Daily Price Research Articles

The risk spillover between China’s economic policy uncertainty and commodity markets: Evidence from frequency spillover and quantile connectedness approaches

Based on the frequency spillover method extended by Baruník and Křehlík (2018), we explore the risk spillover relationship between China’s economic policy uncertainty (CNEPU) and commodity futures in different frequency domains with daily settlement price data of 14 commodity futures in China. The results show that the risk spillover relationship between CNEPU and the commodity market mainly occurs in the short term. Quantile connectedness results show that economic policy uncertainty, which mainly plays the role of risk transmitter, is more closely related to the commodity market during the market boom and recession. Soybeans, soybean meal, and corn have shown high investment value in the process of market recovery, which is exposed to less risk spillover from policy uncertainty. Finally, the economic crisis with different characteristics will have specific impacts on asymmetric risk spillovers based on certain impact mechanisms.

Nonparametric Directional Dependence Estimation and Its Application to Cryptocurrency

This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with asymmetric-threshold GARCH models for marginal distributions, detects the directional dependence better than the copula directional dependence method by an asymmetric GARCH model.

Measuring price efficiency in petroleum markets: New insights using various long-range dependence techniques

This paper empirically measures pricing efficiency in the petroleum markets using a battery of long-range dependence techniques. We analyse the volatility (absolute returns) of fourteen petroleum products using daily prices from January 1990 to April 2020. We use bootstrapping techniques to estimate the confidence intervals of the long-range dependence parameters. Overall results obtained using CMA-MAF, CMA-MSF, GPH and periodogram techniques suggest that there is no evidence of long-range dependence in absolute returns of petroleum markets prices indices at least at the 10% level of significance. For robustness purposes, findings from the rolling windows estimates reveal that the results are not affected by the normality assumption under different rolling window sizes. Furthermore, we find that Gas oil, Biofuel and Heating oil are the most correctly priced while Crude Oil Dubai emerging is the least efficient. Comparing the efficiency index of the all six crude oil returns in our sample, WTI oil was seen to be less efficient than Brent crude oil. In all, our work uncovers crucial implications for investors and policymakers.

An investigation on dynamic connectedness of commodity market with financial market during the Russia–Ukraine invasion

PurposeThis study unravels an attempt to investigate the dynamic connectedness of agri-commodity (wheat) of Russia with 10 financial markets of wheat importing counties during the Russia–Ukraine invasion.Design/methodology/approachThis study took the daily prices of Wheat FOB Black Sea Index (Russia) along with stock indices of 10 major wheat-importing nations of Russia and Ukraine. The time frame for this study ranges from February 24, 2022 to July 31, 2022. This time frame was selected since it fully examines all of the effects of the crisis. The conditional correlations and volatility spillovers of these indices are predicted using the DCC-GARCH model, Diebold and Yilmaz (2012) and Baruník and Křehlík (2018) models.FindingsIt is found that there is dynamic linkage of agri-commodity of with stock markets of Iraq, Pakistan and Tanzania in short run while stock markets of Egypt, Turkey, Bangladesh, Pakistan, Brazil and Iraq are spilled by agri-commodity in long run. In addition, it documents that there is large spillover in short run than medium and long run comparatively. This signifies that investors have more diversification opportunity in short run then long run contemplating to invest in these markets.Originality/valueTo the best of the authors’ understanding this is the first study to undertake the dynamic linkage of agri-commodity (wheat) of Russia with financial market of select importing counties during the Russia–Ukraine invasion.

Estimation of stock market index volatility using the GARCH model: Causality between stock indices

This paper aims to model the volatility of returns for selected stock indices and examine the causal relationships between the markets using the historical daily prices of the Financial Times Stock Exchange (FTSE), Bursa Malaysia Kuala Lumpur Composite Index (KLCI), the Indonesia Stock Exchange Index (LQ45), and the Stock Exchange of Thailand (SET) from January 2008 to November 2019. The study employs univariate GARCH models that are prominent in capturing the volatility clustering of financial instruments in association with the Box–Jenkins methodology for better estimation. Generally, the ARMA-GARCH model is used to capture the volatility series, while the Granger causality test examines the causal directions between the markets. The findings revealed leverage effects on the markets, with the outperformance of the EGARCH in analyzing the empirical properties of stock returns. An initial test that yielded positive correlations suggests the existence of co-movement between the derived volatility series. The study concluded bidirectional causal relationships between the selected markets, and based on the resulting relationships, it is proposed that supervision of markets among the ASEAN members could be advantageous in predicting the corresponding market performance.

