Trading Simulation Research Articles

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Developing Life-long Investment Literacy of Students through Initial Public Offerings (IPO) One-Stop Support Center and Stock Trading Simulation

Initial Public Offerings (IPO) One-Stop Support Center website is created to help students, the public and academic researchers. One of the significant features of this website is a stock trading simulation which enables stock trading practice in a simulated or virtual environment. It simulates the experience of trading equities with virtual currency. The main aim of this trading simulation is to offer students and the public (potential retail investors) a risk-free environment to learn and practice trading before entering the real stock market. This study aims to understand the influence of trading simulation as an educational tool in improving students’ lifelong learning skills through the development of investment literacy. This study was conducted using a qualitative approach. To fulfill the objective of this study, an online survey was distributed through a Google Form. A total of 110 respondents consisting of students enrolling in FIN768 (Investment and Portfolio Analysis) course, public, and academics in Universiti Teknologi MARA (UiTM), Shah Alam and Puncak Alam, Selangor took part in the online survey. This study finds that 89.10 percent of the respondents agree that this stock trading simulation is very useful for their life-long learning. This study contributes mainly to the students by providing them with valuable learning experiences and practical skills that extend beyond traditional classroom teachings.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance. Key Words: Virtual stock trading, Stock market simulation, Financial literacy, Investment education, Online trading platforms, Stock market knowledge, Risk management, Investment skills, User experience, Financial education, Stock trading simulation, Virtual investing, Educational technology, Portfolio management.

Price effects after one-day abnormal returns and crises in the stock markets

We investigate price effects after one-day abnormal returns during crises in US, Japanese, Chinese, Russian and Brazilian stock markets, using the ANOVA, Mann–Whitney, t-tests, the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach. The results suggest that the momentum effect is the most typical case of price behaviour after the days with positive abnormal returns, especially in emerging markets in pre and post crisis periods. Interestingly the momentum effect in developed markets changes into contrarian during crisis periods. However, in emerging markets the momentum effect prevails even in crisis periods. However, the power of the detected effects is weak. These effects do not provide opportunities to beat the market and might result from prevailing positive returns in these stock markets.

Improving the Machine Learning Stock Trading System: An N‐Period Volatility Labeling and Instance Selection Technique

Financial technology is crucial for the sustainable development of financial systems. Algorithmic trading, a key area in financial technology, involves automated trading based on predefined rules. However, investors cannot manually analyze all market patterns and establish rules, necessitating the development of supervised learning trading systems that can discover market patterns using machine or deep learning techniques. Many studies on supervised learning trading systems rely on up–down labeling based on price differences, which overlooks the issues of nonstationarity, complexity, and noise in stock data. Therefore, this study proposes an N‐period volatility trading system that addresses the limitations of up–down labeling systems. The N‐period volatility trading system measures price volatility to address uncertainty and enables the construction of a stable, long‐term trading system. Additionally, an instance‐selection technique is utilized to address the limitations of stock data, including noise, nonlinearity, and complexity, while effectively reducing the data size. The effectiveness of the proposed model is evaluated through trading simulations of stocks comprising the NASDAQ 100 index and compared with up–down labeling trading systems. The experimental results demonstrate that the proposed N‐period volatility trading system exhibits higher stability and profitability than other trading systems.

Uncertainty-Aware Portfolio Management With Risk-Sensitive Multiagent Network.

As deep neural networks (DNNs) have gained considerable attention in recent years, there have been several cases applying DNNs to portfolio management (PM). Although some researchers have experimentally demonstrated its ability to make a profit, it is still insufficient to use in real situations because existing studies have failed to answer how risky investment decisions are. Furthermore, even though the objective of PM is to maximize returns within a risk tolerance, they overlook the predictive uncertainty of DNNs in the process of risk management. To overcome these limitations, we propose a novel framework called risk-sensitive multiagent network (RSMAN), which includes risk-sensitive agents (RSAs) and a risk adaptive portfolio generator (RAPG). Standard DNNs do not understand the risks of their decision, whereas RSA can take risk-sensitive decisions by estimating market uncertainty and parameter uncertainty. Acting as a trader, this agent is trained via reinforcement learning from dynamic trading simulations to estimate the distribution of reward and via unsupervised learning to assess parameter uncertainty without labeled data. We also present an RAPG that can generate a portfolio fitting the user's risk appetite without retraining by exploiting the estimated information from the RSAs. We tested our framework on the U.S. and Korean real financial markets to demonstrate the practicality of the RSMAN.

