Financial teaching method and system integrating game strategy and multi-dimensional evaluation

Abstract

The invention provides a financial teaching method and system fusing a game strategy and multi-dimensional evaluation, and relates to the field of financial teaching. The method comprises the following steps: S1, setting a financial decision-making scene based on the game theory to help students to understand strategy competition and decision making in the market; s2, introducing a multi-dimensional evaluation system which comprises a market fluctuation coefficient, investor risk tolerance, market predictive ability and the like, and comprehensively evaluating decision-making effects of participants; s3, according to the strategy selection and evaluation result of each participant, calculating and outputting a financial income value and a risk income ratio of the participant; and S4, providing personalized decision optimization suggestions through a guiding feedback mechanism, and helping students to improve decision strategies. The system can be realized through a terminal or a server, supports multiple rounds of game interaction, and enhances the financial analysis and decision-making capabilities of students. Through the comprehensive game strategy and multi-dimensional evaluation, the practicability and interactivity of financial teaching are improved, and students can make more accurate decisions in a complex market environment.

Description

Technical Field

[0001] The present invention relates to the field of financial teaching, and in particular to a financial teaching method and system integrating game strategy and multi-dimensional evaluation. Background Art

[0002] Financial decision-making is a complex and uncertain process, especially in today's market environment with frequent fluctuations and complex information. Traditional financial teaching methods usually focus on imparting theoretical knowledge and simulating practical operations, but in the actual teaching process, students often find it difficult to fully understand the effects of various decision-making strategies in the financial market and the game logic behind them. Although some teaching tools and platforms have begun to introduce simulated trading or decision-making models, these methods usually do not fully combine the strategic competition of game theory with multi-dimensional comprehensive evaluation, and cannot effectively improve students' understanding and response capabilities for complex financial decisions.

[0003] Game theory, as a theory for analyzing the interactive behavior between decision makers, is widely used in the fields of economics and finance. In the financial market, various decision-making behaviors often form a complex game process, which involves strategic competition, information asymmetry and dynamic adjustment among participants. Game theory can help students better understand the game relationship between different strategies and how to obtain the best decision in competition. However, the existing financial teaching methods often ignore the dynamic nature of game strategies and the multidimensionality of decision-making, and the learning effect of students in simulated games is limited.

[0004] At the same time, financial decision-making does not rely solely on a single decision-making strategy, but is also affected by multiple factors. For example, market volatility, participants’ risk tolerance, and the accuracy of market expectations all affect decision-making results to varying degrees. Most existing financial decision-making simulation tools lack comprehensive consideration of these multidimensional factors and are unable to provide students with comprehensive and accurate decision-making analysis, which often makes it difficult for students to flexibly adjust their strategies when dealing with the real market.

[0005] Therefore, how to combine game theory and multi-dimensional evaluation system to design a teaching method that can simulate the complex financial market environment, help students fully understand the financial decision-making process and improve their decision-making ability has become an urgent problem to be solved in the current financial education field. Summary of the invention

[0006] In order to solve the technical problems in the prior art of lack of dynamics of strategic competition, singleness of evaluation dimension and limitation of decision feedback, the present invention provides a financial teaching method and system integrating game strategy and multi-dimensional evaluation.

[0007] The technical solution provided by the present invention is as follows:

[0008] First aspect:

[0009] The present invention provides a financial teaching method integrating game strategy and multi-dimensional evaluation, including:

[0010] S1. Set up a financial decision-making scenario based on game theory, which includes at least two participants, multiple decision-making strategies, and the payoff function corresponding to each strategy. The decision-making scenario helps students understand strategic competition and decision-making in the financial market by comparing the game process of different strategies;

[0011] S2. Introduce a multi-dimensional evaluation system, which includes at least three-dimensional parameters, namely, market volatility coefficient MTC, investor risk tolerance IRT and market forecasting ability MPA, and conduct a multi-dimensional evaluation of the financial decisions of participants based on these dimensions;

