Stock Market Simulation Research Articles

Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models

In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that all the relevant patterns associated with the time series have been detected. The paper uses simulated stock market data and risk management scenarios to demonstrate how these tests enhance model reliability, since when white noise characteristics are ascertained, the residuals are confirmed random and independent. The importance of validating models is discussed in the context of economic forecasting and policy analysis, where the validated models are indispensable for anticipating a market upheaval or to make right economic decisions. Therefore, when white noise test is used to confirm that the model meets the white noise criteria, this improves the credibility of predictions and thus influences strategic decision making in the uncertain environment.

From Theory to Practice: Applying the Markowitz Model in Stock Portfolio Management under ESG

The study consisted of a month-long simulated stock market operation focusing on the comprehensive analysis of equity portfolio creation and management. Against the backdrop of growing concern for environmental protection, the S&P 500 Net Zero 2050 Climate Transition ESG Index was deemed the appropriate benchmark for the portfolios due to the average level of risk tolerance of customers. The investigation began with an in-depth assessment of macroeconomic and sector conditions, followed by careful selection of securities using both fundamental and technical analysis techniques. The portfolio was then optimized using the Markowitz model and subsequently managed using a variety of strategies, including trading, monitoring, and rebalancing. The subsequent phase of the research involves a rigorous evaluation of performance utilizing various metrics including single-period returns and the Sharpe ratio. The study culminates in a reflective analysis of the overall investment project. Despite the portfolio's underperformance against the benchmark, the project provides invaluable insights into the intricacies of stock market investment and portfolio management, accentuating the impact of market volatility and the significance of strategic asset allocation.

Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study.

Recent advances in the field of machine learning have yielded novel research perspectives in behavioural economics and financial markets microstructure studies. In this paper we study the impact of individual trader leaning characteristics on markets using a stock market simulator designed with a multi-agent architecture. Each agent, representing an autonomous investor, trades stocks through reinforcement learning, using a centralized double-auction limit order book. This approach allows us to study the impact of individual trader traits on the whole stock market at the mesoscale in a bottom-up approach. We chose to test three trader trait aspects: agent learning rate increases, herding behaviour and random trading. As hypothesized, we find that larger learning rates significantly increase the number of crashes. We also find that herding behaviour undermines market stability, while random trading tends to preserve it.

모의 주식 투자 프로젝트의 학습 성과 분해

The disaggregation and detailed examination of the learning processes’ data that form the basis and the application of the learners’ knowledge allows to obtain additional valuableinformation that would, otherwise, have been overlooked when simply analyzing the aggregated data. This study analyzes the stock and the flow of the economic and financial knowledge among 481 college students who engaged in a stock market simulation project, and further disaggregates the multiple-choice data into four learning types, based on the pattern of pre-test and post-test responses: positive learning, retained learning, zero learning, and negative learning. The difference between pre- and post-test scores is mostly contributed by positive learning, but negative learning is also present. Likewise, the post-test score can be split into retained learning and positive learning components: the larger the contribution from retained learning, the smaller the contribution from positive learning. The regression results show that males, nonfreshmen, and students with experience in stock investing have more prior economic and financial understanding of the stock market. However, after engaging in the stock market simulation project, the differences in the scores mostly disappear due to knowledge catch-up. Gender, academic year, stock investment experience, reading economic articles, and the desire to engage in real stock investments are identified as factors influencing the stock, flow, and learning types of the economic and financial knowledge. Recognizing the categories that reflect zero learning and their content elements, and then incorporating those findings into the development and modification of the economic and financial education programs can further enhance the educational outcomes.

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.

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

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.

Evaluation of the Impact of Utilizing Stock Market Simulators for Enhancing Analytical Competence: An Experiential Learning Approach

Abstract -This research explores the impact of stock marketsimulators on students pursuing technical education, specifically focusing on developing analytical skills in finance. Fifty students enrolled in technical courses with finance components participated in an experimental study. Pre and post-test surveys were conducted to assess their financial analytical skills before and after exposure to a stock market simulator, specifically Money Control. The students were given 60 days to build and operate investment portfolios using acquired analytical skills. Results from the study reveal a significant positive impact on students' analytical skills, demonstrating increased interest and motivation in utilizing stock market simulators for learning subjects like security analysis and portfolio management. The findings suggest that stock market simulators serve as valuable tools for teaching, practising, and enhancing analytical skills in finance, particularly for students pursuing technical courses. Keywords: Analytical skills, stock market simulator, teachinglearning.

