Investment activity risk intelligent assessment and decision-making auxiliary method, system and device and medium thereof

Abstract

The invention provides an investment activity risk intelligent assessment and decision-making auxiliary method, system and device and a medium thereof, and the method comprises the steps: obtaining historical investment data related to an investment activity from a plurality of data sources, and carrying out the preprocessing of the historical investment data; through multi-source data acquisition and integration, various kinds of information related to investment activities can be comprehensively obtained, and assessment one-sidedness caused by a single data source is avoided; the historical investment data is subjected to feature extraction and selection, key feature vectors are effectively screened out, the calculation efficiency and accuracy of a risk assessment model are improved, and interference of data redundancy and noise on the model is reduced; the advanced machine learning algorithm is adopted to construct the risk assessment model, automatic learning and adaptation to a complex and changeable investment market environment can be achieved, investment risks are accurately quantified, more scientific and accurate investment decision assistance is provided for investors, the quality and success rate of investment decision are improved, and the investment risks are reduced.

Description

Technical Field

[0001] The present invention relates to the field of investment risk assessment and decision-making technology, and in particular to an investment activity risk intelligent assessment and decision-making assistance method, system, device and medium thereof. Background Art

[0002] In today's complex financial market environment with deep integration of global economic patterns, rapid development of science and technology, and continuous changes in policies and regulations, the risk factors faced by investment activities are extremely diverse and intertwined. Among them, market volatility is particularly significant. The frequent rise and fall of stock market prices, the unstable changes in bond yields, and the fluctuations in the value of various financial derivatives may have a huge impact on the value of the investment portfolio in a short period of time. In terms of macroeconomic changes, factors such as the slowdown or acceleration of the growth rate of gross domestic product (GDP), the fluctuations in the inflation rate, the rise and fall of interest rates, and the fluctuation of exchange rates will profoundly affect the returns and risks of investment from the perspective of overall economic operation. The competitive situation in the industry cannot be ignored. The rise of emerging technologies may put traditional industries at risk of being subverted. The fierce competition among companies in the same industry will lead to the redistribution of market share, the compression of profit margins, and the increase of innovation pressure. These situations will undoubtedly bring uncertainty to related investments. Furthermore, the business conditions of a company are more directly related to the success or failure of an investment. The financial health of a company, including whether its asset-liability structure is reasonable, whether its cash flow is sufficient, whether its profitability is stable, etc., as well as non-financial factors such as the correctness of its strategic decisions, the strength of its market development capabilities, the leadership level of its management and governance efficiency, all largely determine the level of risk faced by investing in the company.

[0003] Traditional investment risk assessment methods often rely on single financial indicator analysis or simple statistical models, which makes it difficult to fully and accurately quantify investment risks. In addition, when faced with massive amounts of financial data and complex market relationships, they lack effective data processing and analysis capabilities, which greatly reduces the scientificity and accuracy of investment decisions. In addition, with the continuous advancement of financial innovation, new investment products and trading strategies emerge in an endless stream, and traditional methods are even more difficult to adapt to this rapidly changing investment scenario. For this reason, an investment activity risk intelligent assessment and decision-making assistance method, system, device and medium thereof are proposed. Summary of the invention

[0004] In view of this, the embodiments of the present invention hope to provide an investment activity risk intelligent assessment and decision-making assistance method, system, device and medium thereof to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0005] In order to solve the above technical problems, a technical solution adopted in this application is: an intelligent risk assessment and decision-making assistance method for investment activities, comprising the following steps:

[0006] Step 1: Acquire historical investment data related to investment activities based on multiple data sources, and pre-process the historical investment data;

[0007] Step 2: Extract and select features from the preprocessed historical investment data, screen out key feature vectors for risk assessment, and perform standardization to generate a feature variable data set;

[0008] Step 3: Based on the characteristic variable data set, use machine learning or deep learning algorithms to build an investment risk assessment model;

[0009] Step 4: Input the data of the investment project to be evaluated into the risk assessment model, and output the risk assessment results of the investment project to be evaluated;

[0010] Step 5: Based on the risk assessment results, combined with the investor’s risk preference, investment objectives and investment period, provide investors with investment decision-making assistance suggestions;

[0011] Step 6. Regularly obtain new investment data, retrain and update the risk assessment model, and monitor and evaluate the model update process.