[0012] S3. In the game decision-making process, according to the strategy selection and evaluation results of each participant, the corresponding financial return value and the corresponding risk-return ratio RRR are calculated and output. The calculation of the return value and risk-return ratio refers to the existing game theory and multi-dimensional evaluation model, and is dynamically adjusted according to the parameters of each dimension in the evaluation system;

[0013] S4. Based on the final profit value of each participant, use the guided feedback mechanism to give personalized financial decision-making optimization suggestions to help students understand the complexity behind financial decisions and their impact on individuals and the market from the perspective of game strategy.

[0014] Second aspect:

[0015] The present invention provides a financial teaching system integrating game strategy and multi-dimensional evaluation, including:

[0016] processor;

[0017] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the financial teaching method integrating game strategy and multi-dimensional evaluation as described in the first aspect is implemented.

[0018] The beneficial effects brought about by the technical solution provided by the present invention include at least:

[0019] (1) In the present invention, by constructing a financial decision-making scenario based on game theory, it is possible to simulate the strategic competition and game process among multiple participants, helping students to intuitively understand the impact of different financial strategies on decision-making results and enhance their decision-making ability in the actual financial market;

[0020] (2) In the present invention, a multi-dimensional evaluation system (including market turbulence coefficient MTC, investor risk tolerance IRT, market prediction ability MPA, etc.) is introduced to comprehensively evaluate the decision-making effect of participants, helping students realize that financial decision-making not only needs to focus on returns, but also needs to balance multiple factors such as risk, market volatility and prediction accuracy, thereby improving the comprehensiveness and accuracy of their financial decision-making;

[0021] (3) In the present invention, a guided feedback mechanism is combined to provide students with personalized optimization suggestions, and the decision-making strategy is adjusted in real time according to their performance in the game process, helping students to identify deficiencies in decision-making and improve them, thereby improving their coping ability and decision-making level in a complex market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 A flowchart of a financial teaching method integrating game strategy and multi-dimensional evaluation provided by an embodiment of the present invention;

[0024] Figure 2 A schematic diagram of the structure of a financial teaching system integrating game strategy and multi-dimensional evaluation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0026] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0027] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0028] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0029] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0030] Reference Manual Attached Figure 1 , which shows a flow chart of a financial teaching method integrating game strategy and multi-dimensional evaluation provided by an embodiment of the present invention.

[0031] The embodiment of the present invention provides a financial teaching method integrating game strategy and multi-dimensional evaluation. The method can be implemented by a financial teaching device integrating game strategy and multi-dimensional evaluation. The financial teaching device integrating game strategy and multi-dimensional evaluation can be a terminal or a server. The processing flow of the financial teaching method integrating game strategy and multi-dimensional evaluation can include the following steps:

[0032] S1. Set up a financial decision-making scenario based on game theory, which includes at least two participants, multiple decision-making strategies, and the payoff function corresponding to each strategy. The decision-making scenario helps students understand strategic competition and decision-making in the financial market by comparing the game process of different strategies.

[0033] It should be noted that the purpose of setting up a financial decision-making scenario based on game theory is to help students experience the decision-making process under different financial strategies in a simulated environment. Through the strategic competition between participants, students can better understand the decision-making logic in the market and identify the impact of different strategy choices on the final returns, thereby providing a theoretical and practical basis for them in actual financial decision-making. The construction of this scenario enables students to explore and test the effectiveness of different strategies in a risk-free environment, enhancing their financial analysis and decision-making capabilities.

[0034] S2. Introduce a multi-dimensional evaluation system, which includes at least three-dimensional parameters, namely market volatility coefficient MTC, investor risk tolerance IRT and market forecasting ability MPA, and conduct a multi-dimensional evaluation of the financial decisions of participants based on these dimensions.