Market potential and the industrial sectors inclusion sequence in China's national carbon emission trading: From the perspective of maximizing gains

The carbon emission trading scheme (CETS) has been gradually introduced in China to control carbon emissions and boost economic and social development. The existing literature has evaluated the market potential of pilot CETSs or a certain industry, yet no study has focused on the industrial sectors to be covered in the national CETS to analyze trading flexibility and the sector inclusion sequence. This study presents an analytical framework for estimating market potential (i.e., potential gains and carbon emission reduction potential) and industrial sector inclusion sequences for China's national CETS using by-production models. Three simulation scenarios (i.e., no-trading scenario, power sector trading scenario, and multiple-industry trading scenario) and two trading modes (i.e., contemporary and intertemporal trading mode) are simultaneously considered. The novel evidences are: (i) There would be 39.93% (27.7 trillion CNY), 65.26% (273.0 trillion CNY) and 159.42% (666.7 trillion CNY) national aggregated potential gains of CETS in intertemporal power sector trading scenario, contemporary multiple-industry trading scenario and intertemporal multiple-industry trading scenario, and they would help save 3.60%, 5.18% and 6.05% of CO2 emissions reduction, respectively. (ii) CO2 emission permits would transfer from nonmetal minerals, ferrous metals, and electricity, to paper making, non-ferrous metals, petrochemicals, and chemicals under the intertemporal multiple-industry trading simulation. (iii) Based on the principle of potential gain maximization, it is recommended that the sector of smelting and processing of non-ferrous metals should to be included as the next sector in national CETS. The results show that national CETS has great market potential for achieving China's joint carbon control and economic growth goals.

Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy

To mitigate the impact of market uncertainty on trading investments, this paper proposes a forecasting and investing framework for crude oil market by integrating interval models and machine learning models. Firstly, natural language processing technique is employed to analyze text information from social and news media, enabling the capture of market and societal sentiment. Subsequently, deep learning models are integrated to combine sentiment data with other economic variables for more accurate predictions of crude oil prices. Furthermore, this paper introduces a trading strategy with interval constraints based on interval prediction models to reduce trading risk arising from the uncertainty of point forecasts in investments. Through trading simulations, it is discovered that employing the interval constrained strategy is more effective in reducing trading risk and enhancing investment returns compared to point-based trading strategies. This interval-based strategy offers a novel approach to mitigating investment risk in the crude oil market.

An integrated approach of ensemble learning methods for stock index prediction using investor sentiments

It has been evidenced by numerous studies that irrational investor sentiment is one of the critical factors leading to dramatic volatility in financial market prices. Therefore, how to effectively predict market prices by information on investor sentiment is a popular but complex topic for researchers, market investors, and financial regulators. In this research, we aim to investigate the effectiveness of stock index prediction using multiple investor sentiment features, and we propose an advanced price trend prediction and trading simulation approach for the Shanghai Stock Exchange index and the Shenzhen Component index by integrating the Boosting, Bagging, and NSGA-II methods. Additionally, the SHAP method is employed as a model interpretation approach to analyze the importance of the sentiment variables and quantify their contributions to the predictions from both local and global perspectives. According to the experimental results, it can be found that the proposed method outperforms the benchmark methods in terms of the hit ratio, accumulated return, and maximum drawdown. It indicates that the proposed method is capable of achieving high accuracy, low risk, and stable profit in price trend prediction and trading simulation of the Chinese stock indexes. Moreover, the SHAP approach incorporated in the proposed method improved the interpretability of the proposed model, which can provide a beneficial reference for market participants to clarify the important sentiment factors and make relative decisions.

Learning About Market Microstructure and Price Discovery Using Experimental Markets

This study introduces the use of an instructor-developed trading simulation to teach intrinsic value estimation, price discovery,and market mechanics. It provides the reader with a cost efficient solution to deepen students’ understanding of financial markets and the price discovery process. By using a multi-week time frame we allow students to analyze the financial information of the companies in the simulation as well as to develop strategies to exploit the market structure and price discovery process. Thus, this simulation combines the importance of financial analysis with the awareness of the impact of liquidity and market organization on price discovery.

Dynamic graph construction via motif detection for stock prediction

Stock trend prediction is crucial for recommending high-investment value stocks and can strongly assist investors in making decisions. In recent years, the significance of stock relationships has been gradually recognized for trend prediction, and graph neural networks (GNNs) have been introduced to capture useful features from relationships. However, applying GNNs to stock relationship analysis still faces numerous challenges, including inappropriate distance algorithms, non-dynamic stock graphs, and over-fitting. To address these challenges, we propose a dynamic graph construction module. The module offers the following advantages: (1) A dynamic graph construction module is introduced. (2) A novel stock distance algorithm based on motif detection is proposed to reduce the distance between stocks with similar trends. (3) A dynamic graph-based LSTM is proposed to aggregate the changes in historical graphs. We have conduct numerous experiments on 4503 Chinese A-share stocks, spanning 1218 trading days. Our model demonstrates 8.65% and 1.02% relative improvements in accumulated return and accuracy, respectively. In addition, the trading simulation validates that our algorithm outperforms the state-of-the-art (SOTA) algorithms in terms of profitability.

Psychological profile and investment decisions

We conduct a study to test whether psychological factors influence stock trading behavior in a sample of 176 individuals. Using a trading simulation game, we combined financial data with demographic information, psychological traits, and risk attitudes. Our findings indicate that conscientiousness is a significant factor in explaining higher trading volume and greater risk-taking. Furthermore, demographics and risk attitude measures influenced individual investment choices. These results demonstrate that financial decision-making and the frequency of stock trading are influenced not only by our attitudes toward risk but also by our level of conscientiousness. These findings have important implications for modeling decisions under risk and providing financial advice.

