Future Market Prices Research Articles

Research on Stock Price Prediction by LSTM

The stock market’s influence on the contemporary economy is profound, with its fluctuations directing the investment strategies of traders and investors. The intricacies and volatility of the market have consistently drawn the interest of data scientists. Forecasting stock prices is a complex task, influenced by various factors, ranging from individual company financials to global economic indicators. The effectiveness of Long Short-Term Memory (LSTM) models in predicting stock prices has been demonstrated in multiple studies and practical applications. They can capture non-linear trends and seasonal patterns in stock price movements, providing valuable insights for investors and traders. For those interested in implementing LSTM models for stock price prediction, numerous resources are available, including tutorials, research papers, and platforms that offer pre-trained models or APIs for financial forecasting. This study aims to develop a deep learning framework using LSTM networks to identify underlying trends within the data. The objective is to predict future stock market price movements by leveraging LSTM’s ability to model time series data effectively. By tapping into the predictive power of LSTM, this model can seek to provide investors with a sophisticated tool for making well-informed investment decisions, enhancing the precision of stock price predictions.

Asymmetric spot‐futures prices adjustments in Quebec grain markets

AbstractAsymmetric price transmission has been the subject of many studies in agricultural economics, but few has been said on Quebec grain market. This study uses threshold cointegration models to examine the dynamic relationship between Quebec spot and futures prices. Estimation results with daily, weekly, and monthly data from September 1994 to May 2022 are three‐fold. First, the main point to keep from cointegration tests is that futures prices and Quebec local grain prices are integrated but estimations with daily data are consistent to highlight a nonlinear long‐run relationship. Second, results indicate overall positive asymmetric for daily and weekly prices transmissions from futures to spot market. Third, the positive asymmetric prices transmissions shed light on the oligopoly structure of Quebec grain market and inefficiencies in the pricing mechanism which favor local grain sellers. Results with different data frequencies show that aggregated monthly prices data compared to more disaggregated data such as daily and weekly prices may lead to different results. Results imply that policymakers and market stakeholders could facilitate increased grain market competition. Grain users like Quebec hog producers need to better understand additional tools for their management of price risk like futures market and real time prices movement monitoring.

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

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

Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices

Surrogate-based modeling and sensitivity analysis of future European electricity spot market prices

Imagine and Imitate: Cost-Effective Bidding under Partially Observable Price Landscapes

Real-time bidding has become a major means for online advertisement exchange. The goal of a real-time bidding strategy is to maximize the benefits for stakeholders, e.g., click-through rates or conversion rates. However, in practise, the optimal bidding strategy for real-time bidding is constrained by at least three aspects: cost-effectiveness, the dynamic nature of market prices, and the issue of missing bidding values. To address these challenges, we propose Imagine and Imitate Bidding (IIBidder), which includes Strategy Imitation and Imagination modules, to generate cost-effective bidding strategies under partially observable price landscapes. Experimental results on the iPinYou and YOYI datasets demonstrate that IIBidder reduces investment costs, optimizes bidding strategies, and improves future market price predictions.

Study on the profitability of agricultural enterprises in Ukraine during the russian military invasion of Ukraine

This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income.

Electricity price forecasting using quantile regression averaging with nonconvex regularization

AbstractElectricity price forecasting (EPF) is an emergent research domain that focuses on forecasting the future electricity market price both deterministically and probabilistically. EPF has attracted enormous interest from both practitioners and scholars since the deregulation of the power market and wide applications of renewable energy sources, such as wind and solar energy. However, forecasting the electricity price accurately and efficiently is an extremely challenging task because of its high volatility, randomness, and fluctuation. Although quantile regression averaging (QRA) has been demonstrated to be efficacious in probabilistic EPF since the global energy forecasting competition in 2014 (GEFCom2014), it is sensitive to nuisance variables especially when the number of variables is large. The forecasting accuracy will be negatively affected by these nuisance variables. To address these challenges, this study investigates a nonconvex regularized QRA in probabilistic forecasting. Two types of nonconvex regularized QRA select the important inputs obtained from point forecasting to obtain more accurate forecasting outcomes. To demonstrate the effectiveness of the proposed EPF model, two real datasets from the European power market are considered.