[0012] As a further preferred embodiment of the present technical solution, in step one, the multiple data sources include financial market transaction data, macroeconomic data, industry data, corporate financial data and news and public opinion data; the preprocessing of historical investment data includes data cleaning, and the data cleaning specifically includes removing duplicate data, outliers and erroneous data.

[0013] As a further preferred embodiment of the present technical solution, in step 2, the standardization process converts the feature vector into a data distribution with a specific mean and variance by using a normalization method or a standardization method.

[0014] As a further preferred embodiment of the present technical solution, in step three, the method for constructing an investment risk assessment model comprises the following steps:

[0015] Step 1: Divide the feature variable data set into training set, validation set and test set according to the proportion;

[0016] Step 2: training the selected investment risk assessment model based on the training set;

[0017] Step 3: Verify the trained model based on the validation set to evaluate the generalization ability and performance of the model;

[0018] Step 4: Test the model based on the test set, and obtain the final investment risk assessment model based on the test results.

[0019] As a further preferred embodiment of the present technical solution, in step four, the risk assessment result includes a risk level and a risk quantification score, the risk level is divided into low risk, medium risk and high risk, the risk quantification score is a value between 0 and 1, and the risk level is divided according to the risk quantification score, the low risk corresponds to a score range of 0-0.3, the medium risk corresponds to a score range of 0.3-0.7, and the high risk corresponds to a score range of 0.7-1.

[0020] As a further preferred embodiment of the present technical solution, in step 2, the key feature vectors include enterprise financial indicators, market transaction indicators and macroeconomic indicators, the enterprise financial indicators include asset-liability ratio, return on equity and cash flow ratio; the market transaction indicators include stock price volatility and trading volume change rate; the macroeconomic indicators include interest rate, inflation rate and GDP growth rate.

[0021] As a further preferred embodiment of the present technical solution, in step six, the period of regularly acquiring new investment data is monthly, quarterly or semi-annually, which is determined according to the market fluctuation frequency and the data update speed.

[0022] To solve the above technical problems, another technical solution adopted by the present application is: an investment activity risk intelligent assessment and decision support system, comprising: a data acquisition module, a data preprocessing module, a feature engineering module, a model building module, a risk assessment module, a decision support module and a model update module;

[0023] The data acquisition module is used to obtain historical investment data related to investment activities from financial market transaction data, macroeconomic data, industry data, corporate financial data and news and public opinion data, and transmit the acquired historical investment data to the data preprocessing module;

[0024] The data preprocessing module is used to receive the historical investment data from the data acquisition module, and transmit the data to the feature engineering module after performing data cleaning operations;

[0025] The feature engineering module is used to extract and select features from the preprocessed historical investment data from the data preprocessing module, construct feature vectors for risk assessment, and perform standardization to generate feature variable data sets;

[0026] The model building module is used to train the selected investment risk assessment model based on the received feature variable data set using a machine learning or deep learning algorithm, and determine the final investment risk assessment model based on the test results;

[0027] The risk assessment module is used to input the data of the investment project to be assessed into the risk assessment model, output the risk assessment results of the investment project to be assessed, and transmit the risk assessment results to the decision support module;

[0028] The decision-making support module is used to generate and provide investment decision-making support suggestions for investors based on the received risk assessment results and in combination with the risk preferences, investment goals and investment periods pre-set by investors;

[0029] The model updating module is used to regularly acquire new investment data, transmit the new investment data to the data preprocessing module for processing, and then use the processed data to retrain and update the existing investment risk assessment model.

[0030] To solve the above-mentioned technical problems, another technical solution adopted in the present application is: an intelligent risk assessment and decision-making assistance device for investment activities, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of an intelligent risk assessment and decision-making assistance method for investment activities as described above.

[0031] In order to solve the above technical problems, another technical solution adopted in this application is: a computer-readable storage medium storing program instructions capable of implementing an intelligent risk assessment and decision-making assistance method for investment activities as described above.