[0035] It should be noted that the introduction of a multi-dimensional evaluation system is to comprehensively evaluate the decision-making effects of participants in the game. The market turbulence coefficient (MTC), investor risk tolerance (IRT) and market prediction ability (MPA) are evaluated from three dimensions: market volatility, personal risk tolerance and market prediction accuracy, helping students realize that in financial decision-making, they should not only pay attention to returns, but also weigh risks, volatility and market expectations. Through this multi-angle evaluation, students can obtain more comprehensive and in-depth decision analysis and improve their decision-making acumen in a complex market environment.

[0036] S3. In the game decision-making process, according to the strategy selection and evaluation results of each participant, the corresponding financial return value and the corresponding risk-return ratio RRR are calculated and output. The calculation of the return value and risk-return ratio refers to the existing game theory and multi-dimensional evaluation model, and is dynamically adjusted according to the parameters of each dimension in the evaluation system.

[0037] It should be noted that by calculating and outputting the financial return value and risk-return ratio (RRR) of each participant, students can more intuitively see the specific consequences of their decisions. The financial return value reflects the actual return of the decision, while the risk-return ratio reflects the risk of the decision. Through the calculation of these two important indicators, students can understand how to balance the maximization of returns and the minimization of risks in the market, thereby further improving their decision-making quality and risk management capabilities.

[0038] S4. Based on the final profit value of each participant, use the guided feedback mechanism to give personalized financial decision-making optimization suggestions to help students understand the complexity behind financial decisions and their impact on individuals and the market from the perspective of game strategy.

[0039] It should be noted that the role of using the guided feedback mechanism to give personalized optimization suggestions is to help students understand their own decision-making deficiencies and room for improvement. Through feedback, students can obtain real-time guidance related to their decisions during the game, helping them learn from actual operations how to adjust strategies to achieve better market performance. This mechanism effectively enhances the pertinence and practicality of learning, helps students accumulate experience through continuous practice and adjustment, and cultivates more mature and accurate financial decision-making capabilities.

[0040] In a possible implementation, the calculation formula of the market turbulence coefficient MTC is:

[0041]

[0042] Among them, P i (t) represents the market price at time t, P i-1 (t) represents the market price at the previous moment, and n represents the total period in the time series.

[0043] In one possible implementation,

[0044] The calculation method of the investor risk tolerance IRT is:

[0045]

[0046] Among them, R i represents the risk-return ratio of the i-th investment project, W i represents the weight of the ith investment project, and m represents the total number of investment projects.

[0047] In a possible implementation, the market forecasting capability MPA is calculated by the following steps:

[0048] S401. Collect historical market data within a certain time range;

[0049] S402. Use historical data to perform regression analysis to obtain a prediction model for future market trends;

[0050] S403, predicting the future market ups and downs according to the prediction model, and comparing it with the actual market changes to calculate the prediction error;

[0051] The calculation formula of MPA is:

[0052]

[0053] Among them, P forecasted (t i ) represents the prediction time t i The market price, P actual (t i ) is the actual market price, and n represents the number of forecast periods.

[0054] In a possible implementation, the calculation of the profit function refers to the Nash equilibrium model in game theory, and based on the strategies selected by the participants, the optimal strategy combination of each participant is calculated, which is achieved by the following steps:

[0055] S501, determining a strategy set for all participants, where each participant has multiple strategy options;

[0056] S502. In the game model, calculate the profit value corresponding to each strategy combination;

[0057] S503. Using the concept of Nash equilibrium, determine the best response strategy for each participant, and ultimately calculate the optimal strategy and benefit for each participant.

[0058] In a possible implementation, the game strategy for financial decision-making includes:

[0059] High-risk, high-return speculative strategies, stable low-risk, low-return conservative strategies, and dynamic adjustment strategies that adapt to market changes.

[0060] In a possible implementation, the game process further includes:

[0061] Each participant chooses a corresponding strategy in each round based on his or her risk tolerance, market forecasting ability, and payoff function;

[0062] After each round, the system dynamically adjusts the market decision recommendations for each participant based on strategy selection, market volatility coefficient and other evaluation dimensions.

