Future Prices Research Articles

Existence of Weak Solution for Black-Scholes Partial Differential Equation and Application of Energy Estimate Theorem in Sobolev Space

Purpose:This paper aims to solve the BS second-order parabolic equation in Sobolev spaces to obtain weak solutions for financial applications, extending previous work in this field.Methodology:This paper constructs a set of functions that transforms the Black-Scholes partial differential equation into weak formulations. This study focuses on the Mathematics of finance, particularly the evolution of option pricing. An option's underlying asset involves agreements to buy or sell at a future strike price. The Black-Scholes (BS) equation, widely used in financial applications, models this.Findings:The analytical solutions, existence, uniqueness and other estimates were also obtained in weak form using boundary conditions to establish the effects of their financial implications in Sobolev spaces.Implication:The problem's regularity conditions were considered, and the coefficients and boundary of the domain are all smooth functions. To this end, this paper illustrates the definitions and assumptions that led to valuable assertions.

Staggered boards, agency costs and stock price crash risk: Evidence from China

In this study, we leverage unique, hand-collected data to explore the impact of staggered boards on the likelihood of future stock price crashes. Our analysis shows that companies with staggered boards are at a higher risk of future stock price crashes compared to their counterparts with non-staggered boards, particularly in firms with elevated agency costs and those not state-owned. Additionally, we find that staggered boards exacerbate this risk by failing in their monitoring and advisory capacities. Evidence of this failure emerges through various indicators of diminished oversight, including managerial opportunism, financial reporting manipulation, insider trading, and a rise in both investment and operational inefficiencies. This study not only contributes to the ongoing debate about the implications of staggered boards but also sheds new light on the associated agency costs.

How does asset redeployability affect stock price crash risk?

AbstractHow does a firm's asset redeployability affect its future stock price crash risk? Asset redeployability, which refers to the ease of selling corporate assets, allows managers to opportunistically exploit asset transactions to manage earnings to hoard bad news, thereby increasing future crash risk. Using a large sample of US firms, we find that firms with higher asset redeployability are more likely to experience a future stock price crash. We further find that this positive association is stronger for firms experiencing greater internal and external pressure to manage earnings. Our study highlights that relying on redeployable assets to orchestrate earnings undermines shareholders' interests, particularly when internal and external pressures incentivize upward earnings management.

Agricultural commodity market response to Russia's withdrawal from the grain deal

AbstractThis paper assesses the response of agricultural commodity markets to Russia's withdrawal from the Black Sea Grain Initiative (BSGI). Employing daily commodity‐level data and event study methods, we analyse the impact on seven agricultural commodities and four key market metrics, including futures prices, historical and implied volatility, and speculative pressure. Our findings show a statistically insignificant increase of 1.1% in agricultural futures prices within the first seven trading days following the BSGI termination. In the following days, futures prices began to decline, eventually returning to levels below those observed before the withdrawal, a pattern further underscored by our implied volatility analysis. While there is no evidence of heightened speculation, we find some evidence for treatment differences across agricultural commodities. These findings suggest that traders did not believe in the likelihood of a blockade of Black Sea grain shipments.

Navigating the Dynamics of Brent Crude Oil Prices: Factors, Trends, and Insights

Accurate Brent Crude Oil price prediction is essential for informed decision-making and risk control in the global energy market. This paper examines the dynamics of Brent Crude Oil prices over the previous 35 years, utilizing predictive modelling techniques to project future prices and analyzing historical trends and relationships with the GDPs of major economies. The statistical methods used in this paper include the moving average and Pearson correlation coefficient. While correlation studies show significant positive links between Brent Crude Oil prices and the GDPs of major economies like China, Saudi Arabia, Europe, Russia, and the United States, historical data analysis reveals large price volatility. Highly accurate price forecasting using LSTM (Long Short-Term Memory) modelling approaches provides detailed risk management and decision-making information in the global energy market. The application of the Neural Network and the ARIMA model shows an increase in the price of Brent crude oil in the next year, 2024. Identifying the importance of the Brent crude oil price forecast and its effect on the international market is highly needed. This paper thus might help to increase economic growth. These results highlight the importance of advanced modelling techniques and economic indicators in managing oil price changes and help make informed decisions in the energy industry.

