Trading Period Research Articles

Decision on Mixed Trading between Medium- and Long-Term Markets and Spot Markets for Electricity Sales Companies under New Electricity Reform Policies

The introduction of the new round of electricity reform policies has made the electricity sales companies’ trading environment increasingly complex. In the medium- and long-term market and spot market, following the new policy-oriented optimization of trading decisions is the focus of electricity sales companies. The main objective of this study is to consider the impact of the latest policies of China’s current electricity reform on each subject of electricity trading and to propose a method for electricity sales companies to make optimal decisions on renewable energy source (RES) power and conventional energy source (CES) power mixed with power trading in the medium- and long-term and spot markets to improve the efficiency of electricity market trading, promoting the consumption of renewable energy and helping the synergistic development of the electricity market and the tradable green certificate (TGC) market. This paper first discusses the impact of the new electricity reform policies on the transactions of various subjects in the electricity market and constructs the model of the consumer utility function, the profit model of an electricity sales company, and the profit model of power generators with energy storage. Considering the complex power supply and demand relationship among the various subjects of the electricity market, a game model is established for the decision on mixed trading between the medium- and long-term market, the spot market, and the tradable green certificate market to minimize the comprehensive power purchase cost of an electricity sales company. To reduce the decision-making risk caused by the uncertainty of spot price, the prophet model is used to predict the spot price; finally, through the analysis of the decision-making model of the electricity sales companies, the optimal transaction decisions of the electricity sales companies in different trading periods and different scenarios are solved. The test results show that the proposed model can significantly improve the profitability of the electricity sales companies and provide a decision-making reference for electricity sales companies to participate in the medium- and long-term market and spot market.

Trade Liberalization Schedules and Members' Development Characteristics: How are They Connected and Why do They Matter to Trade Agreements?

This study examines whether the length of the implementation periods of regional trade agreement (RTA) in relation to trade liberalization schedules and the level of development of member countries affect members' trade. The analysis finds that RTAs formed by partner countries with a similar level of economic development lead to increased trade among members than among North-South RTAs during a shorter transition period of trade and tariff liberalization, regardless of the degree of integration being shallow or deep. The average long-term effects of RTAs formed by developing countries on members' trade diminish 15 years after their implementation. In distinguishing trade direction from developed to developing countries and vice versa, we noted that direction-specific South-North RTAs appear to take less time to see larger increases in Southern exports to the Northern members than direction-specific North-South RTAs do.

The arbitrage strategy in the crude oil futures market of shanghai international energy exchange

This research conducts an empirical study on arbitrage opportunities in the crude oil futures market of Shanghai International Energy Exchange in the period of China’s economic change, 2020–2022. We use the daily closing price data of crude oil futures sc2303 and sc2212 to test whether there is a statistical arbitrage opportunity in China’s crude oil futures market. Taking the most commonly used 12 + 6 rolling window mode in statistical arbitrage, we select several one-year periods that pass the cointegration test during the formation period and apply the optimal opening, closing, and stop loss thresholds based on the highest yield during the formation period to the trading period. Finally, we draw the following conclusions: (i) the pair trading strategy based on the cointegration model is profitable in China’s crude oil futures market; (ii) the 12 + 6 window model can be applied to the pair trading strategy based on China’s crude oil futures. Our research proves the effectiveness of pair trading strategy in China’s crude oil futures market for institutional and individual investors.