Distributional neural networks for electricity price forecasting

We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network containing a so-called probability layer, i.e., the outputs of the network are parameters of the normal or Johnson’s SU distribution. To validate our approach, we conduct a comprehensive forecasting study complemented by a realistic trading simulation with day-ahead electricity prices in the German market. The proposed distributional deep neural network outperforms state-of-the-art benchmarks by over 7% in terms of the continuous ranked probability score and by 8% in terms of the per-transaction profits. The obtained results not only emphasize the importance of higher moments when modeling volatile electricity prices, but also – given that probabilistic forecasting is the essence of risk management – provide important implications for managing portfolios in the power sector.

LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.

Witching days and abnormal profits in the us stock market

This paper examines price effects related to witching days in the US stock market using both weekly and daily data for three major indices, namely the Dow Jones, S&P500 and Nasdaq, over the period 2000–2021. First it analyses whether or not anomalies in price behaviour arise from witching by using various parametric (Student’s t-test, and ANOVA) and non-parametric (Mann-Whitney) tests as well as an event study method and regressions with dummies; then it investigates whether or not any detected anomalies give rise to profit opportunities by applying a trading simulation approach. The results suggest the presence of the anomaly in daily returns on witching days which can be exploited by means of suitably designed trading strategies to earn abnormal profits, especially in the case of the Nasdaq index. Such evidence is inconsistent with the Efficient Market Hypothesis (EMH).

Exploring Stock Traders’ Cognitive Biases: Research Design and Simulator Framework

Cognitive bias is a phenomenon that has been extensively studied in stock trading and many other fields. This paper presents a framework for a Mobile Stock Trading Simulator (MSTS) that facilitates automatic investment in stocks with minimal human influence, by investigating the behavioral patterns and cognitive errors of stock market investors. The paper aims to determine whether investors’ investment strategies can be improved by detecting investment threats and reducing investment errors based on investors’ transaction histories. To accomplish this, we built a stock exchange simulator and implemented a decision tree to classify cognitive biases into one of six categories. By incorporating the behavioral patterns and cognitive biases of stock market investors into the MSTS's architecture, and by implementing a decision tree and stock exchange simulator, we can minimize the impact of human influence on automatic investments.

Zurich Trading Simulator (ZTS) — A dynamic trading experimental tool for oTree

Recent literature on the intersection of economics, finance and psychology indicates the benefits of simulated experience in tools measuring decision making. Here, we present the Zurich Trading Simulator (ZTS) first used by Andraszewicz et al. (2022) to test the impact of upward social comparison on trading activity. ZTS is a free application for oTree designed to create dynamic investment experiments suitable for measuring trading activity and risk taking. The software is ready-to-use applying the default settings. It can also be freely adapted in the source code depending on the experimenter’s needs. Price paths developed for experiments with the ZTS are freely available online. We also outline recommendations and possibilities for future studies and extensions.

Stock index direction forecasting using an explainable eXtreme Gradient Boosting and investor sentiments

In this article, an explainable eXtreme Gradient Boosting (XGBoost) method is proposed for stock index prediction and trading simulation in the Chinese security market. Sentiment features of three types of investors, including institutional, individual, and foreign investors, are utilized as explanatory variables, and a binary classification model based on XGBoost is constructed to predict the direction of the Shanghai composite index and Shenzhen composite index movements. Additionally, the Gain function of XGBoost and SHapley Additive exPlanations (SHAP) are employed to estimate the importance of sentimental factors affecting index direction forecasting. Experimental results demonstrate that the XGBoost-based approach using multiple investor sentiments achieved the best forecasting accuracy, and sentiment features of the institutional investor were relatively more essential than the individual investor and foreign investor sentiments for index direction prediction in most out-of-sample periods. It demonstrates that the method proposed in this research can provide useful references for market participants to support their investment in the Chinese security market.

Financial Futures Prediction Using Fuzzy Rough Set and Synthetic Minority Oversampling Technique

In this research, a novel approach called SMOTE-FRS is proposed for movement prediction and trading simulation of the Chinese Stock Index 300 (CSI300) futures, which is the most crucial financial futures in the Chinese A-share market. First, the SMOTE- (Synthetic Minority Oversampling Technique-) based method is employed to address the sample unbalance problem by oversampling the minority class and undersampling the majority class of the futures price change. Then, the FRS- (fuzzy rough set-) based method, as an efficient tool for analyzing complex and nonlinear information with high noise and uncertainty of financial time series, is adopted for the price change multiclassification of the CSI300 futures. Next, based on the multiclassification results of the futures price movement, a trading strategy is developed to execute a one-year simulated trading for an out-of-sample test of the trained model. From the experimental results, it is found that the proposed method averagely yielded an accumulated return of 6.36%, a F1-measure of 65.94%, and a hit ratio of 62.39% in the four testing periods, indicating that the proposed method is more accurate and more profitable than the benchmarks. Therefore, the proposed method could be applied by the market participants as an alternative prediction and trading system to forecast and trade in the Chinese financial futures market.