Future Prices Research Articles

Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends

Decentralized Storage Cryptocurrencies: An Innovative Network-Based Model for Identifying Effective Entities and Forecasting Future Price Trends

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

Europe is transitioning towards a sustainable energy economy. Planning these future power and energy systems is multifaceted and complex, requiring long-term projections of future electricity market dynamics. The prognostication of future prices is crucial for power system planning, as these prices will drive investments and impact incentives to build and improve new systems. The price of electricity in different regions impacts transmission investment. Energy systems models have high-dimensional output and are expensive to evaluate, leading to unique challenges in managing the uncertainty in model assumptions. This study presents a novel methodology for determining which cost assumptions are most impactful on the output of a detailed model of the European energy system. Because this model is computationally intensive, only a small number of samples are available to us. A multiple-output support vector regression is used as a surrogate of predicted electricity prices timeseries. We find that solar and wind costs are the primary drivers of electricity prices, but natural gas costs generally drive peak electricity prices and those far in the future.

How Do Futures Contracts on Agricultural Commodities Respond to El Nino Weather Events?

This study examines how futures contracts on wheat, soybeans, corn and sugarcane respond to El Nino weather events over the period March 2010 to April 2024. We use the monthly ENSO forecast data from the Climate Prediction Center of the National Oceanic and Atmospheric Administration to benchmark the start of an El Nino forecast, which we define as the first reporting month in which a current or forecasted El Nino probability exceeds 50%. We examine two periods around each event: the six months leading up to the El Nino forecast, and the six months following the event date. We find that El Nino weather events have significant effects on commodity futures prices. Wheat, soybean and corn futures prices (expressed in dollars) generally decline after an El Nino forecast, consistent with improved harvests due to an increase in rain. We also find that El Nino events are associated with increased volatility in commodity futures markets. Our study can inform hedgers and speculators who participate in the agricultural derivatives markets, as well as policymakers who may be interested in initiating or adjusting any subsidies granted to farmers or food consumers, as compensation for price pressures.

Impact of accounting conservatism and corporate governance on stock price breakdown in firms listed on the Palestine Stock Exchange

PurposeThis paper uses test panel data for the biggest companies listed on the boards of directors of the Palestine Stock Exchange from 2016 to 2022 and will focus on the relationship between the corporate governance index, accounting conservatism, and the comprehensive index of corporate governance.Design/methodology/approachThe relationship between corporate governance and accounting conservatism is experimentally investigated for its impact on the likelihood of stock price breakdown and decline among companies listed on the Palestine Stock Exchange between 2016 and 2022, using a mixed utilities approach.FindingsThe findings demonstrated the adverse correlation between corporate governance, accounting conservatism, and stock prices. Higher levels of corporate governance can effectively reduce the likelihood of future stock price increases, while conservative accounting policies can effectively prevent stock price collapses in these listed companies. Higher levels of corporate governance can greatly lessen the detrimental effect of accounting conservatism on the likelihood of future stock price breakdowns and declines. Both accounting conservatism and corporate governance have substitution effects in decreasing the danger of stock price collapse.Research limitations/implicationsThe limitations of the current research are that higher levels of corporate governance can significantly reduce the harmful effect of accounting conservatism on the probability of stock price breakdown and decline in the future on the study sample used, and these results cannot be generalized to all company stocks that were excluded in this study. The last research limitation is that the sample size of this study is somewhat small, and therefore the effects of the results cannot be used on all unlisted companies, and they cannot be generalized to all of these companies except only to companies listed on the Palestine Stock Exchange.Practical implicationsOur findings have interesting managerial and policy implications. Listed firms should first strengthen external audit oversight, improve the method of disclosing accounting information, and improve the system architecture to raise the level of accounting conservatism. Moreover, it is imperative to enhance and improve the ownership structure of publicly traded firms, construct a robust mechanism for replacing shareholders, fortify the duties of the board of directors, proficiently fulfil the role of independent directors, and develop and refine the internal and external framework for corporate governance.Originality/valueThis study provides insights about reducing the probability of a stock market breakdown and collapse from two sides: enhancing corporate governance, improving accounting conservatism, enhancing the reliability and integrity of disclosure, and growing the number of sustainable disclosures. These suggestions can also be used as a template for Palestine's capital market's gradual and sustainable expansion.