Planiranje i rad elektroenergetskog sistema u uslovima velike proizvodnje električne energije iz solarnih elektrana

Drop of solar panels prices at the world marked has caused construction of large number of solar power plants in relatively short time period. That is the reason why this type of renewable electricity sources is constructed far intensively than the other types of power plants. Capacities of solar power plants vary from those of several kW that are connected to distribution low voltage grid, to large ones, the capacities of which are order of magnitude of hundreds of MW, connected to transmission system. Expansion of solar power plants construction in Europe and world changes current electricity production profile opening questions of balancing of such production, its evacuation and future price. Future market price of electricity produced in solar power plants is the issue the investors are the most interested in and which is the most difficult to answer. On the other side, issue is raised about power system operation in conditions of large and variable solar power plants production. The paper considers and summarizes a number of issues related to power system operation in Southeast Europe in case of large electricity production in solar power plants.

Price discovery in Chinese PVC futures and spot markets: Impacts of COVID-19 and benchmark analysis

Despite being a minor futures category, Polyvinyl chloride (PVC) futures have emerging as a vital element in energy and chemical futures domain. Employing three benchmark models component share (CS), information share (IS), and information leadership share (ILS), this study explores the price discovery function of Chinese PVC futures and spot markets. It assesses whether PVC futures have matured into an effective hedging tool and reference point for spot markets, and also examines the impact of the COVID-19 pandemic on this price discovery relationship. Empirical analysis reveals that the futures market has become the primary site for price discovery in the Chinese PVC market. All the models consistently demonstrate a mature price discovery function in PVC futures, providing risk mitigation tools for industry players. However, post-pandemic dynamics indicate that price discovery in PVC markets primarily occurs within the spot market. This suggests that compared to the futures market, the PVC spot market is able to respond more quickly to the strong signals of industrial recovery after the end of the pandemic. The feedback and pricing efficiency of the PVC futures market in response to new market information are also influenced. Furthermore, our study offers better anticipation of future market prices.

Do spot market auction data help price discovery?

This paper contributes to the price discovery literature by establishing, for the first time, the role of commodity spot market auction data. Using the New Zealand whole milk powder market as an example, we show that auction-level data explain the price discovery dynamics above and beyond determinants previously identified as being relevant to spot and futures market price formation. In particular, the price discovery of the futures market rises with the volume of dairy products traded at the auction, signaling that the volume auctioned induces a change in the trading strategies of futures market participants. The whole milk powder discovery process is found to primarily take place in the spot market, which aligns well with the auction predating the introduction of the futures market, its higher volume, and lower trading costs.

AGROPEDIA - One Stop for all Agricultural Needs

In India, more than half of the workforce is employed in agriculture and related sectors, which account for around 17 percent of the country's Gross Value Added (GVA). In India, 60–70% of the workforce is employed in agriculture directly or indirectly. Given the importance of food to human existence, it is always difficult to produce food profitably, efficiently, and with minimum land usage. The project's approach offers farmers a one-stop shop for all of their agricultural requirements. It uses a crop forecast system, a weather analysis model, information about government programmes, a system for renting equipment, and a system for analysing market prices. The system for renting out tools that has been developed here gives users the option of lending out their unused agricultural equipment in order to generate passive money from it. The farmer may make a claim for crop insurance or during crop losses with the aid of proper understanding of these programmes. The farmers are informed by our model, which is now in use, about potential future market price patterns.

The Transmission Effect Test of China’s Rotation Mechanism on the Cotton Reserve Market

As an important strategic material in China, cotton policy has positive significance in stabilizing the safety of domestic cotton, ensuring cotton income, and stabilizing cotton output. At the same time, affected by the future market price, the cotton industry supply chain cost management is particularly important. Based on the data of China’s cotton price, consumption, yield, and wheeling-in and wheeling-out from 1978 to 2016, the simultaneous equation model is used to empirically analyze the transmission effect of the rotation mechanism of reserve cotton (RMRC) on cotton supply and demand. Studies have shown that China’s cotton reserve policy and the RMRC play a positive role in balancing the supply and demand of the domestic cotton market and preventing excessive fluctuations in cotton prices. The transmission effect of the RMRC on domestic cotton yield and consumption is significant and there are certain differences. The transmission effect of domestic cotton yield is stronger than the transmission effect on domestic cotton consumption. The difference is owing to the transfer path of the RMRC being different. We should improve the regulation and control effect of the RMRC on cotton demand, realize the normalization of the RMRC, and clarify the policy objectives of the RMRC.