Generative Adversarial Neural Networks for Realistic Stock Market Simulations

Generative Adversarial Neural Networks for Realistic Stock Market Simulations

The influence of upward social comparison on retail trading behaviour

Online investing is often facilitated by digital platforms, where the information of peer top performers can be widely accessible and distributed. However, the influence of such information on retail investors’ psychology, their trading behaviour and potential risks they may be prone to is poorly understood. We investigate the impact of upward social comparison on risk-taking, trading activity and investor satisfaction using a tailored experiment with 807 experienced retail investors trading on a dynamically evolving simulated stock market, designed to systematically measure various facets of trading activity. We find that investors presented with an upward social comparison take more risk and trade more actively, and they report significantly lower satisfaction with their own performance. Our findings demonstrate the pitfalls of modern investment platforms with peer information and social trading. The broad implications of this study also provide guidelines for improving retail investor satisfaction and protection.

From genes to performance: Dopaminergic modulation of decision making in a stock market simulation.

From genes to performance: Dopaminergic modulation of decision making in a stock market simulation.

A bibliometric analysis on the application of deep learning in finance: status, development and future directions

PurposeDeep learning (DL) is a new and relatively unexplored field that finds immense applications in many industries, especially ones that must make detailed observations, inferences and predictions based on extensive and scattered datasets. The purpose of this paper is to answer the following questions: (1) To what extent has DL penetrated the research being done in finance? (2) What areas of financial research have applications of DL, and what quality of work has been done in the niches? (3) What areas still need to be explored and have scope for future research?Design/methodology/approachThis paper employs bibliometric analysis, a potent yet simple methodology with numerous applications in literature reviews. This paper focuses on citation analysis, author impacts, relevant and vital journals, co-citation analysis, bibliometric coupling and co-occurrence analysis. The authors collected 693 articles published in 2000–2022 from journals indexed in the Scopus database. Multiple software (VOSviewer, RStudio (biblioshiny) and Excel) were employed to analyze the data.FindingsThe findings reveal significant and renowned authors' impact in the field. The analysis indicated that the application of DL in finance has been on an upward track since 2017. The authors find four broad research areas (neural networks and stock market simulations; portfolio optimization and risk management; time series analysis and forecasting; high-frequency trading) with different degrees of intertwining and emerging research topics with the application of DL in finance. This article contributes to the literature by providing a systematic overview of the DL developments, trajectories, objectives and potential future research topics in finance.Research limitations/implicationsThe findings of this paper act as a guide for literature review for anyone interested in doing research in the intersection of finance and DL. The article also explores multiple areas of research that have yet to be studied to a great extent and have abundant scope.Originality/valueVery few studies have explored the applications of machine learning (ML), namely, DL in finance, which is a much more specialized subset of ML. The authors look at the problem from the aspect of different techniques in DL that have been used in finance. This is the first qualitative (content analysis) and quantitative (bibliometric analysis) assessment of current research on DL in finance.

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

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

Winner Strategies in a Simulated Stock Market

In this study, we explore the dynamics of the stock market using an agent-based simulation platform. Our approach involves creating a multi-strategy market where each agent considers both fundamental and technical factors when determining their strategy. The agents vary in their approach to these factors and the time interval they use for technical analysis. Our findings indicate that investing heavily in reducing the value–price gap was a successful strategy, even in markets where there were no trading forces to reduce this gap. Furthermore, our results remain consistent across various modifications to the simulation’s structure.

Supplemental Material for From Genes to Performance: Dopaminergic Modulation of Decision Making in a Stock Market Simulation

Supplemental Material for From Genes to Performance: Dopaminergic Modulation of Decision Making in a Stock Market Simulation

Impact of gamification on mitigating behavioral biases of investors

We investigate whether gamification can help mitigate behavioral biases of investors by conducting a unique stock market experiment that is free from observer-expectancy and subject-expectancy effects. Utilizing the trading data of investors who simultaneously have active portfolios in an investment firm and stock market simulation game, we show that investors have different biases in real versus simulated settings. We find that participating in a stock market game affects all biases differently, with different degrees of participation to the game. While overconfidence bias and disposition effect can be mitigated and decrease with more active participation in the game, familiarity and status quo biases increase. We also show that young, inexperienced investors with average-sized portfolios and men are more likely to participate. These findings will especially be of interest to researchers, financial institutions and governmental bodies that plan to conduct similar experiments and design services promoting better financial decision-making and investment behavior.

Multi-agent reinforcement learning approach for hedging portfolio problem.

