Daily Price Research Articles

Uncovering time and frequency co-movement among green bonds, energy commodities and stock market

PurposeThis paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.Design/methodology/approachGeneric 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.FindingsResults from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.Originality/valueThis study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

Income over feed cost of the Indonesian-Bali pure breed cattle fed the formulation supplement of agricultural by-product in the urea red palm sugar block

Introduction and aim: The specific nutrients needed for tissue synthesis in ruminant animal’s growth required balanced nutrients arising from ruminant animal fermentative and intestinal digestion. Thus, this study aimed to evaluate feed supplement utilization of agricultural waste ingredients providing specific valuable nutrients in balancing the absorbed products of digestion to meet animal's requirements increasing animal economical production. Methods: Data of dried matter consumption, average daily gain feed conversion, feed cost and animal live weight price of yearling calves in Bali pure breed cattle were used to assess the economical animal production focusing on income over feed cost of different the levels of agricultural waste ingredients formulated in the urea red palm sugar block (URPS-Block). This formulated URPS-Block was fed to the Indonesian-Bali pure breed cattle with basal diets of king grass (Pennisetum purpureum). The unused waste of expired red palm sugar was used to change molasses function due to its unavailability and high prices in some areas. The yearling calves generated by Bali pure breed bull mated naturally with Bali pure breed cows were used in this study. Data were analyzed by a covariance model in the Double Latin Square design. Results: The economical productivity focusing on the income over feed cost (IOFC) of both male and female yearling calves by treatment comparisons of different levels of URPS-Block feed supplement showed that the treatment of URPS-Block supplement with 300 g/animal/day (R2) produced the highest income over feed cost (IOFC) of IDR 18,975.- and 21,100.- in female and male yearling calves, respectively. This IOFC of male yearling calves indicated the comparative outstanding of 6.95 percent more efficient for economical production over female yearling calves in Bali-pure breed cattle Conclusion: This study gives preliminary information on the treatment of the URPS-Block feed supplement of 300 g/animal/day (R2) producing the highest income over feed cost (IOFC). The results may help animal producers in planning strategies for beef farm to keep animals with the economical productivity using URPS-Block supplement from agricultural by-product for beef cattle farm sustainability. Implications: The treatment of the URPS-Block feed supplement of 300 g/animal/day (R2) could be used as the first rank choice of the highest feed supplement, followed by the treatment of the URPS-Block feed supplement of 150 g/animal/day (R1) and 450 g/animal/day (R3) to be the alternative choices for the economical animal production compared with treatment without URPS-Block (R0) in male and female calves of Bali-pure breed cattle.

The Menganalisis Pergerakan Harga Saham PT Indofood CBP Sukses Makmur Tbk: Sebuah Tinjauan Sebelum dan Sesudah Masa Pandemi

This study aims to determine the share price of Indofood CBP Sukses Makmur TBK (ICBP) using three different methods, namely Long Short-Term Memory (LSTM), Logistic Regression and Support Vector Machine (SVM). Daily stock price data from October 2010 to January 2023 are used for input in this study. The LSTM method is used to predict ICBP's stock price by taking information from several previous periods, while Logistic Regression and SVM are used to predict stock price trends.

The drivers of market-based inflation expectations in the euro area and in the US

In this paper, we propose a methodology to assess the structural drivers of inflation expectations, as measured by inflation-linked swaps. To this end, we estimate a Bayesian Vector Autoregressive (BVAR) model for the euro area (EA) and the United States (US) on daily asset price movements in the two economies. Shocks are identified using sign and magnitude restrictions, taking also into account international spillovers. The inclusion of inflation expectations allows to neatly differentiate between supply and demand innovations. The results suggest that over the course of 2021–23​ inflation expectations in the US were steadily sustained by domestic demand, while in the EA they mostly reflected the role of supply shocks, and only more recently a growing strength of demand factors. Our evidence also indicates that monetary policy shocks progressively contributed in soothing inflation expectations in both jurisdictions, although with a different timing and vigour.

