Intelligent valuation analysis and stock selection method based on large model

Abstract

The invention relates to the technical field of financial stock selection analysis, and discloses an intelligent valuation analysis and stock selection method based on a large model. The method collects basic information data and real-time market data of a stock from a plurality of data sources. And performing time sequence sorting on the valuation indexes in the stock valuation index set, identifying sections with similar and different index change trends in the sequence, and obtaining a trend mode classification result. And for stocks in a trend similar section in a trend mode classification result, calculating an adjustment amplitude in combination with evaluation index fluctuation, evaluating evaluation correction intensity under a market dynamic state, and generating evaluation correction comprehensive analysis data. And screening out the abnormal points in the stocks of which the correction values exceed the reference level and which are located in the trend difference section, and establishing a stock abnormal point set. And performing risk attribute analysis on all the stocks in the stock abnormal point set, and marking the stocks with high risks, thereby generating intelligent stock selection and risk assessment output.

Description

Technical Field

[0001] This invention relates to the field of financial stock selection and analysis technology, specifically to an intelligent valuation analysis and stock selection method based on a large model. Background Technology

[0002] In the investment field of financial markets, stock analysis and stock selection have always been core aspects of investor focus. Traditional stock analysis and selection methods mainly rely on fundamental analysis, technical analysis, and quantitative analysis. Fundamental analysis focuses on a company's financial condition, operating performance, and industry competitiveness, assessing the company's intrinsic value by studying the balance sheet, income statement, and cash flow statement. However, financial statements can be manipulated, and for companies in emerging industries or undergoing transformation, traditional financial indicators are difficult to accurately reflect their true value. Technical analysis uses data such as stock price and trading volume, employing charts and technical indicators to predict stock price trends. However, it is based on historical data, and market uncertainty makes past trends unreliable for predicting the future, and it is easily affected by short-term market sentiment and random factors. Quantitative analysis uses mathematical models and statistical methods to process large amounts of stock data and screen for stocks with potential investment value, but it requires strong computing power and professional knowledge. Model construction and parameter setting are subjective, and the model may fail when the market environment changes.

[0003] These traditional methods all require manual processing of large amounts of data, making them inefficient and prone to errors. Furthermore, investment decisions largely depend on investors' experience and subjective judgment; different investors interpret data and have different judgment standards, making it difficult to guarantee the accuracy and reliability of stock selection results. Faced with the ever-changing financial market, traditional analytical methods struggle to comprehensively and promptly capture market changes, potentially leading to missed investment opportunities or investment mistakes.

[0004] Stock data is characterized by its wide range of sources, diverse types, and rapid updates. In terms of data sources, it covers multiple channels, including stock exchanges, financial information platforms, company annual reports, and macroeconomic data publishing institutions. The data types include both basic stock information, such as company overview, financial statements, and shareholding structure, and real-time market data, such as stock prices, trading volume, turnover rate, and order book data. Furthermore, the stock market trades in real time, and the data is constantly updated; new financial news and policy changes can also affect stock data at any time.

[0005] Traditional data processing methods fall short when faced with such massive and complex datasets. Data from different sources has inconsistent formats and standards, making integration difficult; the rapid growth in data volume exceeds the capabilities of traditional data processing technologies, making efficient storage and processing challenging; real-time updated data requires timely analysis and response, but traditional methods cannot meet this timeliness requirement, resulting in an inability to fully utilize the value of data for accurate stock valuation and stock selection decisions. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent valuation analysis and stock selection method based on a large model to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, this invention provides an intelligent valuation analysis and stock selection method based on a large model, the method comprising: Basic information data and real-time market data of stocks are collected from multiple data sources, and data fusion processing is performed through a large model to generate initial valuation indicators. Timestamps and stock codes are associated to form a set of stock valuation indicators. The valuation indicators in the stock valuation indicator set are sorted by time series, and segments with similar and different trend changes in the indicators in the series are identified to obtain trend pattern classification results. For stocks in the trend similarity segment in the trend pattern classification results, time series data of macroeconomic variables and market sentiment index are extracted, and the adjustment magnitude is calculated by combining the fluctuation of valuation indicators. The strength of valuation correction under market dynamics is assessed, and comprehensive valuation correction analysis data is generated. Based on the comprehensive analysis data of the valuation correction, outliers in stocks whose correction values ​​exceed the benchmark level and are located in the trend difference range are screened out, and a set of stock outliers is formed. Risk attribute analysis is performed on all stocks in the set of stock anomalies, stocks with high risk are marked, and intelligent stock selection and risk assessment outputs are generated.

[0008] Preferably, the stock valuation indicator set includes valuation indicator values, stock code identifiers, and standardized financial data. The trend pattern classification results specifically include trend similarity segment markings, trend difference segment markings, and the change rate of valuation indicators for adjacent stocks. The comprehensive valuation correction analysis data includes the impact of the rate of change of economic variables on valuation, the impact of the rate of change of sentiment index on valuation, and a comparison of indicator correction responses under each market dynamic condition. The stock anomaly set includes anomaly stock codes, anomaly market sentiment fluctuation characteristics, and the ratio of anomaly indicators to trading volume changes. The intelligent stock selection and risk assessment output includes a list of high-risk stocks and multi-dimensional judgment tags for high-risk stocks.

[0009] Preferably, the step of collecting basic stock information data and real-time market data from multiple data sources and performing data fusion processing through a large model to generate initial valuation indicators includes: The process involves acquiring historical price sequences, financial statement data, and industry classification information for stocks; collecting market capitalization data and liquidity indicators corresponding to the stocks; and recording the collected results as two types of data elements: market capitalization factor and liquidity factor, forming a stock data element group. Based on the market capitalization factor and liquidity factor data in the stock data element group, normalization processing is performed on each of them. The normalized results are then mapped to the stock code and timestamp. The weighted average of the normalized market capitalization value and the normalized liquidity value is calculated as the initial valuation indicator, generating a set of stock valuation indicators.