An Investigation of the Co-Movement between Spot and Futures Prices for Chinese Agricultural Commodities

We employed a non-parametric causality test based on Singular Spectrum Analysis (SSA) and used the Vector Error Correction Model (VECM) and Information Share Model (IS) to measure the relationship between the futures and spot prices for seven major agricultural commodities in China from 2009 to 2017. We found that the agricultural futures market has potential leading information in price discovery. The results of an Impulse Response Function (IRF) analysis also showed that the spot prices react to shocks from the future market and have a lasting impact. This confirms our findings reported for the causality test and information share analysis.

The role of biodiversity risk in stock price crashes

This study examines whether and how firms’ biodiversity risk exposure affects future stock price crash risk. By analyzing data from US listed companies, we find a significant and positive association between biodiversity risk exposure and future crash risk. The additional analysis indicates that the biodiversity-related transition risks caused by regulations still significantly increase future crash risk, but management's sentiment toward biodiversity-related issues does not significantly affect future crash risk.

Stock Market Prediction Analysis using Deep Learning

The abstract provides an overview of a proposed approach for stock market value prediction using deep learning techniques, specifically focusing on artificial neural networks (ANN) and long-short-term memory (LSTM) algorithms. The stock market is a dynamic environment influenced by various factors, such as economic conditions and market sentiment, making accurate prediction challenging yet crucial for investors.In this study, the objective is to leverage deep learning methodologies to enhance the accuracy and robustness of stock market predictions compared to traditional methods. By harnessingthe Python programming language, the research aims to develop a model capable of analyzing historical stock data and generating forecasts of future stock prices

Audit committee personnel training and stock price crash risk

This study examines whether audit committee personnel training decreases future stock price crash risk. Using a hand-collected Korean sample comprising 2,378 firm-year observations from 2018 to 2022, we find that audit committee personnel training reduces future stock price crash risk by lowering real earnings management and curbing overinvestment. Additionally, our findings are more pronounced for firms with high information reporting opacity, high information asymmetry, audit committee chairs with accounting and financial expertise, and firms audited by high-quality auditors. The findings shed light on the value of training to audit committee personnel.

An EEMD‐LSTM, SVR, and BP decomposition ensemble model for steel future prices forecasting

AbstractThe forecasting of steel futures prices is important for the steel futures market, even for the steel industry. We propose a decomposition ensemble model that incorporates the Ensemble Empirical Mode Decomposition (EEMD), Long Short‐Term Memory (LSTM), Support Vector Regression (SVR), and Back Propagation (BP) neural network to forecast steel futures prices. The forecasting procedures are as follows: (1) The price data are initially decomposed into several relatively independent Intrinsic Mode Functions (IMFs) and a residue using EEMD. (2) The IMFs are then reconstructed as components representing short‐term, medium‐term, and long‐term frequencies via fine‐to‐coarse. (3) LSTM, SVR, and BP neural network are utilized to forecast the short‐term, medium‐term, and long‐term reconstructed components, respectively. (4) The prediction results for each component are simply added to the final prediction results. The accuracy of the proposed model is compared with several benchmark models by experiments and evaluated by some prediction evaluation indexes. The experimental results show that our model outperforms other models in terms of forecast accuracy, confirming its strong predictive capabilities. This study provides some suggestions for investment and decision making by participants in the steel futures market. It may promote the smooth operation of the steel futures market and shed some light on the operation of the steel industry.

Investigating the influence of energy dynamics on price forecasts in tropical and traditional agricultural futures markets

ABSTRACT This research aims to investigate the intricate dynamics between energy futures prices and both tropical and traditional commodities futures prices, with a particular focus on the critical role of oil futures prices in predicting the trajectory of agricultural futures prices. The study employs the Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC-MGARCH) model to analyse the interplay among oil and agricultural futures price series over 2008:01–2021:06. Additionally, it incorporates the innovative Spatio-temporal Information Recombination Hypergraph Neural Network (STIR-HGNN) model to highlight the differences in how energy futures prices predict tropical and traditional agricultural futures prices. The findings reveal numerous connections between oil prices and both tropical and traditional agricultural futures prices, underscoring the significant role of oil prices in forecasting agricultural futures price movements. The empirical insights from this study provide valuable guidance for futures market participants, encouraging them to use these findings to refine and optimize their market strategies, thus enhancing their ability to navigate and capitalize on the complexities of these interconnected markets.