[0063] In a possible implementation, the feedback mechanism includes:

[0064] According to the performance of participants in the game, optimization suggestions are given in real time to help them adjust their decisions;

[0065] After each feedback, participants adjust the weights of future strategies based on historical strategy selection and feedback optimization results.

[0066] In a possible implementation, the multidimensional evaluation system further includes the following evaluation dimensions:

[0067] The market expected volatility MEV and financial leverage effect FLE, and the weight of each dimension on the decision result is obtained through the following calculation method:

[0068]

[0069] Among them, V(t i ) is the time t i The market price fluctuation, E(t i ) is the expected return of investors, A(t i ) is the total assets of the investor, and n is the evaluation period.

[0070] In one possible implementation, the system guides students to make strategy choices and market analysis by setting a game scenario of financial decision-making. The game scenario includes two participants, each of whom has three strategy options: a high-risk speculative strategy, a low-risk conservative strategy, and a dynamic adjustment strategy that adapts to market fluctuations. Each strategy corresponds to a different profit function, and these profit functions are adjusted in real time according to the market environment and the choices of the participants.

[0071] Each participant's decision is evaluated based on different profit functions. Take participant A as an example. Assuming that the strategy he chooses is "high-risk speculative strategy", the system will calculate his profit value based on factors such as market price fluctuations, risk tolerance and leverage effect. The basic form of the profit function is as follows:

[0072] U A =f(P t ,Risk,Leverage)

[0073] Among them, P t Represents the current market price, Risk represents the market risk coefficient, and Leverage represents the financial leverage effect. The system adjusts the profit function in real time according to market fluctuations, thereby helping participants understand the impact of different strategy choices on decision-making results.

[0074] In the game decision-making process, the system introduces a multi-dimensional evaluation system covering the following key dimensions:

[0075] Market Turbulence Coefficient (MTC): This indicator is used to measure the volatility of the market. The specific calculation method is as follows:

[0076]

[0077] Among them, P i (t) represents the market price at time t, P i-1 (t) represents the market price at the previous moment, and n represents the total period in the time series.

[0078] Investor Risk Tolerance (IRT): This indicator measures the risk tolerance of participants. The calculation formula is as follows:

[0079]

[0080] Among them, R i represents the risk-return ratio of the i-th investment project, W i represents the weight of the ith investment project, and m represents the total number of investment projects.

[0081] Market Prediction Ability (MPA): This indicator is used to evaluate the accuracy of participants in predicting market trends. The calculation formula is as follows:

[0082]

[0083] Among them, P forecasted (t i ) represents the prediction time ti The market price, P actual (t i ) is the actual market price, and n represents the number of forecast periods.

[0084] The system has further added two new evaluation dimensions to the multi-dimensional evaluation system: Market Expected Volatility (MEV) and Financial Leverage Effect (FLE). These two dimensions are used to measure market volatility and the effect of investors using leverage, and affect the final decision-making recommendations. The specific calculation method is as follows:

[0085] Market Expected Volatility (MEV): This indicator is used to evaluate the expected volatility of market price fluctuations. The calculation formula is:

[0086]

[0087] Among them, V(t i ) is the time t i The market price fluctuation, V(t i-1 ) represents the market price at the previous moment, E(t i ) is the expected return of investors, A(t i ) is the total assets of the investor, and n is the evaluation period.

[0088] Through these two new evaluation dimensions, the system can more accurately evaluate the decision-making results of participants when facing different market conditions and provide them with more personalized optimization suggestions.

[0089] After each round of gaming, the system calculates the Risk-Reward Ratio (RRR) based on the participant's choice and the evaluation results of each dimension. The participant's final return and risk-reward ratio will affect the subsequent decision-making guidance. The system provides each participant with personalized strategy optimization suggestions through a feedback mechanism. For example, if a participant adopts an overly risky strategy during market turmoil, the system will recommend that he or she adjust to a more conservative strategy in the next round.