Improvements of Stock Price Prediction Based on LSTM by Fama-French Five Factors on Market Capitalization

Stock price prediction refers to determining the trend of a budgetary trade's stock projection. The precise prognosis of offer value growth will urge greater profit for speculators. The research based on previously obtained massive data with the use of AI techniques is applicable to a variety of disciplines. The primary goal is to create an analysis for the purpose of producing well-quality information that will be beneficial for decision-making. There are several machine-learning approaches with varying degrees of accuracy. However, choosing of the optimal technique to attain the maximum level of precision is an important requirement. The LSTM techniques are supervised learning-based and will require training to increase accuracy. This paper adopts LSTM algorithms combining with the Fama-French five-factors (FF5F) daily to the daily close price prediction of 10 companies represent as large and small market capitalizations from October 31, 2012 till October 28, 2022. It determines the relationship of the proposed LSTM model and market capitalization of companies. The results reveal that the LSTM model combined with FF5F for the low market capitalization companies outperforms the large market capitalization on stock price prediction.

Stock Price Prediction of Walmart Based on Combination of SVM and LS-SVM Models

One of the most significant operations in the finance sector is stock trading. The stock market is an essential part in the economy of a country and serves as the indicators of the situation of a country’s economy as the stock prices go up or down. Therefore, stock price prediction, the behavior of attempting to predict the potential worth of a corporation or any financial instruments successfully, will maximize investor’s gain, enhance market’s confidence, and help government policymakers to make economic decisions. In order to forecast the price of a stock, a machine learning approach is constructed in this study. The suggested algorithm includes random forest, support vector machine (SVM), and least square support vector machine (LS-SVM). In particular, the random forest is employed to select the most important features from the technical indicators calculated for stock price prediction. The SVM and the LS-SVM models are employed to predict the daily stock prices. Besides, R-Squared (R²), mean squared error (MSE) and mean absolute error (MAE) are used for model evaluation. According to the results, both SVM and LS-SVM models can predict stock price well, but both algorithms are not suitable for large datasets, and overfitting problem exists. These results shed light on guiding further exploration of stock price predictions.

Exploring the pattern of price interdependence in rice market in Indonesia in the presence of quality differential

Being the main food commodity, the dynamics of rice prices is one of the most important issues for Indonesian economy. The prices at the retail level and at the farm level are influenced not only by the demand and supply in each of these markets but also by price behavior at the wholesale market. This study aims to analyse the dynamics of the relationship and behaviour of the prices of various varieties and qualities of rice in the wholesale market. The dynamics of rice prices are investigated by employing multivariate vector error correction model (VECM) and using daily price series at wholesale level during the period of 1 October 2014 until 12 February 2018. The results show a strong price relationship between premium-quality rice and medium-quality rice and between medium-quality rice and low-quality rice. Changes in the price of premium-quality rice and changes in the price of low-quality rice will have a large influence on the price of medium quality rice, but not vice versa. Furthermore, regarding the price stabilization policy, the results suggested that the policies aimed at regulating medium-quality rice prices are estimated to have relatively weak effects on the prices of premium-quality rice and low-quality rice.

Cranes among chickens: The general-attention‐grabbing effect of daily price limits in China's stock market

This paper examines the general-attention-grabbing effect of daily price limits in China's stock market. We show that stocks with large exposure to daily price limits attract more investor attention and have lower future returns. The exposure is measured empirically through the absolute beta with respect to the daily proportion of stocks that hit price limits. The general-attention-grabbing effect is not solely caused by stocks that recently hit price limits, is not subsumed by market volatility exposure, and does not reflect other stock market characteristics. Moreover, the effect is stronger among stocks that are heavily invested in by retail investors.

EU Climate Change News Index: Forecasting EU ETS prices with online news

Carbon prices have been rapidly increasing in the EU since 2018 and accurate forecasting of EU Emissions Trading System (ETS) prices has become essential. This paper proposes a novel method to generate alternative predictors for daily ETS price returns using relevant online news information. We devise the EU Climate Change News Index by calculating the term frequency–inverse document frequency (TF–IDF) feature for climate change-related keywords. The index is capable of tracking the ongoing debate about climate change in the EU. Finally, we show that incorporating the index in a simple predictive model significantly improves forecasts of ETS price returns.