[0032] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0033] 1. The present invention can comprehensively obtain various types of information related to investment activities through multi-source data collection and integration, avoiding the one-sidedness of evaluation caused by a single data source;

[0034] 2. The present invention extracts and selects features from historical investment data to effectively screen out key feature vectors, thereby improving the computational efficiency and accuracy of the risk assessment model and reducing the interference of data redundancy and noise on the model;

[0035] 3. The present invention adopts advanced machine learning algorithms to construct a risk assessment model, which can automatically learn and adapt to the complex and changing investment market environment, accurately quantify investment risks, provide investors with more scientific and accurate investment decision-making assistance, improve the quality and success rate of investment decisions, and reduce investment risks.

[0036] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 A schematic diagram of a flow chart of an intelligent risk assessment and decision-making assistance method for investment activities of the present invention;

[0039] Figure 2 A schematic diagram of a process for constructing an investment risk assessment model according to the present invention;

[0040] Figure 3 This is a functional module diagram of an investment activity risk intelligent assessment and decision support system of the present invention;

[0041] Figure 4 A schematic diagram of the structure of an investment activity risk intelligent assessment and decision-making support device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0043] It should be clear that the following embodiments of the present disclosure are described by specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other in the absence of conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0044] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0045] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0046] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0047] Figure 1 1 is a flow chart of an intelligent investment activity risk assessment and decision-making assistance method according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not based on Figure 1 The process sequence shown is limited. Figure 1-Figure 2 As shown: An investment activity risk intelligent assessment and decision-making assistance method, comprising the following steps:

[0048] Step 1: Acquire historical investment data related to investment activities based on multiple data sources, and pre-process the historical investment data;

[0049] Specifically: First, historical investment data related to investment activities is obtained from multiple data sources. These data sources include but are not limited to financial market transaction data, macroeconomic data, industry data, corporate financial data, and news and public opinion data. The historical investment data obtained may be duplicated, abnormal, or erroneous, so data preprocessing is required. The specific steps of data preprocessing include data cleaning, that is, removing duplicate data, outliers, and erroneous data to ensure the accuracy and effectiveness of subsequent analysis.

[0050] Step 2: Extract and select features from the preprocessed historical investment data, screen out key feature vectors for risk assessment, and perform standardization to generate a feature variable data set;

[0051] Specifically: First, according to the needs of investment analysis and risk assessment, define a series of potential features, which may include historical returns, volatility, maximum drawdown, Sharpe ratio, beta coefficient, industry distribution, market value distribution, etc.; then, use the preprocessed historical investment data to calculate the value of each potential feature; by calculating the correlation between features, identify highly correlated or redundant features, and consider removing one of them to reduce the dimension of the data set; use feature importance evaluation methods (such as feature importance of tree-based models, coefficients of Lasso regression, etc.) to identify key features that have a significant impact on risk assessment; combine professional knowledge and experience in the investment field to conduct The feature set is screened and optimized in one step; the standardization process is to ensure that all features are comparable in value and to avoid some features with a larger numerical range from dominating the model training; the commonly used standardization methods include Z-score standardization and Min-Max standardization; Z-score standardization converts the feature value into a normal distribution with a mean of 0 and a standard deviation of 1; Min-Max standardization scales the feature value to between the specified minimum and maximum values ​​(usually 0 and 1); the screened and standardized key features are combined to form a feature variable data set, and the feature variable data set is verified to ensure the accuracy and completeness of the data.

[0052] Step 3: Based on the characteristic variable data set, use machine learning or deep learning algorithms to build an investment risk assessment model;

[0053] Specifically: Divide the feature variable data set into training set, validation set and test set, and perform appropriate preprocessing on the data, such as normalization and standardization; then, select the appropriate algorithm according to the complexity of the problem and the characteristics of the data, considering the trade-offs in algorithm interpretability, computational efficiency and accuracy; then, use the training set data to train the model and adjust the hyperparameters to achieve the best results. At the same time, monitor indicators such as loss function and accuracy during training to ensure the effectiveness of the model; finally, use the validation set data to verify the model, evaluate its generalization ability, and adjust and optimize the model based on the verification results; evaluate the performance of the model by using indicators such as accuracy, recall, F1 score, AUC-ROC curve, etc.

