Daily Price Research Articles

Improving Operational Efficiency through Quality 4.0 Tool: Blockchain Implementation and Subsequent Market Reaction

Purpose: This article aims to observe and measure how modern and innovative blockchain technology improves the data quality and transparency and thus affect the stock prices of publicly traded companies after announcing its implementation in their operations. Additionally, the objective is to compare the results with control group of non-adopters. Methodology/Approach: We selected 30 public companies across various sectors, obtained daily stock price data, identified peer companies, and employed an event study approach to examine the statistical impact of blockchain adoption announcements. Findings: A significant negative reaction (-0.4%) was observed in stock prices the day following a blockchain adoption announcement, but overall, the market response was unsystematic, indicating no consistent reaction in stock prices post-announcement. Research Limitation/Implication: The event study approach assumes that markets are always efficient. This methodology has some limitations because we live in a world that is not perfect, and stock prices do not necessarily fully reflect all available information. Originality/Value of paper: Blockchain implementation is a current and intriguing subject that has attracted limited scholarly research. Each new study contributes valuable insights to the understanding of how this innovative technology impacts corporate operations. Furthermore, this research endeavours to draw comparisons between companies that have announced their adoption of blockchain and their non-adopters counterparts.

Price forecasts of ten steel products using Gaussian process regressions

Addressing price forecasting problems is an important exercise to policymakers and market participants in the resource business sector. In this work, we build Gaussian process regression models through cross validation and Bayesian optimizations over different kernels and basis functions for daily price index forecasting of ten major steel products in the Chinese market during July 20, 2011–April 15, 2021. This study aims to be the first attempt of exploring potential of Gaussian process regressions for price forecasting exercises with the coverage of all of the ten most important steel products that carry enormous economic significance in China as the largest steel consumer and producer in the world. The models offer accurate out-of-sample forecasts for the two-year period of April 16, 2019–April 15, 2021 with relative root mean square errors ranging from 0.07404% to 0.22379% across the ten price indices and correlation coefficients above 99.9%. They also lead to better forecast performance than some traditional econometric models and some other machine learning models as benchmarks. The models constructed here could be utilized by policymakers as part of policy design and implementations and by market participants as part of market assessments and decision making.

A cross-quantile correlation and causality-in-quantile analysis on the relationship between green investments and energy commodities during the COVID-19 pandemic period

PurposeThis paper aims to examine the cross-quantile correlation and causality-in-quantiles between green investments and energy commodities during the outbreak of COVID-19. To be specific, the authors aim to address the following questions: Is there any distributional predictability among green bonds and energy commodities during COVID-19? Is there exist any directional predictability between green investments and energy commodities during the global pandemic? Can green bonds hedge the risk of energy commodities during a period of the financial crisis.Design/methodology/approachThe authors use the nonparametric causality in quantile and cross-quantilogram (CQ) correlation approaches as the estimation techniques to investigate the distributional and directional predictability between green investments and energy commodities respectively using daily spot prices from January 1, 2020, to March 26, 2021. The study uses daily closing price indices S&P Green Bond Index as a representative of the green bond market. In the case of energy commodities, the authors use S&P GSCI Natural Gas Spot, S&P GSCI Biofuel Spot, S&P GSCI Unleaded Gasoline Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI, OPEC Oil Basket Price, Crude Oil Oman, Crude Oil Dubai Cash, S&P GSCI Heating Oil Spot, S&P Global Clean Energy, US Gulf Coast Kerosene and Los Angeles Low Sulfur CARB Diesel Spot.FindingsFrom the CQ correlation results, there exists an overall negative directional predictability between green bonds and natural gas. The authors find that the directional predictability between green bonds and S&P GSCI Biofuel Spot, S&P GSCI Gas Oil Spot, S&P GSCI Brent Crude Spot, S&P GSCI WTI Spot, OPEC Oil Basket Spot, Crude Oil Oman Spot, Crude Oil Dubai Cash Spot, S&P GSCI Heating Oil Spot, US Gulf Coast Kerosene-Type Jet Fuel Spot Price and Los Angeles Low Sulfur CARB Diesel Spot Price is negative during normal market conditions and positive during extreme market conditions. Results from the non-parametric causality in the quantile approach show strong evidence of asymmetry in causality across quantiles and strong variations across markets.Practical implicationsThe quantile time-varying dependence and predictability results documented in this paper can help market participants with different investment targets and horizons adopt better hedging strategies and portfolio diversification to aid optimal policy measures during volatile market conditions.Social implicationsThe outcome of this study will promote awareness regarding the environment and also increase investor’s participation in the green bond market. Further, it allows corporate institutions to fulfill their social commitment through the issuance of green bonds.Originality/valueThis paper differs from these previous studies in several aspects. First, the authors have included a wide range of energy commodities, comprising three green bond indices and 14 energy commodity indices. Second, the authors have explored the dependency between the two markets, particularly during COVID-19 pandemic. Third, the authors have applied CQ and causality-in-quantile methods on the given data set. Since the market of green and sustainable finance is growing drastically and the world is transmitting toward environment-friendly practices, it is essential and vital to understand the impact of green bonds on other financial markets. In this regard, the study contributes to the literature by documenting an in-depth connectedness between green bonds and crude oil, natural gas, petrol, kerosene, diesel, crude, heating oil, biofuels and other energy commodities.