A privacy protection scheme based on linkable ring signature for user payment of Peer-to-Peer uniform-price double auction transaction in the microgrid day-ahead market

In the microgrid day-ahead market Peer-to-Peer (P2P) transaction, the uniform-price double auction mechanism has advantages in the microgrid day-ahead market because it can prevent power price fluctuations during the one hour trading period, and give priority to trading Distributed Energy Resources(DERs) based on trading volume, which is conducive to the consumption of new energy. In this kind of transaction, aiming at the problem that the attacker can obtain the privacy information such as the collection and payment address and transaction amount of the producer and consumer through the account book information on the blockchain, so that the relationship between the producer and consumer and the payment behavior is subject to inference attacks, and thus the identity of the participants is inferred, a privacy protection scheme based on linkable ring signature for user payment of P2P uniform-price double auction transaction in the microgrid day-ahead market is proposed. This scheme has the following novelty and contribution: Firstly, the linkable ring signature is used to sign the one-time account address and payment information of the producer and consumer, which ensures the untraceability of transaction payment in the case of anonymity of the producer and consumer, and it has low computational cost; Secondly, in order to hide its real output address, destroy the association between the input address and the real output address, and provides the unlinkability of transactions, the one-time address is proposed; Finally, a one-time address generation method based on elliptic curve is designed, and point multiplication is used to improve the efficiency of address generation. Theoretical analysis proves that the scheme is untraceable and unlinkable while ensuring privacy. Experiments show that this scheme has lower computational cost.

Making Money: a LR+ARIMA and Multi-Index Based Investment Model

In order to achieve the goal of maximizing returns, market traders tend to buy and sell assets with high volatility. Now, in this paper, traders will buy and sell only two assets, namely gold, and bitcoin, based on their requirements, using past daily prices to determine the trading prices to determine whether they should buy, continue to hold, or sell the assets in their portfolio on that date. Determine a portfolio consisting of cash, gold, and bitcoin that is based on an investment model for a five-year trading period starting at $1,000 on September 11, 2016, and ending on September 10, 2021. During this period, they will follow a trading strategy when appropriate. (The gold market is only open on weekdays and the bitcoin market is open daily with commission fees for each trade).

내부자거래와 정보비대칭의 관계에 관한 연구

This study analyzes the pre-transaction period accounting earnings-stock price relation among companies with and without insider trade(insider sell and buy) to find out whether information asymmetry increase potential insider’s profit, thereby, motivate insider to trade their own stocks.
 In order to figure out correct relationship between insider trading and information asymmetry, this study recognize succesive insider trading, and use only first trade observation of succetive insider sell and buy, respectively. Therefore the total number of stocks and the first trade of each successive insider tradings are respectively presumed as an insider trading volume and as an observation of the the succesive insider trading.
 Also, all ownership information of directors and major shareholder is cellected from DART using Python, and quarterly financial information is used for our analysis to alleviate the possible problem coming from assumption of period of insider trading.
 The analysis period is from 2012 to 2018, and non-financial companies among listed companies on the Korea Stock Exchange and KOSDAQ are targeted.
 Empirical analysis shows as follow.
 First, it was found that the greater the over-estimate of earnings-return relation incurs greater probability of occurrence of insider stock sales and greater insider sales stock volume. These relationships are larger and more significant than those between insider trading and future earnings level, future earnings growth, and future stock return, which represent insiders' information advantage on future prospects of the firm. Second, the effect of estimation bias of earnings-return relation on the probability of insider sales was stronger and more immediate than those of insider purchases. The positive relationship between future prospects and insider trading, reported in previous studies, was stronger in insiders purchases than in insider sales.
 This study directly analyzed whether information asymmetry between insiders and external stakeholders plays a major role of insider trading, comparing pre-period earning-return relation estimates of insider trading firms with those of control firms. It is also meaningful in that it shows that the information asymmetry difference between insiders and external stakeholders can have different effects on insider trading for different properties of insider sales and purchase.

Social media and price discovery: The case of cross‐listed firms

In this article, we examine whether social media information affects the price-discovery process for cross-listed companies. Using over 29 million overnight tweets mentioning cross-listed companies, we examine the role of social media for a link between the last periods of trading in the US markets and the first periods in the UK market. Our estimates suggest that the size and content of information flows on social networks support the price-discovery process. The interactions between lagged US stock features and overnight tweets significantly affect stock returns and volatility of cross-listed stocks when the UK market opens. These effects weaken and disappear 1 to 3 hr after the opening of the UK market. We also develop a profitable trading strategy based on overnight social media, and the profits remain economically significant after considering transaction costs.This article is protected by copyright. All rights reserved.