Pump It: Twitter Sentiment Analysis for Cryptocurrency Price Prediction

This study demonstrates the significant impact of market sentiment, derived from social media, on the daily price prediction of cryptocurrencies in both bull and bear markets. Through the analysis of approximately 567 thousand tweets related to twelve specific cryptocurrencies, we incorporate the sentiment extracted from these tweets along with daily price data into our prediction models. We test various algorithms, including ordinary least squares regression, long short-term memory network and neural hierarchical interpolation for time series forecasting (NHITS). All models show better performance once the sentiment is incorporated into the training data. Beyond merely assessing prediction error, we scrutinise the model performances in a practical setting by applying them to a basic trading algorithm managing three distinct portfolios: established tokens, emerging tokens, and meme tokens. While NHITS emerged as the top-performing model in terms of prediction error, its ability to generate returns is not as compelling.

Prediction of Carbon Price in EU-ETS Using a Geometric Brownian Motion Model and Its Application to Analyze the Economic Competitiveness of Carbon Capture and Storage

To achieve carbon neutrality, many countries and regions are making efforts to promote the commercialization of greenhouse gas (GHG) mitigation technologies using emissions trading systems (ETSs). Accurate predictions of when the cost of GHG reduction technologies will become competitive below carbon prices could be invaluable to engineers and policy makers. In this study, carbon price movement in the EU-ETS was analyzed using a geometric Brownian motion (GBM) model. Using daily price data for the last 10 years, it tested whether the price pattern of the latter three years could be predicted by applying the first seven years of data to the GBM model. The results showed that the GBM model could well predict the upper and lower bounds of the actual carbon price. Based on the acceptable predictability of the GBM model, simulations were performed using carbon price data over the last decade, showing that carbon prices would reach around 200 EUR/tCO2 by the start of 2026. This is higher than the cost of CO2 avoided evaluated from the costs of commercial-scale carbon capture facilities for coal-fired power plants. This means that carbon capture technologies in the coal-fired power sector could become economically competitive within the next several years.

Financial time series prediction under Covid-19 pandemic crisis with Long Short-Term Memory (LSTM) network

In this paper, we design and apply the Long Short-Term Memory (LSTM) neural network approach to predict several financial classes’ time series under COVID-19 pandemic crisis period. We use the S&P GSCI commodity indices and their sub-indices and consider the stock market indices for different regions. Based on the daily prices, the results show that the proposed LSTM network can form a robust prediction model to determine the optimal diversification strategies. Our prediction model achieved RMSEs and MAEs too small for the different selected financial assets, showing the predictive power of our LSTM network especially during the COVID-19 health crisis. In addition, our LSTM network outperforms ARIMA-type models for all selected assets.

Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning

AbstractIn recent years, cryptocurrencies' price prediction has attracted the interest of many people including investors, researchers and practitioners. In this study, we proposed a hybrid model for predicting the daily close price of cryptocurrencies based on different neural networks such as long short‐term memory, convolutional neural network and attention mechanism. Using an ensemble of three pre‐trained language models, we extracted sentiment of cryptocurrency‐related tweets posted between 1 January 2021 and 31 December 2021. We constructed 20 different versions of our model and evaluated their performance on data of 27 most traded cryptocurrencies using a history of previous days' sentiment data along with close prices as input data. The flexible input layer of our model enables different ways of feeding data into the model to adjust it for different cryptocurrencies to obtain better predictions. Our analysis revealed several important findings. We showed that longer sequences of input data achieve most accurate predictions on average. More specifically, using a history of 14‐ and 21‐days' data results in lowest RMSE values on average compared to using a history of 7 days. However, there is no significant difference between the results related to the input sequences with lengths of 14 and 21. In addition, our findings suggest that sentiment data can be useful in predicting prices for more than 70% of the studied cryptocurrencies. Thus, peoples' emotions, opinions, and sentiment that are expressed through their posts on Twitter platform play a significant role in prediction of cryptocurrencies' prices.