Forecasts of the real price of oil revisited: Do they beat the random walk?

In macroeconomic forecasting, the real price of oil is traditionally computed as the monthly average price of oil deflated by the price index. Consequently, the no-change forecast used to benchmark forecasts of the real price of crude oil is a monthly average price. We demonstrate that an alternative no-change forecast which reflects the random walk forecast from daily oil prices – the end-of-month price – is significantly more accurate in predicting the real price of oil up to one year ahead. We find that at the one-step-ahead prediction, all existing forecasts that outperform the monthly average no-change forecast perform worse than the end-of-month no-change forecast. The results call into question the usefulness of existing forecasting approaches for the real price of crude oil relative to naive forecasts.

A dynamic connectedness analysis between rare earth prices and renewable energy

Current world environmental challenges put pressure on clean energy produced mostly through renewables. There is an undeniably important role of rare earth minerals in renewable energy technologies. This study aims to infer the relationship between rare earth, clean energy, renewable energy technologies, and carbon emissions, focusing on daily stock price index data and applying the novel quantile time-frequency connectedness model, and the cross-quantilogram dependence approach during 2012–2022. Results show that spillovers among rare earth minerals and renewable energy are dependent on market conditions, time horizons, and analyzed quantiles. They also highlight the net receiver role of rare earth, especially in the short term. Findings might help investors understand diversification benefits and support policymakers in developing strategies for lessening import dependence on rare earth metals, as important as they are for renewable technology adoption to ensure green growth.

From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling

Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations.

Predicting Indian electricity exchange‐traded market prices: SARIMA and MLP approach

AbstractThis research investigates the short‐term (ST) forecasting performance of the daily prices of the Indian exchange‐traded day‐ahead (DAM) market, divided into 13 bid areas, each consisting of states with varied fundamentals. Forecasts are built employing SARIMA (seasonal autoregressive integrated moving average) and MLP (multilayer perceptron) methods. Moreover, the robustness and performance of the model is compared using the lowest error and the Diebold–Mariano (DM) test statistic values. The results indicates that the SARIMA model has high prediction accuracy with error values ranging from 1% to 5% with Southern region having the highest error of 4.53% and Northern having the least error of 1.27%. However, validation by the DM test suggests no statistical significant difference between the two models. The power generators, distribution companies, traders, policymakers, strategists and managers could use the findings for effective power management through proper planning.

Wheat price volatility regimes over 140 years: An analysis of daily price ranges

We analyze Chicago based daily wheat price volatility over more than 140 years using a novel data set of daily high and low futures prices starting in 1877. We identify five long-run regimes and find that volatility shifts between regimes are statistically more pronounced than fluctuations within regimes, even when conditioning on economic states. Historical volatility estimates derived from average commodity price data, a common practice in empirical studies, exhibit a regime-dependent upward bias between 0% and 22%. The magnitude of the bias and the importance of regimes potentially explain contradictory findings on volatility patterns in earlier studies.

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets.

The isotropy of cryptocurrency volatility

AbstractWe examine the fractal volatility and long‐range dependence of Bitcoin, Ethereum, Tether and USD Coin by employing the continuous wavelet transform, maximal overlap discrete wavelet transform and rescaled range. Our dataset consists of daily prices spanning from January 2017 through to October 2022, encapsulating pre‐ and post‐epidemic eras. Generally, our findings suggest that Tether presents the least overall volatility throughout the time‐frequency spectrum. USD Coin demonstrates ephemeral turbulence, contrary to Tether's maturity in influencing market equilibrium through token issuance and trade responses. In the post‐epidemic sample, both stablecoins indicate mean reversion, with USD Coin showing marginally better efficiency. Conversely, investment tokens display persistent clusters due to retail traders and long‐term fundamental institutions. Although both tokens illustrate multifractal volatility, Ethereum unveils more essence of self‐similarity than Bitcoin. Hence, there is no evidence that Ethereum truly duplicates Bitcoin since policy‐related events differ between them, as both return series move incongruously. Conditional dynamics signify that all cryptocurrencies, except Tether, were affected by the pandemic transition of COVID‐19 and subsequent macroeconomic news. The unconditional volatility of stablecoins evinces zero‐mean errors, antithetical to investment tokens exhibiting annual cycles. The fractal geometry suggests that investment tokens simulate one‐dimensional lines, whereas stablecoins mimic two‐dimensional planes.