Predicting veal-calf trading events in France

Global trade has been ranked as one of the top five drivers of infectious disease threat events. More specifically, livestock trade is known to increase the speed at which infectious diseases circulate and to facilitate their dissemination over large distances Therefore, predicting animal movements arising from trade is crucial for assessing epidemic risk and the impact of preventive measures. In this study, we developed a statistical framework for predicting trading events using predictors accessible from routinely collected data. We focused on veal calves, a category of animals with significant commercial value; the dataset considered the veal calf trade in France between January 2011 and June 2019. A subset of farms with consistent trade behaviour over time was built to be used throughout the study. To predict sale or purchase event occurrences, our predictive framework was based on random forests as a binary classification tool, an approach that allows a large number of potential predictors. We explored the robustness of model predictions with respect to the delay in data acquisition and prediction lag time. Overall, sales were more accurately predicted than purchasing events. Unsurprisingly, a delay in data acquisition led to a decrease in the performance of indicators, whereas prediction lag time had little impact. Sale-related predictors mostly reflected past trading events, whereas purchase-related predictors were associated with past trading events, farm management and general farm characteristics. The model outputs also suggested that the veal calf trading network is driven by sales rather than by purchases. Regardless of the length of the delay in data acquisition and prediction lag, the random forest approach fitted on data with municipality as trading unit and a 28-day trading period provided better performance scores (F1-score, positive predictive value and negative predictive value) than scenarios with finer temporal and spatial aggregation units. Predicted trade events can therefore be used to reconstruct the entire veal calf trading network and transfers between selling and purchasing units for each period. This predicted network could be further used to simulate the spread of pathogens via animal trade.

A comparison of women role models in architecture firms featured in Architectural Record and Architectural Review

How are women represented, specifically women of colour, in architecture library collections? I focus on two major trade periodicals to find out: Architectural Review (a British publication) and Architectural Record (American). I analyse firms highlighted in these periodicals in 2019, researching the proportion of women in leadership per each featured company. I compare against demographic and occupational statistics in the respective countries to see if these periodicals accurately represent the field, or further reinforce antiquated notions. My findings suggest that who the periodicals highlight is roughly proportionate or more diverse than the field itself, but the field continues to mostly represent white men as successful architects. Library collections reify that notion. In order to decentre the monolithic nature of many library collections, I further suggest approaches to increasing diversity based on my own initiatives.

High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19

This study investigates intraday patterns in the eleven sectors of the United States (U.S.). Key contributions are (i) risk and return patterns at specific trading periods on the New York Stock Exchange (NYSE), (ii) whether a specific day return model can predict the next 15-minute positive return, and (iii) the impact of the first vaccination rollout in the U.S. on intraday Exchange-Traded-Funds (ETF) returns. Time-dependent regressions capture risk and return relationships, decision trees in machine learning compare return models, and impulse responses capture the effect of the 2019 coronavirus (COVID-19) vaccine rollout in U.S. 15-minute Standard & Poor’s Depository Receipts (SPDR) Select Sector ETF data is used over 12th March 2020-23rd February 2021. Findings support sector ETF returns fluctuate the most in the first and last 15 minutes. Average returns in the first 15 minutes are the highest, converging to near zero as the trading session continues. Overnight returns contribute the most to volatility. U-shaped patterns into both return and risk exist, especially on Mondays. Mondays and Fridays have the most significant positive returns 15 minutes after the open. Prediction scores using an all-return model were superior to any specific day return model. The first vaccination rollout has a positive effect only in energy, technology, and financial sector ETFs, however with a short-lasting effect on ETFs returns.

