Future Prices Research Articles

Dynamics of a Price Adjustment Model with Distributed Delay

This paper deals with the stability and occurrence of Hopf bifurcation of a distributed delay differential cobweb model using the chain trick technique. This is a generalized form of the fixed delay cobweb model to which it is compared using the same parameter values. The results from the delay distribution showed that whenever less weight (γ=0.146) is put on past prices, the current equilibrium price is adjusted upwards while the reverse is observed when a higher weight (γ=0.186) is put on the previous price. It is also observed that if the initial price is set below/above the equilibrium price, the price adjustment either affects the consumers or benefits the suppliers. However, the fixed delay cobweb model does not display the consumers or suppliers benefits of the price dynamics in either direction. These are unique, underlying patterns in price dynamics discovered when using a distributed delay model compared to traditional fixed delay cobweb models. Furthermore, our model challenges the traditional cobweb model’s requirement for divergence, as it is based on the weight assigned to past prices rather than the relationship between the elasticities of supply and demand, which is the determining factor in the classical model. Based on these insights, we recommend that future price adjustment models incorporate distributed delays, as they reveal more intricate price dynamics and provide a more comprehensive understanding of market behavior than fixed delay models.

The Fluctuation Characteristics and Risk Investigation of China's Gold Future Market

This paper deeply explores the fluctuation characteristics and risks of China's gold futures market. Through the collection of data on macroeconomic factors, stock index interest rates, and energy futures, the paper employs multiple linear regression models and intermediary variable models to quantitatively analyze the impact of these factors on the volatility of gold futures prices. The research finds that there is a significant positive correlation between the gold futures price and the growth of the U.S. GDP and the silver futures price index, while the impact of other macroeconomic indicators such as China's gold reserves and the inflation rate is not significant. Policy support and market regulation play a key role in the healthy development of the market, and the broad participation of market participants and the innovation of financial products have driven the diversification of the market. The intermediary effect analysis shows that the impact of macroeconomic factors on the gold futures price is partially indirect through the intermediary variable of stock index interest rates.

A Financial Analysis and Valuation of Shell

In todays era of global energy transition, the strategic choices made by energy giants like Shell carry immense significance, shaping not only their own future blueprint but also profoundly influencing the overall energy sectors trends. The article provides a comprehensive analysis of Shell plc with liquidity, solvency, profitability, and valuation analysis of 2023 with its main competitors in the oil and gas industry. Despite facing challenges from fluctuating commodity prices and regulatory changes, Shell maintains a competitive advantage and favorable market position. The company demonstrates robust financial health, with strong liquidity, solvency, and profitability ratios compared to industry peers. Nevertheless, the analysis reveals the companys profitability ratios lag behind competitors, indicating room for improvement. Although facing uncertainties in future commodity prices and regulatory environments, the analysis highlights Shells commitment to balancing environmental goals with shareholder value, as evidenced by its energy transition strategy. Shells stock is still fairly valued according to the analysis, presenting a compelling investment opportunity with its well-constructed financial framework. Overall, Shells strategic plans and adjustments aim to ensure resilience and adaptability, promising investors sustainable development amidst evolving market dynamics.

A Systematic Review on Graph Neural Network-based Methods for Stock Market Forecasting

Financial technology (FinTech) is a field that uses artificial intelligence to automate financial services. One area of FinTech is stock analysis, which aims to predict future stock prices to develop investment strategies that maximize profits. Traditional methods of stock market prediction, such as time series analysis and machine learning, struggle to handle the non-linear, chaotic, and sudden changes in stock data and may not consider the interdependence between stocks. Recently, graph neural networks (GNNs) have been used in stock market forecasting to improve prediction accuracy by incorporating the interconnectedness of the market. GNNs can process non-Euclidean data in the form of a knowledge graph. However, financial knowledge graphs can have dynamic and complex interactions, which can be challenging for graph modeling technologies. This work presents a systematic review of graph-based approaches for stock market forecasting. This review covers different types of stock analysis tasks (classification, regression, and stock recommendation), a generalized framework for solving these tasks, and a review of various features, datasets, graph models, and evaluation metrics used in the stock market. The results of various studies are analyzed, and future directions for research are highlighted.

