and the rand/US$ exchange rate. The study uses a mixed bag of time series approaches such as cointegration, Granger causality, impulse response functions and forecasting error variance decompositions. The paper identifies a bi-directional causality from the Standard & Poor’s 500 stock price index to the rand/US$ exchange rate in the Granger sense. It was also found that the Standard & Poor’s stock price index accounts for a significant portion of the

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Senanayaka, N. I. M. B., and H. A. Pathberiya. "Effectiveness of Using Candlestick Charts to Forecast Ethereum Price Direction: A Machine Learning Approach." Sri Lankan Journal of Applied Statistics 25, no. 1 (2024): 34–48. http://dx.doi.org/10.4038/sljas.v25i1.8131.

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Cryptocurrency is a form of decentralized digital currency. Ethereum is the second-largest cryptocurrency by market capitalization and the largest altcoin. Cryptocurrencies including Ethereum are highly volatile. Hence, shortterm directional forecasts in the cryptocurrency market have become a widely discussing topic. Candlestick charts are useful visualizations of the open, high, low and close prices which can identify patterns and gauge the near-term direction of prices. This research explores the effectiveness of forecasting hourly Ethereum closing price direction based on candlestick chart

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Yan, Haoyang. "Research on Gold Price Prediction Based on LSTM Modeling." Advances in Economics, Management and Political Sciences 94, no. 1 (2024): 202–10. http://dx.doi.org/10.54254/2754-1169/94/2024ox0166.

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Gold plays a pivotal role in asset allocation, and the construction of gold price prediction models represents a complex yet rewarding task within the field of finance. The problem of international gold price is addressed in this paper forecasting by proposing a standard Long-Short Term Memory (LSTM) model and introducing bi-directional LSTM (Bi-LSTM) networks and multivariate analysis to compare the forecasting accuracy of the relevant models. This comparison is based on the daily gold price of the London Bullion Market Association (LBMA) for 2013-2022. This methodology takes into account var

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XIONG, TAO, YUKUN BAO, ZHONGYI HU, RUI ZHANG, and JINLONG ZHANG. "HYBRID DECOMPOSITION AND ENSEMBLE FRAMEWORK FOR STOCK PRICE FORECASTING: A COMPARATIVE STUDY." Advances in Adaptive Data Analysis 03, no. 04 (2011): 447–82. http://dx.doi.org/10.1142/s1793536911000878.

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In this study, a hybrid decomposition and ensemble framework incorporating Ensemble empirical mode decomposition (EEMD) and selected modeling methodologies are proposed for stock price forecasting. Under the framework, the original stock price series was first decomposed into several subseries including a number of intrinsic mode functions (IMFs) and a residue using EEMD technique. Then, extracted subseries was modeled to generate forecasts respectively. Finally, the forecasts of all extracted subseries were aggregated to produce an ensemble forecasts for the original stock price series. An ex

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Kumari, Prity, Viniya Goswami, Harshith N., and R. S. Pundir. "Recurrent neural network architecture for forecasting banana prices in Gujarat, India." PLOS ONE 18, no. 6 (2023): e0275702. http://dx.doi.org/10.1371/journal.pone.0275702.

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Objectives The forecasting of horticulture commodity prices, such as bananas, has wide-ranging impacts on farmers, traders and end-users. The considerable volatility in horticultural commodities pricing estimates has allowed farmers to exploit various local marketplaces for profitable sales of their farm produce. Despite the demonstrated efficacy of machine learning models as a suitable substitute for conventional statistical approaches, their application for price forecasting in the context of Indian horticulture remains an area of contention. Past attempts to forecast agricultural commodity

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Wu, Jiang, Yu Chen, Tengfei Zhou, and Taiyong Li. "An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting." Energies 12, no. 7 (2019): 1239. http://dx.doi.org/10.3390/en12071239.

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Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy. Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society. To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CE

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Zou, Yiyang. "Forecasting Apple Inc. Stock prices: A comparative analysis of ARIMA, LSTM, and ARIMA-LSTM models." Advances in Operation Research and Production Management 4, no. 1 (2025): None. https://doi.org/10.54254/3029-0880/2025.23870.