A study of European wheat price series forecasting based on multiple variants of Holt-Winters and LSTM models

Given the importance of wheat in global food security and agricultural economy, accurate price forecasts are of significant practical significance for growers, the food chain, and government macro-control. Four different time series forecasting models were used in this study, including the traditional Holt-Winters three-parameter exponential smoothing method and three improved models based on the Long Short-Term Memory Network (LSTM): the CNN-LSTM, the GRU, and the VMD-LSTM. By comparing the forecasting effects of these models under the same training and test sets, this study aims to select the best models using the best model for predicting European wheat prices from 2010 to 2023 after training. The results show that the CNN-LSTM model performs the best in predicting the European wheat price series, and the future wheat price will show a slow downward trend, and the CNN-LSTM model combines the feature extraction ability of convolutional neural network and the long-term dependency learning ability of LSTM, which can effectively capture the fluctuation characteristics of the price series and the trend characteristics. The GRU model also shows a better prediction ability, especially in dealing with long-term dependencies. in dealing with long-term dependencies. However, the VMD-LSTM model has certain shortcomings in its prediction effect when facing time series data with large fluctuations in the later period. In addition, the Holt-Winters model predicts that after a period of slow decline, wheat prices may rebound, which may be related to the adjustment of market supply and demand and changes in external factors.

Product Market Competition and Stock Price Crash Risk: Evidence from China

Synopsis The research problem This study analyzes the relationship between product market competition (PMC) and stock price crash risk in China, especially for firms with weak corporate governance and a lower-quality information environment. Motivation Emerging capital markets like China have less-developed financial systems and institutions compared to developed capital markets like Europe and the United States, making them more susceptible to stock price crash risk. The impact of product market competition (PMC) on corporate decision-making has attracted considerable attention from scholars, especially given the increasingly intense and complex strategic competition in global markets and uncertainty in the world today. However, the effect of PMC on a firm’s stock price crash risk has not been thoroughly explored. Therefore, this study investigated how PMC affects managers’ tendency to hoard bad news and how this behavior contributes to the risk of stock price crashes. The test hypotheses We tested two competing hypotheses: PMC is negatively correlated with crash risk, and PMC is positively correlated with crash risk. Target population This study should be of interest to stakeholders, including firm managers, practitioners, regulatory authorities, policymakers, and investors. Adopted methodology This study employed ordinary least squares (OLS), difference-in-differences analysis (DID), and path analysis. Analysis Using a sample of Chinese A-share listed manufacturing firms from 2000 to 2021, this study found that competitive pressure from the product market encouraged managers to hide bad news, thereby increasing future stock price crash risk. Moreover, we performed additional tests to examine how PMC affected stock price crash risk. Findings We found robust evidence that PMC significantly increases stock price crash risk. Further tests showed that the effect of PMC on crash risk is more pronounced for firms with weak corporate governance and a lower-quality information environment. We also provide evidence that firms in highly competitive industries tend to disclose fewer negative words in the management discussion and analysis section of the annual report and fewer risk factors in the internal control report. Moreover, we show that operational risk is the underlying driver through which PMC affects crash risk.

Multi-objective optimization of forest ecosystem services under uncertainty

Forest ecosystems deliver multiple services crucial for human wellbeing and welfare, with complex relationships among forest ecosystem services (FESs). Implementing sustainable forest management (SFM) policy requires insights into trade-offs, conflicts and synergies among key FESs, necessitating decision-support tools for multiple objectives. However, uncertainty in key parameters adds complexity. We formulated a tri-objective optimization problem concentrating on three vital ecosystem services in Nordic boreal forests: timber supply, climate change mitigation, and biodiversity conservation. Furthermore, future timber price uncertainty was integrated into the optimization model. Utilizing the TreeSim individual-tree growth and yield simulator, we simulated forest growth and yields, carbon stocks, and biodiversity values. Pareto-optimal solutions were found for four distinct management strategies using the epsilon-constrain method to solve the optimization problem. These management strategies differed in spatial restrictions on harvest location and timing, including considerations like harvest adjacency, green-up and maximum harvest opening area (MOA). The analysis revealed trade-offs among all three objectives, with variations influenced by management intensity. Furthermore, within the Pareto solutions for each strategy, a compromise solution was identified based on the knee-point method. The study demonstrated nuanced impacts of green-up constraints related to harvest practices, showing minor effects on harvesting, carbon sequestration, and biodiversity conservation when MOA was restricted to 35 hectares or less. However, more substantial impacts were observed with a MOA limited to 60 hectares or in a strategy without spatial restrictions. Our results underscore the importance of considering multiple objectives in forest management and policy under uncertain wood prices, preventing undesired effects from a singular or deterministic approach. This work provides insights into trade-offs and synergies, contributing to strategic planning and policy design.