[0010] Preferably, the step of sorting the valuation indicators in the stock valuation indicator set by time series and identifying segments with similar and different trends in the indicator changes within the series includes: Extract the valuation indicator values ​​and corresponding stock code data from the stock valuation indicator set. Sort all stock data points according to timestamp information. Call the time series sequence and, based on adjacent time intervals, perform sliding window calculation and sorting on the data points that are continuous in time to generate a time series of adjacent data points. Based on the time series of adjacent data points, calculate the rate of change of valuation indicators between every two adjacent data points and integrate them to generate a sequence of changes in valuation indicators. According to the sequence of changes in valuation indicators, analyze the direction of market value change and liquidity change between adjacent data points. Classify and label each pair of data points according to whether the change patterns in the two directions are consistent. Record and group segments with similar trends and different trends respectively to obtain the trend pattern classification results.

[0011] Preferably, the step of extracting time series data of macroeconomic variables and market sentiment indices for stocks in the trend similarity segment of the trend pattern classification results includes: Based on the trend pattern classification results, segments marked as having similar trends are selected. Economic growth rate data and sentiment fluctuation data within each stock trading cycle are detected and arranged chronologically to form economic variable time series and sentiment index time series, generating a market dynamic time series set. Based on this market dynamic time series set, the rate of change of economic variables and the rate of change of sentiment index between consecutive time points in each stock's time series are calculated. The rates of change of economic variables and the rate of change of sentiment index are compared in parallel under the same valuation conditions. By jointly analyzing the two types of speed indicators, the adjustment magnitude under the indicator values ​​is quantitatively identified, and the correction value series of each stock is integrated to establish a comprehensive valuation correction analysis data set.

[0012] Preferably, the step of screening outliers among stocks whose adjusted values ​​exceed the benchmark level and are located in the trend difference range based on the comprehensive analysis data of the valuation correction includes: Based on the comprehensive analysis data of valuation correction, stocks with correction values ​​exceeding the benchmark level and stocks in the trend difference range are selected. Continuous trading records of these stocks are extracted in chronological order, and valuation indicator values ​​and trading volume data corresponding to each time point are collected to generate a continuous trading record set. The continuous trading record set is then used to extract indicator values ​​and trading volumes for two consecutive time points, and the indicator change ratio and trading volume change ratio are calculated respectively. These are integrated into an indicator change ratio sequence and a trading volume change ratio sequence to establish a trading fluctuation dataset. Based on the trading fluctuation dataset, market sentiment fluctuation data and liquidity change data for the corresponding time period are extracted. It is determined whether both the indicator change ratio and the trading volume change ratio exceed the set anomaly identification threshold, and whether both sentiment fluctuation and liquidity change exceed the anomaly judgment threshold simultaneously. Time points that meet the conditions are marked as anomalies, generating a set of stock anomaly points.

[0013] Preferably, the step of performing risk attribute analysis on all stocks in the stock anomaly set and marking stocks with high risk includes: Obtain all stocks and their corresponding codes and attribute information from the set of stock anomalies, calculate the comprehensive risk assessment value for each stock, and establish a comprehensive risk assessment value sequence. Based on the comprehensive risk assessment value sequence, screen stocks whose correction values ​​exceed the risk threshold, whose valuation index values ​​are lower than the safety benchmark value, and whose labels in the trend pattern classification results are in the difference segment, extract the corresponding stock codes, name identifiers, and industry classifications, and mark them as high-risk stocks. Sort and output the stocks that meet the conditions according to the classification format to generate intelligent stock selection and risk assessment output.

[0014] Preferably, the step of generating intelligent stock selection and risk assessment output further includes: integrating the high-risk stock list and multiple judgment tags to generate a visual report structure, including a stock risk distribution chart and a trend comparison visualization component.

[0015] Preferably, the step of generating the visual report structure includes: using a large model to perform in-depth feature extraction on high-risk stock data, identifying key risk factors and opportunity indicators, and optimizing the layout of the report content.

[0016] Preferably, the steps for optimizing the report content layout include: updating the set of stock valuation indicators and trend pattern classification results based on real-time market data, and dynamically adjusting stock selection recommendations and risk warning outputs.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention utilizes a large-scale model to collect fundamental information data and real-time market data from multiple data sources, and then integrates and processes this data to generate initial valuation indicators, forming a set of stock valuation indicators. Data from different data sources has different characteristics: fundamental information data reflects a company's long-term fundamentals, while real-time market data reflects short-term market performance. Leveraging its powerful data processing capabilities, the large-scale model can integrate this multi-source data, uncover potential connections between data points, and comprehensively reflect stock characteristics. For example, by integrating revenue and profit data from a company's financial statements with real-time stock price and trading volume data, investors can not only understand the company's profitability but also its market reaction to current performance, providing a more accurate and comprehensive basis for stock valuation. This allows investors to gain a more comprehensive understanding of multi-dimensional stock information and make more rational investment decisions.

[0018] This method involves sorting valuation indicators within a stock valuation metric set by time series and identifying segments with similar and differing trends to obtain trend pattern classification results. Time series data contains information about stock price changes over time, and this analysis can reveal patterns in stock price fluctuations. For example, if certain stock valuation indicators show similar upward or downward trends over a period of time, it indicates that these stock price movements are correlated and may be influenced by the same market factors. Based on the trend pattern classification results, investors can grasp the characteristics of different stock movements, discover potential investment opportunities, make early arrangements or adjust investment strategies in a timely manner, and seize opportunities amidst market fluctuations.