Analyzing the dynamics between crude oil spot prices and futures prices by maturity terms: Deep learning approaches to futures-based forecasting

Predicting crude oil prices is critical due to their significant impact on the global economy. While numerous studies have focused on time series forecasting and machine learning methods to predict crude oil spot prices, the use of futures prices segmented by the maturity terms as predictors is comparatively underexplored. This study explored a deep learning approach to investigate using futures prices across different maturities to predict spot prices. Specifically, this paper analyzes the predictive power of one-, two-, three-, six-, and 12-month crude oil futures contracts. This study employs multiple deep learning algorithms, including Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Temporal Convolutional Neural Network (TCN), to forecast crude oil spot prices. This research investigates the performance of these machine learning models when exploring the relationship between futures and spot prices of crude oil. In addition, this research incorporates extensive hyperparameter tuning to enhance the interpretability of the machine learning models when forecasting spot prices using futures prices, thereby contributing to the field of Explainable Artificial Intelligence (XAI) with an optimal set of hyperparameters. In summary, this research systematically shows results demonstrating predictive power in terms of XAI between spot prices and futures prices with different maturities, and machine learning algorithms.

Internal Ties and Stock Price Crash Risk: Evidence from Chinese Listed Firms

Drawing on information advantage and executive entrenchment perspectives, this study examines the impact of internal ties between independent directors and non‐CEO executives (hereafter, internal ties) on stock price crashes. Utilizing a dataset covering Chinese listed firms spanning 2005 to 2021, we find that internal ties alleviate information asymmetry between the board and management, reducing future stock price crash risk. This supports the information advantage hypothesis. Further analysis reveals the effect of internal ties on stock price crash risk is more evident in high social trust regions and among non‐state‐owned enterprises (non‐SOEs). Mechanism tests demonstrate that financial opacity and financial reporting conservatism are economic mechanisms through which internal ties affect stock price crashes. Our research sheds new light regarding the significance of internal ties in corporate governance and enriches the literature on internal governance.

Forecasting crude oil prices with global ocean temperatures

This paper explores the role of ocean temperature (OT) in forecasting crude oil prices. Global OT shows statistically and economically significant predictive power, with corresponding in-sample and out-of-sample R2s of 1.81 % and 1.74 %, respectively. Both southern and northern hemisphere OTs can predict future oil prices, and the former is more predictive. Compared to the Atlantic, OTs in the Indian and Pacific Oceans display considerable predictive gains. In addition, we find that OTs have stronger forecasting power for crude oil prices in the recent period, with the in-sample R2 of global OT for the recent decade's sample reaching as high as 2.74 %. Correspondingly, we find that OTs' predictive power in the full sample can be explained through two channels: market risk and investor attention. In the recent period, in addition to these two channels, OTs have also influenced crude oil prices through the oil fundamental channel.

FROM CALENDAR TIME TO BUSINESS TIME: THE CASE OF COMMODITY MARKETS

We address the problem of modeling commodity forward curves, while preserving empirical features observed in commodity markets. By letting a commodity market to “ live” in the tempo of a business rather than calendar clock, we create a model with a rich but realistic set of features, such as stochastic volatility, stochastic rate of mean reversion and various shapes of forward curves such as backwardation and contango. The model, when applied to extensive historical datasets of crude oil and natural gas forward curves, shows a remarkably good fit to the observed futures prices, also in periods of high volatility and negative prices. The model is developed in such a way that it can be used for a wide variety of applications, ranging from exotic derivatives pricing to risk management of commodity portfolios.