Analyzing the volatility spillover and cointegration relationship between daily spot West Texas intermediate crude oil price and US dollar

In the study, it is aimed to analyze the diffusion and cointegration relationship between WTI and US Dollar in the period of 2016-2021. In the study, after a comprehensive literature review of the theoretical review, the econometric analysis section was started. In the first part of the analysis, the short and long-term relationships between the variables were examined with the autoregressive distributed lag methodology and the existence of a cointegration relationship was reached. According to the findings, the effect of WTI on foreign exchange volatility in the long run is statistically significant and negative. In the short-term evaluation, ECT is negative and significant within expectations. In this context, the changes between the variables approach the long-term equilibrium level. According to the results obtained in the causality and variance causality analyzes applied in the last part of the analysis, it is understood that there is a volatility spillover effect from WTI to foreign currency.

The Estimation of Heavy Tails in Non-linear Models

A generalized student t distribution technique based on estimation of bilinear generalized autoregressive conditional heteroskedasticity (BL-GARCH) model is introduced. The paper investigates from empirical perspective, aspects of the model related to the economic and financial risk management and its impacts on volatility forecasting. The purposive sampling technique was applied to select four banks for the study, namely First Bank of Nigeria (FBN), Guaranty Trust Bank (GTB), United Bank for Africa (UBA) and Zenith Bank (ZEB). The four banks are selected, because their daily stock prices are considered to be more susceptible to volatility than those of other banks within the sampled period (January 2007–May 2022). The data collected were analyzed using MATLAB R2008b Software. The results show that the newly introduced generalized student t distribution is the most general of all the useful distributions applied in the BL-GARCH model parameter estimation. It serves as a general distribution for obtaining empirical characteristics such as volatility clustering, leptokurtosis and leverage effects between returns and conditional variances as well as capturing heavier and lighter tails in high frequency financial time series data.

Employing Recurrent Neural Networks to Forecast the Dollar Exchange Rate in the Parallel Market of Iraq

In this study, neural recurrent neural networks (RNN) have been used to forecast the price of dollars in Iraqi dinars, as it is clear that the government's efforts to control prices in the parallel markets, the commercial markets witnessed a decline in the exchange rate, but it rose again. Which indicates an economic problem that is still present in the country. Despite adjusting the exchange rate of the dinar, the dollar crisis in Iraq has not ended yet. Here we want to forecast the daily price of the dollar against Iraqi dinars for the common next 30 days. According to the results the RNN model have been performed for the data under consideration with different numbers of hidden layer and nodes. The best architecture for the RNN model was [1,10,1,1] using soft plus activation function, which gives the performance of 85% for the training dataset and (92% and 90%) for the testing and validation datasets respectively, with Mse (0.018, 0.000417, and 0.000477) for training, testing, and validation respectively at epoch 4. According to the results of the forecasted values which start from 15 May 2023 to 13 June 2023 the price of dollars will be between 1390 to 1435.

Price limit performance: New evidence from a quasi-natural experiment in China's ChiNext market

This study examines the impact of daily price limits on market performance and trading activity by using a quasi-natural experiment in China. It focuses on the case of the ChiNext market, where the daily price limits of stocks increased from 10% to 20% in 2020. The results show that, initially, the stock prices and occurrences of 10% price limit hits increase, but then decline after the new price limits have been implemented. The level of trading liquidity and volatility increases significantly, with a greater impact on the short term than the long term. These price limit performances are more pronounced for stocks with additional retail interest. The analysis of detailed trading data reveals that institutional investors initially purchase ChiNext stocks in large quantities, followed by retail investors who purchase smaller quantities. In the long run, institutional investors tend to increase their holdings, while retail investors tend to sell their holdings. Additionally, there is a temporary increase in investor attention, price synchronicity, and stock risks, followed by a decline. The findings suggest that wider price limits increase trading volumes and enhance long-term market efficiency, but encourage immediate price manipulation, causing short-term overreactions and long-term reversals. This study provides valuable insights for building an effective price limit system.

