Daily Market Research Articles

Energy forecast for a cogeneration system using dynamic factor models

Cogeneration is used in different sectors of industry and it allows that two types of energy to be efficiently obtained from a single source. Accurate predictions are fundamental to optimize energy production, considering the variability that occurs in the daily market. This study adjusts and predicts cogeneration using real data from a Spanish energy technology center, using dynamic factor analysis methodology and incorporating covariates such as temperature and relative humidity. A comparative analysis is performed to evaluate the improvements achieved by implementing cluster-structured dynamic models versus other methods. Furthermore, a robust interpolation method has been implemented to handle missing data in both the main variable and the covariates.

Short-term electricity price forecasting through demand and renewable generation prediction

Electricity market prices depend on various variables, including energy demand, weather conditions, gas prices, renewable generation, and other factors. Fluctuating prices are a common characteristic of electricity markets, making electricity price forecasting a complex process where predicting different variables is crucial. This paper introduces a hybrid forecasting model developed for the Spanish case. The model comprises four forecasting tools, with three of them relying on artificial neural networks, while the demand forecasting model employs a similar-day approach with temperature correction. This model can be employed by electrical energy trading companies to enhance their trading strategies in the day-ahead market and in derivative markets with a time horizon ranging from two to ten days. The results indicate that, with a forecasting horizon of two days, the price forecast has a rMAE of 8.18%. Furthermore, the model enables a market agent to accurately decide whether to purchase energy in the daily market or in the derivatives market in 69.9% of the days.

A rapid increase in tropical species of grouper (Perciformes: Serranidae) in the temperate waters, the Goto Islands, Japan.

Global warming has resulted in rapid poleward shifts in the geographical distributions of many tropical fish species. This study conducted daily market surveys from 2008 to 2013 to investigate catch trends of seven commercially important grouper species in the temperate Goto Islands, Japan. Our results revealed that the catch numbers of tropical grouper species increased rapidly by an average of 5.9-fold (12.3-fold at maximum) within six years, whereas the temperate and subtropical species did not exhibit substantial changes. Based on the findings of several previous studies, the rapid increase in the number of tropical groupers in temperate waters was most likely caused by the successful settlement of larvae transported from tropical waters. Large-scale ocean currents may facilitate larval transport from tropical waters because the Goto Islands face the Tsushima Warm Current, which branches from the Kuroshio Current. Meanwhile, the transition processes of size distribution in tropical groupers suggest a possible hypothesis that adults migrating from tropical waters first settle in temperate waters and then enhance their populations by reproduction. Further studies are required to determine how tropical grouper species settle and how their populations increase in temperate waters.

Is democracy costly? The effect of political turmoil on Kuwait’s stock market

This study is the first to analyze the impact of domestic political unrest on Kuwait’s stock market. Our data indicates a daily market decline of 0.16 percent during periods when the parliament is suspended. This translates to a 7.1 percent loss in the value of the Kuwait Stock Exchange for each period of suspension. Our findings suggest that Kuwait, despite having a more democratic political system, experiences greater economic instability than Saudi Arabia. This research highlights the nuanced relationship between the political structure and economic performance, particularly in emerging markets, challenging the notion that more democracy invariably leads to better economic outcomes.

Fine sandy concrete reinforced with polypropylene fibre for rural and desert housing

This research aimed to design a low-cost fibre concrete as an alternative to earth-concrete reinforced with biomaterial fibres, where the latter is characterized by mechanical defects such as low strength, water degradation and air erosion, as well as the continuous deformation related to the self-compression and unlimited subsidence of the bearing surface soil.The designed concrete is composed of fine sand, cement at small quantities and polypropylene fibre available on the daily market. This material is easy to form due to the availability of fine sand in nature and the possibility of its production without much skill. At the same time, it is made at a low cost and short implementation time, unlike earth concrete where it needs time to dry and acquire the appropriate strength. Laboratory work has shown good results as the light density, the large ductility, and an acceptable strength capacity of this material.

