Future Prices Research Articles

Impact of COVID-19 trade suspension on price discovery in FPO-traded agri-derivatives

Purpose This study aims to examine whether agricultural commodities exhibited deviations in the lead-lag relationship between future and spot prices of farmer producer organizations (FPOs) traded commodities in the Indian derivative market after trade suspensions during Covid-19. The study may help buyers and sellers to get a fair price for their commodities after lockdown-trade disruptions. Design/methodology/approach The study applied the Granger causality (GC) test and the vector error correction model (VECM) to analyse short-run and long-run lead-lag relationships. Moreover, the study examined the pre-post-trade suspension effect on the lead-lag relationship of commodity prices. Findings The GC test results show that five out of the 13 agri-commodities have changed their lead-lag relationship from future to spot in the short run. Simultaneously, VECM captured changes in the lead-lag relationship for the same five commodities in the long run due to trade suspensions. Practical implications The findings indicate a reverse lead-lag relationship between future and spot prices for aforesaid commodities after trade suspension. The stakeholders may use the lead prices for these commodities to perform a fair trade. The study may be helpful in structuring price discovery strategy to achieve optimal price and efficient derivative trading. Originality/value To the best of the authors’ knowledge, this is the first study examining the effects of trade suspension on price discovery in FPO-traded agri-derivatives caused by the COVID-19 pandemic.

Applications of machine learning in quantitative trading

Abstract. This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.

Gold’s Value as an Investment

Abstract This paper presents an approach for pricing gold from investors’ perspective. The model is based on no-arbitrage principles with minimal structural assumptions. There is no need to specify investor preferences. When fitted to match 10-year real U.S. Treasury rates, the model can replicate the salient fluctuations in the time series of gold prices since 2007. The model is also able to capture key patterns of CME Comex gold futures prices. The model implies that the majority of the value of gold is due to its role as an investment asset. (JEL G12, G13)

Carbon option pricing and carbon management under uncertain finance theory

Against the backdrop of increasingly severe global climate change issues, more and more countries and regions are adopting carbon emission trading, which is an effective market mechanism. As an important component of the carbon market, American carbon options provide more flexible trading tools to help companies avoid price risks. Therefore, the pricing research of American carbon options is of great significance for promoting the healthy development of the carbon market. Nonetheless, existing pricing methods are all based on probability theory, which need to satisfy the premise that the distribution function is close to the true frequency sufficiently. However, usually such premise is ill-suited in carbon market. To alleviate the predicament of the need for satisfying probability theory’s premise, this work investigates American carbon option pricing under the framework of uncertainty theory, which is another axiomatic mathematical system. For this purpose, uncertain differential equation is adopted to model the price of carbon future, and pricing formulae for American call and put carbon options are derived based on this model. Real data analyses illustrate the proposed method in details. Furthermore, it provides a detailed schema of applying the American carbon option for carbon asset management, which can optimize risk control for carbon consuming enterprises.

Machine Learning Approaches for Modelling Water Futures

Modelling water futures is challenging due to the dynamics of several variables and non-linearity. The traditional models are often inefficient to capture such hidden patterns. Thus, utilizing datasets that include daily price and volume information of water futures, this study evaluates the performance of SVM, Random Forest Regressor, LSTM, GBM, and XGBoost models. The findings indicate that XGBoost outperforms other models in accuracy; however, all models tested perform well and provide accurate predictions. Consequently, it makes valuable contributions to the field of modelling financial products based on water-adjacent entities. Furthermore, it underscores the potential for machine learning in enhancing market predictions for emerging and niche sectors that have thus far been overlooked by market participants but will increasingly become more important as climate change progresses. Lastly, as futures markets for water as a commodity mature (with higher trading volume of futures contracts), this study will serve as a benchmark for better modelling of water futures.

Probabilistic oil price forecasting with a variational mode decomposition-gated recurrent unit model incorporating pinball loss

Prediction methods have garnered significant attention in intelligent decision-making. Most existing approaches to predicting crude oil prices prioritize accuracy and stability while providing precise prediction intervals (PIs) that can offer valuable insights. Thus far, we introduce a novel hybrid model to forecast future crude oil prices. Our approach leverages the variational mode decomposition (VMD) to simplify the complexity of the original time series, yielding a set of subseries. These subseries are then modeled using a deep neural network architecture called a gated recurrent unit (GRU). To address the prediction uncertainty, we employed the pinball loss function rather than the mean square error (MSE) to guide the proposed VMD-GRU. This adaptation extends the traditional GRU-based point forecasting to probabilistic forecasting by estimating quantiles. We evaluated our proposed model on a well-established crude oil price series by conducting both single- and multi-step-ahead forecasting analyses. Our findings underscore the efficacy of the combined model, demonstrating its superior predictive performance compared to benchmark models.