Time-frequency connectedness between food commodities: New implications for portfolio diversification

This study examines the time-frequency connectedness of food commodities (cereals, dairy, food, meat, vegetable oil, and sugar) in the Food and Agriculture Organization's world food indices. With a monthly-frequency dataset covering February 1990 to October 2021, we utilise the returns spillover index approach of Baruník and Křehlík (2018). The results are robust to the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The findings explicate that food commodities are highly connected in the short- and long-term horizons. The dynamic total connectedness varies from 15% to 80% in the short term, 1% to 70%, in the medium-term, and 1% to 75% in the long-term, with high fluctuations in notable crisis periods. Contagious returns spillovers between food commodities are propagated by vegetable oil. Diversification and safe-haven properties of food commodities are consistently found in the meat market across tranquil and exuberant trading periods, respectively. The safe-haven role of sugar in recent episodes of financial crises is highlighted by our findings. The economic, policy, and portfolio implications of our results are discussed.

Perlindungan Hukum Terhadap Barang atau Merek KW Di Indonesia

The period of global trade, in accordance with Indonesia's ratification of international accords. As the role of the brand becomes increasingly significant, particularly in ensuring healthy business rivalry, adequate brand regulation is required to deliver community services. Trademark is a subset of Intellectual Property Rights, in which the brand provides a specific amount of value to the circulation of goods. In tandem with a rise in market demand, the development of the circulation of items with in-demand brands will increase, as will the requirements and lifestyles of the community. Frequently, business actors infringe the mark in order to meet the price and quality requirements of the community with counterfeit or KW goods. So that the establishment of unhealthy companies conducted by unlicensed business actors of counterfeit goods. The existence of infractions in the society necessitates legal protection for brand owners. The author employs a qualitative research approach and normative research in this study. Legal protection against counterfeit goods or KW and consumer protection has been regulated in Indonesian laws and regulations, according to the study's findings.

Workplace Emotions and Burnout: Implications for Practice and Research

This paper observes the possible grounds for job burnout among human resources professionals who receive unfortunate attention. Not only are emotions critical, but the inability to successfully manage those emotions can often lead to various forms of outbursts, affecting the general stakeholders. The gatekeeper of the business organization’s workforce, the human resource professionals, need to shine and brighten the entire organization. Emotional and spiritual intelligence and emotional labour strategies assessed by the authors from a paradigmatic view help frame the bondings as the several conduct emotions play out in the work atmosphere and bear burnout. We present a framework that draws attention to the institutionalization of Emotional Contagion Theory in connecting the antecedents of job burnout in business organizations. From the Emotional Contagion Theory’s point of view, job burnout and its direct and indirect influencers will open new perspectives in interpreting and analyzing small and medium-sized businesses’ knowledge that circulates in ubiquitous trade periodicals. Accordingly, in this paper, we have alluded to opportunities for researchers and human resource professionals to engage in spaced areas of job burnout and connect across paradigms of knowledge. We anticipate fueling future research and extent organizational dynamism of the complex yet impactful phenomenon of emotional intelligence, spiritual intelligence and emotional labour strategies towards plummeting job burnout.