A novel market sentiment measure: assessing the link between VIX and the Global Consciousness Projects data

PurposeThe primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.Design/methodology/approachThis study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.FindingsThe results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.Research limitations/implicationsOne limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.Practical implicationsThe study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.Originality/valueUtilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

TESTING THE MARKET EFFICIENCY WHEN INTEREST RATES CHANGE: CASE IN INDONESIA

Until the end of September 2023, the interest rate policy in the Republic of Indonesia shifted from 3.50% to 5.75%. The objective of this study is to examine whether changes in interest rates have an impact on the market efficiency in Indonesia. The daily market returns of Indonesia Stock Exchange (IDX) indices from January 3rd, 2022, until October 9th, 2023 are employed. To examine the market efficiency in weak form, this study conducts the normality test by the Kolmogorov-Smirnov test, the unit root problem test by Augmented Dickey-Fuller test, and the variance ratio test. This study finds that most market indices experienced a decline in returns and were less volatile when interest rates shifted to higher levels. This study also finds that most market indices tend to remain efficient in weak form at both low and high interest rates. The finding implies that those indices tend not to be influenced by policy changes in interest rates. However, the findings also show that several market indices experience changes in conditions when interest rates shift. As the studies on the relationship between changes in interest rates and market efficiency are scarce, the novelty of this study is the use of event windows to get specific impact of changes in interest rates on market efficiency.

The nexus between non-fungible tokens (NFTS) and cryptocurrencies

BACKGROUND: Like Bitcoin or any other cryptocurrencies, non-fungible tokens (NFTs) count on blockchain technology, and NFTs are the latest and the most popular in a series of blockchain solutions. Traders in this ecosystem need to pay a dynamic fee, called a gas fee, for making any transactions on the Ethereum blockchain. The gas fee is measured by gwei, and traders must consider this as an additional cost. So, the current price of this fee may affect the decision of NFT creators or traders. OBJECTIVE: This study investigates the interrelationships between NTFs, cryptocurrencies (Ethereum and BTC), and gas fees using daily market data from January 2019 to November 2021. METHOD: Fourier Shin’s (2016) cointegration test, Fully Modified Ordinary Least Squares, and Group Dynamic Least Squares tests were employed to analyze the data. Then, the variance Decomposition method was applied to determine what other variables explain the percentage of the total variance on NFTs— it also used Impulse-response functions for measuring the response of the NFTs variable for one standard deviation shock. RESULTS: Results show that an increase in gas fees, the daily volume of Bitcoin, and the daily volume of Ethereum decrease NFTs sales. There is a unidirectional relationship between lnSales and lnGasFee variables. Also, there is a determined unidirectional relationship between lnBTC and lnSales variables. Lastly, there is a one-way causality relationship between lnSales and lnETH variables. CONCLUSIONS: The primary causation of the relationship between NFTs, gas fees and Ethereum fees is most likely related to the use of Ethereum as the primary means of payment in the NFTs market and gas fees being a significant cost element in NFTs trading. Another point of view is that the dominance of Bitcoin in the market is very effective in pricing of other cryptocurrencies and in the sales and pricing of NFTs indirectly. It is supported by empirical findings that the main elements in the blockchain ecosystem are interrelated.

Prediction of Matching Prices in Electricity Markets through Curve Representation

In the Spanish electricity market, after the daily market is held in which prices are set for the next day, the secondary and tertiary markets take place, which allow companies more accurate adjustment of the electricity they are able to offer. The objective of this paper is to predict the final price reached in these markets by predicting the supply curve in advance, which is the aggregate of what companies offer. First, we study a procedure to represent the supply curves, and then we consider different machine learning approaches to obtain the day-ahead supply curves for the secondary market. Finally, the predictions of the supply curves are crossed with the system requirements to obtain the expected price predictions. Histogram-Based Gradient Boosting is the best performing algorithm for predicting supply curves. The most relevant variables for the prediction are the lagged values, the daily market price, the price of gas and values of the wind recorded in the Spanish provinces.