Modernizing the Stock Pricing Mechanism: An Effective Path to Improve the Stock Market Efficiency

This study investigates whether regulating the stock pricing mechanism by adopting an agreed-upon efficient stock valuation model can enhance the stock market efficiency. The study involves a simulated stock market experiment with 65 traders who provide daily stock price predictions for a virtual company under unregulated and regulated scenarios. In the regulated scenario, traders agree on one of three valuation models to generate stock prices. Moreover, 20 evaluators acted as “Homo-Economicus” to determine a stock's fair value trend, serving as a benchmark to assess the information efficiency of the simulated market. The study finds that the NAPV-regulated market shows a strong linear relationship and high R-squared, indicating the highest level of information efficiency, while the DDM- and RIM-regulated markets show moderate correlations. The study suggests that modernizing the stock pricing mechanism, by regulating shareholders to mutually agree on one efficient stock valuation model to be used by the company to generate fair values alternative to market prices, could significantly enhance the stock market efficiency by focusing on fundamentals rather than irrational speculators. However, model choice matters, as NAPV explains more variation. The study suggests that appropriate regulation is crucial for realizing this potential. Although the results are promising, limitations like small evaluator samples, inability of models to always generate stock values, and trader biases should be considered. Future research with larger samples and more models could strengthen these insights.

Algorithmic trading using continuous action space deep reinforcement learning

Finding a more efficient trading strategy has always been one of the main concerns in financial market trading. In order to create trading strategies that lead to higher profits, historical data must be used. Due to a large amount of financial data and various factors affecting them, algorithmic trading and, more recently, artificial intelligence are employed to overcome the decision-making complexity. This paper aims to introduce a new approach using Twin-Delayed DDPG (TD3) and the daily close price to create a trading strategy. As a continuous action space deep reinforcement learning algorithm, in contrast to the discrete ones, the TD3 provides us with both the number of trading shares and the trading positions. In order to evaluate the performance of the proposed algorithm, the comparison results of our approach and other commonly-used algorithms such as technical analysis, reinforcement learning, supervised learning, stochastic strategies, and deterministic strategies are reported. By employing both position and the number of trading shares, we show that the performance of a trading strategy can be improved in terms of Return and Sharpe ratio.

Forecasting Day-Ahead Brent Crude Oil Prices Using Hybrid Combinations of Time Series Models

Crude oil price forecasting is an important research area in the international bulk commodity market. However, as risk factors diversify, price movements exhibit more complex nonlinear behavior. Hence, this study provides a comprehensive analysis of forecasting Brent crude oil prices by comparing various hybrid combinations of linear and nonlinear time series models. To this end, first, the logarithmic transformation is used to stabilize the variance of the crude oil prices time series; second, the original time series of log crude oil prices is decomposed into two new subseries, such as a long-run trend series and a stochastic series, using the Hodrick–Prescott filter; and third, two linear and two nonlinear time series models are considered to forecast the decomposed subseries. Finally, the forecast results for each subseries are combined to obtain the final day-ahead forecast result. The proposed modeling framework is applied to daily Brent spot prices from 1 January 2013 to 27 December 2022. Six different accuracy metrics, pictorial analysis, and a statistical test are performed to verify the proposed methodology’s performance. The experimental results (accuracy measures, pictorial analysis, and statistical test) show the efficiency and accuracy of the proposed hybrid forecasting methodology. Additionally, our forecasting results are comparatively better than the benchmark models. Finally, we believe that the proposed forecasting method can be used for other complex financial time data to obtain highly efficient and accurate forecasts.

Relaxing Daily Price Limits and Stock Market Cross-Correlation: Evidence from MF-X-DMA Analysis

The daily price limits in the ChiNext stock market were relaxed from [Formula: see text]% to [Formula: see text]% on 24 August 2020. Using the multifractal detrended moving average cross-correlation analysis (MF-X-DMA) method, we find that relaxing daily price limits leads to a greater degree of multifractality of the ChiNext stock market, suggesting that the relaxation of daily price limits harms stock market efficiency. In addition, the positive cross-correlation between ChiNext and Chinese main board stock markets becomes weaker, i.e., relaxing daily price limits also decreases the connection between ChiNext and other stock markets. Moreover, there is an increase in the degree of the cross-correlation multifractality between ChiNext and Chinese main board stock markets, suggesting that the linkage of ChiNext and other stock markets is more complicated and risky after the relaxation of daily price limits. Our findings fulfill related literature from the perspective of multifractality and have important implications for investors.