Quantitative trading decision model based on LSTM neural network

As trading markets are vulnerable to national policies and economies, some investors choose to leave their assets in the hands of market traders for management. To help market traders find the best portfolio, the best trading strategies were dynamized to obtained by predicting the daily price movements of gold and bitcoin. BP neural network, time series analysis, and LSTM neural network were chosen for modeling quantitative trading decisions and modify the dynamic model of optimal trading. The LSTM neural network was used to indicate the time series because of the best fit of 0.991. And the investment weights for gold and bitcoin were calculated based on the particle swarm algorithm to be 0.09:0.91 and over the five-year trading period an initial $1,000 investment would yield $66,588.987. In addition, the optimal daily trades dynamically were re-solved by changing the values of the investment weights by 2:3, 2:1, 3:2, and 1:2 and obtained that the total number of assets of gold and bitcoin is smaller than the total number of assets with an allocation ratio of 0.09:0.91 for either allocation ratio. Thus, the model in the present paper provides the optimal strategy.

Forecasting on metal resource spot settlement price: New evidence from the machine learning model

Accurate prediction of the price of metal mineral resources is of great practical significance for guiding the production of non-renewable resource enterprises and maintaining the stability of related economic price indexes. Based on the daily frequency spot settlement price data of lead, aluminium, aluminium alloy, tin, copper(A) and other metal minerals from Sep. 21, 2005 to Dec. 1, 2021, machine learning method is used to analyze the accuracy of hybrid models for predicting spot settlement prices of metal minerals. Firstly, we compared and analyzed the in-sample prediction accuracy of different models in spot settlement price of metal minerals, and found that the prediction accuracy of LSTM-GRU and LSTM-CNN models is significantly better than other models. Secondly, we verified the out-of-sample prediction results of spot settlement prices of metal minerals, which further demonstrates the robustness of the prediction accuracy of the LSTM hybrid model. Finally, we considered the impact of COVID-19 and explored the prediction accuracy of different hybrid models on spot settlement prices of metal minerals. We found that LSTM-GRU and other models also perform well with strong robustness. Therefore, we believed that the LSTM hybrid model, especially the LSTM-GRU model, is suitable for analyzing the prediction of spot settlement price of metal minerals.

Pay cycles and gasoline price: When low-cost and independent stations take advantage of the needy

This paper analyses the daily price behaviour of different types of gasoline stations in the presence of potential temporary demand shocks caused by an increased availability of disposable income, generated by the payment of a subsidy to a specific part of the population (the unemployed). Using 2015–2016 Spanish data on daily fuel prices and a difference-in-difference approach, we show that independent and low-cost gasoline stations take advantage of the neediest consumers. We find that their prices increase on the day the unemployed receive the government subsidy, while, on the same day, major branded companies decrease their prices. This phenomenon underlines the effect of payment cycles on consumer choices and their economic impact. The paper fills an important gap in the knowledge of how gasoline stations strategically exploit the income patterns of low-income motorists. The findings are also relevant for antitrust and for industry regulators, who generally focus on the activities of major branded gasoline stations, giving less emphasis to the activities of other operators, which also cover significant market shares.

Role of proliferation COVID-19 media chatter in predicting Indian stock market: Integrated framework of nonlinear feature transformation and advanced AI

The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intelligence (AI) driven frameworks have been propounded to gauge the proliferation of COVID-19 news towards Indian stock markets through the lens of predictive modelling. Two hybrid predictive frameworks, UMAP-LSTM and ISOMAP-GBR, have been constructed to accurately forecast the daily stock prices of 10 Indian companies of different industry verticals using several systematic media chatter indices related to the COVID-19 pandemic alongside several orthodox technical indicators and macroeconomic variables. The outcome of the rigorous predictive exercise rationalizes the utility of monitoring relevant media news worldwide and in India. Additional model interpretation using Explainable AI (XAI) methodologies indicates that a high quantum of overall media hype, media coverage, fake news, etc., leads to bearish market regimes.