[0054] Step 4: Input the data of the investment project to be evaluated into the risk assessment model, and output the risk assessment results of the investment project to be evaluated;

[0055] Specifically: First, collect relevant data of the investment project to be evaluated, including but not limited to financial statements, market analysis reports, industry trends, management team background, etc., to ensure the accuracy and completeness of the data and avoid using outdated or erroneous information; then, convert the collected data into a form that matches the input format of the risk assessment model, and perform pre-processing operations such as cleaning, normalization or standardization on the data to ensure the consistency and comparability of the data; then, input the pre-processed data of the investment project to be evaluated into the risk assessment model to ensure the correctness and completeness of the data and avoid deviations in the assessment results caused by data input errors; the risk assessment model will perform calculations and inferences based on the input data and the learned feature relationships, and output the risk assessment results of the investment project to be evaluated.

[0056] Step 5: Based on the risk assessment results, combined with the investor’s risk preference, investment objectives and investment period, provide investors with investment decision-making assistance suggestions;

[0057] Specifically: First, the results of the risk assessment model output should be interpreted in detail, which usually include quantitative indicators such as risk level, risk probability, risk impact, and corresponding risk descriptions and suggestions. These results will provide important references for subsequent investment decisions; then, through questionnaires, interviews, etc., understand investors' risk tolerance, risk aversion, etc., and divide investors' risk preferences into conservative, stable, balanced, aggressive and aggressive levels; according to the investor's risk preference level, adjust the risk level of the investment portfolio. For example, conservative investors may prefer low-risk, low-return investment products, while aggressive investors may prefer low-risk, low-return investment products. Aggressive investors may be more willing to take high risks in pursuit of high returns. Next, communicate with investors to clarify their main investment goals, such as long-term appreciation, capital preservation, short-term returns, etc. According to different investment goals, adjust investment strategies and asset allocations, and determine the investment time span of investors, such as short-term (1-3 years), medium-term (3-5 years) or long-term (more than 5 years). The length of the investment period will affect the construction of the investment portfolio and risk management strategy. Finally, build a suitable investment portfolio based on risk assessment results, investors' risk preferences and investment goals. The investment portfolio should include a variety of asset categories and industries to diversify risks and improve overall returns.

[0058] Step 6: Regularly obtain new investment data, retrain and update the risk assessment model, and monitor and evaluate the model update process;

[0059] Specifically: First, regularly obtain new investment data through financial news websites, government and international organizations, investment banks and research institutions, social media and professional forums, and perform pre-processing operations such as cleaning, normalization or standardization on the obtained data to ensure the consistency and comparability of the data. At the same time, verify the accuracy and completeness of the data to avoid using outdated or erroneous information; then, integrate the new investment data into the existing data set for retraining and updating the risk assessment model; retrain the risk assessment model by using the updated data set, and adjust the parameters and structure of the risk assessment model as needed, so as to improve the accuracy and predictive ability of the risk assessment model; then , use historical data or cross-validation methods to verify the retrained risk assessment model, evaluate the predictive accuracy and stability of the risk assessment model, and ensure the effectiveness of the risk assessment model; finally, establish a monitoring system to regularly monitor the performance of the risk assessment model. The monitoring system should include functions such as data quality monitoring, model performance monitoring, and anomaly detection; through the monitoring system, timely discover problems and deficiencies in the risk assessment model, and make corresponding adjustments and optimizations according to the type of problem, such as parameter adjustment, feature selection, etc.; and continuously update and optimize the risk assessment model according to market changes and changes in investor demand, and continuously improve the accuracy and reliability of the risk assessment model.

[0060] In one embodiment, specifically, in step 1, the multiple data sources include financial market transaction data, macroeconomic data, industry data, corporate financial data, and news and public opinion data;

[0061] Among them, financial market transaction data include stock prices, trading volumes, market indices, etc. These data reflect the real-time dynamics of the market and investor sentiment;

[0062] Macroeconomic data include GDP growth rate, inflation rate, unemployment rate, etc. These data provide an overview of the macroeconomic environment and are crucial for assessing overall market risks;

[0063] Industry data includes industry growth rate, competition landscape, policy changes, etc. These data help to understand the risks and opportunities of specific industries;

[0064] Corporate financial data includes financial statements (balance sheet, income statement, cash flow statement), financial indicators (such as price-earnings ratio, price-to-book ratio, ROE, etc.), which are important bases for assessing the investment risks of individual companies;

[0065] News and public opinion data include news reports, social media comments, investor sentiment, etc. These data provide unstructured information about the market on enterprises or industries, which helps to capture potential risks and opportunities.