Pricing cloud stocks: Evidence from China

AbstractUsing factor models, we examine two pricing issues of cloud stocks in China's stock market. In particular, we test whether the Fama and French factor models are useful to explain the stock prices of cloud stocks and whether there are abnormal returns unexplained by these models. Using the daily stock prices of 1670 cloud stocks from 2012 to 2022, we find that the factor models explain up to nearly 97% of the stock return variations of the cloud stocks, and mispricing. The results are robust to alternative measure of factors, outliers, sampling period and different approaches of factor modelling.

Reactions of Stock Prices to Changes in Exchange Rate and Monetary Policy Rate of Central Banking System: Symmetry or Asymmetry

This study examines the reactions of stock prices to variations in the monetary policy rate of the central banks and exchange rate shock, which is if the responses are similar or differ in a comparative analysis between developed and emerging stock markets. Data were obtained on daily stock market prices, exchange rates, and variations in policy rate. The panel non-linear autoregressive distributed lag (NARDL) estimation method was utilized for this study. The NARDL showed the existence of a significant asymmetric long-run link between stock market price, exchange rate, and variation in monetary policy rate variables. For developing countries, the long-run results show that a percentage rise in the currency rate stimulated a 0.596% increase in the price of stock while it resulted in a 0.574% drop in the prices of stock. The same results were obtained for the short-run analysis. Also, the results show that a positive shock to the policy rate led to a 0.26% drop in the price of stock in the long term while it induced a 0.35% rise in the prices of stock. These estimates are similar to those obtained for developed economies. By and large, the findings imply that variations in the currency and monetary policy rate of the central banks have significant implications for stock market prices. There is a need for central monetary authorities to base effective regulations of exchange and monetary policy rates on market mechanisms while stock market investors are advised to frequently study the behavior of the two variables when dealing with the stock market.

Sustainability and Inflation: A Bi-front Malaise

The singular presence together of inflation and deflation is only apparently improper because we must always distinguish on the subject of inflation, about which phenomenon we are talking. In general, we can say that the two phenomena - called classics - cannot share. With an important distinction. If inflation or deflation is - as already said "classical" - that is, it affects the economic system due to errors made by governments in managing the economy. In this case, inflation has its own characteristics: that is, it is subject to constant acceleration and its race continues until monetary policy intervenes with the rise in the interest rate. Different the case of inflation from international prices, due to the increase in the cost of energy and rare earths, as well as cereals, etc. Well in this case, the subjection of daily prices is linked to the evolution to which they are subject due to speculation and to the selfishness of producers. In the case of international price inflation, the decision of central banks to raise the cost of borrowing has a marginal effect on cost inflation. However, it has a strong indirect effect because the rising cost of credit puts lead into the pockets of speculators. With the result of containing the speculative thrust to the advantage of domestic prices which are slowing down. We are in a period of creeping deflation and tensions on the stock markets and capital assets front, two phenomena that have direct repercussions on daily spending and aggravate the difference between the rich and classes close to poverty. It is therefore essential to mitigate the social tensions that accumulate that the issue of international price controls, starting with wheat and energy, are addressed at the G7 and G20 level for a policy of moderation on these prices. On the topic of the recurring pressure of stock market values and of equity assets, in search of the reasons for the unjustified rise, we point out the hypothesis that this for the moment moody rise in values has the possibility that it could be a sort of hypothetical foretelling of human society when we have achieved full neutrality with nature. At the time, a sharp acceleration in development could change our economics of scarcity.

Analysis of Gold Price Movements Through a Financial Forecasting Model Approach

The resilience of gold to situations full of risk has been proven over a long period of time. Forecasting the movement of gold which is in a safe range is done to prove that gold is still stable. The purpose of this study is to obtain the best model, estimate the parameters, and predict the daily gold price change in the last two years. The AR-GARCH(1.1) model is proven to be able to form the best forecasting model so that future gold resistance can be known with a low error rate. This model can be reliably applied to predict gold prices over the next 30 days. This may prompt investors to consider investing in or out of gold.