Environmental corporate social responsibility and stock price crash risk: The role of environmental performance and ISO 14001

This paper examines the impact of environmental corporate social responsibility (CSR) on stock price crash risk. Using a sample of publicly listed Chinese firms for the period 2010 to 2020, we find that environmental performance negatively affects crash risk. ISO 14001 certification, however, does not exert a significant effect. The negative relationship between environmental performance and crash risk is mediated by the visibility of the firm, and moderated by the certification of ISO 14001, respectively. In addition, the documented relationship is more evident for firms belonging to heavily polluting industries, non-SOEs and firms with greater media coverage and higher market competitive status. Our results remain consistent after controlling for endogeneity concerns and a batch of robustness tests. This study provides new insights into the CSR literature by highlighting the risk-mitigating role of environmental CSR in firms’ future stock price crashes.

Factors affected housing prices: taking Boston as an example

Abstract. Real estate price prediction plays a vital role in urban planning, investment decision-making, and risk management. However, existing prediction models often show problems such as insufficient generalization ability and susceptibility to outliers when faced with complex nonlinear relationships, multidimensional features, and noisy data. Therefore, choosing a model that can accurately capture complex patterns and has strong robustness has become the focus of research. This paper introduces the random forest model and compares it with multivariate linear regression, XGBoost, and support vector machine (SVM). Compared with the traditional regression model, the random forest model combines the flexibility of decision trees and the multi-level feature extraction ability of deep learning, and can better handle the complex nonlinear relationships in the Boston housing price dataset. The experimental results show that the random forest model has achieved excellent performance in all evaluation indicators, and the model accuracy indicators are distributed as MSE=8.2502, RMSE=2.8723, MAE=2.0668, and R^2=0.8875. These results show that the random forest model not only outperforms other models in prediction accuracy but also shows significant advantages in dealing with data complexity and improving generalization ability. Therefore, the random forest model provides an efficient and reliable tool for future real estate price prediction research and applications.

Geopolitical Risks’ Spillovers Across Countries and on Commodity Markets: A Dynamic Analysis

We investigate the transmission of geopolitical risks between G7-BRICS countries and commodity futures prices. We find geopolitical instability after the outbreak of the Russia-Ukraine conflict. In a context where the G7 countries geopolitically influence the BRICS and vice versa, the United States, Germany, India, and Russia emerge as the predominant sources of spillovers. The commodity futures prices most influenced by geopolitical risks are those relating to energy.

Analysis of the Effectiveness of Multi-Armed Bandit Algorithms in Crop Pricing

This study explores the application of multi-armed bandit (MAB) algorithms in dynamic pricing of crops, with a focus on evaluating the adaptive upper confidence bound (asUCB) and Thompson Sampling (TS) algorithms. Through simulation experiments on historical data, the study analyzed the performance of these algorithms in fitting actual market price trends and responding to future price fluctuations. The results indicate that the asUCB algorithm performed particularly well in both the training dataset and simulation tests, demonstrating low mean squared error (MSE) and minimal cumulative regret, reflecting its rapid convergence and stable pricing capabilities. In contrast, although the TS algorithm initially performed slightly less effectively, it demonstrated unique advantages in dealing with market volatility due to its strong adaptability. This study demonstrates the potential application of MAB algorithms in dynamic pricing, providing valuable insights for pricing strategies in the agricultural product market.

Forecast Analysis of the Stock Market Based on Hidden Markov Model and Long Short-Term Memory Model- Taking the S and P500 Index as an Example

This paper proposes a hybrid method that combines the advantages of HMM and LSTM models to improve the accuracy of stock market predictions, using the S&P500 index as a case study. HMMs are used to identify and model potential market states, while LSTMs predict future stock prices based on these states. Through this integration, this paper hopes to take advantage of HMM’s ability to model market conditions and LSTM’s advantages in forecasting to provide a more robust forecasting framework. This article is mainly divided into five parts. The first part is an introduction to the research background and objectives. The second part is the review and arrangement of relevant literature. The third part is the elaboration of the theoretical basis and the collection and analysis of data. The fourth part is an explanation of the data analysis results. The fifth part is the conclusion and suggestions of this study.