[0066] Preprocessing historical investment data includes data cleaning, which specifically involves removing duplicate data, outliers, and erroneous data. The specific steps of preprocessing historical investment data include:

[0067] Deduplication: Check and remove duplicate records in the dataset to avoid introducing bias in subsequent analysis;

[0068] Dealing with outliers: Identify and deal with outliers in the data (such as extremely high or low values), which may be caused by data entry errors or abnormal market events. For outliers, you can choose to delete, replace them with the mean or median, or use other statistical methods to correct them;

[0069] Remove erroneous data: Check the logical errors or inconsistencies in the data, such as incorrect date format, incorrect calculation of financial indicators, etc., and make corresponding corrections or deletions;

[0070] Data integration: Integrate data from different data sources to ensure data consistency and comparability, which may involve data format conversion, time series alignment, and missing value processing;

[0071] Data normalization / standardization: Normalize or standardize data of different dimensions or distributions to ensure stability and accuracy during model training.

[0072] In one embodiment, specifically, in step 2, the standardization process uses a normalization method or a standardization method to convert the feature vector into a data distribution with a specific mean and variance; specifically, after the data preprocessing stage, the feature vector is usually required to be standardized to ensure the consistency of different features in terms of magnitude and distribution, thereby improving the performance and stability of the machine learning model. The standardization process mainly includes a normalization method and a standardization method;

[0073] The normalization method is to scale the feature values ​​to a specific range (usually between 0 and 1) so that all features are on the same scale. This method is particularly useful for features with different dimensions or distributions.

[0074] The normalization formula is usually as follows:

[0075]

[0076] Among them, X is the original eigenvalue, X min and X max are the minimum and maximum eigenvalues, respectively, and X norm is the normalized eigenvalue;

[0077] The standardization method is to convert the feature values ​​into normally distributed data with a specific mean (usually 0) and variance (usually 1). This method is suitable for most machine learning algorithms, especially those that are sensitive to feature scale, such as support vector machines (SVM), linear regression, and logistic regression.

[0078] The formula for normalization is usually as follows:

[0079]

[0080] Where X is the original eigenvalue, μ is the mean of the eigenvalue, σ is the standard deviation of the eigenvalue, and X std is the standardized eigenvalue.

[0081] In one embodiment, specifically, in step three, the method for constructing an investment risk assessment model includes the following steps:

[0082] Step 1: Divide the feature variable data set into training set, validation set and test set according to the proportion;

[0083] Specifically: First, divide the dataset into 70% training set, 15% validation set and 15% test set (this ratio can be adjusted according to actual conditions); the training set is used to train the model, the validation set is used to adjust the model parameters during the training process and evaluate the generalization ability of the model, and the test set is used to finally evaluate the performance of the model.

[0084] Step 2: training the selected investment risk assessment model based on the training set;

[0085] Specifically: Input the training set data into the model, adjust the model parameters through iterative optimization algorithms (such as gradient descent, stochastic gradient descent, Adam, etc.) to minimize the loss function of the model on the training set.

[0086] Step 3: Verify the trained model based on the validation set to evaluate the generalization ability and performance of the model;

[0087] Specifically: Input the validation set data into the model and calculate the performance indicators of the model on the validation set (such as accuracy, recall rate, F1 score, AUC-ROC curve, etc.); according to the performance indicators, tune the model, such as adjusting model parameters, adding feature engineering, etc.

[0088] Step 4: Test the model based on the test set, and obtain the final investment risk assessment model based on the test results;

[0089] Specifically: input the test set data into the model and calculate the performance indicators of the model on the test set. These performance indicators will serve as the final basis for evaluating the performance of the model. If the model performs well on the test set, it can be considered that the model has high generalization ability and accuracy and can be used for actual risk assessment tasks.

[0090] In one embodiment, specifically, in step 4, the risk assessment result includes a risk level and a risk quantification score. The risk level is divided into low risk, medium risk and high risk. The risk quantification score is a value between 0 and 1. The risk level is divided according to the risk quantification score. The score range for low risk is 0-0.3, the score range for medium risk is 0.3-0.7, and the score range for high risk is 0.7-1.

[0091] The risk level is a qualitative evaluation result, which is used to intuitively indicate the level of investment risk. In this embodiment, the risk level is divided into three levels: low risk, medium risk and high risk;

[0092] The risk quantification score is a quantitative assessment result used to more accurately represent the size of investment risk. The risk quantification score is defined as a value between 0 and 1, where 0 represents no risk at all and 1 represents extremely high risk.