The interplay of geopolitics and agricultural commodity prices

AbstractThis article uses the time‐varying parameter vector autoregressive model to assess the impact of geopolitical risk and shocks on agricultural commodity markets, accounting for demand—exports; supply—input prices; inventory; speculation; and economic fluctuations. The results show that geopolitical risks significantly impact corn and soybean futures prices and market behaviors with context‐specific implications in the short to medium term. In addition, heightened geopolitical risk during the Russia–Ukraine war increased oil prices and indirectly elevated agricultural commodity prices. These insights are crucial for agricultural risk management, informing federal policies and forecasting future price trends in an increasingly uncertain global market environment.

IMPLEMENTASI MACHINE LEARNING ALGORITMA NEURAL NETWORK DALAM MEMPREDIKSI HARGA SAHAM MENGGUNAKAN RAPIDMINER

The research is aimed at implementing the Neural Network algorithm in predicting stock prices using RapidMiner. Historical stock price data is analyzed to train neural network models in identifying patterns and trends that can be used to predict future stock prices. The research phases include data collection, data preprocessing, Neural Network model development, model training, and model performance evaluation. Data used include daily closing prices, trade volumes, and other technical indicators. The data is processed through several stages of preprocesing, including normalization and filling of missing values, before being used to train Neural network models. The model was built with several layers and the number of neurons adjusted based on early experiments to find the optimal configuration. Models are trained using datasets that are divided into training sets and testing sets. The predicted result is compared to the actual value to evaluate the performance of the model. The results showed that the Neural Network model was able to predict stock prices with a fairly high accuracy rate, with an RMSE of 0.989 indicating a low prediction error rate. The research concludes that the Neural Network algorithm implemented in RapidMiner is effective in predicting stock prices and has significant potential to be used in investment decision-making. The use of RapidMiner as a data processing tool has proven to be efficient and facilitating the development and evaluation of predictive models. Keywords: Neural Network, Stock Price Prediction, RapidMiner, Machine Learning, Investment

Cross-modal scenario generation for stock price forecasting using Wasserstein GAN and GCN

The forecasting of stock price has always been a difficult problem, as the various inputs like company performance, technical innovation, political factors are intricate and often assessed with uncertainty. While most of the existing studies belong to deterministic forecast that focused on either the trend or exact value of future stock price, the forecasted results contain little uncertain information, thus bringing certain risks to investors. In this study, an effective scenario generation method based on wasserstein generative adversarial network (WGAN) is proposed for stock price forecasting, which can characterize the temporally and spatially correlations among different stocks, so as to help investors make proper investment strategies in complicated market environments. To the best of our knowledge, the scenario generation of stock price has been rarely studied by existing publications. Specifically, considering the inherent correlation among stocks, graph convolutional network (GCN) is used to capture the features of related stocks, whereafter the features of target stocks and related stocks are combined. In terms of feature fusion, this paper further uses a pre-trained model to obtain the key information of news headlines as text features, and fully integrates them with historical data through the attention mechanism, thus improving the overall performance of scenario generation. In summary, the proposed model can generate promising scenarios for future stock prices over time based on cross-modal information like historical data and news headlines. Numerical experiments show that our method outperforms other baseline methods in terms of root-mean-square error and average absolute percentage error (the average of several scenarios). For each part of the method, stock correlation is the most helpful for the results, followed by cross-modal information, with scenario generation contributing less to the results. Besides, experiments on a real-life portfolio selection problem also demonstrate that our method brings the highest returns, which proves its effectiveness in practical application.

How does finance and accounting supervision affect stock price crash risk?

As an essential component of China's national supervision system, finance and accounting supervision plays an important role in regulating financial order, promoting economic development, and maintaining social stability. This study selects the special action of finance and accounting supervision as a quasi-natural experiment and conducts a difference-in-differences estimation to alleviates the problem of endogeneity. Using a sample of A-share listed firms from 2007 to 2013, this study examines the effect of finance and accounting supervision on stock price crash risk in the context of China. Our results indicate that firms subject to finance and accounting supervision are less likely to suffer a stock price crash. We explore the underlying mechanism and find that finance and accounting supervision mitigates firms' information asymmetry, thereby reducing future stock price crash risk. We also find that the negative effect of finance and accounting supervision on stock price crash risk is more pronounced for highly financialized firms, firms with larger management shareholding, and firms with poorer stockholder checks and balances. This study has significance for enriching research on the factors influencing stock price crash risk and assessing the effectiveness of finance and accounting supervision in the capital market.