Peramalan Return Saham Menggunakan Model Integrated Moving Average

A popular investment that is in great demand among investors is stocked. Stocks are another type of financial instrument offering returns but carrying a higher risk level. Price time series are more difficult to manage than return time series. To equip investors with the knowledge to forecast future stock prices, mathematical models can be used to simulate stock price fluctuations. The time series method, especially the Integrated Moving Average (IMA) model, is a model that can be used to observe changes in stock prices. The Integrated Moving Average (IMA) model will be used in this study to simulate stock returns. The Integrated Moving Average (IMA) model is a Moving Average model that is carried out with a differencing process or an Autoregressive Integrated Moving Average model with a value of Autoregressive being zero. This study uses secondary data simulations from secondary sources, such as data on daily business stock prices for one year, to conduct a literature review and test experiments. The Integrated Moving Average (IMA) model is used in data processing, especially to test the differencing data process. The results obtained are the IMA (1,1) model with the following equation Zˆt = Zt-1 + 0, 5782at-1, which can be used to anticipate future stock returns. Based on these results, it is expected that investors can predict the value of shares within a certain period of time.

Optimising a Biogas and Photovoltaic Hybrid System for Sustainable Power Supply in Rural Areas

This paper proposes a method for evaluating the optimal configuration of a hybrid system (biomass power plant and photovoltaic plant), which is connected to the electrical grid, to achieve minimum energy costs. The study is applied to a small rural municipality in the Valencian Community, Spain, as an energy community. The approach takes into account the daily energy demand variation and price curves for energy that are either imported or exported to the grid. The optimal configuration is determined by the highest internal rate of return (IRR) over a 12-year period while providing a 20% discount in electricity prices for the energy community. The approach is extrapolated to an annual period using the statistical data of sunny and cloudy days, considering 23.8% of the year as cloudy. The methodology provides a general procedure for hybridising both plants and the grid to meet the energy needs of a small rural population. In the analysed case, an optimal combination of 140 kW of rated power from the biogas generator was found, which is lower than the maximum demand of 366 kW and 80 kW installed power in the photovoltaic plant, resulting in an IRR of 6.13% over 12 years. Sensitivity studies for data variations are also provided.

Study and Analyze the Time Series of Daily Prices Oil World by Using ARIMA(p,d,q) Models

The research aims to use the ARIMA models to study and analyze the time series of Daily Prices Oil World 2010-2013 to find the best model for prediction. The results of the application showed that the appropriate and efficient model for the representation of time series data is the ARIMA (2.1.1). According to the estimation results of this model, the prediction results time series of Daily Prices Oil World for the period 2010-2013 were consistent with those of the original time series

Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods

Significant fluctuations in the price of crude oil in recent years make accurate price estimations of critical importance. A reliable method for crude oil price forecasting is beneficial in guiding production and investment. This study presents two new hybrid predictors for forecasting oil prices based on recurrent neural networks using variational modal decomposition (VMD), sample entropy (SE), and gated recurrent units (GRUs). We consider the West Texas Intermediate daily closing oil prices from August 2, 2010, to December 31, 2019. The hybrid prediction methods first decompose the original time series and then reconstruct the decomposed data using SE. The reconstructed data are used to formulate independent price predictions, which are subsequently combined to produce an ensemble output. With the West Texas Intermediate dataset, the VMD-SE-GRU framework proposed in this paper gives a root mean square error of 0.6735, mean absolute error of 0.4585, mean absolute percentage error of 0.8059, and R2 value of 0.9272. Overall, the hybrid VMD-SE-GRU framework has several advantages over previous models and produces highly accurate forecasts with a shorter runtime. Thus, the new approach provides an effective tool for predicting oil prices.

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency.

The Influencing Factors of the Carbon Trading Price: A Case of China against a “Double Carbon” Background

The Carbon trading price (CTP) can best reflect the fluctuations of the carbon trading market. This paper comprehensively analyzes the CTP mechanism of China’s carbon trading market, discusses the main factors affecting China’s CTP, which include macroeconomic factors, energy price factors, policy factors, and environmental factors, and provides three hypotheses. In order to highlight and test the three hypotheses about the CTP, five representative carbon trading pilot markets were included: Beijing, Shenzhen, Shanghai, Hubei, and Guangdong, and the daily average price data (over years) were adapted from January 2017 to December 2021, using a dynamic heterogeneous panel PMG model. The current paper selects the China air quality index (AQI), Bohai-Rim steam-coal price index (BSPI), Liquefied natural gas index (LNGI), and the Shanghai stock exchange industrial index (SSEII) as the explanatory variables. The empirical results show that there is a long-term equilibrium relationship between the CTP, AQI, energy price, and macroeconomics. Strengthening environmental governance, optimizing the energy structure, and expanding the carbon trading market coverage should be adopted to improve the China carbon emission trade exchange (CCETE) and stabilize the CTP.