Optimal multi-layer economical schedule for coordinated multiple mode operation of wind–solar microgrids with hybrid energy storage systems

The aim of this paper is the design and implementation of an advanced model predictive control (MPC) strategy for the management of a wind–solar microgrid (MG) both in the islanded and grid-connected modes. The MG includes energy storage systems (ESSs) and interacts with external hydrogen and electricity consumers as an extra feature. The system participates in two different electricity markets, i.e., the daily and real-time markets, characterized by different time-scales. Thus, a high-layer control (HLC) and a low-layer control (LLC) are developed for the daily market and the real-time market, respectively. The sporadic characteristics of renewable energy sources and the variations in load demand are also briefly discussed by proposing a controller based on the stochastic MPC approach. Numerical simulations with real wind and solar generation profiles and spot prices show that the proposed controller optimally manages the ESSs, even when there is a deviation between the predicted scenario determined at the HLC and the real-time one managed by the LLC. Finally, the strategy is tested on a lab-scale MG set up at Khalifa University, Abu Dhabi, UAE.

The Marketing Strategy for Small-Scale Garment Products on Post-Pandemic Era

The small-scale garment industry has gained significant influence in Indonesia. However, with the challenges posed by the pandemic era, the industry must adapt to ensure its long-term sustainability. The objective of this research is to analyze the marketing strategy of small-scale garment products, specifically examining the proportion of online and offline marketing strategies, as the success of these products in the market relies on the effective utilization of both strategies. The urgency of this research stems from the daily market demand for garment products and the crucial role played by dominating small-scale garment companies in contributing to the economy’s development. This research adopts a mixed-method approach, incorporating both qualitative and quantitative methods. The qualitative approach involves conducting interviews with owners of small-scale garment businesses, commonly known as convection businesses. Meanwhile, the quantitative approach is used to analyze the proportion of online and offline marketing strategies. The qualitative analysis utilizes a Markov approach to provide insights into future marketing strategy proportions and possibilities. The research was conducted at a small-scale garment center located in Kutawaringin Village, West Bandung Regency. The results indicate that during the pandemic era, garment businesses have increasingly embraced online marketing strategies to enhance their marketing performance. Furthermore, businesses that have traditionally relied on offline marketing are also increasing their proportion of online marketing strategy implementation.
 Keywords: marketing strategy, small-scale garment business, online, offline

Technical analysis as a sentiment barometer and the cross-section of stock returns

This paper explores an unexamined sentiment channel through which technical analysis can add value. We use a spectrum of technical trading strategies to build a daily market sentiment indicator that is highly correlated with other commonly used sentiment measures. This technical-analysis-based sentiment indicator positively predicts near-term returns and is inversely related to long-term returns in the cross-section. Simple trading strategies based on this sentiment indicator yield substantial abnormal returns. These results are consistent with the explanation that lack of synchronization induces rational arbitrageurs to exploit the mispricing before it is corrected.

Memory Effects, Multiple Time Scales and Local Stability in Langevin Models of the S&P500 Market Correlation.

The analysis of market correlations is crucial for optimal portfolio selection of correlated assets, but their memory effects have often been neglected. In this work, we analyse the mean market correlation of the S&P500, which corresponds to the main market mode in principle component analysis. We fit a generalised Langevin equation (GLE) to the data whose memory kernel implies that there is a significant memory effect in the market correlation ranging back at least three trading weeks. The memory kernel improves the forecasting accuracy of the GLE compared to models without memory and hence, such a memory effect has to be taken into account for optimal portfolio selection to minimise risk or for predicting future correlations. Moreover, a Bayesian resilience estimation provides further evidence for non-Markovianity in the data and suggests the existence of a hidden slow time scale that operates on much slower times than the observed daily market data. Assuming that such a slow time scale exists, our work supports previous research on the existence of locally stable market states.