[0019] For stocks exhibiting similar trends, this invention extracts time-series data of macroeconomic variables and market sentiment indices, combines this data with valuation indicator fluctuations to calculate the adjustment magnitude, assesses the strength of valuation corrections under market dynamics, and generates comprehensive valuation correction analysis data. Macroeconomic variables such as GDP growth rate and interest rates, along with market sentiment indices, reflect the overall market situation and investor sentiment, significantly impacting stock value. During periods of strong economic growth, rising corporate profit expectations may lead to higher stock valuations; in times of optimistic market sentiment, investors are willing to pay higher prices for stocks. This invention assesses the strength of valuation corrections by comprehensively considering these factors, making valuation analysis more aligned with market dynamics and providing investors with more reasonable investment references that reflect actual market conditions, avoiding investment decision-making errors caused by ignoring macroeconomic factors and market sentiment.

[0020] Based on comprehensive valuation correction analysis data, outliers are identified among stocks whose correction values ​​exceed the benchmark level and fall within the trend difference range. This forms an outlier set, and risk attribute analysis is performed on these stocks, marking high-risk stocks. Outlier stocks exhibit price movements that differ from most stocks, potentially implying either risk or opportunity. By screening outliers and analyzing their risk attributes, investors can identify potentially high-risk stocks, such as those experiencing sudden deterioration in company performance or financial fraud leading to abnormal price fluctuations, thus avoiding investment losses. Investors with low risk tolerance can directly avoid high-risk stocks; while investors with higher risk appetite can make prudent investment decisions based on a thorough understanding of the risks, optimizing their portfolios and reducing overall investment risk. Attached Figure Description

[0021] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent valuation analysis and stock selection method based on a large model as described in this invention. Figure 2 A detailed flowchart illustrating the methods for defining a set of stock valuation metrics, etc. Figure 3 A flowchart for sorting and identifying trend segments in the time series of stock valuation metrics. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1This invention provides an intelligent valuation analysis and stock selection method based on a large-scale model. The method involves systematically collecting stock-related data from multiple data sources, including financial databases, real-time trading platforms, and macroeconomic data interfaces. The collected data covers basic stock information such as company financial reports and industry classifications, as well as real-time market data such as price fluctuations and trading volume changes. The collected data is fused and processed using a large-scale model. This model employs a deep learning architecture to align and normalize the multi-source heterogeneous data, generating initial valuation indicators. These initial valuation indicators are then associated with timestamps and stock codes to construct a set of stock valuation indicators. The valuation indicators in this set are sorted by time series. The sorting process uses a sliding window algorithm to group consecutive time points, identifying segments with similar trends and segments with differing trends. Segments with similar trends show adjacent data points changing in the same direction, while segments with differing trends show abnormal fluctuations. This identification result forms a trend pattern classification result. For trend-similar segments in the trend pattern classification results, time-series data of macroeconomic variables such as GDP growth rate or inflation rate are extracted, along with market sentiment indices such as investor sentiment survey data. These are combined with the volatility of valuation indicators to calculate adjustment values, assessing the strength of valuation corrections under dynamic market conditions such as policy changes or event-driven events, and generating comprehensive valuation correction analysis data. This comprehensive valuation correction analysis data is used to screen stocks whose correction values ​​exceed a preset benchmark level and fall within the trend difference segment. Outliers are identified by comparing the rate of change of indicators with thresholds, forming a set of stock anomalies. All stocks in the stock anomaly set undergo risk attribute analysis, which involves calculating a comprehensive risk score and marking high-risk stocks. Finally, an intelligent stock selection list and risk assessment report are output.

[0024] Example 1: See Figure 2The stock valuation metric set includes valuation metric values, stock code identifiers, and standardized financial data. Valuation metric values ​​are the core quantitative output generated through large-scale model processing. Stock code identifiers use the globally unified ISIN encoding system to ensure uniqueness. Standardized financial data is obtained by aligning accounting items and unifying units in the original financial statements through the large-scale model. The trend pattern classification results are specifically represented by trend similarity segment labels, trend difference segment labels, and adjacent stock valuation metric change rates. Trend similarity segment labels are used to identify intervals in which valuation metrics move in the same direction for three or more consecutive data points in a time series. Trend difference segment labels are used to mark data segments where valuation metrics show sharp reversals or abnormal fluctuations. Adjacent stock valuation metric change rates are derived by calculating the percentage change in metric values ​​between consecutive time points. The comprehensive valuation correction analysis data includes the impact of the rate of change of economic variables on valuation, the impact of the rate of change of sentiment index on valuation, and a comparison of indicator correction responses under various market dynamics. The impact of the rate of change of economic variables on valuation is quantified by constructing a multiple regression model to quantify the elasticity coefficient of GDP growth rate changes on valuation indicators. The impact of the rate of change of sentiment index on valuation is measured using time series analysis to determine the lead-lag relationship between investor sentiment fluctuations and valuation adjustments. The comparison of indicator correction responses under various market dynamics tests the response patterns of valuation indicators to macroeconomic shocks in different market state partitions. The stock anomaly set includes anomaly stock codes, anomaly market sentiment fluctuation characteristics, and the ratio of anomaly indicators to trading volume changes. Anomaly stock codes are updated synchronously with the official stock exchange code mapping table. Anomaly market sentiment fluctuation characteristics extract high-frequency volatility components from social media sentiment analysis data and traditional sentiment indices. The ratio of anomaly indicators to trading volume changes detects price-volume divergence by calculating the ratio of the valuation indicator change rate to the trading volume change rate. The intelligent stock selection and risk assessment output includes a list of high-risk stocks and multi-dimensional labels for high-risk stocks. The list of high-risk stocks is sorted from high to low according to risk scores and includes a snapshot of fundamental data. The multi-dimensional labels for high-risk stocks integrate three categories of judgment dimensions: valuation risk label, liquidity risk label, and financial health label.