Applications of Neuro Deep Learning Models in Predictive Data Analytics for the Movements and Trends of the Indian Stock Market: Financial Data Mining, Nonlinearity, and Quantum Finance

The financial world of today is high-frequency data-driven and characterized by the application and use of information technology for better business development and decision-making. The price movements of stock markets are mainly influenced by micro and macroeconomic variables, legal frameworks, and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical prices. Achieving accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behaviour in the stock market and volatility in the stock prices. This paper analyzes the stochastic movement pattern of the highly volatile thirty individual company stocks (in terms of market capitalization) of BSE-Sensex, using the Neuro Deep Learning method, a model of deep learning approach. The findings of the study would help the investors, to make rational and well-informed investment decisions, and to optimize the returns by investing in the most valuable stocks of listed corporate enterprises.

Populating HIA of tobacco policies with empirical evidence on behavioral response to price increases

Abstract Performing Health Impact Assessment of tobacco policies requires to have insights into how smokers will react to policy measures such as price increases. In the Netherlands, a series of 3 substantial price increases has been implemented in 2020 (10% increase), 2023 (10% increase) and 2024 (24% increase). We performed three consecutive studies comparing the ex-ante stated preferences about behavioural response to actual and hypothetical future price increases, and compared these stated preferences with actual behaviour changes 6 months after the increase in excise tax. Two of these possible behavioural reactions actually will impact smoking prevalence or smoking intensity, e.g. stopping altogether and decreasing the number of cigarettes smoked. Other behavioural responses are less favourable and will not impact health, such as buying more and cheaper tobacco abroad and smoking less expensive brands or transferring from cigarettes to roll-your-own or e-cigarettes. For the price increases of 2020 and 2023, we have observed a substantial number of about 10% of smokers quitting successfully and about one third attempting to quit smoking. (Attempting to) quit smoking was much more prevalent among those with a more pronounced intention to stop smoking at the ex-ante measurements. At the same time, cross-border shopping increased significantly as well, with a 10 percent-point growth in the absolute amount of all tobacco consumed originating from abroad following the 10 percent price increase of April 2023. This big increase in cross-border shopping appeared to be independent of distance to the border. We conclude that every significant price increase stimulates smokers to consider to stop smoking. However, unilateral price increases in one country will eventually also stimulate smokers to buy cross-border. Behavioural data as presented should inform HIA to make them more realistic with regard to actual behaviour changes and health impacts of smoking policies.

Features to Involve and Collect in Future Auto Insurance Pricing

The property insurance industry is a highly data-driven sector. In the traditional model, insurance pricing largely depends on the self-reported risk characteristics of clients, such as age, gender, vehicle type, etc. These characteristics help insurance companies categorize clients into different risk levels and set premiums accordingly. With the development of telematics, insurance companies can now collect and analyze more dynamic data that is more directly related to risk. This study presents a comprehensive analysis of three feature selection models for vehicle insurance pricing. Utilizing regression techniques, it evaluated a multitude of factors believed to influence claim frequency and severity. Through a rigorous comparative study, key factors that significantly impact the vehicle are assessed and fit to different regression models. The articles findings indicate that factors such as policy bonus, mileage base data, and car usage are paramount in determining insurance rates. Moreover, the study revealed that incorporating these factors into the pricing model enhances its accuracy and fairness.

A Financial Analysis and Valuation of Pfizer

Abstract: The rational valuation of the healthcare industry and the analysis and research of the financial risks faced by enterprises can not only provide a basis for rational investment and transaction pricing decisions, but also help enterprises to find out the shortcomings of the production and operation, so as to improve the management mode of the enterprise, and help enterprises to achieve the maximisation of management. Firstly, this paper analyse the risks faced by Pfizer from a financial point of view by quantitatively comparing the data of Pfizer and its two competitors, Amgen and Unitedhealth Group, and then qualitatively analysing their characteristics in terms of liquidity, solvency and profitability and the reasons for this. Next, Pfizer is valued by looking at its leverage, business risk, earnings per share, etc. to measure the financial risk of the business and the reasons for the capital structure. It then gives investors an objective investment recommendation by forecasting Pfizer's future share price. The study concludes that Pfizer Inc. is illiquid, has poor corporate solvency, is at risk of debt servicing, and has suffered a significant decline in profitability. Valuing Pfizer, the forecast suggests that Pfizer's stock is currently undervalued.