An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data

Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms are utilized, significantly improving the accuracy of price trend predictions and sentiment analysis. Technical indicators from stock market data are processed using the Bi-LSTM model to predict future stock prices. Concurrently, sentiment data from the Economic Times is pre-processed through Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and pre-trained transformer models to extract key sentiment scores. The ABC algorithm enhances textual feature selection, while the Firefly algorithm reduces skewness in the error distribution of technical indicators, thereby improving the alignment between forecasted and actual stock prices. The fusion mechanism integrates predicted price movements with sentiment analysis to provide actionable insights for stock selection. The decision-making framework classifies stocks based on the consistency between price movements and sentiment: stocks predicted to rise with positive sentiment are labelled as "Sure Selection"; rising prices with negative sentiment are categorized as "Dicey Selection"; declining prices with positive sentiment result in a "Risky Selection"; and negative sentiment with falling prices trigger an "Avoid Selection". The proposed approach achieved a prediction accuracy of 67.00% for the Bi-LSTM model, with a precision of 0.714, recall of 0.695, and an F1 score of 0.677, demonstrating the model's robustness. The mean absolute error (MAE) for the Bi-LSTM model was 4.30, indicating strong predictive performance. The combined use of ABC and Firefly algorithms optimizes both technical and sentiment features, significantly contributing to the overall performance of the prediction model. By fusing multiple data sources and employing advanced optimization techniques, this methodology offers a unique and effective solution for stock market prediction and decision-making

Deep Reinforcement Learning in Cryptocurrency Trading: A Profitable Approach

This study proposes an Automatic Cryptocurrency Trading System using Deep Reinforcement Learning (DRL). Six popular cryptocurrencies were used: Bitcoin, Ethereum, BinanceCoin, DogeCoin, Cardano, and WAVES. Development of the trading system started with building three timeseries models – Temporal Convolutional Neural Network (TCNN), Long Short-Term Memory Network (LSTM), and Gated Recurrent Unit Network (GRU) – to predict future prices. Then, cryptocurrency sentiment data was scraped using the Alternative.me API. Data on historical prices, predicted future prices, cryptocurrency sentiment index, technical indicators, and trading account information was fed as input states to three DRL Agents — Deep Q Network (DQN), Advantage Actor Critic (A2C), and Recurrent Proximal Policy Optimization (RPPO) — which were trained using a custom-developed trading environment. Each agent was given $1000 initial capital for all six cryptocurrencies to trade using three possible actions — Buy, Sell and Hold — and were back-tested on one year of unseen data. Our DQN model had the highest overall return on investment (ROI) of $740, an average 12.3% ROI across all six cryptocurrencies, with an ROI of 63.98% achieved for BinanceCoin. However, A2C and RPPO both had negative ROI.

Stock Market Prediction Using Fuzzy Logic and Technical Indicators: A Case Study of Indian Banks

This study presents a decision support system for stock market investors that combines technical analysis with fuzzy logic. The system utilizes four essential technical indicators—Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator (SO), and On-Balance Volume (OBV)—to predict future price movements. These indicators are mapped as inputs into a fuzzy inference system, where fuzzy rules and membership functions are applied to generate trading recommendations, categorized as Bullish, Neutral, or Bearish. Experiments using MATLAB with data from 10 Indian banks demonstrate that the system effectively predicts stock movements and improves investment decisions when integrated with additional market information and investor insights

The Influence of Speculation in Food and Commodities and the Impact of Futures Prices on Spot Prices- Evidence from two Behavioral Experiments

This paper examines the impact of speculation on commodity and food futures on spot prices. Econometric studies contradict each other, coming not to a clear empirical outcome. Therefore, we take an alternative methodological approach with two behavioral experiments. The outcome shows, that tendencies in futures prices influence the price-setting behavior of market players in the spot market in the same direction. Since futures prices had no direct influence on profits and there were no other influences, futures prices must have influenced the expectations of market participants psychologically. Speculation with futures prices thus has an influence on spot prices via expectations. When speculation involves commodities and food in the future market serving crucial real economic functions it can lead to substantial misallocations and damages, why speculation should be regulated.