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Stock price fluctuation and prediction is a problem that has attracted much attention. There exist many mathematical and statistical problems behind it. In essence, the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements. This study investigates the predictive capabilities of two distinct methodologiesLong Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA) modelsusing Apple Inc. (AAPL) stock price data spanning 2016 to 2024. By synthesizing theoretical frameworks with emp

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Zhang, Jilin, Lishi Ye, and Yongzeng Lai. "Stock Price Prediction Using CNN-BiLSTM-Attention Model." Mathematics 11, no. 9 (2023): 1985. http://dx.doi.org/10.3390/math11091985.

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Accurate stock price prediction has an important role in stock investment. Because stock price data are characterized by high frequency, nonlinearity, and long memory, predicting stock prices precisely is challenging. Various forecasting methods have been proposed, from classical time series methods to machine-learning-based methods, such as random forest (RF), recurrent neural network (RNN), convolutional neural network (CNN), Long Short-Term Memory (LSTM) neural networks and their variants, etc. Each method can reach a certain level of accuracy but also has its limitations. In this paper, a

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Li, Jianyao. "A Comparative Study of LSTM Variants in Prediction for Tesla’s Stock Price." BCP Business & Management 34 (December 14, 2022): 30–38. http://dx.doi.org/10.54691/bcpbm.v34i.2861.

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Long short-term memory (LSTM) is widely used in the stock market to train the prediction model and forecast future stock prices. Applying the LSTM method to research may incur some problems and facilitate the improvement of the method. Therefore, many LSTM variants are put forward under different circumstances. This paper surveys four LSTM variants, including Vanilla, Stacked, Bi-directional, and CNN LSTM on two different data sets regarding Tesla's stock price. Two data sets mentioned in this paper represent different stock types. To be more specific, data set 1 refers to stocks with a single

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Moazzen, Farid, and M. J. Hossain. "Multivariate Deep Learning Long Short-Term Memory-Based Forecasting for Microgrid Energy Management Systems." Energies 17, no. 17 (2024): 4360. http://dx.doi.org/10.3390/en17174360.

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In the scope of energy management systems (EMSs) for microgrids, the forecasting module stands out as an essential element, significantly influencing the efficacy of optimal solution policies. Forecasts for consumption, generation, and market prices play a crucial role in both day-ahead and real-time decision-making processes within EMSs. This paper aims to develop a machine learning-based multivariate forecasting methodology to account for the intricate interplay pertaining to these variables from the perspective of day-ahead energy management. Specifically, our approach delves into the dynam

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Stádník, Bohumil. "The Riddle of Volatility Clusters." Business: Theory and Practice 15, no. (2) (2014): 140–48. https://doi.org/10.3846/btp.2014.14.

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In this financial engineering research we evaluate if observed non normalities in the market price distributions are caused mainly by a volatility clustering or also by another nonclustering mechanism. Such findings allow us to assess accor d ing to which rules the market price is actually developing or even make conclusions about market price directional forecasting chances, based on the realistic financial processes which we assign to the clustering and non clustering mechanisms. In the research we suggest certain methodology how to recognize these processes behind the market price devel

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Ez-zaiym, Mustapha, Yassine Senhaji, Meriem Rachid, Karim El Moutaouakil, and Vasile Palade. "Fractional Optimizers for LSTM Networks in Financial Time Series Forecasting." Mathematics 13, no. 13 (2025): 2068. https://doi.org/10.3390/math13132068.

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This study investigates the theoretical foundations and practical advantages of fractional-order optimization in computational machine learning, with a particular focus on stock price forecasting using long short-term memory (LSTM) networks. We extend several widely used optimization algorithms—including Adam, RMSprop, SGD, Adadelta, FTRL, Adamax, and Adagrad—by incorporating fractional derivatives into their update rules. This novel approach leverages the memory-retentive properties of fractional calculus to improve convergence behavior and model efficiency. Our experimental analysis evaluate

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Hadizadeh, Anita, Mohammad Jafar Tarokh, and Majid Mirzaee Ghazani. "A convolutional deep reinforcement learning architecture for an emerging stock market analysis." Decision Science Letters 14, no. 2 (2025): 313–26. https://doi.org/10.5267/j.dsl.2025.1.006.

