SIMULATION RESEARCH ON CARBON EMISSIONS TRADING BASED ON BLOCKCHAIN

Global warming caused by greenhouse gases is one of the problems that need to be solved urgently. Blockchain technology can achieve automatic quota certification and settlement, providing a new direction for carbon emissions trading. This paper provides a quantitative analysis of blockchain-based carbon emissions trading through the Repast simulation platform. Firstly, it designs the blockchain-based carbon emissions trading simulation framework from a macro perspective, including identity and quota certification, quota trading, risk prevention and smart contracts management. Then, it establishes a blockchain-based carbon emissions trading simulation model and formulates the behavior rules of the government, investors and company agents and market transaction processes. Finally, it simulates the carbon emissions trading based on public chain and private chain on the Repast platform, and analyzes the simulation results.

Gold and oil prices: abnormal returns, momentum and contrarian effects

This paper explores price (momentum and contrarian) effects and their timing parameters on the days characterised by abnormal returns and the following ones in two commodity markets. Specifically, using daily gold and oil price data over the period 01.01.2009–31.03.2020 the following hypotheses are tested: (H1) there is a time gap between the detection of an abnormal return day and the end of that day, (H2) there are price effects on the day after abnormal returns occur; (H3) price effects after 1-day abnormal returns have identifiable timing parameters; (H4) the detected timing parameters can be used to “beat the market”. For these purposes average analysis, t tests, CAR and trading simulation approaches are used. The main results can be summarised as follows. Prices tend to move in the direction of abnormal returns till the end of the day when these occur. The presence of abnormal returns can usually be detected before the end of the day by estimating specific timing parameters, and a momentum effect can be detected. On the following day two different price patterns are detected: a momentum effect for oil prices and a contrarian effect for gold prices, respectively. These effects are limited in time, and the corresponding timing parameters are estimated. Trading simulations show that these effects can be exploited to generate abnormal profits with an appropriate calibration of the timing parameters.

Momentum and contrarian effects in the Ukrainian stock market: case of daily overreactions

This paper examines momentum and contrarian effects in the Ukrainian stock market after one-day abnormal returns. To do this, UX futures data over the period 2010–2018 are used. The following hypotheses are tested: H1) hourly returns on overreaction days differ from hourly returns on normal days, H2) there are price patterns on overreaction days, and H3) to test these hypotheses, visual inspection and average analysis are used, as well as t-tests, cumulative abnormal returns, and trading simulation approaches. The results suggest that there are statistically significant differences between intraday dynamics during the usual days and the overreactions day. There is a strong momentum effect present on the day of overreaction: prices tend to change only in the direction of the overreaction during the whole day. The fact of the overreaction becomes clear after 13:00-14:00. This gives a lot of time to explore the momentum effect in the day of overreaction. On the day after the overreaction, prices tend to go in the opposite direction: contrarian pattern is detected, which is in line with the overreaction hypothesis. Based on detected price patterns, rules of trading and trading strategies for the Ukrainian stock market are developed. Momentum Strategy (based on price patterns on the day of overreaction) generates several successful trades; close to with 90%, and their number being is profitable (trading results differ from the random ones – confirmed by t-tests). Contrarian Strategy (based on price patterns on the day after the overreaction) demonstrates low efficiency, and results do not differ from random trading.

Blockchain-Enabled Charging Right Trading Among EV Charging Stations

Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.

Artificial Intelligence Modelling Framework for Financial Automated Advising in the Copper Market

Financial innovation by means of Fintech firms is one of the more disruptive business model innovations from the latest years. Specifically, in the financial advisor sector, worldwide assets under management of artificial intelligence (AI)-based investment firms, or robo-advisors, currently amount to US$975.5 B. Since 2008, robo-advisors have evolved from passive advising to active data-driven investment management, requiring AI models capable of predicting financial asset prices on time to switch positions. In this research, an artificial neural network modelling framework is specifically designed to be used as an active data-driven robo-advisor due to its ability to forecast with today’s copper prices five days ahead of changes in prices using input data that can be fed automatically in the model. The model, tested using data of the two periods with a higher volatility of the returns of the recent history of copper prices (May 2006 to September 2008 and September 2008 to September 2010) showed that the method is capable of predicting in-sample and out-of-sample prices and consequently changes in prices with high levels of accuracy. Additionally, with a 24-day window of out-of-sample data, a trading simulation exercise was performed, consisting of staying long if the model predicts a rise in price or switching to a short position if the model predicts a decrease in price, and comparing the results with the passive strategies, buy and hold or sell and hold. The results obtained seem promising in terms of both statistical and trading metrics. Our contribution is twofold: 1) we propose a set of input variables based on financial theory that can be collected and fed automatically by the algorithm. 2) We generate predictions five days in advance that can be used to reposition the portfolio in active investment strategies.

Exploring price gap anomaly in the Ukrainian stock market

This paper analyzes price gaps in the Ukrainian stock market for the case of UX index over the period 2009–2018. Using different statistical tests (Student’s t-tests, ANOVA, Mann-Whitney test) and regression analysis with dummy variables, as well as modified cumulative approach and trading simulation, the authors test a number of hypotheses searching for price patterns and abnormal market behavior related to price gaps: there is seasonality in price gaps (H1); price gaps generate statistical anomalies in the Ukrainian stock market (H2); upward gaps generate price patterns in the Ukrainian stock market (H3) and downward gaps generate price patterns in the Ukrainian stock market (H4). Overall results are consistent with the Efficient Market Hypothesis: there is no seasonality in price gaps and in most cases there is no evidences of price patterns or abnormal price behavior after the gaps in the Ukrainian stock market. Nevertheless, the authors find very strong and convincing evidences in favor of momentum effect on the days of negative gaps. These observations are confirmed by trading simulations: trading strategy based on detected price pattern generates profits and demonstrates overall efficiency, which is against the market efficiency. These results can be interesting both for academicians (further evidences against market efficiency) and practitioners (real and effective trading strategy to generate profits in the Ukrainian market market).

Can Stock Analysts Predict Market Risk? New Evidence from Copula Theory

We assess investment value of stock recommendations from the standpoint of market risk. We match I/B/E/S (Institutional Brokers’ Estimates System) consensus recommendations issued in January 2015 for a cross-section of u.S. public equities with realized volatility of these papers, showing that these recommendations signifcantly correlate with subsequent changes in market risk. Thus, the results indicate that to some extent the analysts can predict an increase or decrease in risk, which can beneft asset management. However, the relationship between the recommendations and the risk is not linear and depends on the specifc recommendation. using a semi-parametric copula model, we fnd recommendation levels to be associated with future changes in volatility. We further fnd this relationship to be asymmetric and most pronounced among the best-rated stocks which experience largest volatility declines. We conduct a trading simulation showing how stock selection based on such ratings can lead to a reduction in portfolio-level value-at-risk.