Links between US and Turkish agricultural commodity Markets: Nonlinear dependence and tail risk

In these unprecedented times, marred by the effects of the Covid-19 pandemic, global warming, and the war in Ukraine that began in February 2022, new approaches such as tail dependence have attracted more interest than conventional market dependence methodologies in analyzing time series in order to evaluate market linkage. In this study, we use the tail-restricted integrated regression function (IRF), introduced as a new methodology for nonlinear tail-mean dependence analysis. The IRF approach has several advantages over the existing tail-dependence measures focused solely on the occurrence of single tail events. To examine market dependence and tail risk, we analyze the nexus between the US and Turkish agricultural commodity markets over the period January 5, 2016–May 31, 2022. In addition to the daily prices of barley, corn, and wheat on agricultural commodity markets, we use Brent oil as a representative for the energy market, interest, and foreign exchange rates for financial markets as variables in our study. The results of upside and downside asymmetric risk spillovers show the direction of impact from the US agricultural market to the Turkish agricultural market. The findings suggest that, following the launch of a spot market in Türkiye, the launch of an agricultural commodity futures market will enhance the completeness and the link between the spot and derivatives markets there.

DAMPAK PENGUMUMAN COVID-19 TERHADAP ABNORMAL STOCK RETURN DAN TRADING VOLUME ACTIVITY

This study aims to determine the impact of the announcement of COVID-19 on abnormal stock returns and trading volume activity. The variables were abnormal return and trading volume activity in 61 days, 30 days before and after the incident, and the day the COVID-19 incident occurred. This research is an event study. This study uses secondary data, namely the daily stock price and the active trading volume of each infrastructure company during the study period. The data analysis method used is the paired sample t-test and Wilcoxon signed rank test to determine the significant impact before and after the announcement of COVID-19. This study shows that there is no significant abnormal return, and the average abnormal return before the announcement of COVID-19 is smaller than after the announcement of COVID-19. There is significant trading volume activity on the day of the announcement of COVID-19 in the infrastructure sector in Indonesia, and the average trading volume activity before the announcement of COVID-19 is smaller than after the announcement of COVID-19.

Exploring the Multifractality in the Precious Metal Market

This study proposes a novel approach to investigating the multifractality of time series using the multifractal cross-correlation detrended moving average analysis (MF-X-DMA). The study demonstrates the behavioral differences of MF-X-DMA in coherent and non-coherent time periods. Due to the lack of a mechanism to capture the dynamical cross-correlation in time series, correlated time series with multifractal structure present a barrier for analysis. The study shows that when the wavelet coherence method is applied to time series, co-movement between time series can be easily captured in certain time intervals, providing an efficient way to find time intervals to apply MF-X-DMA. The study applies the wavelet coherence method to the daily spot prices of gold and platinum from January 1987. It shows that the wavelet coherence method is an excellent engine to extract designated time series in certain frequency and time intervals, eliminating the need for windowing or shuffling methods. Additionally, the study observes a long-term power law cross-correlation using detrended cross-correlation analysis coefficients of inversed series for both low-correlated and high-correlated series. Finally, the findings indicate that MF-X-DMA leads to superior results compared to MF-DFA when provided with highly correlated data.

Carbon tax pass‐through in Canadian retail gasoline markets

AbstractA carbon tax's pass‐through is one factor influencing its effectiveness in internalizing the externality created by greenhouse gas emissions. This paper measures the pass‐through of carbon taxes introduced in retail gasoline markets of four Canadian provinces that did not meet the carbon pollution pricing federal benchmark stringency requirements. Those four provinces are Saskatchewan, Manitoba, Ontario and New Brunswick. Using daily retail gasoline prices for 40 treated cities and nine control cities we find the pass‐through rates are city‐specific and vary from 0% to over 140%. City‐specific pass‐through rates imply that estimations at a higher level of geographical aggregation assume homogeneous effects where heterogeneous effects might be present. Our results also suggest it would be difficult for a government to impose an optimal and nationwide carbon tax on automotive greenhouse gas emissions. Although the degree of competition can explain city‐specific pass‐through rates, it cannot explain over‐shifting. Over‐shifting can be explained, however, by the demand functional form.