[0093] In order to convert the risk quantification score into risk level, the following classification criteria are formulated:

[0094] The risk quantification score range for low risk is from 0 to 0.3. Within this range, the investment risk is relatively low and investors can invest with relative confidence.

[0095] The risk quantification score range for medium risk is 0.3 to 0.7. Within this range, the investment risk is moderate and investors need to consider it carefully and may need to conduct more in-depth investigation and analysis.

[0096] High risk corresponds to a risk quantification score range of 0.7 to 1. Within this range, the investment risk is high and investors need to be extra careful and may need to avoid or seek professional advice before investing.

[0097] In one embodiment, specifically, in step 2, the key feature vectors include enterprise financial indicators, market transaction indicators and macroeconomic indicators. Enterprise financial indicators include asset-liability ratio, return on net assets and cash flow ratio; market transaction indicators include stock price volatility and trading volume change rate; macroeconomic indicators include interest rate, inflation rate and GDP growth rate;

[0098] Among them, the debt-to-asset ratio reflects the proportion of total assets of an enterprise financed through debt financing, and is an important indicator for evaluating the level of debt and risk of an enterprise;

[0099] Return on net assets reflects the efficiency of an enterprise in using its own capital and is an important indicator for measuring the profitability of an enterprise.

[0100] The cash flow ratio reflects the proportional relationship between the net cash flow generated by the company's operating activities and the company's current liabilities. It is an important indicator for measuring the company's short-term debt repayment ability and liquidity risk.

[0101] Stock price volatility measures the volatility of stock prices, reflecting the market's uncertainty about the future trend of the stock;

[0102] The volume change rate reflects the changes in stock trading volume and is an important indicator for judging market activity, investor sentiment and potential trading opportunities.

[0103] Interest rates affect corporate financing costs, investment returns, and consumer spending and savings behavior, and are an important tool for macroeconomic policy regulation;

[0104] The inflation rate reflects the degree of decline in the purchasing power of money, and has an important impact on the pricing strategy, cost structure of enterprises, and the purchasing power of consumers;

[0105] The GDP growth rate measures the growth rate of a country's overall economy, reflecting the health of the macroeconomic environment and potential growth opportunities.

[0106] In one embodiment, specifically, in step six, the period of regularly acquiring new investment data is monthly, quarterly or semi-annually, which is determined according to the market fluctuation frequency and data update speed;

[0107] Specifically: In this embodiment, three possible cycles are set: monthly, quarterly, or semi-annually;

[0108] If the market fluctuates frequently and the data is updated quickly, you can choose to obtain new investment data once a month to ensure that the model can capture the latest changes in the market in a timely manner;

[0109] If market fluctuations are relatively stable and the data update speed is moderate, you can choose to obtain new investment data once a quarter. This can reduce the cost of data acquisition and model retraining while ensuring the timeliness of the model.

[0110] If market fluctuations are small and data updates are slow, you can choose to obtain new investment data every six months. This can further reduce the cost of data acquisition and model retraining, but you need to be careful to ensure that the model can maintain a certain level of accuracy over a longer period of time.

[0111] In summary, an embodiment of the present invention provides an intelligent investment activity risk assessment and decision-making assistance method, which obtains historical investment data through multiple data sources and performs preprocessing, feature extraction and selection, builds a risk assessment model, outputs risk assessment results, provides investment decision-making assistance suggestions, and regularly updates data and models. This method comprehensively considers multi-dimensional characteristics such as corporate finance, market transactions, and macroeconomics, can accurately assess investment risks, and provide investors with a scientific decision-making basis.