Crop tracker - A web application to sell or buy crops and predict crop price using machine learning

In India, Number of mediators for selling the crops were raised, so that the crop price was increased. Mediators get the crops from the farmers with less cost and sell it to high price. Farmers does not get full independence to choose the price and customers. Most of the states in India, buying and selling of crops were done through the mediators. According to several surveys, Mediators take more than 75% of the Farmer's profit. Farmers have the rights to get the full profit which was gained by their crops. Our Proposed System consists of the progressive web application which will help the farmers and retailers to Buy or Sell their Crops. In our system, we also predicting the price of the crops to know the current and future market price. This will help the farmers to predict which crops will give more profit in the future and also, they can set the on fair price to sell their crops. Our System helps to eliminate the mediators and help the farmers to get the full profit. This System also aims to provide uninterrupted location service for Transportation and 24x7 Customer support. For delivering the crops, offline maps are available in the driver portal by which they can see the location even in poor signal areas. Many Farmers and suppliers do not know English language. So, we providing the system with more than 18 language support. This application has the ability to predict the crop price and sell the crops to the customer.

Multidimensional Risk-Based Real Options Valuation for Low-Carbon Cogeneration Pathways

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogeneration configurations using combined-cycle gas turbine (CCGT) integrated with solar photovoltaics (PV) and carbon capture and storage (CCS) technologies were assessed. A combined price risk was initially estimated by transforming the given price factors representing maximum covariance using principal component analysis (PCA). The trend and volatilities in the major principal component scores (the combined price risk indicator) were modelled using the geometric Brownian motion stochastic process, whose parameters were determined and then used to perform time-series simulation and generate multiple realisations of the principal component. A back transformation was then applied to obtain the simulated values representing future uncertainties in the price factors. The effect of price risk and uncertainties were subsequently evaluated using a recombining binomial lattice model for real options analysis (ROA). There were financial gains when PV was mixed with conventional natural gas-fired technology. Investment in cogeneration configurations with (a) 25% PV share provided a 53% gain in the extended net present value (e–NPV); and (b) 50% PV share provided a 124% e–NPV gain when compared to the baseline cogeneration system with no PV shares. The analyses demonstrate that PV technology is a better hedging option than CCS against future market uncertainty and price volatility.

Influence of Increasing Renewable Power Penetration on the Long-Term Iberian Electricity Market Prices

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study’s findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation.

Remote sensing monitoring of planting area of Panax notoginseng: A case study of Kaiyuan City in Honghe Prefecture

Panax notoginseng is second only to ginseng Panax notoginseng in the Chinese traditional medicine industry. It has extremely high economic value and medicinal value. However, due to the unsustainable planting characteristics of P. notoginseng. The original planting area of P. notoginseng has been exhausted, so the planting area of P. notoginseng has gradually expanded to other areas, and the planting area of P. notoginseng is also changing. Based on Sentinel-2 and Landsat 8 OLI remote sensing image data from 2018 to 2021, the study used Google Earth engine and Random Forest classification to analyze the spatial distribution information of P. notoginseng planting areas in Kaiyuan City, Honghe Prefecture. The results show that, based on Sentinel-2 data, Landsat 8 OLI data, and other multisource data, comprehensively considering the image spectral characteristics and habitat characteristics of P. notoginseng, and applying the Random Forest classification method. The spatial distribution information of P. notoginseng planting areas can be quickly extracted. After calculation, the transfer matrix indicates that the accuracy of the results is high and can meet the requirements of research and application. According to the analysis of the results, the cultivated area of P. notoginseng showed no significant change in 2018 and 2019. It shows an increasing trend during this period. In 2020, the cultivated area of P. notoginseng decreased slightly. While in 2021, the cultivated area of P. notoginseng increased sharply, and the cultivated area of P. notoginseng was more than twice that of the previous year. However, the price of P. notoginseng has shown a trend of decline year by year. By monitoring the planting area of P. notoginseng and analyzing the relationship between the planting area and the price of P. notoginseng, we can predict the future market price of P. notoginseng and plan the rational planting of P. notoginseng.