Price transmission relationships in Taiwan seafood supply chain: Tilapia (Oreochromis niloticus) and milkfish (Chanos chanos)

Tilapia and milkfish are aquaculture species associated with the highest production volume in Taiwan. With both fish species as the research focus, this study explored their price transmission relationships along the seafood supply chain. Our study results revealed that tilapia and milkfish prices had a long-run equilibrium relationship at different stages of the seafood supply chain and presented inconsistent price transmissions in the short run. There are domestic and foreign market channels for tilapia, enabling producers and wholesalers to transmit prices to retailers. By contrast, milkfish are mostly supplied to domestic markets, there is not perfect price transmission in the milkfish supply chain besides between farmers and wholesalers. An accurate price forecasting model can effectively indicate future price volatility and predict seafood prices, especially following government policy changes or in the face of external impacts. Such model can also serve as a crucial reference for future research of tilapia and milkfish industry, producers, and aquaculture farmers. Finally, seafood prices are a key determinant of fisheries and supply chain sustainability. Therefore, the stability of seafood prices, fish market wholesale pricing, and retail pricing are issues meriting continuous attention for a sustainable seafood supply chain.

Media Content, African Swine Fever, a Trade War, and U.S. Lean Hog Futures

Abstract In 2018 and 2019, China’s outbreak of African swine fever (ASF) and the U.S.–China trade war captured media headlines worldwide. This research uses a unique data set of media headlines and sentiments to estimate the impact of media on U.S. lean hog futures prices for nearby and distant expiration contracts. Findings suggest futures prices are influenced by news media content, with results differing by time to contract expiration and sentiment of the headline. International headlines with positive and negative connotations toward ASF and trade war have more significant effects, indicating sensationalist media creates the greatest price movements compared to neutral headlines.

Potential formation mechanism and prediction of crude oil price based on underdetermined independent component analysis

In most crude oil price prediction, a few of them explored the potential formation mechanism of oil price. This study proposes a novel multi-scale integrated (MSI) learning paradigm to mine the potential formation mechanism of the crude oil price and predict the crude oil price. Firstly, we propose a novel underdetermined independent component analysis (UICA) model based on extreme-point symmetric mode decomposition (ESMD) and independent component analysis (ICA) to study the potential formation mechanism of crude oil price. Based on the representative independent components (ICs), we found and analyzed some factors affecting the crude oil price from high frequency to low frequency, as well as the potential formation mechanism of crude oil price, such as geopolitical implications, natural disasters, historical pricing data and copper futures price. Moreover, the ICs are predicted and generated the final forecasting result of crude oil price via radial basis function neural network (RBFNN). The experimental results based on the West Texas Intermediate (WTI) crude oil price, confirm that the proposed MSI model is superior to the existing prediction models in terms of statistical and error performance criteria and Diebold-Mariano (DM) test with the optimal D stat = 99.0220 , MAE = 0.0050 , RMSE = 0.0537 , MAPE = 0.0116 , ESD = 0.0007 , R 2 = 0.9960 .

Unpacking the relation between media sentiment and house prices: A topic modeling approach

This study uses a topic modeling approach to investigate the relation between news media sentiment and house price movements. By examining real estate related articles published in local newspapers of 16 major cities in Canada and Australia, we find that housing media sentiment has significant relation with future house price movements in the Canadian market but not in the Australian market. We identify the specific topics discussed in news media related to the housing market and report differences in their themes and media sentiment's predictive power between Canada and Australia. This analysis presents novel inferences of qualitative and hard-to-quantify information related to the housing market in two different countries.