[0025] The process of collecting basic stock information and real-time market data from multiple data sources requires the establishment of a multi-threaded data acquisition engine. Basic information data is obtained from the Compustat global database, including historical data from balance sheets and cash flow statements. Real-time market data is obtained by subscribing to tick-by-tick transaction data and market information through the Bloomberg terminal API interface. The market capitalization data corresponding to the stock is dynamically calculated by multiplying the closing price by the number of outstanding shares. Liquidity indicators are selected by comprehensively measuring the daily turnover rate and the Amihud illiquidity indicator. The collected results are recorded as two types of data elements: market capitalization factor and liquidity factor. The market capitalization factor includes logarithmic market capitalization and market capitalization percentile ranking data, while the liquidity factor includes standardized turnover rate and bid-ask spread indicators. These stock data element groups are stored in a columnar database for unified management. Based on the market capitalization factor and liquidity factor data in the stock data element group, normalization processing is performed separately. The normalization processing adopts the Z-score standardization method to eliminate the influence of dimensions. The normalized results are mapped to stock codes and timestamps. The mapping relationship is realized by constructing a database table with a stock code-timestamp composite primary key to achieve the association index. The weighted average of the normalized market capitalization value and the normalized liquidity value is calculated as the initial valuation indicator. The weight allocation of the weighted average is dynamically optimized according to the market volatility to adjust the weight allocation ratio. A set of stock valuation indicators is generated and stored in a distributed file system in time series data format.

[0026] In constructing the stock valuation indicator set, the large model adopts a Transformer architecture with an attention mechanism. The Transformer architecture includes 12 encoder layers to achieve deep feature extraction from multi-source data. During the data fusion processing stage, the basic information data is first seasonally and adjusted for inflation, and outlier filtering and smoothing are performed on the real-time market data. The initial valuation indicator calculation incorporates momentum and volatility factors for enhancement. The momentum factor is measured using the past 12 months' returns, and the volatility factor uses a GARCH model to estimate the conditional variance. The generation of trend pattern classification results relies on a dynamic time warping algorithm. This algorithm sets curved path constraints to improve pattern matching accuracy. Labeling trend-similar segments requires that continuous data points change in the same direction and that the magnitude of change exceeds the minimum volatility threshold. The criteria for identifying trend-difference segments include a rate of change exceeding three times the standard deviation and the presence of gaps or jumps. The rate of change sequences of adjacent stock valuation indicators are smoothed and denoised using an exponentially weighted moving average method. The valuation correction analysis integrates data calculations using a vector autoregression model. This model includes three endogenous variables: macroeconomic variables, market sentiment index, and valuation indicators. The impact of the rate of change in economic variables on valuation is quantified by an impulse response function to determine the duration of the impact. The contribution ratio of the rate of change in the sentiment index to valuation is calculated in the generalized variance decomposition. The indicator correction response under each market dynamic condition is compared and tested to demonstrate the asymmetric response characteristics under three market conditions: bull market, bear market, and sideways market.

[0027] The stock data element group is organized using a star schema data warehouse model. The fact table stores the numerical measures of market capitalization and liquidity factors, while the dimension table includes descriptive attributes such as time, stock, and industry dimensions. During the normalization process, statistical parameter estimates are established for each factor using a historical rolling window. The mean and standard deviation used for Z-score standardization are dynamically updated based on data from the past five years. The weighted average calculation of initial valuation indicators employs robust statistical methods, with the weights adjusted quarterly based on factor effectiveness test results. The update mechanism for the stock valuation indicator set adopts an event-driven model, triggering real-time incremental calculations upon the arrival of new data. The trend pattern classification results are stored using a graph database structure. Trend similarity segments are stored as time-continuous subgraph nodes, while trend difference segments are marked as special edges with abnormal attributes. An inverted index is built for the change rate sequences of adjacent stock valuation indicators to support fast similarity queries. The generation of valuation correction comprehensive analysis data utilizes a distributed computing framework. The calculation of the impact of the rate of change of economic variables on valuation is deployed in parallel processing across multiple computing cores. The analysis of the impact of the rate of change of the sentiment index on valuation integrates a high-frequency data stream processing engine. The comparison results of indicator correction responses under each market dynamic condition are stored in the form of a multi-dimensional data cube. The calculation of the market capitalization factor includes adjustments for size premium and industry neutralization. The construction of the liquidity factor considers the market condition dependence characteristic. During the normalization stage, Winsorize is used to shrink extreme values ​​to avoid the influence of outliers. A two-way index is established to map stock codes to timestamps, supporting time-range queries. The initial valuation indicator generation process includes a quality inspection step, checking data integrity and logical consistency. Version management of the stock valuation indicator set uses timestamps to support historical backtracking. The trend pattern classification algorithm is trained using labeled historical data. Validation of trend similarity segment annotations involves fine-tuning the model using manually labeled samples. The sensitivity parameters for detecting trend difference segments are dynamically optimized based on market volatility. The recalibration cycle of the valuation correction comprehensive analysis model is set to a monthly frequency. The model incorporates policy dummy variables to capture institutional changes, and the analysis of the impact of the sentiment index's change rate on valuation controls for changes in liquidity conditions and investor structure.

[0028] The large-scale model's data fusion module includes a feature selection mechanism that uses LASSO regression to screen for the most predictive variables. Preprocessing of basic information data includes accounting standard conversion and financial statement reclassification. Real-time market data cleaning includes jump point detection and transaction time alignment. The construction of the market capitalization factor includes dual-dimensional measurements of free-float market capitalization and total market capitalization. The calculation of the liquidity factor uses liquidity indicators constructed from intraday high-frequency data. Normalization employs industry-specific standardization to eliminate industry-specific biases. The storage format of the stock valuation indicator set supports multi-dimensional analysis operations, and the time-series characteristics of the initial valuation indicators are ensured to be stationary through unit root tests. Real-time calculation of trend pattern classification uses online learning algorithms. Identification of trend-similar segments utilizes dynamic clustering methods, and the detection of trend-difference segments integrates abrupt change point detection algorithms. The construction of the valuation correction comprehensive analysis database includes out-of-sample testing. The estimation of the rate of change of economic variables uses a state-space model, and the calculation of the rate of change of the sentiment index includes a sentiment factor derived from text analysis.