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In the complex and dynamic stock market landscape, investors seek to optimize returns while minimizing risks associated with price volatility. Various innovative approaches have been proposed to achieve high profits by considering historical trends and social factors. Despite advancements, accurately predicting market dynamics remains a persistent challenge. This study introduces a novel deep reinforcement learning (DRL) architecture to forecast stock market returns effectively. Unlike traditional approaches requiring manual feature engineering, the proposed model leverages convolutional neura

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Kim, Eunsol, and Jaegi Jeon. "Stock Price Prediction Model Based on LSTM Reflecting Interest Rate Fluctuations." Korean Institute of Smart Media 13, no. 12 (2024): 99–108. https://doi.org/10.30693/smj.2024.13.12.99.

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Due to the recent impact of inflation, central banks have maintained a policy of raising interest rates, highlighting the growing importance of financial market forecasting that reflects these economic conditions. However, there has been a lack of recent studies in Korea that consider interest rate in stock price prediction. In this study, we aimed to improve prediction accuracy by incorporating interest rates as a variable into stock price prediction models. For this purpose, we utilized the LSTM algorithm and trained the model on historical data from periods of high stock market volatility t

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Sandeep, Yadav. "Predictive Modeling of Cryptocurrency Price Movements Using Autoregressive and Neural Network Models." International Journal on Science and Technology 14, no. 1 (2023): 1–9. https://doi.org/10.5281/zenodo.14288541.

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Cryptocurrency markets are highly volatile and driven by complex, non-linear dynamics, posing significant challenges for price prediction. This research explores the predictive modeling of cryptocurrency price movements by integrating traditional statistical techniques, such as Autoregressive (AR) models, with advanced Neural Network (NN) architectures. The study evaluates the performance of these models in forecasting short-term price trends for major cryptocurrencies like Bitcoin, Ethereum, and Binance Coin. The dataset consists of historical price data and technical indicators, preprocessed

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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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Iftikhar, Hasnain, Murad Khan, Josué E. Turpo-Chaparro, Paulo Canas Rodrigues, and Javier Linkolk López-Gonzales. "Forecasting stock prices using a novel filtering-combination technique: Application to the Pakistan stock exchange." AIMS Mathematics 9, no. 2 (2024): 3264–88. http://dx.doi.org/10.3934/math.2024159.

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<abstract><p>Traders and investors find predicting stock market values an intriguing subject to study in stock exchange markets. Accurate projections lead to high financial revenues and protect investors from market risks. This research proposes a unique filtering-combination approach to increase forecast accuracy. The first step is to filter the original series of stock market prices into two new series, consisting of a nonlinear trend series in the long run and a stochastic component of a series, using the Hodrick-Prescott filter. Next, all possible filtered combination models ar

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Gupta, Priyank, Sanjay Kumar Gupta, and Rakesh Singh Jadon. "Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (2023): 309–18. http://dx.doi.org/10.17762/ijritcc.v11i11s.8103.

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In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introd

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Lee, Sangheon, and Poongjin Cho. "Graph-Based Stock Volatility Forecasting with Effective Transfer Entropy and Hurst-Based Regime Adaptation." Fractal and Fractional 9, no. 6 (2025): 339. https://doi.org/10.3390/fractalfract9060339.

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This study proposes a novel hybrid model for stock volatility forecasting by integrating directional and temporal dependencies among financial time series and market regime changes into a unified modeling framework. Specifically, we design a novel Hurst Exponent Effective Transfer Entropy Graph Neural Network (H-ETE-GNN) model that captures directional and asymmetric interactions based on Effective Transfer Entropy (ETE), and incorporates regime change detection using the Hurst exponent to reflect evolving global market conditions. To assess the effectiveness of the proposed approach, we compa

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Din, Riaz Ud, Salman Ahmed, Saddam Hussain Khan, Abdullah Albanyan, Julian Hoxha, and Bader Alkhamees. "A novel decision ensemble framework: Attention-customized BiLSTM and XGBoost for speculative stock price forecasting." PLOS ONE 20, no. 4 (2025): e0320089. https://doi.org/10.1371/journal.pone.0320089.