[0112] Figure 3 Schematic diagram of the functional modules of an intelligent risk assessment and decision support system for investment activities according to an embodiment of the present application. Figure 3 As shown, an investment activity risk intelligent assessment and decision support system includes: a data acquisition module, a data preprocessing module, a feature engineering module, a model building module, a risk assessment module, a decision support module and a model update module;

[0113] The data collection module is used to obtain historical investment data related to investment activities from financial market transaction data, macroeconomic data, industry data, corporate financial data and news and public opinion data, and transmit the acquired historical investment data to the data preprocessing module;

[0114] The data preprocessing module is used to receive the historical investment data from the data acquisition module, and transmit it to the feature engineering module after performing data cleaning operations;

[0115] The feature engineering module is used to extract and select features from the preprocessed historical investment data from the data preprocessing module, construct feature vectors for risk assessment, and perform standardization to generate feature variable data sets;

[0116] The model building module is used to train the selected investment risk assessment model based on the received feature variable data set using a machine learning or deep learning algorithm, and determine the final investment risk assessment model based on the test results;

[0117] The risk assessment module is used to input the data of the investment project to be assessed into the risk assessment model, output the risk assessment results of the investment project to be assessed, and transmit the risk assessment results to the decision support module;

[0118] The decision support module is used to generate and provide investment decision support suggestions for investors based on the received risk assessment results and in combination with the risk preferences, investment objectives and investment periods set by investors in advance;

[0119] The model updating module is used to regularly obtain new investment data, transmit the new investment data to the data preprocessing module for processing, and then use the processed data to retrain and update the existing investment risk assessment model.

[0120] In summary, the intelligent investment activity risk assessment and decision-making support system provided by the embodiment of the invention integrates the functions of multiple modules to achieve a comprehensive assessment of investment risks and the generation of personalized investment advice. It can not only improve the decision-making efficiency of investors, but also reduce investment risks and create greater value for investors.

[0121] For other details about the technical solutions for implementing each module in the system of the above embodiment, please refer to the description of an intelligent risk assessment and decision-making assistance method for investment activities in the above embodiment, which will not be repeated here.

[0122] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0123] like Figure 4 The present invention provides a schematic diagram of the structure of an intelligent risk assessment and decision-making support device for investment activities in accordance with an embodiment of the present invention. The present invention provides a schematic diagram of the structure of an intelligent risk assessment and decision-making support device for investment activities in accordance with an embodiment of the present invention. Figure 4 The investment activity risk intelligent assessment and decision-making support device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0124] like Figure 4 As shown, an investment activity risk intelligent assessment and decision-making auxiliary device may include a processor (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the investment activity risk intelligent assessment and decision-making auxiliary device are also stored. The processor, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0125] Typically, the following devices can be connected to the I / O interface: input devices such as sensors or visual information acquisition devices; output devices such as display screens; storage devices such as magnetic tapes and hard disks; and communication devices. The communication device can allow the investment activity risk intelligent assessment and decision-making assistance device to communicate wirelessly or wired with other devices (such as edge computing devices) to exchange data. Figure 4The investment activity risk intelligent assessment and decision-making assistance device with various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0126] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processor, all or part of the steps of an investment activity risk intelligent assessment and decision-making assistance method of an embodiment of the present disclosure are executed.

[0127] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0128] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of an investment activity risk intelligent assessment and decision-making assistance method of each embodiment of the present disclosure are executed.

[0129] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0130] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0131] The basic principles of the present disclosure are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present disclosure are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present disclosure. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, and are not limitations. The above details do not limit the present disclosure to the necessity of adopting the above specific details to be implemented.

[0132] In the present disclosure, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. The block diagrams of the devices, devices, equipment, and systems involved in the present disclosure are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, devices, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0133] Additionally, as used herein, "or" used in a list of items beginning with "at least one" indicates a separate list, so that, for example, a list of "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not mean that the example described is preferred or better than other examples.

[0134] It should also be noted that in the system and method of the present disclosure, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present disclosure.

[0135] Various changes, substitutions, and modifications of the techniques described herein may be made without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of the present disclosure is not limited to the specific aspects of the processes, machines, manufactures, compositions of events, means, methods, and actions described above. Currently existing or later to be developed processes, machines, manufactures, compositions of events, means, methods, or actions that perform substantially the same functions or achieve substantially the same results as the corresponding aspects described herein may be utilized. Thus, the appended claims include such processes, machines, manufactures, compositions of events, means, methods, or actions within their scope.

[0136] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present disclosure. Therefore, the present disclosure is not intended to be limited to the aspects shown herein, but rather to the widest range consistent with the principles and novel features disclosed herein. The above description has been given for purposes of illustration and description. In addition, this description is not intended to limit the embodiments of the present disclosure to the forms disclosed herein. Although a number of example aspects and embodiments have been discussed above, those skilled in the art will recognize some variations, modifications, changes, additions, and sub-combinations thereof.

