Price Prediction Research Articles

Machine learning gold price forecasting

ABSTRACT Price projections for main metal commodities have been highly valued by many market players for a substantial period of time. Our research examines the daily reported price of gold in order to address the issue. The sample that is being investigated covers a period of ten years, beginning on 22 April 2014 and ending on 17 April 2024, and the price series that is being examined has significant business repercussions. When it comes to this particular scenario, Gaussian process regression models are constructed by using cross-validation approaches and Bayesian optimization methodologies. The strategies that are produced as a consequence are then used in order to supply price predictions. With a relative root mean square error of 0.8706%, our empirical prediction technique generates price projections that are relatively accurate for the out-of-sample measurement period that spans from 4 May 2022 to 17 April 2024. Investors and governments are provided with the knowledge they need to make informed decisions on the gold market due to the availability of models that anticipate prices.

Do stock markets care about ESG and sentiments? Impact of ESG and investors' sentiment on share price prediction using machine learning

Do stock markets care about ESG and sentiments? Impact of ESG and investors' sentiment on share price prediction using machine learning

Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability

Using a machine learning approach, this study examines how operational and financial efficiency metrics influence stock prices in the aviation industry. A CatBoost regression model enhanced with SHapley Additive exPlanations (SHAP) was developed using data from 65 global aviation companies collected between 2015 and 2023. The model predicts stock prices based on various operational and financial indicators, including Total Revenue per Available Seat Mile (ASM), Passenger Load Factor, liquidity ratios, and debt-to-assets ratios. The findings suggest that operational efficiency metrics, particularly Total Revenue per ASM and Passenger Load Factor, play a significant role in predicting stock prices within the aviation sector. Financial metrics, such as the Quick Ratio and Debt-to-Assets Ratio, also contribute to the model but appear to have a secondary influence compared to operational factors. SHAP values provided interpretable insights into the model's predictions, allowing for a better understanding of the relative importance of different features. Furthermore, the study's findings offer support for the semi-strong form of the Efficient Market Hypothesis (EMH), demonstrating that operational and financial metrics are reflected in stock prices. These results indicate that aviation companies demonstrating higher operational efficiency may be better positioned for favorable stock market performance, although financial health remains important. This study contributes to the existing literature by integrating operational and financial metrics into a machine learning framework, offering a comprehensive and interpretable model for stock price prediction in the aviation industry.

A Comparative Study of Neural Network and Wavelet Decomposition Models for Price Forecasting of Tomato

Tomato price forecasting in Bihar marketplaces is the subject of a study. The study compares and contrasts many time series models. Three models are compared: WT-TDNN (Wavelet Transform with TDNN), TDNN (Time-Delay Neural Network), and ARIMA. This study evaluates the efficacy of advanced and traditional time series models for forecasting tomato prices in Bihar markets, with a focus on ARIMA, Time Delay Neural Networks (TDNN), and Wavelet-TDNN (WT-TDNN). ARIMA models were optimized using the Akaike Information Criterion (AIC), identifying (0, 1, 2) and (1, 1, 1) as optimal configurations, and validated through Wald's test. TDNN models with 2:4s:1l and 1:6s:1l architectures exhibited robust performance based on RMSE, MAE, and MAPE metrics. The WT-TDNN model, which integrates wavelet decomposition with TDNN, demonstrated superior predictive accuracy over both ARIMA and standard TDNN models. By employing the Haar filter for series decomposition into orthogonal components and applying TDNN to each component, WT-TDNN effectively captured the complex, non-linear dynamics of agricultural price series. This innovative approach enhances the precision of price forecasting, offering significant implications for agricultural economics and machine learning applications. The findings provide valuable insights for policymakers, farmers, and market analysts, enabling improved decision-making and fostering resilience in the agricultural sector. The study highlights WT-TDNN as a transformative tool for addressing the challenges of agricultural price prediction.

A taxonomy of literature reviews and experimental study of deepreinforcement learning in portfolio management

Portfolio management involves choosing and actively overseeing various investment assets to meet an investor’s long-term financial goals, considering their risk tolerance and desired return potential. Traditional methods, like mean–variance analysis, often lack the flexibility needed to navigate the complexities of today’s financial markets. Recently, Deep Reinforcement Learning (DRL) has emerged as a promising approach, enabling continuous adjustments to investment strategies based on market feedback without explicit price predictions. This paper presents a comprehensive literature review of DRL applications in portfolio management, aimed at finance researchers, data scientists, AI experts, FinTech engineers, and students seeking advanced portfolio optimization methodologies. We also conducted an experimental study to evaluate five DRL algorithms—Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed DDPG (TD3)—in managing a portfolio of 30 Dow Jones Industrial Average (DJIA) stocks. Their performance is compared with the DJIA index and traditional strategies, demonstrating DRL’s potential to improve portfolio outcomes while effectively managing risk.

Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market

In this work, we introduce an innovative approach to managing electricity costs within Germany’s evolving energy market, where dynamic tariffs are becoming increasingly normal. In line with recent German governmental policies, particularly the Energiewende (Energy Transition) and European Union directives on clean energy, this work introduces a risk management strategy based on a combination of the well-known risk measures of the Value at Risk (VaR) and Conditional Value at Risk (CVaR). The goal is to optimize electricity procurement by forecasting hourly prices over a certain horizon and allocating a fixed budget using the aforementioned measures to minimize the financial risk. To generate price predictions, a Gaussian process regression model is used. The aim of this hybrid approach is to design a model that is easily understandable but allows for a comprehensive evaluation of potential financial exposure. It enables consumers to adjust their consumption patterns or market traders to invest and allows more cost-effective and risk-aware decision-making. The potential of our approach is shown in a case study based on the German market. Moreover, by discussing the political and economical implications, we show how the implementation of our method can contribute to the realization of a sustainable, flexible, and efficient energy market, as outlined in Germany’s Renewable Energy Act.

Impact of Artificial Intelligence on Stock Price Prediction in India

Impact of Artificial Intelligence on Stock Price Prediction in India

Peanut oil price change forecasts through the neural network

Purpose For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market. Design/methodology/approach The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification. Findings With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions. Originality/value On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.

Time Series Analysis in Financial Markets and Biological Phenomena

This paper discusses the application of time series analysis in financial markets and biological phenomena. It expounds the basic theory of time series analysis, including the concepts and characteristics of time series and commonly used time series models. It also provides an in-depth analysis of its application in financial markets, covering the characteristics of financial time series and specific applications in stock markets and foreign exchange markets, such as stock price forecasting, risk measurement, foreign exchange rate forecasting and volatility analysis. It also discusses the application of time series analysis in biological phenomena, including the characteristics of biological time series and its application in biological growth and development, such as modeling the growth curves of animals and plants, and studying the dynamics of microbial populations. The application of time series analysis in these two fields is summarized, providing a valuable reference for related research and practice.

Stock Price Forecasting Using Long Short-Term Memory Networks: A Comprehensive Analysis of Financial Time-Series Data

In this study, we employ Long Short-Term Memory (LSTM) networks to predict stock prices using historical data from Tesla Inc. spanning 10 years. The research emphasizes the importance of LSTM's capability to model complex temporal dependencies in financial time-series data, outperforming traditional statistical methods. Various feature combinations and time steps are tested, identifying a 30-day window as the optimal setup. Key evaluation metrics, including Mean Squared Error (MSE), training loss, and validation loss, are utilized to assess model performance. The results show that LSTM networks are particularly effective for short-term stock price predictions, while longer-term forecasts experience decreasing accuracy. This work contributes to the growing body of knowledge on deep learning applications in financial markets, offering practical insights for investors and financial institutions

Modelling and Forecasting Future Retail Price of Capsicum Annum L. Var. Kulai for Food Security and SDG Issues in Perlis, Malaysia

Objective: This study focuses on the modeling and forecasting of the retail price of Capsicum Annum L. Var. Kulai in Perlis, Malaysia, as a measure to support food security initiatives. Theoretical Framework: Food security is a pressing global issue exacerbated by factors such as climate change, conflicts, and the COVID-19 pandemic. Malaysia, ranked 41st in The Global Food Security Index (GFSI) 2022, has seen a concerning rise in food insecurity, driven by increased agricultural input costs and a high dependency on food imports. Method: Using historical monthly time series data from January 2018 to May 2023, various univariate time series models were employed, including Naïve, Seasonal Naïve, Holt’s Method, and ARIMA models. Results and Discussion: The study found that Holt’s Method provided the most accurate forecasts, with a mean absolute percentage error (MAPE) of 0.4607. The model predicts a continued increase in chili prices, which could pose challenges to food security in the region. Research Implications: These findings underscore the importance of accurate price forecasting in formulating effective agricultural policies and supporting local communities in mitigating food insecurity. This research contributes to worldwide initiatives aimed at fulfilling SDGs for goal 2 (Zero Hunger and Sustainable Agriculture), goal 11 (Sustainable Cities and Communities), and goal 13 (Climate Action). Originality/Value: This study offers valuable insights into forecasting the price of Capsicum Annum L. Var. Kulai in Perlis, Malaysia. Food security challenges have increasingly affected this area as various univariate time series models have been applied, and Holt’s method has been identified as the most effective. Providing policymakers with reliable forecasts guide strategic agricultural policies and supports food security efforts.

Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy.

Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance. PSO-LSTM model leveraging PSO's efficient swarm intelligence and strong optimization capabilities is proposed in this article. The experimental results on six global stock indices demonstrate that PSO-LSTM effectively fits real data, achieving high prediction accuracy. Moreover, increasing PSO iterations lead to gradual loss reduction, which indicates PSO-LSTM's good convergence. Comparative analysis with seven other machine learning algorithms confirms the superior performance of PSO-LSTM. Furthermore, the impact of different retrospective periods on prediction accuracy and finding consistent results across varying time spans are. Conducted in the experiments.

The Role of Artificial Intelligence in Enhancing Commodity Trading and Risk Management Solutions

This paper explores the transformative impact of Artificial Intelligence (AI) on commodity trading and risk management. It delves into how AI technologies can enhance the efficiency and effectiveness of front office, middle office, and back office operations. The paper discusses various applications of AI, including price forecasting, real- time market insights, risk assessment, fraud detection, and the generation of Business Intelligence (BI) reports and dashboards. Additionally, it examines how AI can automate reconciliation processes, simulate prices, and audit master and transactional data for compliance purposes. Real-life examples of AI-enhanced Commodity Trading and Risk Management (CTRM) software are provided to illustrate these benefits. The paper also addresses the challenges and future prospects of AI implementation in this sector. Keywords Artificial Intelligence, Commodity Trading, Risk Management, CTRM, Price Forecasting, Market Insights, Risk Assessment, Fraud Detection, Business Intelligence, Reconciliation, Price Simulation, Data Auditing, Compliance, Automation.

ANALYZING SOCIAL MEDIA SENTIMENT TOWARD SPECIFIC COMMODITIES FOR FORECASTING PRICE MOVEMENTS IN COMMODITY MARKETS

This study adopts a systematic literature review to analyze social media sentiment towards specific commodities to enhance the accuracy of price movement forecasts in commodity markets. Drawing from the field of applied mathematics, the research gathered literature from Scopus, DOAJ, and Google Scholar databases, covering publications from 2014 to 2024. A rigorous search strategy yielded 66 journal articles, with 30 being selected for their close relevance to keywords such as "social media sentiment," "commodity markets," and "price forecasting." Results indicate that social media sentiment significantly influences commodity prices, with particular variations based on commodity type and geographical context. Specific sentiment factors—especially intensity, polarity, and timing—were found to have a pronounced impact on price dynamics, with sentiment polarity being particularly influential in volatile markets. Additionally, advanced analytical methods, like Bayesian Dynamic Linear Models and LSTM neural networks, enhance predictive accuracy when applied to sentiment analysis in this context. These findings underscore the value of social media sentiment in refining forecasting models, while also highlighting gaps in understanding regional sentiment variations and their effects on different commodity types. By synthesizing these insights, this study emphasizes the importance of considering social media sentiment for more accurate price predictions and identifies key areas for future research to explore the multifaceted impacts of sentiment in commodity markets.

A Vegetable-Price Forecasting Method Based on Mixture of Experts

The accurate forecasting of vegetable prices is crucial for policy formulation, market decisions, and agricultural market stability. Traditional time-series models often require manual parameter tuning and struggle to effectively handle the complex non-linear characteristics of vegetable price data, limiting their predictive accuracy. This study conducts a comprehensive analysis of the performance of traditional methods, deep learning approaches, and cutting-edge large language models in vegetable-price forecasting using multiple predictive performance metrics. Experimental results demonstrate that large language models generally outperform other methods, but do not have consistent performance for all kinds of vegetables across different time scales. As a result, we propose a novel vegetable-price forecasting method based on mixture of expert models (VPF-MoE), which combines the strengths of large language models and deep learning methods. Different from the traditional single model prediction method, VPF-MoE can dynamically adapt to the characteristics of different vegetable types, dynamically select the best prediction method, and significantly improve the accuracy and robustness of the prediction. In addition, we optimized the application of large language models in vegetable-price forecasting, offering a new technological pathway for vegetable-price prediction.