Market uncertainty and information content in complex seasonality of prices

In the context of heightened economic and financial market uncertainty, identifying and measuring uncertainty have become an important research question. This study offers a novel approach to uncover uncertainty and its sources by leveraging the complex seasonality inherent in futures prices. Our findings indicate that the temporal hierarchical forecasting method is not universally effective across 35 different commodity futures. However, further analysis reveals that this method outperforms single time series forecasting models when its manually set seasonal components align with those identified through singular spectrum analysis (SSA). The study demonstrates the efficacy of SSA in revealing complex seasonality patterns in various futures prices. Additionally, our event analysis results indicate that shifts in the seasonality patterns of commodity futures prices provide a new perspective for understanding uncertainty and exploring its origins. This research contributes to the literature by extending market uncertainty measures through the linkage with complex seasonality analysis, and offering valuable insights for risk management.

The impact of corporate financialization on stock price crash risk

In recent years, the continuous occurrence of stock price crashes and the significant financialization trend of non-financial firms have aroused widespread attention from regulators, investors and academia. Based on the panel data of China’s A-share non-financial listed companies from 2010 to 2023, this paper designed a rigorous empirical research program and utilized descriptive statistical analysis, correlation analysis, multiple regression analysis and other analytical methods to explore the impact of enterprise financialization on stock price crash risk. The empirical results show that enterprise financialization is conducive to attenuating future stock price crash risk.

Amazon’s Stock Trends Prediction based on ARIMA Model

The rapid development of e-commerce in recent years has made stock market prediction a challenging yet important task for investors and traders. The ARIMA model has been extensively applied for forecasting in financial time series data. This study applied the ARIMA model to predict the Amazon stock price, utilizing historical stock price data from a period of a decade. The results of the analysis indicated that the ARIMA model can effectively predict short-term stock price movements with a certain level of accuracy. The model managed to encapsulate the predominant trend in the variations of Amazon’s stock prices, providing insights into potential future price movements. The predicted stock prices closely matched the actual prices, with a reasonable level of precision. Furthermore, the ARIMA model can be useful for investors and traders in making informed decisions. By utilizing the model’s predictions, investors can better assess the potential risks and returns associated with their investment strategies. It should be noted that the ARIMA model is not without its limitations. The model may not perform well during periods of structural changes or when there are significant events affecting the stock price. Besides, it can also be influenced by other factors such as policies, market sentiment and corporate fundamentals. In conclusion, the ARIMA model can assist in predicting short-term Amazon stock price movements, serving as a valuable tool for investors and traders. However, it is essential to integrate the model's forecasts with other pertinent information and analysis to form comprehensive investment decisions.

AN EVALUATION OF ML TECHNIQUES ON NASDAQ DATASET FOR STOCK MARKET FORECASTING

The uncertainty of stock pricing has popularized stock market prediction as a common practice. Forecasting prices in the market are viewed as problematic, as the hypothesis of efficient markets (EMH) explains. According to the EMH, all accessible information is represented in market prices, and price variations are only the consequence of newly available information. The approach to prediction forecasts the market as either positive or negative based on a variety of input parameters. A combination of derived, fundamental, and pure technical data is utilized in stock forecasts to project future stock prices. Algorithms for machine learning (ML) are made to find patterns in data and utilize those patterns to forecast future events. K Nearest Neighbour (KNN) can process relationships between the numerical data, it is particularly effective in numerical prediction problems for predicting changes in stock value the following day. KNN categorizes freshly input data according to how similar it is to previously taught data, and it does this by clustering the data into coherence subsets or clusters. Using the KNN approach in conjunction with technical analysis, the closest neighbour search strategy yielded the desired outcome. KEYWORDS: Hypothesis, K Nearest Neighbour, Prediction, Stock prices,