APPLYING OF RANDOM FOREST AND SUPPORT VECTOR MACHINE IN PREDICTING PRICES OF URANIUM COMPANIES

Due to the war in Ukraine and restrictions on the export of hydrocarbons from Russia by the European countries, uranium companies are again becoming an interesting sector in terms of investment. Consequently, it is important for investors to have accurate forecasts of uranium sector. This article applies machine learning algorithms such as the Random Forests and the Support Vector Machine to predict future URA ETF prices for the next five periods. The study was conducted using data on the ETF Global X Uranium for the period from 08/11/2010 to 31/05/2023 was obtained from investing.com. The data contains information about the stock such as High, Low, Open, Close, Adjacent close and Volume and several well-known technical indicators. The research showed that both the Random Forest and the Support Vector Machine forecast prices with less bias than the classic ARIMA model. The Random Forest algorithm forecasted prices with a constant level of bias over the forecasting period, while the error of the forecasts calculated by the Support Vector Machine algorithm for the first three periods was the lowest compared to the rest of the analyzed models. Research showed that the Random Forest algorithm and the Support Vector Machine can be used to make correct predictions for uranium sector.

Deductibles and health care utilization: An experiment on the role of forward-looking behavior

We investigate the effects of nonlinear deductible contracts on health utilization behavior by using a laboratory experiment in which we can control the likelihood of hitting the deductible. We also evaluate the effect of subjects receiving regular information updates on their remaining deductible. Our results show that varying the future price has a significant effect on health care consumption. At an individual level, we identify and richly characterize heterogeneity. We find fully forward-looking, fully myopic, as well as mixed types after controlling for risk preferences. We show that there is a substantial welfare loss due to a lack in forward-looking behavior. The distribution of the welfare loss are characterized and differ sharply according to the model of risk preferences adopted for normative evaluation. Potential drivers of welfare effects are investigated and have a tendency to differ across risk preference models.

Do OPEC+ policies help predict the oil price: A novel news-based predictor

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Evolving Efficiency of Cryptocurrency Market: Evidence from Leading Cryptocurrencies

The article investigated the efficiency of the cryptocurrency market, with a focus on Bitcoin (BTC), Ethereum (ETH), Tether (USDT), and Binance Coin (BNB-USD), based on their substantial market capitalization. For this purpose, we observed the daily closing prices spanning from January 2018 to December 2023 and employed a set of robust tests, including the generalized spectral test, automatic portmanteau test, and automatic variance ratio test. The findings of the study reveal a random pattern in price fluctuations, indicating weak form efficiency. Furthermore, we adopted the rolling window approach to investigate whether market efficiency is dynamic or static over time. The empirical result illustrated that the crypto market efficiency remains static over time, except for USDT. In conclusion, the overall empirical results support the notion of the random walk hypothesis, indicating that past price movements offer no predictive insight into future prices. These findings have significant implications for investors, emphasizing the lack of predictive insight from past price movements. Policymakers are urged to establish a robust framework for market integrity and reliable price discovery.

Federal judge ideology, securities class action litigation, and stock price crash risk

AbstractThis study investigates whether and how federal judge ideology affects firm‐specific stock price crash risk. Using a comprehensive sample of US firms, we find a decline in the likelihood of future stock price crashes for firms headquartered in more liberal circuits. In identifying potential mechanisms, we show that liberal judge ideology reduces information opacity, risk‐taking behaviours and overinvestment, and thus curbs stock price crash risk. Furthermore, the curbing effect is more salient for firms with poor monitoring quality and those in low social capital areas. Overall, this study elucidates how federal judge ideology affects capital markets.

Productivity Shocks of Dominant Companies and Local Housing Markets

We extend the literature on the influence of firm-level characteristics on housing markets by exploring the association between the labor productivity shocks of dominant firms and local housing prices. Using a sample of all U.S. firms from COMPUSTAT during 1980-2017, we find that the aggregate shocks of labor productivity of dominant firms at the metropolitan statistical area (MSA)-level explain for a significant portion of the local housing price changes in MSAs while controlling for other housing price determinants. About a year or more is required for the shocks to propagate through the local housing markets, which make them a viable predictor of future housing price. The productivity shock – housing price relation is stronger in areas that have more concentrated high-tech dominant firms or where dominant firms have closer links to their local non-dominant industry peers. Shocks are also more influential during economic expansion than economic contraction. Furthermore, the relation also exists at the zipcode level but the shocks propagate faster than at the MSA-level. The findings provide helpful insights for real estate practitioners and policymakers, especially in areas with a higher concentration of large companies.