Claims

1. An intelligent risk assessment and decision-making support method for investment activities, characterized in that: The following steps are involved: Acquire historical investment data related to investment activities based on multiple data sources and pre-process the historical investment data; Extract and select features from the preprocessed historical investment data, screen out key feature vectors for risk assessment, and perform standardization to generate feature variable data sets; Based on the characteristic variable data set, use machine learning or deep learning algorithms to build an investment risk assessment model; Input the data of the investment project to be evaluated into the risk assessment model, and output the risk assessment results of the investment project to be evaluated; Provide investors with investment decision-making assistance based on risk assessment results, combined with their risk preferences, investment objectives and investment period; Regularly obtain new investment data, retrain and update the risk assessment model, and monitor and evaluate the model update process.

2. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The multiple data sources include financial market transaction data, macroeconomic data, industry data, corporate financial data and news and public opinion data; the preprocessing of historical investment data includes data cleaning, and the data cleaning specifically includes removing duplicate data, outliers and erroneous data.

3. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The standardization process converts the feature vector into a data distribution with a specific mean and variance by using a normalization method or a standardization method.

4. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The method for constructing an investment risk assessment model comprises the following steps: Step 1: Divide the feature variable data set into training set, validation set and test set according to the proportion; Step 2: training the selected investment risk assessment model based on the training set; Step 3: Verify the trained model based on the validation set to evaluate the generalization ability and performance of the model; Step 4: Test the model based on the test set, and obtain the final investment risk assessment model based on the test results.

5. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The risk assessment result includes a risk level and a risk quantification score. The risk level is divided into low risk, medium risk and high risk. The risk quantification score is a value between 0 and 1. The risk level is divided according to the risk quantification score. The low risk corresponds to a score range of 0-0.3, the medium risk corresponds to a score range of 0.3-0.7, and the high risk corresponds to a score range of 0.7-1.

6. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The key feature vectors include enterprise financial indicators, market transaction indicators and macroeconomic indicators. The enterprise financial indicators include debt-to-asset ratio, return on equity and cash flow ratio; the market transaction indicators include stock price volatility and trading volume change rate; the macroeconomic indicators include interest rate, inflation rate and GDP growth rate.

7. The method for intelligent risk assessment and decision support for investment activities according to claim 1, characterized in that: The period for regularly acquiring new investment data is monthly, quarterly or semi-annually, which is determined according to the frequency of market fluctuations and the speed of data updating.

8. An investment activity risk intelligent assessment and decision support system, applied to an investment activity risk intelligent assessment and decision support method according to any one of claims 1 to 7, characterized in that: include: Data acquisition module, data preprocessing module, feature engineering module, model building module, risk assessment module, decision support module and model updating module; The data acquisition module is used to obtain historical investment data related to investment activities from financial market transaction data, macroeconomic data, industry data, corporate financial data and news and public opinion data, and transmit the acquired historical investment data to the data preprocessing module; The data preprocessing module is used to receive the historical investment data from the data acquisition module, and transmit the data to the feature engineering module after performing data cleaning operations; The feature engineering module is used to extract and select features from the preprocessed historical investment data from the data preprocessing module, construct feature vectors for risk assessment, and perform standardization to generate feature variable data sets; The model building module is used to train the selected investment risk assessment model based on the received feature variable data set using a machine learning or deep learning algorithm, and determine the final investment risk assessment model based on the test results; The risk assessment module is used to input the data of the investment project to be assessed into the risk assessment model, output the risk assessment results of the investment project to be assessed, and transmit the risk assessment results to the decision support module; The decision-making support module is used to generate and provide investment decision-making support suggestions for investors based on the received risk assessment results and in combination with the risk preferences, investment goals and investment periods pre-set by investors; The model updating module is used to regularly acquire new investment data, transmit the new investment data to the data preprocessing module for processing, and then use the processed data to retrain and update the existing investment risk assessment model.

9. An intelligent risk assessment and decision-making support device for investment activities, characterized in that: It includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of an investment activity risk intelligent assessment and decision-making assistance method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which are used to enable a processor to execute the steps of an investment activity risk intelligent assessment and decision-making assistance method as described in any one of claims 1-7.

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