[0029] Example 2: See Figure 3 The process extracts valuation indicator values ​​and corresponding stock code data from a set of stock valuation indicators. The valuation indicator values ​​are standardized values ​​obtained from the initial valuation indicators generated through large-scale model data fusion processing. The corresponding stock code data uses the globally unified ISIN encoding format to ensure the unique identification of assets. All stock data points are sorted according to timestamp information, accurate to the millisecond level and using the international standard time format Coordinated Universal Time (UTC). The sorting process uses an efficient red-black tree-based sorting algorithm to ensure the consistency of the time order of massive data points. The process then calls a time series sequence, which is a sequence structure composed of valuation indicator data points arranged in chronological order, based on adjacent time intervals. The adjacent time interval is set to a regular trading day cycle, but can be configured to minute-level intervals for high-frequency data. A sliding window calculation is performed on consecutive data points and sorted. The sliding window calculation uses a mechanism combining a fixed window size and a variable step size to generate time series of adjacent data points and store them as an array format with time indexes.

[0030] Based on the time series of adjacent data points, the rate of change of valuation indicators between every two adjacent data points is calculated. The rate of change of valuation indicators is calculated using the logarithmic return method to improve the statistical properties of the series. The results are integrated to generate a sequence of valuation indicator rate of change. This sequence uses a circular buffer data structure to support continuous calculation of real-time streaming data. Based on the sequence of valuation indicator rate of change, the direction of market capitalization change and liquidity change between adjacent data points are analyzed. The direction of market capitalization change is determined by comparing the positive and negative changes of the market capitalization factor at consecutive time points. The direction of liquidity change is determined based on the combined changes in trading volume and bid-ask spread. Each pair of data points is classified and labeled according to whether their change patterns in the two directions are consistent. Consistent change patterns are defined as situations where the direction of market capitalization change and the direction of liquidity change are simultaneously positive or simultaneously negative. Segments with similar trends and those with different trends are recorded and grouped separately. Segments with similar trends record multiple data point intervals with consecutive consistent change patterns, while segments with different trends record data point intervals with divergent or drastic change patterns. The trend pattern classification results are obtained and stored as a set of time-stamped classification labels.

[0031] The time series sorting process constructs a multi-level index structure, including a primary index of stock codes and a secondary index of timestamps. The sorting algorithm employs an external sorting method to handle large datasets exceeding memory capacity. A sliding window calculation implements an overlapping window mechanism to ensure no short-term patterns are missed. The calculation of the valuation indicator change rate series incorporates a volatility adjustment factor, which is standardized based on historical volatility. Outlier filtering and smoothing are performed on the change rate series to eliminate the impact of instantaneous noise. The direction of market capitalization and liquidity changes is determined using the cosine of the vector angle; a cosine value greater than a set threshold indicates a consistent direction of change. The classification and labeling process is automated, generating a visualized trend pattern map. The trend pattern classification results are stored in a time series database. Similar trend segments are labeled with additional duration and intensity indicators, while different trend segments are labeled with the type of difference and volatility amplitude. Data extraction from the stock valuation indicator set is achieved through a distributed query engine. Valuation indicator values ​​are checked for completeness and rationality by a data quality verification module. Timestamp sorting considers the time synchronization issues of cross-timezone trading data. The generation of time series data for adjacent data points employs a pipelined processing architecture. The sliding window size is dynamically adjusted based on asset liquidity characteristics, and the window calculation results are cached in a high-performance in-memory database. The calculation of the valuation indicator change rate series integrates a real-time risk control mechanism to prevent division-by-zero errors and numerical overflow issues. The change rate series analysis module detects structural breakpoints. The comparison of market capitalization change direction and liquidity change direction uses multiple threshold judgments. The threshold parameters are adaptively adjusted according to market volatility, and the classification labeling results are reverse-verified to ensure logical consistency. The application of trend pattern classification results includes pattern matching and similarity search. The identification of trend similarity segments emphasizes the persistence and stability of trends, while the identification of trend difference segments focuses on market state transition nodes.

[0032] A parallel computing framework is introduced during data sorting, which shards the data by stock code and sorts it in parallel. Timestamp alignment uses a reference timeline to unify the trading time of all assets. Sliding window calculation optimizes boundary handling issues, and a mirror expansion method is used to process data points at both ends of the sequence. The storage format of adjacent data point time series supports fast range queries. Statistical analysis of the valuation indicator change rate series includes autocorrelation and partial autocorrelation tests, and the change rate distribution characteristics are used for parameter tuning of subsequent pattern recognition algorithms. Market capitalization change direction determination integrates price momentum information, liquidity change direction analysis incorporates order book depth data, and consistency testing of two-way change patterns adds a weight adjustment mechanism. Trend pattern classification results are updated using an incremental calculation method; when new data arrives, only the affected time interval is recalculated. Version management of classification results supports historical pattern backtracking analysis.

[0033] The trend pattern recognition algorithm integrates multiple machine learning methods, including Hidden Markov Models (HMMs) for identifying market state transitions and Dynamic Time Warping (RTW) algorithms for measuring the similarity of trends across different stocks. The definition of trend similarity segments incorporates three constraints: consistency of change direction, stability of change magnitude, and duration threshold. Change point analysis is used to detect trend difference segments. Classification and labeling results are correlated with fundamental event data, including earnings release dates and major announcement dates, enhancing the economic interpretability of trend pattern classification. Quality control for time series sorting includes data gap detection, which automatically identifies and processes missing data from non-trading periods to ensure the continuity of the time series. Sliding window calculation parameters are optimized through grid search, which determines the optimal window size and step size combination based on historical data backtesting. Standardization of the valuation indicator change rate series eliminates the influence of dimensions and facilitates comparison of trend patterns across different asset classes. The synergistic analysis of market capitalization and liquidity change directions introduces lead-lag relationship detection, which identifies the order of change in the two directions. The output of the trend pattern classification results includes a confidence score, which is calculated based on the pattern matching degree and historical accuracy to assist in subsequent analysis and decision-making.