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Forecasting speculative stock prices is essential for effective investment risk management and requires innovative algorithms. However, the speculative nature, volatility, and complex sequential dependencies within financial markets present inherent challenges that necessitate advanced techniques. In this regard, a novel framework, ACB-XDE (Attention-Customized BiLSTM-XGB Decision Ensemble), is proposed for predicting the daily closing price of speculative stock Bitcoin-USD (BTC-USD). The proposed ACB-XDE framework integrates the learning capabilities of a customized Bi-directional Long Short-

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Kumar, Manish. "Returns and volatility spillover between stock prices and exchange rates." International Journal of Emerging Markets 8, no. 2 (2013): 108–28. http://dx.doi.org/10.1108/17468801311306984.

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PurposeThe purpose of this paper is to analyze the nature of returns and volatility spillovers between exchange rates and stock price in the IBSA nations (India, Brazil, South Africa).Design/methodology/approachThe study uses VAR framework and the recently proposed Spillover measure of Diebold and Yilmaz to examine the returns and volatility spillover between exchange rates and stock prices of IBSA nations. In addition, multivariate GARCH with time varying variance‐covariance BEKK model is used as a benchmark against the spillover methodology proposed by Diebold and Yilmaz.FindingsThe results

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Li, Shuaishuai, and Weizhen Chen. "A Study on Interpretable Electric Load Forecasting Model with Spatiotemporal Feature Fusion Based on Attention Mechanism." Technologies 13, no. 6 (2025): 219. https://doi.org/10.3390/technologies13060219.

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Driven by the global “double carbon” goal, the volatility of renewable energy poses a challenge to the stability of power systems. Traditional methods have difficulty dealing with high-dimensional nonlinear data, and the single deep learning model has the limitations of spatiotemporal feature decoupling and being a “black box”. Aiming at the problem of insufficient accuracy and interpretability of power load forecasting in a renewable energy grid connected scenario, this study proposes an interpretable spatiotemporal feature fusion model based on an attention mechanism. Through CNN layered ext

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Liu, Yezhen, Xilong Yu, Yanhua Wu, and Shuhong Song. "Forecasting Variation Trends of Stocks via Multiscale Feature Fusion and Long Short-Term Memory Learning." Scientific Programming 2021 (September 21, 2021): 1–9. http://dx.doi.org/10.1155/2021/5113151.

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Forecasting stock price trends accurately appears a huge challenge because the environment of stock markets is extremely stochastic and complicated. This challenge persistently motivates us to seek reliable pathways to guide stock trading. While the Long Short-Term Memory (LSTM) network has the dedicated gate structure quite suitable for the prediction based on contextual features, we propose a novel LSTM-based model. Also, we devise a multiscale convolutional feature fusion mechanism for the model to extensively exploit the contextual relationships hidden in consecutive time steps. The signif

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Liu, Bingchun, Xingyu Wang, Shiming Zhao, and Yan Xu. "Prediction of Baltic Dry Index Based on GRA-BiLSTM Combined Model." International Journal of Maritime Engineering 165, A3 (2024): 217–28. http://dx.doi.org/10.5750/ijme.v165ia3.1212.

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The Baltic dry index (BDI) is not only one of the most important indicators of shipping costs but is also an important barometer of global trade and manufacturing sentiment. The BDI is highly volatile and subject to complex factors, which make it difficult to predict. In this paper, a neural network model-based BDI forecasting system was proposed to effectively forecast the BDI. We used the gray relational degree analysis method to select seven factors with higher correlation from 15 factors affecting the variation of BDI index to be used as input indicators for the bi-directional long short-t

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Das, Asit Kumar, Debahuti Mishra, Kaberi Das, et al. "A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market." Mathematics 10, no. 19 (2022): 3632. http://dx.doi.org/10.3390/math10193632.