[0090] During multiple rounds of gaming, the system dynamically adjusts the participants' future strategy choices based on their historical decisions and evaluation results. If a participant consistently chooses a higher-risk strategy and obtains higher returns in multiple rounds, the system may recommend that the participant increase investment leverage to further amplify the returns. If the participant loses money in most rounds, the system will guide the participant to transition to a conservative strategy.

[0091] In one possible implementation, after each round of gaming, the system will evaluate the performance of the participants and provide feedback based on the evaluation results. Through consecutive rounds of gaming, participants gradually adjust their strategies and learn to choose appropriate strategies in different market environments. For example, when the market is volatile, the system recommends participants to adopt a low-risk strategy; when the market is stable, it recommends a higher-risk speculative strategy.

[0092] Participants accumulate rich experience in the game, and the system will adjust their strategy options according to each participant's performance and risk tolerance. In this way, learners can gradually improve their financial decision-making ability in a simulated environment.

[0093] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0094] (1) In the present invention, by constructing a financial decision-making scenario based on game theory, it is possible to simulate the strategic competition and game process among multiple participants, helping students to intuitively understand the impact of different financial strategies on decision-making results and enhance their decision-making ability in the actual financial market;

[0095] (2) In the present invention, a multi-dimensional evaluation system (including market turbulence coefficient MTC, investor risk tolerance IRT, market prediction ability MPA, etc.) is introduced to comprehensively evaluate the decision-making effect of participants, helping students realize that financial decision-making not only needs to focus on returns, but also needs to balance multiple factors such as risk, market volatility and prediction accuracy, thereby improving the comprehensiveness and accuracy of their financial decision-making;

[0096] (3) In the present invention, a guided feedback mechanism is combined to provide students with personalized optimization suggestions, and the decision-making strategy is adjusted in real time according to their performance in the game process, helping students to identify deficiencies in decision-making and improve them, thereby improving their coping ability and decision-making level in a complex market environment.

[0097] Reference Manual Attached Figure 2 , showing a structural diagram of a financial teaching system integrating game strategy and multi-dimensional evaluation provided by an embodiment of the present invention.

[0098] The present invention further provides a financial teaching system 20 integrating game strategy and multi-dimensional evaluation, which is applied to the financial teaching method integrating game strategy and multi-dimensional evaluation, and comprises:

[0099] Processor 201.

[0100] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201, a financial teaching method integrating game strategy and multi-dimensional evaluation as in the method embodiment is implemented.

[0101] The financial teaching system integrating game strategy and multi-dimensional evaluation provided by the present invention can execute the financial teaching method integrating game strategy and multi-dimensional evaluation and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0102] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0103] (1) In the present invention, by constructing a financial decision-making scenario based on game theory, it is possible to simulate the strategic competition and game process among multiple participants, helping students to intuitively understand the impact of different financial strategies on decision-making results and enhance their decision-making ability in the actual financial market;

[0104] (2) In the present invention, a multi-dimensional evaluation system (including market turbulence coefficient MTC, investor risk tolerance IRT, market prediction ability MPA, etc.) is introduced to comprehensively evaluate the decision-making effect of participants, helping students realize that financial decision-making not only needs to focus on returns, but also needs to balance multiple factors such as risk, market volatility and prediction accuracy, thereby improving the comprehensiveness and accuracy of their financial decision-making;

[0105] (3) In the present invention, a guided feedback mechanism is combined to provide students with personalized optimization suggestions, and the decision-making strategy is adjusted in real time according to their performance in the game process, helping students to identify deficiencies in decision-making and improve them, thereby improving their coping ability and decision-making level in a complex market environment.