[0034] Example 3: Valuation Correction Analysis of Stocks in Trend-Similar Segments from Trend Pattern Classification Results. Starting with segments marked as "trend-similar" from the trend pattern classification results, these segments refer to intervals where stock valuation indicators show changes in the same direction over consecutive time points. The screening process uses a database query language to retrieve all stock codes and time interval lists marked "trend-similar" from the trend pattern classification result table. Economic growth rate data and sentiment fluctuation data are examined within each stock trading cycle. Economic growth rate data is obtained from the macroeconomic indicator database released by the National Bureau of Statistics, including core indicators such as GDP growth rate and industrial added value growth rate. Sentiment fluctuation data is obtained by crawling financial news texts and social media data, and a sentiment analysis model is used to calculate the sentiment index. These are arranged chronologically to form economic variable time series and sentiment index time series. The economic variable time series is aligned and interpolated at a quarterly frequency, while the sentiment index time series is smoothed and standardized at a daily frequency. This generates a market dynamic time series set and stores it as a multi-dimensional array structure in the time series database.

[0035] Based on a market dynamic time series dataset, the rate of change of economic variables and the rate of change of sentiment index are calculated between consecutive time points in each stock's time series. The rate of change of economic variables is obtained by dividing the first difference of economic indicator values ​​at adjacent time points by the time interval. The rate of change of sentiment index is calculated by processing the raw sentiment data using an exponentially weighted moving average method to calculate the instantaneous rate of change. The rates of change of economic variables and the rate of change of sentiment index are compared in parallel under the same valuation conditions. The same valuation conditions refer to a unified timestamp and market environment setting. The parallel comparison uses a multi-threaded computing framework to synchronously process the time alignment and numerical normalization of the two types of rate indicators. By jointly analyzing the two types of rate indicators, the adjustment magnitude under the indicator values ​​is quantitatively identified. The joint analysis uses a vector autoregression model to establish the dynamic relationship between the rate of change of economic variables, the rate of change of sentiment index, and the fluctuation of valuation indicators. The quantitative relationship identification is achieved by estimating model coefficients and testing statistical significance. The adjusted value series of each stock is integrated, and the adjusted value series contains the calculated valuation adjustment value at each time point. A comprehensive valuation adjustment analysis data is established and output as a structured data file.

[0036] The screening criteria for trend-similar segments include a minimum duration threshold and a consistency test for the direction of change. The minimum duration threshold is set to a variable range of five consecutive trading days to three months, and the consistency test for the direction of change requires that the sign of the rate of change of the valuation indicator remains constant within the range. Economic growth rate data is collected and cross-validated using multiple authoritative data sources. Sentiment fluctuation data processing includes text cleaning, feature extraction, and a sentiment score calculation pipeline. During the generation of economic variable time series and sentiment index time series, missing values ​​are handled using spline interpolation, and time alignment considers publication lag and revision issues. The calculation of the rate of change of economic variables incorporates a seasonal adjustment factor to eliminate cyclical effects, and the derivation of the rate of change of the sentiment index controls for noise during market closures. Parallel comparison operations are executed on a distributed computing cluster, and resampling of the velocity indicator ensures consistency in time granularity. The standard error estimation of the joint analysis model uses a robust standard error method, and the output of the quantitative relationship identification results includes elasticity coefficients and contribution decomposition. The storage format of the valuation correction comprehensive analysis data supports time range querying and batch export.

[0037] The construction of the market dynamics time series dataset includes a data quality monitoring module, which checks for outliers and logical inconsistencies. The stationarity of the economic variable time series is achieved through differencing or logarithmic transformation. The formula for calculating the rate of change of economic variables is: in: The rate of change of economic variables at point t This represents the value of the economic variable at time point t. This represents the value of the economic variable at the previous point in time. The calculation of the sentiment index's rate of change is similar, but uses the sentiment index value. Time intervals are uniformly converted to an annualized ratio for comparison. Parallel comparisons are implemented using a matrix operation library to efficiently process large-scale data. For valuation conditions, accurate matching of timestamps and market state classification are ensured. The joint analysis model uses maximum likelihood estimation, with a significance level of 5% set for quantifying relationships. The generation of the corrected value series includes confidence interval calculations, and the valuation correction comprehensive analysis data covers both point and interval estimation results.

[0038] Stock data extraction for trend-similar segments is performed in batches using automated scripts. Economic growth rate data is updated in sync with official releases, and real-time acquisition of sentiment fluctuation data utilizes a stream processing platform. The construction of economic variable time series considers the integration of leading and lagging indicators, while derivative variables for the sentiment index time series include volatility and extreme value indicators. Standardization of the rate of change of economic variables uses the Z-score method, and the smoothing parameter for the rate of change of the sentiment index is optimized through cross-validation. Visual outputs for parallel comparisons include scatter plots and correlation matrices, with grouping under the same valuation conditions based on industry and market capitalization stratification. Granger causality tests of the joint analysis model determine the direction of causal relationships between variables, and the results of quantified relationship identification are stored as coefficient tables and graph structures. Validation of the valuation correction comprehensive analysis data is conducted through out-of-sample testing, and anomaly detection for the corrected value series uses the Isolation Forest algorithm. The collaborative analysis of the rate of change of economic variables and the rate of change of the sentiment index introduces interaction terms, which test the multiplier effect of the two types of rate indicators. Maintenance of the market dynamics time series set includes version control and backtesting, and breakpoint handling of the economic variable time series uses a structural change detection algorithm. The calculation of the rate of change in the sentiment index integrates multiple sentiment dictionaries to improve coverage, and statistical tests for parallel comparison include unit root tests and cointegration tests. Residual diagnostics of the joint analysis model ensure correct model specification, and the economic significance of quantified relationship identification is evaluated by domain experts. The valuation correction and comprehensive analysis data are applied in scenarios including stress testing and scenario analysis, and the optimized storage of the corrected value series supports high-frequency access.