유통물류 환경 변화에 따른 사후송금방식 (Open Account) 수출거래특성과 신용위험 최소화 방안 연구

In recent decades, export trends have rapidly changed. As building long-term relationships with clients, the preference for payment is changing from documentary credit transactions to remittances. Accordingly, an extensive study on credit risk assessment on Open Account (from now on O/A) from the perspective of exporters is essential to reduce credit risk. The purpose of our study is to analyze the relationship between the characteristics of O/A and credit risk management performance mediated by the level of risk. In addition, the moderated mediating effect of the overseas trading period on the relationship will be analyzed. Our research model and hypothesis were designed through an in-depth interview with a person in charge of practical affairs and previous literature. The target of the survey is shippers who perform general forms of exports through forwarders or shipping companies in Seoul, Gyeonggi-do, and Incheon. In order to secure objectivity of the questionnaire responses, simple random sampling was used with the list of members such as the Korea International Trade Association (KITA), Certified Trading Company, and Korea Certified Customs Attorneys Association. The analysis results are as follows: First, the characteristics of O/A had a positive effect on the level of risk. These results are consistent with the book by Harlaar & de Leeuw (2020). Considering that O/A is a deal on credit, it should be possible to increase the level of risk awareness through pre-negotiations. Second, the level of risk had a positive effect on credit risk management performance. These results are consistent with those of Niepmann & Schmidt-Eisenlohr (2017), Lee Jung-sun, and Huh Eun-sook (2021). In other words, the greater the recognition of risk, the lower the likelihood of credit risk. Third, the characteristics of O/A had a positive effect on credit risk management performance. This finding is consistent with those in the book of Lin (2022). In O/A, the exporter’s withdrawal of payment often depends only on the importer’s credit. Therefore, using appropriate insurance for each transaction can have a significant positive effect on credit risk management performance. Fourth, in the relationship between the characteristics of O/A and the level of risk, the overseas trading period had a significant moderated mediation effect. Our findings show that the longer the overseas transaction period, the lower the level of risk recognition due to accumulated transaction experience. This study is different from the study of Han Woo-jung and Cho Hyuk-soo (2019) in that trade experience and trading period are designed as moderating variables rather than independent variables.

A real-time mix-adjusted median property price index enabled by an efficient nearest neighbour approximation data structure

Homeowners, first-time buyers, banks, governments and construction companies are highly interested in following the state of the property market. Currently, property price indexes are published several months out of date and hence do not offer the up-to-date information which housing market stakeholders need in order to make informed decisions. In this article, we present an enhanced version of a mix-adjusted median based property price index which uses geospatial property data and stratification in order to compare similar houses sold in different trading periods. The expansion of the algorithm to include additional parameters, enabled by both a richer dataset and the introduction of a new, efficient data structure for nearest neighbour approximation, allows for the construction of a far smoother and more robust index than the original algorithm produced.

Dynamics of information leadership in the volatility complex with trading time changes: Evidence from VIX futures and VIX ETPs

We examine the effect of VIX futures’ new trading hours on price discovery as these causal relations have not been investigated before and are consequential for regulators and practitioners involved in the VIX futures market. Our data include VIX futures and VIX ETPs for four different periods in which trading hours were changed. Employing three different measures of information share, we find that VXX ETN leads VIX futures in 2009 and 2010, while in 2011 and 2013, the ETPs’ leadership varies depending on the exchange-traded product under consideration. Furthermore, in 2013 before the change of trading hours, the VIX futures contribute more to price discovery than they do after trading hours expansion. Less of the price discovery occurs from the exchange-traded products in the latter half of the trading period in 2010. OLS regression results of the determinants of price discovery as well as panel regression results show that the effect of volume and spread, which are the main determinants of price discovery in the prior literature, change significantly before and after futures trading hour expansions, for both VIX futures and ETPs.

Research on Quantitative Trading Investment Strategy Based on LSTM and Dynamic Programming

As financial markets matured, more standardized and quantitative research emerged. In this paper, we model the historical price returns of U.S. gold and BTC over a five-year trading period from September 11, 2016, to September 10, 2021, and estimate the total investment returns as of September 10, 2021. First, we build a variety of price-prediction models to fit the image to the data to get the most accurate image of the actual price trend. It was found that the LSTM neural network-based multi-interval segmentation price prediction model, which has the best prediction effect by comparison with the actual value. Therefore, it was used as the core prediction model for this paper. Then, we use the planning model to find the optimal investment plan by using the difference between the price change of gold and bitcoin trading in the next three days as the decision variable, the highest Sharpe ratio on the third day as the objective function. At the same time, the variance of the forecast value in the next three days is used as the risk characterization, and different weights are assigned to the risk and return as the objective function to characterize the investment strategy under different investment personalities. In order to accelerate the convergence of the planning model, we added a particle swarm algorithm for optimization. In the end, we obtained the results for the specific daily investment scenarios for aggressive investors, intermediate investors, and steady investors.