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In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm a

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Wang, Bingxing. "Empirical Evaluation of Large Language Models for Asset‑Return Prediction." Academic Journal of Sociology and Management 3, no. 4 (2025): 18–25. https://doi.org/10.70393/616a736d.333035.

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In an era of exploding financial‐market information and rapid algorithmic iteration, traditional asset‑return forecasting models struggle to exploit unstructured text. Using cross‑asset data—equities, Treasuries and commodity futures—from 2004 to 2024, we build an integrated prediction framework that fuses semantic factors extracted by Large Language Models (LLMs) with price‑volume and macro‑numerical factors. We benchmark it against Logit, Random Forest, LightGBM and bidirectional LSTM. A comprehensive evaluation with weighted F₁, ROC‑AUC, Information Ratio and Sharpe Ratio shows that (i) LLM

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Ivanov, Illia. "RETURNS FORECASTING WITH A MACROECONOMIC FACTOR-BASED DECISION TREE MODEL (MARPFM)." Європейський науковий журнал Економічних та Фінансових інновацій 1, no. 15 (2025): 193–209. https://doi.org/10.32750/2025-0117.

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This paper aims to develop and test the efficiency of the Multi-Asset Returns Prediction Factor-Based Model (MARPFM), designed to forecast asset returns across a wide range of asset classes and diverse investment timeframes. Motivated by the persistent pursuit of market advantages through innovative technologies, despite the inherent challenges posed by market efficiency, MARPFM innovatively integrates decision tree algorithms with crucial macroeconomic factors, specifically focusing on recession probability, unexpected inflation shifts, investor sentiment indicators, and the slope of the yiel

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Ahmadian, Ali, Kumaraswamy Ponnambalam, Ali Almansoori, and Ali Elkamel. "Optimal Management of a Virtual Power Plant Consisting of Renewable Energy Resources and Electric Vehicles Using Mixed-Integer Linear Programming and Deep Learning." Energies 16, no. 2 (2023): 1000. http://dx.doi.org/10.3390/en16021000.

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Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make th

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Bui, Thanh Khoa, and Trong Huynh Tran. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6625–34. https://doi.org/10.11591/ijece.v12i6.pp6625-6634.

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Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous. The continuous development of pow

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Khoa, Bui Thanh, and Tran Trong Huynh. "Forecasting stock price movement direction by machine learning algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 6 (2022): 6625. http://dx.doi.org/10.11591/ijece.v12i6.pp6625-6634.

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<p><span lang="EN-US">Forecasting stock price movement direction (SPMD) is an essential issue for short-term investors and a hot topic for researchers. It is a real challenge concerning the efficient market hypothesis that historical data would not be helpful in forecasting because it is already reflected in prices. Some commonly-used classical methods are based on statistics and econometric models. However, forecasting becomes more complicated when the variables in the model are all nonstationary, and the relationships between the variables are sometimes very weak or simultaneous.

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M., Jeyakarthic, and Punitha S. "Hybridization of Bat Algorithm with XGBOOST Model for Precise Prediction of Stock Market Directions." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 3375–82. https://doi.org/10.35940/ijeat.C5535.029320.

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In recent days, prediction of stock market returns is generally treated as a forecasting problem. The implicit volatile nature of stock market across the world makes the prediction process highly challenging. As a result, prediction and diffusion modeling undermine a wide range of issues present in the stock market prediction. The minimization in prediction error will greatly minimize the investment risks. This paper presents a new method to determine the direction of stock market variations indicating gain and loss. A new machine learning ML based model is applied to predict the direction of

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Rathore, Anupriya, and Prof Priyanka Khabiya. "Predicting the Direction of Stock Markets Employing Back Propagation in Neural Networks." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem24209.

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Stock Market prediction is a category of time series prediction which extremely challenging due to the dependence of stock prices on several financial, socio-economic and political parameters etc. Moreover, small inaccuracies in stock market price predictions may result in huge losses to firms which use stock market price prediction results for financial analysis and investments. Off late, artificial intelligence and machine learning based techniques are being used widely for stock market prediction due to relatively higher accuracy compared to conventional statistical techniques. The proposed

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