Predicting the unpredictable: New experimental evidence on forecasting random walks

Predicting the unpredictable: New experimental evidence on forecasting random walks

Power-to-heat for the industrial sector: Techno-economic assessment of a molten salt-based solution

• A molten salts based power-to-heat solution for the industrial sector is proposed. • Levelized cost of heat of 55 €/MWh th is attained in cheap electricity markets. • Operational cost savings of 0.5 to 3 M€/y are obtained against electric boilers. • Oversized systems are preferred to maximize the provided flexibility. • More volatile electricity prices could reduce the levelized cost of heat by 20 % The industrial sector is a major source of wealth, producing about one-quarter of the global gross product. However, industry is also a major emitter of CO 2 and it represents a key challenge towards achieving the worldwide CO 2 emission reduction targets. Nowadays, about 22 % of the overall energy demand is heating for the industrial sector, generating about 40 % of the global CO 2 emissions. Solutions to decarbonize the industrial sector are needed. This work presents the techno-economic assessment of a molten salts based power-to-heat solution aiming at decarbonizing the industrial sector, requiring medium temperature heat (150–450 °C). The system is studied under different electric markets considering electricity prices of 2021, future electricity market price modifications are assessed via sensitivity analyses. Dispatch strategies and system sizing are identified to ensure optimal techno-economic performance. The main performance indicators investigated are the levelized cost of heat, the operational expenditure, and the attainable savings with respect to traditional solutions. The system scalability and sizing criteria are investigated accounting for different typical industrial load profile curves and nominal thermal power demands. The results highlight that nominal levelized cost of heat as low as 55 €/MWh th are attainable in cheap electricity markets (i.e. Denmark) and 80 €/MWh th in more expensive scenarios (i.e. United Kingdom). The proposed system can provide operational cost savings between 0.5 and 3 M€/y against traditional non-flexible electric boilers and even wider benefits if fossil fuels-based boilers are considered. Future electricity markets, subjected to higher renewable penetration causing cheaper average electricity prices and higher price volatility, could lead to further reductions of the levelized cost of heat of more than 20 % achieving values below 45 €/MWh th in markets such as Denmark. This study sets the ground for further power-to-heat techno-economic investigations addressing different industrial sectors and identifies main system design strategies.

Research on the Application of Time Convolution Series in Futures Price Forecasting

Aiming at the problem of anomaly detection of time series data with unbalanced distribution among classes, a detection method based on depth convolution neural network is proposed. With the increasing trading scale of the futures market, it covers more and more economic and financial fields, and the volatility of the futures market is more and more intense, which constantly presents many complex phenomena that cannot be explained by other classical financial theories. Investors predict stock prices based on statistical analysis or simple machine learning methods, but because the stock market is a complex nonlinear dynamic system, these methods have huge limitations. By using the existing data and the chart obtained by the game master software for prediction analysis, the advantages of several technical indicators have been effectively complemented, and the accuracy of prediction has been improved. On the basis of the concept of attribute set and attribute measure, aiming at the problem of pattern recognition, this paper proposes a recognition method, establishes an attribute mathematical model, and combines it with other methods, which has been gradually applied to artificial intelligence, neural network and other fields. It has been successfully applied in the prediction of futures price trend. Building a futures price forecasting model to reveal the inherent law implied in futures market price index and show its evolution mechanism can improve the control ability to deal with economic and financial risks, and provide an objective and rigorous basis for regulatory authorities to formulate relevant policies, which is also the significance of this study.

An online algorithm for the inventory retrieval problem with an uncertain selling duration, uncertain prices, and price-dependent demands

We study a multi-period inventory retrieval problem with uncertain selling duration, uncertain future prices, and price-dependent demands. At the beginning of a finite selling horizon, a fixed amount of inventory is available for sale in future periods. In each period, the decision-maker observes the current market price and makes the retrieval decision, and only at the beginning of the last period, he knows that it is the last period. The objective is to maximize the expected total revenue and the difficulty stems from the lack of information on probability distributions of both the selling duration and future market prices, which also causes uncertainty in the random demands. Proposing an online algorithm ALG-IR to solve the problem based on competitive analysis theory, we derive its constant competitive ratio, which is optimal under certain conditions, showing the robust performance of the algorithm. We further indicate that, when applying other price-dependent demand models or considering the impact of reference prices on the current demand, the adjusted algorithm ALG-IR-M retains all the theoretical properties. When considering the holding cost of inventory, a similar algorithm ALG-IR-H is proposed to solve the problem. Through numerical studies, we show the algorithm achieves good performance in both optimality and robustness, especially when the market is not over-supplied or the selling duration will not suddenly end at an early period. • We study the multi-period inventory retrieval problem with an uncertain selling duration, uncertain prices, and price-dependent demands. • No replenishment is allowed and the inventory is given as a limited resource. • We apply the duality theory to the analysis of the (clairvoyant) online optimal result. • We develop an online algorithm that achieves the optimal competitive ratio under some conditions and is effective in solving the problem in general. • We provide a novel and simple approach that helps sale managers maximize the expected total revenue with limited inventory resources.