[0039] The entire technical process relies on high-performance computing infrastructure, and fault-tolerant mechanisms are employed in the data processing stage to ensure reliability. The accuracy of screening for trend-similar segments affects the quality of subsequent analysis, and the accuracy of economic growth rate data is ensured through multi-source verification. Real-time processing of sentiment fluctuation data requires a low-latency architecture, and the generation of economic variable time series considers international comparison benchmarks. The calibration of the sentiment index time series uses survey data as a standard, and the calculation of the rate of change of economic variables addresses the cardinality effect. The derivation of the rate of change of the sentiment index controls text quality variation, and algorithm optimization for parallel comparison reduces computational complexity. Variable selection for the joint analysis model is guided by information criteria, and visualization tools for identifying quantitative relationships enhance the interpretability of the results. The integration of comprehensive analysis data for valuation correction supports automated report generation. Representative indicators for economic growth rate data cover output, income, and employment dimensions, and sentiment fluctuation data is collected from news media and social media platforms.

[0040] The stationarization of economic variable time series data avoids spurious regression issues, while dimensionality reduction techniques for the sentiment index time series extract core sentiment factors. Instantaneous calculation of the rate of change of economic variables utilizes numerical differentiation methods, and the rolling window adjustment of the sentiment index rate of change adapts to market changes. Fairness in parallel comparisons is ensured through data standardization, and stratified processing under the same valuation conditions identifies heterogeneity effects. Dynamic expansion of the joint analysis model includes time-varying parameter settings, and machine learning methods for quantifying relationships supplement traditional econometric models. The structural design of the valuation correction comprehensive analysis data facilitates system integration, and the interface for the corrected value series supports downstream application calls. Data security and privacy protection measures are embedded in all stages of data processing. Access control for economic growth rate data is based on role management, and anonymization of sentiment fluctuation data removes personal information. Encrypted storage of economic variable time series data prevents unauthorized access, and secure protocols are used for the transmission of the sentiment index time series. The calculation logs for the rate of change of economic variables record operational traces, and the audit trail for the rate of change of the sentiment index meets compliance requirements. The results of parallel comparisons are validated through multiple rounds of testing, and sensitivity analysis of the joint analysis model tests parameter robustness. Backup strategies for the valuation correction comprehensive analysis data ensure disaster recovery, and real-time performance indicators are monitored throughout the system.

[0041] Example 4: Screening Outliers and Conducting Risk Analysis Based on Valuation Correction Comprehensive Analysis Data. The process begins by extracting key information from the valuation correction comprehensive analysis data. The screening targets stocks with correction values ​​exceeding the benchmark level and stocks in trend difference zones. A correction value exceeding the benchmark level refers to a valuation adjustment exceeding two standard deviations of the historical average for the same period. Trend difference zones are time intervals marked "difference" extracted from the trend pattern classification results. Continuous trading records of the stocks are extracted in chronological order. These records include the opening price, highest price, lowest price, closing price, and trading volume for each trading day. Valuation indicator values ​​and trading volume data are collected for each time point. Valuation indicator values ​​are obtained from the latest calculated values ​​in the stock valuation indicator set, and trading volume data is obtained from the real-time interface of the market data provider. A continuous trading record set is generated and stored in a time-series database format. The system retrieves continuous trading records and extracts stock indicator values ​​and trading volumes at two consecutive time points. Standardized valuation metrics are used for indicator values, and trading volume data is adjusted for stock splits and dividends to ensure comparability. The change ratios of both indicators and trading volume are calculated. The indicator change ratio is calculated using the logarithmic return formula, while the trading volume change ratio is calculated using a simple percentage change method. These are integrated into two sequences: an indicator change ratio sequence and a trading volume change ratio sequence. Both sequences undergo outlier handling and smoothing filtering to create a trading volatility dataset and construct a corresponding index structure. Based on this dataset, market sentiment fluctuation data and liquidity change data for the corresponding time periods are extracted. Liquidity change data is derived by calculating bid-ask spreads and order book depth changes. The system determines whether both the indicator change ratio and trading volume change ratio exceed a set anomaly detection threshold. This threshold is dynamically set based on the asset's historical volatility characteristics. The system then determines whether both sentiment fluctuations and liquidity changes simultaneously exceed anomaly judgment thresholds, determined based on backtesting results of extreme market events. Time points meeting these conditions are marked as anomalies, generating a set of stock anomaly points and recording the timestamps and characteristic indicators of the anomalies.

[0042] Referring to Table 1, obtain all stocks in the stock anomaly set along with their corresponding codes and attribute information. Attribute information includes fundamental data such as industry classification, market capitalization, and financial indicators. Calculate the comprehensive risk assessment value for each stock. The comprehensive risk assessment value is weighted by multiple dimensions, including valuation deviation, volatility, liquidity indicators, and financial leverage ratio, to establish a comprehensive risk assessment value sequence and sort them according to risk level. Based on the comprehensive risk assessment value sequence, screen stocks whose correction values ​​exceed the risk threshold, whose valuation indicator values ​​are lower than the safety benchmark value, and whose labels in the trend pattern classification results are in the difference range. The risk threshold is dynamically adjusted according to the portfolio's risk preference. The safety benchmark value adopts the median level of the industry price-to-earnings ratio and price-to-book ratio. Extract the corresponding stock code, name identifier, and industry to which the stock belongs, and mark it as a high-risk stock. Sort and output the stocks that meet the criteria according to the classification format to generate an intelligent stock selection and risk assessment report.