Is soft information substitutive or complementary to hard news for investor attention? Evidence from corporate advertising in Japan

Corporate advertising is a type of soft information that may not directly target the financial markets but may attract investors’ attention. In this study, we consider television advertisements (TV ads) as soft information, whereas corporate news via disclosure and mass media as hard news. We explore the interactive effect of TV ads (soft information) with corporate news (hard news) on stock market outcomes. Based on daily market data with the multi-dimensional information sources, we find that TV ads significantly increase trading volume and returns, conditional on less corporate news being released. Our evidence supports the information substitution hypothesis that TV ads compete with corporate news owing to investors’ limited attention, while rejecting the complementary relationship between soft and hard information.

Uncovering the Effect of News Signals on Daily Stock Market Performance: An Econometric Analysis

The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal that political and global news has a significant impact on KSE-100 index. Blue chip stocks are considered safer investments, while short-term panic responses often overshadow rational decision-making in the stock market. Investors tend to quickly react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting stock market fluctuations compared to the simple OLS method.

A comparison of machine learning and econometric models for pricing perpetual Bitcoin futures and their application to algorithmic trading

AbstractBitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.

Ameliorated graph sample and aggregate network and convolutional neural network for stock trading decisions

Both graph neural network and convolutional neural network (CNN) perform well in predicting the trading signals of time series data. However, these models often encounter challenges in profitability due to either inadequate information or algorithmic limitations. A model AG-MCNN is proposed by combining ameliorated graph sample and aggregate network (GraphSAGE) and multi-CNN for stock trading decisions. Ameliorated GraphSAGE (AG) is used to learn graph embedding in daily market network constructed from comprehensive market information, with neighbors having higher correlation exerting stronger influence on the target node. Moreover, a more reasonable graph modeling approach is adopted to preserve the important features of time series while filtering out noise. Multi-scale time series and comments with sentiment information are collected to analyze short-term and long-term trends. The short-term features of the weighted graph are extracted by a dual concatenated CNN. The pre-trained model is fine-tuned based on transfer learning to address the issue of insufficient sentiment information in the long-term trend. Experimental results demonstrate that AG-MCNN achieves an average profit rate of 68.4094%, compared to the highest value of 46.1091% for comparison models and 18.2234% for the buy-and-hold strategy. AG-MCNN’s high level of profitability in stock investments makes it a promising choice for investors.

Cost of health problems caused by stock market volatility: An empirical study in Taiwan

This study highlights the often-overlooked health impacts and related costs endured by individual investors due to market volatility. Leveraging Taiwan's unique market characteristics and healthcare system, we underscore the economic repercussions of health degradation induced by market instability. Analyzing daily medical costs for depression and hypertension patients over 11 years reveals the impact of market turbulence. Specifically, a standard deviation increase in daily market volatility aligns with 4% and 12% surges in daily emergency and outpatient spending, respectively. To avert confounding biases, our empirical models account for market regimes and weather factors, and the outcomes are corroborated through robustness tests.

Evaluating the Effects of the COVID-19 Pandemic on Daily Market Return in Pakistan Stock Exchange

Evaluating the Effects of the COVID-19 Pandemic on Daily Market Return in Pakistan Stock Exchange

METAL, PAPER, AND RUBBER WASTE MANAGEMENT SCENARIO A CASE STUDY OF KHULNA CITY

Because of modern day overpopulation, rapid industrial and lifestyle growth, and Khulna citys geographic location, natural resource consumption is rising while their availability is deteriorating. This paper has been designed with an attempt to inform the policymakers of Bangladesh regarding the potential of waste management and recycling as a renewable source of energy. Due to economic and environmental initiatives, metal, paper, and rubber waste recycling rates continue to rise.The total daily waste collection volume considering four areas of the city (Fulbarigate, Khalishpur, Sheikhpara, and Zero Point) was found to be9.91tons. Its daily output is made up of metal (7.1tons), paper (2.2tons), and rubber (0.59tons). The substantial amount of these components in Khulnas waste composition emphasizes the significance of frequent collection and disposal.The current daily market value of this waste has been estimated to be BDT 4,36,879 in total. Each year, the income is close to BDT 160million.