STOCK PRICE PREDICTION AND SIMULATION USING GEOMETRIC BROWNIAN MOTION-KALMAN FILTER: A COMPARISON BETWEEN KALMAN FILTER ALGORITHMS

Stocks have high-profit potential but also have high risk. Many people have ways to forecast stock prices. The Geometric Brownian Motion (GBM) method forecasts stock prices. The data used in this study are closing stock price data from July 1, 2021 to August 31, 2021 taken from Yahoo! Finance. The stocks used in this research are Bank Rakyat Indonesia (BBRI), Indofood Sukses Makmur (INDF), and Telkom Indonesia (TLKM). A strategy is carried out to improve prediction accuracy by utilising the Kalman Filter (KF). This research will compare the mean absolute percentage error (MAPE) value between GBM-KF, which was manually computed and computed using the Python library. As an example of this research, for BBRI stock, the high GBM MAPE value of 9.02% can be reduced to 3.52% with manually computed GBM-KF and 3.68% with Python library computed GBM-KF. Similarly, INDF and TLKM stocks are showing a significant reduction in MAPE values to deficient levels in some cases. The GBM-KF method employing manual computing may enhance the overall precision of stock price forecasting. Future research may enhance this study by using the GBM-KF model on alternative financial instruments, integrating supplementary market data, or evaluating its efficacy under extreme market conditions.

GEOMETRIC BROWNIAN MOTION WITH JUMP DIFFUSION AND VALUE AT RISK ANALYSIS OF PT BANK NEGARA INDONESIA STOCKS

Investments in stocks are made to make a profit, where the higher the expected profit, the greater the risk undertaken. The return on investing in stocks can be influenced by changes in the price of stocks that are difficult to predict, which can lead to uncertainty in the value of the return and the risk of the stock. The application of the Geometric Brownian Motion (GBM) model with Jump Diffusion is crucial for enhancing the accuracy of stock price forecasting and risk analysis by incorporating price jumps resulting from external events within complex market dynamics. The data used in this study are the closing price data of the daily stock of PT Bank Negara Indonesia for the period 1 December 2022 to 31 January 2024, where the stock return data has a kurtosis value greater than 3 (leptokurtic) so that the data indicates a jump. The GBM with Jump Diffusion model was implemented to predict the stock price with a simulation repetition of 1000 times. The analysis shows that the GBM model with Jump Diffusion has an excellent accuracy rate with the smallest MAPE value of 0.86%. The average value of the VaR with Monte Carlo simulation obtained at the reliability levels of 80%, 90%, 95%, and 99% in a row is 0.96%, 1.53, 1.97%, and 2.64%. This result shows that the higher the confidence level used, the greater the risk.

An enhanced Transformer framework with incremental learning for online stock price prediction.

To address the limitations of existing stock price prediction models in handling real-time data streams-such as poor scalability, declining predictive performance due to dynamic changes in data distribution, and difficulties in accurately forecasting non-stationary stock prices-this paper proposes an incremental learning-based enhanced Transformer framework (IL-ETransformer) for online stock price prediction. This method leverages a multi-head self-attention mechanism to deeply explore the complex temporal dependencies between stock prices and feature factors. Additionally, a continual normalization mechanism is employed to stabilize the data stream, enhancing the model's adaptability to dynamic changes. To ensure that the model retains prior knowledge while integrating new information, a time series elastic weight consolidation (TSEWC) algorithm is introduced to enable efficient incremental training with incoming data. Experiments conducted on five publicly available datasets demonstrate that the proposed method not only effectively captures the temporal information in the data but also fully exploits the correlations among multi-dimensional features, significantly improving stock price prediction accuracy. Notably, the method shows robust performance in coping with non-stationary and frequently changing financial market data.

Forecasting China’s Short-Term Energy Futures Price Using a Novel Secondary Decomposition-Optimized System

AbstractEnergy futures price forecasting is challenging due to the nonlinear and fluctuant characteristics. Existing literature mainly uses decomposition and ensemble method which neglects the intrinsic mode function obtained by the first decomposition could be irregular and thus reduces the prediction accuracy. To fill the research gap, a novel secondary decomposition-optimized-KELM-ensemble forecasting system is proposed to perform short-term forecasting in this study, which synthesizes two-stage data decomposition method, Sparrow search optimization algorithm, and extreme learning machine with kernel. We test the method with two energy futures prices in China, demonstrating that both one-day and three-day ahead forecasting results obtained are more accurate and stable compared to existing models in the literature, such as BPNN (improved by 58.42% on one-day ahead and 56.44% on three-day ahead by MAE) and KELM (improved by 56.40% on one-day ahead and 49.04% on three-day ahead by MAE). Therefore, the forecasting system introduced in this paper can provide useful implications for both policy makers and financial practitioners in the energy sector.

Implementation of Fundamental Analytical Dashboard and Stock Price Forecasting of ADRO, ANTM, INCO with Arima Approach

Implementation of Fundamental Analytical Dashboard and Stock Price Forecasting of ADRO, ANTM, INCO with Arima Approach