Research on Grain Futures Price Prediction Based on a Bi-DSConvLSTM-Attention Model

Grain is a commodity related to the livelihood of the nation’s people, and the volatility of its futures price affects risk management, investment decisions, and policy making. Therefore, it is very necessary to establish an accurate and efficient futures price prediction model. Aiming at improving the accuracy and efficiency of the prediction model, so as to support reasonable decision making, this paper proposes a Bi-DSConvLSTM-Attention model for grain futures price prediction, which is based on the combination of a bidirectional long short-term memory neural network (BiLSTM), a depthwise separable convolutional long short-term memory neural network (DSConvLSTM), and an attention mechanism. Firstly, the mutual information is used to evaluate, sort, and select the features for dimension reduction. Secondly, the lightweight depthwise separable convolution (DSConv) is introduced to replace the standard convolution (SConv) in ConvLSTM without sacrificing its performance. Then, the self-attention mechanism is adopted to improve the accuracy. Finally, taking the wheat futures price prediction as an example, the model is trained and its performance is evaluated. Under the Bi-DSConvLSTM-Attention model, the experimental results of selecting the most relevant 1, 2, 3, 4, 5, 6, and 7 features as the inputs showed that the optimal number of features to be selected was 4. When the four best features were selected as the inputs, the RMSE, MAE, MAPE, and R2 of the prediction result of the Bi-DSConvLSTM-Attention model were 5.61, 3.63, 0.55, and 0.9984, respectively, which is a great improvement compared with the existing price-prediction models. Other experimental results demonstrated that the model also possesses a certain degree of generalization and is capable of obtaining positive returns.

Believe me when I say green! Heterogeneous expectations and climate policy uncertainty

We develop a dynamic model where heterogeneous firms take investment decisions depending on their beliefs on future carbon prices. A policy-maker announces a forward-looking carbon price schedule but can decide to default on its plans if perceived transition risks are high. We show that weak policy commitment, especially when combined with ambitious mitigation announcements, can trap the economy into a vicious circle of credibility loss, carbon-intensive investments and increasing risk perceptions, ultimately leading to a failure of the transition. The presence of behavioural frictions and heterogeneity - both in capital investment choices and in the assessment of the policy-maker's credibility - has strong non-linear effects on the transition dynamics and the emergence of ‘high-carbon traps’. We identify analytical conditions leading to a successful transition and provide a numerical application for the EU economy.

The long-term memory of stock markets: unveiling patterns and predictability

The efficient market hypothesis assumes that financial markets fully incorporate all available information, rendering past information irrelevant for predicting future prices. However, numerous studies challenge this notion and suggest the presence of long-term memory in market dynamics. Understanding long-term memory in financial markets has important implications for investors and policymakers. The aim of this study was to empirically investigate long term memory in financial markets. This study employed a Hurst model for a sample of 5 financial markets from June 1, 2018, to June 1, 2023. The findings revealed that four out of the five sampled financial market exhibits long term memory which challenges the efficient market hypothesis concept. Therefore, portfolio managers and active market participants can utilize long-term memory to optimize asset allocation decisions by considering the persistent effects of past returns and adjust portfolio weights to take advantage of potential return predictability and manage risk.

Volatility analysis and forecasting of vegetable prices using an ARMA‐GARCH model: An application of the CF filter and seasonal adjustment method to Korean green onions

AbstractThe vegetable market experiences significant price fluctuations due to the complex interplay of trend, cyclical, seasonal, and irregular factors. This study takes Korean green onions as an example and employs the Christiano–Fitzgerald filter and the CensusX‐13 seasonal adjustment methods to decompose its price into four components: trend, cyclical, seasonal and irregular fluctuations. To estimate the price volatility and forecast future prices, we apply an ARMA‐GARCH model and use various statistical measures to assess the prediction accuracy. The empirical results demonstrate the effectiveness of ARMA(1, 2)‐GARCH(1, 1) model in modeling and forecasting the seasonally adjusted price of Korean green onions. Our study provides an important reference for expanding research in the field of vegetable price fluctuations and formulating strategies for stabilizing prices. [EconLit Citations: Q11, C22, Q02].