[0106] The above contents are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0107] There are a few points to note:

[0108] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0109] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0110] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0111] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A financial teaching method that integrates game strategy and multi-dimensional evaluation, characterized by: include: S1. Set up a financial decision-making scenario based on game theory, which includes at least two participants, multiple decision-making strategies, and the payoff function corresponding to each strategy. The decision-making scenario helps students understand strategic competition and decision-making in the financial market by comparing the game process of different strategies; S2. Introduce a multi-dimensional evaluation system, which includes at least three-dimensional parameters, namely, market volatility coefficient MTC, investor risk tolerance IRT and market forecasting ability MPA, and conduct a multi-dimensional evaluation of the financial decisions of participants based on these dimensions; S3. In the game decision-making process, according to the strategy selection and evaluation results of each participant, the corresponding financial return value and the corresponding risk-return ratio RRR are calculated and output. The calculation of the return value and risk-return ratio refers to the existing game theory and multi-dimensional evaluation model, and is dynamically adjusted according to the parameters of each dimension in the evaluation system; S4. Based on the final profit value of each participant, use the guided feedback mechanism to give personalized financial decision-making optimization suggestions to help students understand the complexity behind financial decisions and their impact on individuals and the market from the perspective of game strategy.

2. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: include: The calculation formula of the market turbulence coefficient MTC is: Among them, P i (t) represents the market price at time t, P i-1 (t) represents the market price at the previous moment, and n represents the total period in the time series.

3. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 2, characterized in that: include: The calculation method of the investor risk tolerance IRT is: Among them, R i represents the risk-return ratio of the i-th investment project, W i represents the weight of the ith investment project, and m represents the total number of investment projects.

4. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: include: The market forecast ability MPA is calculated by the following steps: S401. Collect historical market data within a certain time range; S402. Use historical data to perform regression analysis to obtain a prediction model for future market trends; S403, predicting the future market ups and downs according to the prediction model, and comparing it with the actual market changes to calculate the prediction error; The calculation formula of MPA is: Among them, P forecasted (t i ) represents the prediction time t i The market price, P actual (t i ) is the actual market price, and n represents the number of forecast periods.

5. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: include: The calculation of the profit function refers to the Nash equilibrium model in game theory, and based on the strategies selected by the participants, the optimal strategy combination of each participant is calculated, which is achieved through the following steps: S501, determining a strategy set for all participants, where each participant has multiple strategy options; S502. In the game model, calculate the profit value corresponding to each strategy combination; S503. Using the concept of Nash equilibrium, determine the best response strategy for each participant, and ultimately calculate the optimal strategy and benefit for each participant.

6. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: The game strategy of the financial decision-making includes: High-risk, high-return speculative strategies, stable low-risk, low-return conservative strategies, and dynamic adjustment strategies that adapt to market changes.

7. A financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: The game process further includes: Each participant chooses a corresponding strategy in each round based on his or her risk tolerance, market forecasting ability, and payoff function; After each round, the system dynamically adjusts the market decision recommendations for each participant based on strategy selection, market volatility coefficient and other evaluation dimensions.

8. The financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1 is characterized in that: The feedback mechanism includes: According to the performance of participants in the game, optimization suggestions are given in real time to help them adjust their decisions; After each feedback, participants adjust the weights of future strategies based on historical strategy selection and feedback optimization results.

9. The financial teaching method integrating game strategy and multi-dimensional evaluation according to claim 1, characterized in that: The multidimensional evaluation system further includes the following evaluation dimensions: The market expected volatility MEV and financial leverage effect FLE, and the weight of each dimension on the decision result is obtained through the following calculation method: Among them, V(t i ) is the time t i The market price fluctuation, E(t i ) is the expected return of investors, A(t i ) is the total assets of the investor, and n is the evaluation period.

10. A financial teaching system integrating game strategy and multi-dimensional evaluation, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the financial teaching method integrating game strategy and multi-dimensional evaluation as described in any one of claims 1 to 9 is implemented.

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