A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets

Algorithmic trading based on machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for trading. Some financial theories, such as the fractal market hypothesis, believe that the markets behave based on the collective psychology of investors who trade with different investment horizons and interpretations of information. Accordingly, a multi-agent deep reinforcement learning framework is proposed in this paper to trade on the collective intelligence of multiple agents, each of which is an expert trader on a specific timeframe. The proposed framework works in a hierarchical structure in which the flow of knowledge is from the agents trading at higher timeframes to the agents trading at lower timeframes, making them highly robust to the noise in financial time series. The Deep Q-learning algorithm is utilized for training the agents in the framework. The performance of the proposed framework is evaluated through extensive numerical experiments conducted on a historical dataset of the EUR/USD currency pair. The results demonstrate that the proposed multi-agent framework, based on several return-based and risk-based performance measures, outperforms single independent agents and several benchmark trading strategies in all investigated trading timeframes. The robust performance of the multi-agent framework throughout the trading period makes it suitable for algorithmic trading in financial markets.

Modelling Multiperiod Carbon Markets Using Singular Forward-Backward SDEs

We introduce a model for the evolution of emissions and the price of emissions allowances in a carbon market, such as the European Union Emissions Trading System (EU ETS). The model accounts for multiple trading periods, or phases, with multiple times at which compliance can occur. At the end of each trading period, the participating firms must surrender allowances for their emissions made during that period, and additional allowances can be used for compliance in the following periods. We show that the multiperiod allowance pricing problem is well-posed for various mechanisms (such as banking, borrowing, and withdrawal of allowances) linking the trading periods. The results are based on the analysis of a forward-backward stochastic differential equation with coupled forward and backward components, a discontinuous terminal condition, and a forward component that is degenerate. We also introduce an infinite-period model for a carbon market with a sequence of compliance times and with no end date. We show that, under appropriate conditions, the value function for the multiperiod pricing problem converges, as the number of periods increases, to a value function for this infinite-period model and that such functions are unique. Finally, we present a numerical example that demonstrates empirically the convergence of the multiperiod pricing problem. Funding: The Engineering and Physical Sciences Research Council and Climate-KIC funded all parts of H. Chotai’s PhD programme in the centre for doctoral training and provided many opportunities for travel to conferences and events during the programme. D. Crisan received financial support from École Polytechnique and Université Paris Diderot through visitor grants.

Quantitative trading portfolio optimization based on machine learning

The capital asset pricing model (CAPM) is to study the quantitative relationship between the expected return rate of assets and risky assets in the securities market. With the development of computer network technology and the trading market, quantitative trading has gradually become a new way of investment. Its essence lies in the decision of trading strategy. This paper obtains the relative price data of the subsequent short-term investment period through the time series method, then uses the PA algorithm in machine learning to update the portfolio and verify the effectiveness of the strategy in the data set. Based on the exchange rate change trend of the US dollar, gold, and bitcoin within five years given in the question, we constructed dynamic portfolio models for the three assets to ensure maximum profits. First, we use the time series model to predict the relative price data of the next period in a continuous trading period. Then, we implement the PA passive attack algorithm by calling the Sklearn package in Python to update the asset components in real-time every day and calculate the average annual return rate in 5 years. Based on the above analysis, we used the ADF unit root to test the stationarity of the time series. Meanwhile, we also carried out a white noise test of time series. The specific test indexes include: According to CAPM's theory, this product has a high yield, with an annual interest rate of 195.849%, which is entirely objective. However, the annual standard deviation reached 0.919164, and the maximum retractable rate exceeded 50%, indicating a sizeable overall risk. Despite this, the composite product's Calmar ratio is 3.556, Sharpe ratio is 2.104623, and its return per unit risk is still high. Compared with other quantitative trading models, this paper combines time series, machine learning, Monte Carlo, and other algorithms to effectively solve the portfolio investment optimization problem of nonlinear time-varying correlation structure among multiple assets. It enriches the results of portfolio investment structure decisions.