Techno-economic study of chimneyless electric arc furnace plants for the coproduction of steel and of electricity, hydrogen, or methanol

Electric arc furnace (EAF) is the most common technology for steel production from steel scrap. Although the input energy is mostly constituted by electricity, significant amounts of carbon dioxide are emitted with the exhaust gases, most of which are classifiable as process-related. The main goal of this study is to perform a techno-economic analysis of chimneyless electric arc furnace plants, fed by either scrap or direct reduced iron (DRI), and able to coproduce steel as well as electricity, hydrogen, or methanol. Several plant configurations are investigated, featuring different combinations of oxy-postcombustion, carbon capture, carbon monoxide-rich gas recovery, hydrogen or syngas production by high-temperature electrolysis or coelectrolysis, and methanol synthesis. These configurations are also characterized by decreased false air leakage and by heat recovery for steam production. Results show that all cases allow achieving a substantial reduction of direct carbon dioxide emissions, close to 99% compared to the unabated conditions. From an economic perspective, in a long-term scenario, the internal rate of return is always above 8%, and up to 73% for the DRI-fed case. However, in a short-term scenario, only cases with sole power production are economically viable. Hydrogen and methanol are competitive with market prices only for low electricity costs. In a higher electricity cost scenario, the case of carbon capture and storage is more competitive than the case of carbon capture and utilization. With an electricity cost of 100 €/MWh, a steel premium of 10–40 €/t allows to reach economic feasibility if methanol or hydrogen selling prices are in line with current market conditions. In general, the configurations with DRI-fed furnaces obtain more favorable economic performance than scrap-fed ones. The competitiveness of sole electricity, hydrogen or methanol production configurations depends on the case study and on the future market prices.

Exploring the impact of cluster bagging-based weather attention on oil market volatility under the GARCH-MIDAS framework

ABSTRACT Many studies have confirmed the impact of weather and Google Trends attention on oil markets. However, research examining the predictive influence of weather attention on oil price volatility is lacking. To fill this research gap, we have developed an extended GARCH-MIDAS forecasting model based on TBATS and clustering bagging. We utilize weather-related keywords from Google Trends to obtain weather attention data and assess its predictive capability. By decomposing the data using TBATS and combining the decomposed trend and seasonal components with the remaining portion through clustering bagging and arithmetic mean aggregation, we construct a series of GARCH-MIDAS models, and specifically focus on the clustering bagging-based TBATS decomposition GARCH-MIDAS model. Empirical analysis demonstrates a significant impact of weather attention magnitude on in-sample testing, with the clustering bagging-based TBATS model outperforming other models in out-of-sample forecasting. Our findings provide new evidence that incorporating weather attention information into a GARCH-MIDAS model can improve prediction results and highlight the influence of weather attention on future oil price fluctuations. Finally, robustness analysis confirms the robustness of the proposed model. The proposed extended GARCH-MIDAS model based on TBATS and clustering bagging offers new insights into oil market volatility prediction.

Comparative Analysis of Multiple – Linear Regression Algorithm with Random Forest Regression for Prediction of House Plot Prices

Humans basically have a basic need to have a place to live, which can be a house or shelter. Along with the rapid population growth in Indonesia, which continues to increase every year, many people do not have or have a decent place to live. Therefore, careful planning is needed so that every family can have a decent home. One very important aspect in planning investment in the form of property is predicting future house prices. One approach that can be used is to use a Random Forest and Multiple Linear Regression algorithm, which is an algorithm from Machine Learning. There are several factors that can influence the price of a house, including land area, building area, number of bedrooms, bathrooms and garage. In this research, multiple linear regression and random forest regression methods were chosen. The aim of this research is to find the best prediction results between the two methods. To achieve accurate predictions, research was carried out repeatedly by dividing the dataset into 80% for training and 20% for testing. The research results show that the random forest regression algorithm provides the best results, with an accuracy of 81.6%.