Developing a hedging strategy to reduce risk of losses for a given set of stocks in a portfolio is a difficult task due to cost of the hedge. In Vietnam stock market, cross-hedge is involved hedging a long position of a stock because there is no put option for the stock. In addition, only VN30 stock index futures contracts are traded on Hanoi Stock Exchange. Inspired by recently achievement of deep reinforcement learning, we explore feasibility to construct a hedging strategy automatically by leveraging cooperative multi-agent in reinforcement learning techniques without advanced domain knowledge. In this work, we use 10 popular stocks on Ho Chi Minh Stock Exchange, and VN30F1M (VN30 Index Futures contracts within one month settlement) to develop a stock market simulator (including transaction fee, tax, and settlement date of transactions) for reinforcement learning agent training. We use daily return as input data for training process. Results suggest that the agent can learn trading and hedging policy to make profit and reduce losses. Furthermore, we also find that our agent can protect portfolios and make positive profit in case market collapses systematically. In practice, this work can help Vietnam’s stock market investors to improve performance and reduce losses in trading, especially when the volatility cannot be controlled.

COMPLEXITY ANALYSIS OF TIME SERIES BASED ON GENERALIZED FRACTIONAL ORDER DUAL-EMBEDDED DIMENSIONAL MULTIVARIATE MULTISCALE DISPERSION ENTROPY

Based on the theories of multivariate multiscale dispersion entropy, we propose an improved new model — dual-embedded dimensional multivariate multiscale dispersion entropy (mvMDE), and generalize the new model to fractional order (GmvMDE). The mvMDE and GmvMDE simultaneously consider the cross-correlation between multiple channels, and they provide a dynamic complexity measure for measuring the multivariable data observed in one system. We introduce dual embedded dimensions to construct subsequences, which makes the model more flexible and applicable to a variety of application scenarios. Through empirical analysis of simulated data and stock market data, this paper verifies that both mvMDE and GmvMDE can effectively measure and distinguish the sequence complexity, and GmvMDE can better capture the small evolution of sequences, with higher stability and accuracy. On the basis of mvMDE research, we also propose the multiscale system dispersion entropy (MSDE) and its fractional order form (GMSDE). Using the vector-to-vector distance quantization method, the relationship between individuals in the system can be better extracted, and the systemic complexity of financial portfolio can be measured from the time dimension and the space dimension simultaneously. The empirical analysis based on the data of major industries in Chinese stock market shows that MSDE and GMSDE can accurately capture the structural information in the system and effectively distinguish the complexity of financial portfolios.

Pyramid scheme in stock market: A kind of financial market simulation* *Project supported by the National Natural Science Foundation of China (Grant Nos. 71932008 and 91546201).

Artificial stock market simulation based on agent is an important means to study financial market. Based on the assumption that the investors are composed of a main fund, small trend and contrarian investors characterized by four parameters, we simulate and research a kind of financial phenomenon with the characteristics of pyramid schemes. Our simulation results and theoretical analysis reveal the relationships between the rate of return of the main fund and the proportion of the trend investors in all small investors, the small investors’ parameters of taking profit and stopping loss, the order size of the main fund and the strategies adopted by the main fund. Our work is helpful to explain the financial phenomenon with the characteristics of pyramid schemes in financial markets, design trading rules for regulators and develop trading strategies for investors.

Does Gender and Family Income Impact Stock Trading of B-School Students? Findings from a Stock Simulation Exercise

Educators across the globe utilize online stock market simulation games to introduce students to trading in the stock market. The primary objective of the simulation exercise is to expose students to the practical application of financial theories on fundamental analysis, stock selection, building an optimal portfolio, monitoring the risk-return characteristics and continuously improving the portfolio based on changing realities. This article utilizes the trading data from a simulation exercise conducted by a leading B-school in India. The exercise was conducted as part of Security Analysis and Portfolio Management (SAPM) course offered by the B-school. The objective of the article is to understand the role of gender and family income in the trading patterns of students in the simulation exercise. The article covers 163 students who were part of the simulation exercise in 2019. The results indicate that male students trade more aggressively than female students, both in terms of number of trades and the number of companies traded. However, the female students reported higher stock trading performance, measured in stock returns. This is observed to be true at all the quartiles, with the largest magnitude of the difference in the mid-quartiles. The study also indicates that the students from wealthier families perform better than those from poorer backgrounds. However, family income is an insignificant differentiating factor. Further, regression analysis indicates that gender is a significant determinant of stock returns. Based on these findings, the authors argue that gender has a significant role in the stock trading performance of B-schoolers. The article contributes to the field of behavioural finance, especially on the literature of gender and performance in financial markets.