Smart Electricity Meter Monitoring and Prediction using I-Socket

As the world's population and energy needs continue to grow, the demand for power is ever-increasing. This has put a strain on natural resources and renewable sources of power, which are not yet developed enough to operate self-sufficiently. In this context, optimal power sharing and efficient energy consumption is essential. To this end, a smart electricity meter monitoring and prediction system has been developed using i-Socket, a hardware module that tracks the energy consumed by each device in a house and estimates the monthly electricity bill. This system notifies the user when the bill surpasses a set limit and sets the monitoring rate of a connected device to the socket based on the type of device, making it an energy efficient system. Current sensor used is the ACS712 sensor and the microcontroller is Arduino UNO. A Boost converter is used for the power supply and the software application is developed for monitoring electricity bills. This system takes input of present data of current and predicts the meter reading for the following months. However, the same can also be achieved by using a python server for holding an ML model that will be developed to predict future price of the bill based on the current usage of power. This is acts as an affordable alternative to that by using the iSocket

Risk Management and Different Financial Approaches to Reduce Business Risk

This article examines three different risk management methods: asset hedging by shorting stocks, cash flow hedging using forward contracts, and futures contracts. The article explains each method's rationale, application, advantages, and disadvantages. By presenting and explaining these methods, this study aims to help readers gain insights into their effectiveness in managing risk in different types of transactions. The content of the article supports the readers to have a better understanding and gives some strategies for risk management, and provides guidance for decision-makers to mitigate potential risks in their financial operations. Asset hedging by shorting stocks involves selling borrowed stocks in anticipation of a decline in their price, thereby offsetting the potential loss of a declining asset. This strategy allows an investor to profit from a decline in stock prices. Cash flow hedging through forward contracts involves entering into an agreement to buy or sell an asset at a pre-determined future price, thereby reducing the risk of adverse price movements and ensuring a stable cash flow. On the other hand, futures contracts are standardized agreements to buy or sell an asset at a future date, are traded on public markets, provide legal protection for both parties through financial intermediaries, and allow investors to lock in prices. While these risk management methods have obvious advantages, such as potential gains to offset the risk of asset depreciation during market downturns, protection against adverse price movements, and price stabilization, they also have inherent risks and limitations that need to be carefully considered.

Forecasting Corn Futures Prices Using the LSTM Model

The central government's No. 1 document introduced new requirements for “maintaining the stability of agricultural product prices.” However, recent years have seen significant fluctuations in corn prices in China, highlighting the importance of exploring the trends of corn futures prices for stabilizing spot corn prices. Given the increasingly complex financial market environment, traditional linear econometric methods and machine learning approaches face inherent limitations in price forecasting. This study utilizes extensive data and applies the Long-Short Term Memory (LSTM) deep learning model to forecast corn futures prices. During the model training process, historical corn futures data were used as hidden input feature variables, along with additional indicators such as spot corn prices, real-time quotes from corn deep processing enterprises, downstream product prices in the corn industry chain, prices of similar substitutes, and the Baidu Search Index for “corn prices.” The model was trained and fine-tuned with hyperparameters to assess its predictive accuracy under different time windows, learning rates, and iteration counts. Experimental results indicate that incorporating external influencing factors significantly enhances the model's predictive accuracy compared to using only historical price data. The model with a learning rate of 0.01, 50 iterations, a time window of 5, and 4 hidden layers achieved the highest accuracy. The findings demonstrate that integrating external factors and optimizing hyperparameters substantially improve the LSTM model's predictive precision, outperforming models that rely solely on historical data. The study provides policy recommendations on promoting advanced technology, enhancing data collection and information sharing, and improving market transparency and fairness. Additionally, the conclusion discusses the extensive application potential and promising development prospects of the LSTM model in corn futures price forecasting. This model enables market participants to obtain more accurate market trend forecasts, facilitating more informed investment decisions. The research also offers new insights and methodological references for scholars in financial forecasting, contributing to the further advancement of this field.Funding: This study was supported by the 2024 Key Research Project of Higher Education Institutions of the Henan Provincial Department of Education [Project Number 24B790006], the Henan Provincial Department of Education [Project Number 2021GGJS120], and the Henan Provincial Department of Education [Project Number 2021SJGLX478]. Additionally, this study received support from the Xinxiang City Federation of Social Sciences [Project Number SKL-2024-157].

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

Pendekatan Metode Monte Carlo untuk Simulasi Pergerakan Harga Saham

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.