Multi-Step Vector Output Prediction of Time Series Using EMA LSTM

This research paper proposes a novel method, Exponential Moving Average Long Short-Term Memory (EMA LSTM), for multi-step vector output prediction of time series data using deep learning. The method combines the LSTM with the exponential moving average (EMA) technique to reduce noise in the data and improve the accuracy of prediction. The research compares the performance of EMA LSTM to other commonly used deep learning models, including LSTM, GRU, RNN, and CNN, and evaluates the results using statistical tests. The dataset used in this study contains daily stock market prices for several years, with inputs of 60, 90, and 120 previous days, and predictions for the next 20 and 30 days. The results show that the EMA LSTM method outperforms other models in terms of accuracy, with lower RMSE and MAPE values. This study has important implications for real-world applications, such as stock market forecasting and climate prediction, and highlights the importance of careful preprocessing of the data to improve the performance of deep learning models.

Wavelets in Combination with Stochastic and Machine Learning Models to Predict Agricultural Prices

Wavelet decomposition in signal processing has been widely used in the literature. The popularity of machine learning (ML) algorithms is increasing day by day in agriculture, from irrigation scheduling and yield prediction to price prediction. It is quite interesting to study wavelet-based stochastic and ML models to appropriately choose the most suitable wavelet filters to predict agricultural commodity prices. In the present study, some popular wavelet filters, such as Haar, Daubechies (D4), Coiflet (C6), best localized (BL14), and least asymmetric (LA8), were considered. Daily wholesale price data of onions from three major Indian markets, namely Bengaluru, Delhi, and Lasalgaon, were used to illustrate the potential of different wavelet filters. The performance of wavelet-based models was compared with that of benchmark models. It was observed that, in general, the wavelet-based combination models outperformed other models. Moreover, wavelet decomposition with the Haar filter followed by application of the random forest (RF) model gave better prediction accuracy than other combinations as well as other individual models.

Global energy supply risk: Evidence from the reactions of European natural gas futures to Nord Stream announcements

Motivated by recent geopolitical challenges to globalization, finance scholarship has a growing interest in the risks to economies of key supply chains. We explore the effect of Nord Stream pipeline development on daily natural gas prices in Europe. Reviewing a comprehensive list of 24 announcements related to Nord Stream pipeline developments, stoppages, and sanctions for 2013–2022, we investigate their direct impact on the volatility and returns of Dutch TTF natural gas futures. Employing bootstrap quantile regression, we find that the effects of such announcements are highly significant, especially during periods of low TTF volatility and returns distributions. Moreover, results show that both COVID-19 and the Russia-Ukraine war strenghtened the correlation between Nord Stream pipeline announcements and TTF market reactions, consistent with energy markets pricing geopolitical risks. Results, reflective of informed trading, warn of concentration risk in European gas markets, suggesting the need for policy actions to reduce supply-chain dependence. Taken together, we support the importance of diversifying both natural gas supply infrastructures and providers to mitigate geopolitical risk exposures and associated welfare costs to the real economy.

Measuring the Potential Integration of Indonesian Sharia Capital Market with the OIC Member Countries

The Indonesian Composite Index (ICI) experienced a critical session, the data showed the ICI fell by 6.58% which was the biggest decline in the last 9 years. The prevailing notion was that this pressure stemmed from the prevalence of the Covid-19 concern and is anticipated to be alleviated once Covid-19 disseminates further. The purpose of this study is to determine the causality and long-term cointegration relationship between sharia stock in Indonesia and the Organization of Islamic Cooperation (OIC) member countries, as well as to determine the contribution of shares of OIC member countries, such as Egypt, Oman, Saudi Arabia, Kuwait, Turkey, Bahrain, and Malaysia in the collapse of the Islamic capital market in Indonesia. The method used in this research is quantitative by using secondary data in the form of daily prices of the Sharia index sourced from the website www.investing.com. The population of the countries related to this research is 56 members of the OIC that are active until 2021. For data analysis, several tests were conducted, such as; Testing Time Series Data, Vector Autoregressive (VAR), Granger Causality Test, Johansen Model Cointegration Test, and Contagion Effect Test. The study found that; has not been able to prove the existence of cointegration between the Indonesian Islamic stock exchange and the stock exchanges of OIC member countries other than the Malaysian stock exchange. However, this study proves the interdependence between the Malaysian and Indonesian Islamic stock exchanges, but in the long term, the contribution of the Indonesian stock exchange is greater than the Malaysian Islamic stock index. In addition, another finding was that during the Covid-19 shock, the Malaysian stock position index showed more independence, whereas the decline that occurred at the beginning of the pandemic was caused without the JII or ISSI intervention index.

Analyzing and forecasting electricity price using regime‐switching models: The case of New Zealand market

AbstractThis paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities.

Effectiveness of price limits: Evidence from China's ChiNext market.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.