Forecasting Orange Juice Futures: LSTM, ConvLSTM, and Traditional Models Across Trading Horizons

This study evaluated the forecasting accuracy of various models over 5-day and 10-day trading horizons to predict the prices of orange juice futures (OJ = F). The analysis included traditional models like Autoregressive Integrated Moving Average (ARIMA) and advanced neural network models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Backpropagation Neural Network (BPNN), Support Vector Regression (SVR), and Convolutional Long Short-Term Memory (ConvLSTM), incorporating factors like the Commodities Index and the S&P500 Index. We employed loss function metrics and various tests to assess model performance. The results indicated that for the 5-day horizon, the LSTM and ConvLSTM consistently outperformed the other models. LSTM achieved the lowest error rates and demonstrated superior capability in capturing temporal dependencies, especially in single-factor and S&P500 Index predictions. ConvLSTM also performed strongly, effectively modeling spatial and temporal data patterns. In the 10-day horizon, similar trends were observed. LSTM and ConvLSTM models had significantly lower errors and better alignment with actual values. The BPNN model performed well when all factors were included, and the SVR model maintained consistent accuracy, particularly for single-factor predictions. The Diebold–Mariano (DM) test indicated significant differences in forecasting accuracy, favoring advanced neural network models. In addition, incorporating multiple influencing factors further improved predictive performance, enhancing investment outcomes and reducing risk.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

Matrix GARCH model: Inference and application*

Matrix-variate time series data are largely available in applications. However, no attempt has been made to study their conditional heteroskedasticity that is often observed in economic and financial data. To address this gap, we propose a novel matrix generalized autoregressive conditional heteroskedasticity (GARCH) model to capture the dynamics of conditional row and column covariance matrices of matrix time series. The key innovation of the matrix GARCH model is the use of a univariate GARCH specification for the trace of conditional row or column covariance matrix, which allows for the model identification. Moreover, we introduce a quasi-maximum likelihood estimator (QMLE) for model estimation and develop a portmanteau test for model diagnostic checking. Simulation studies are conducted to assess the finite-sample performance of the QMLE and portmanteau test. To handle large dimensional matrix time series, we also propose a matrix factor GARCH model, and establish its theoretical properties. Finally, we demonstrate the superiority of the matrix GARCH and matrix factor GARCH models over existing multivariate GARCH-type models in volatility forecasting and portfolio allocations using three applications on credit default swap prices, global stock sector indices, and future prices.

Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction

Financial time series is one of the most challenging applications of modern time series forecasting. The financial time series is closely related to noise, non-stationary, and deterministic chaos. The characteristics suggest that no complete information can be obtained from the past behavior of financial markets to fully capture the dependency between future prices and that of the past. The data collection method was collected from the Stock Market Online Application "MetaTrader version 4" type "Daily" with a time range from "03/09/2001 to 25/07/2012", as many as 2052 data", with the attributes "Date, Open, High, Low, Close, Volume" with the main attribute "Close" using the Support vector machine algorithm, artificial neural network, and multiple linear regression. The conclusion of the value that is close to the series value is the value by testing on the support vector machine algorithm, with the parameter for the RMSE value that is close to the "0" value obtained from the measurement results on the SVM algorithm on the RBF kernel (radial base function) with a value of "gamma" γ = 100 with the value of RMSE = 0.000, and SE = 0.000. with prediction accuracy error = 0.976

The Effect Of Credit Derivatives On Hedging Risk Of Non-Payment: Applied Research

The purpose of the current research is to reveal the impact of credit derivatives by (purchased credit derivatives, the value of credit derivatives sold) in hedging the risk of non-payment at a sample of commercial banks in the Iraq Stock Exchange by (Middle East Bank, Gulf Commercial Bank, Trade Bank of Iraq, Bank of Baghdad, Credit Bank) for the financial period from (2016-2020), and for the purpose of analyzing the results, the Office package was adopted (Excel.V.2016) in order to extract the level of credit derivatives, And the risks of non-payment at the commercial banks surveyed, has been using a set of statistical methods to determine these variables, represented in (credit derivatives, arithmetic mean, correlation coefficient, and regression coefficient), as a result, the research came out with a set of results, foremost of which was that there is an effect of credit derivatives in hedging the risks of non-payment, and this shows that credit derivatives contribute to improving the ability to hedge against non-payment risks, allowing investors to reduce financial risks. The focus of the surveyed commercial banks on the use of credit derivatives to protect investors from fluctuations in interest rates and currency rates, which works to reduce the impact of non-payment risks on their profits, which means that these contracts allow the determination of a future price for financial assets.