[0043] Table 1: Threshold Parameters for Anomaly Identification The anomaly identification process employs a multi-level verification mechanism. The calculation of indicator change ratios and trading volume change ratios uses a rolling window method to eliminate seasonal effects, and the extraction of market sentiment fluctuation data considers the volatility characteristics at different time scales. Liquidity change data calculations include two dimensions: market depth and tightness. The dynamic adjustment of the anomaly identification threshold is based on machine learning algorithms to predict market state changes. The calibration of the anomaly judgment threshold uses extreme value theory, and the construction of the stock anomaly set includes a false positive filtering step. The calculation of the comprehensive risk judgment value uses principal component analysis to determine the weights of each dimension, and the risk threshold setting references the results of the Value at Risk (VaR) model. The determination of the safety benchmark value uses an industry relative valuation method, and the marking process for high-risk stocks includes a manual review step.

[0044] The generation of continuous trading record sets ensures data integrity and continuity. Valuation indicator values ​​undergo quality checks to prevent data anomalies. Adjustments to trading volume data consider the impact of company actions such as stock splits and rights issues. The calculation of indicator change ratio sequences uses logarithmic returns to improve the statistical properties of the sequences, and the processing of trading volume change ratio sequences uses Box-Cox transformation to improve the distribution shape. The construction of the trading volatility dataset includes data standardization and missing value imputation. Market sentiment volatility data is extracted and integrated from multiple sentiment data sources. Liquidity change data is calculated using high-frequency order book data, and the setting of anomaly identification thresholds has been validated through backtesting. The adjustment of anomaly judgment thresholds considers changes in the macroeconomic environment, and the updating of the stock anomaly point set uses real-time stream processing technology. The screening criteria for high-risk stocks implement multi-factor combination logic, and the detection of correction values ​​exceeding the risk threshold uses statistical hypothesis testing methods. The judgment of valuation indicator values ​​below the safety benchmark value uses a percentile ranking mechanism. The verification of difference segment labels in the trend pattern classification results is performed through a time series pattern matching algorithm. The generation of intelligent stock selection and risk assessment outputs includes detailed risk factor decomposition. The entire technical solution is implemented on a high-performance computing platform, and the data processing stage achieves fully automated pipeline operation. The anomaly identification algorithm has been validated in multiple markets to ensure robustness, and the risk analysis model is continuously optimized to improve its accuracy.

[0045] The input data for the comprehensive valuation analysis undergoes format conversion and consistency checks, while stock screening for trend difference segments utilizes a complex event processing engine. Continuous transaction record set storage optimizes query performance, and the calculation of indicator change ratios and trading volume change ratios is parallelized. The construction of the trading volatility dataset employs a columnar storage format to improve compression ratios, and natural language processing technology is used to extract market sentiment volatility data. Liquidity change data calculation integrates real-time market data streams, and a dynamic adjustment mechanism for anomaly identification thresholds monitors changes in market volatility regimes. Anomaly threshold calibration includes stress testing scenarios, and the maintenance of the stock anomaly point set supports multi-dimensional query analysis. The comprehensive risk assessment module includes risk factor exposure measurement, and risk thresholds are set based on portfolio constraints. The determination of the safety benchmark value references international valuation standards, and label verification in trend pattern classification results is achieved through pattern recognition algorithms. The high-risk stock marking process implements a tiered early warning mechanism, and the format of intelligent stock selection and risk assessment outputs conforms to industry reporting standards. The integration of various components in the technical solution is achieved through standardized interfaces, and real-time monitoring of system operation status ensures processing reliability.

[0046] Data quality control is implemented throughout the entire process, and cross-validation is used to verify the comprehensive analysis data for valuation correction. The integrity check of the continuous trading record set includes data source comparison, and the stationarity processing of the indicator change ratio sequence employs the differencing method. Anomaly detection of the trading volume change ratio sequence uses the isolated forest algorithm, and data lineage tracing is implemented in the construction of the trading volatility dataset. The quality assessment of market sentiment fluctuation data is conducted through correlation tests with market volatility, and the accuracy verification of liquidity change data uses live trading data. The setting of anomaly identification thresholds is based on sensitivity analysis, and the optimization of anomaly judgment thresholds uses a grid search method. The analysis function of stock anomaly point sets supports drill-down queries, and the calculation of the comprehensive risk judgment value supports real-time updates. The risk threshold adjustment mechanism responds to major market events, and the update of the safety benchmark value is synchronized with the financial report release cycle. The tag system in the trend pattern classification results supports version management, and the distribution of intelligent stock selection and risk assessment outputs achieves multi-channel coverage. The entire technical solution is designed with scalability and maintainability in mind, and algorithm parameters are exposed as configurable options to support different market environments.

[0047] Example 5: Visualization and Dynamic Optimization of Intelligent Stock Selection and Risk Assessment Output. This begins with integrating a list of high-risk stocks and multiple judgment tags, including multi-dimensional classification indicators such as valuation risk, liquidity risk, and financial health. The process of generating a visual report structure employs a web-based interactive data visualization framework. The report structure contains multiple parallel component modules, including a stock risk distribution map and a trend comparison visualization component. The stock risk distribution map uses a heatmap format to display the risk concentration across different industries and market capitalization groups. The trend comparison visualization component enables the overlay and comparison of time series data for multiple stock valuation indicators. The steps for generating a visual report structure involve using a large model to perform deep feature extraction on high-risk stock data. The large model employs a pre-trained Transformer architecture neural network to process fundamental, market, and technical indicator data of the stocks. Deep feature extraction identifies key factors affecting the risk-return characteristics of stocks through a multi-layer attention mechanism of the neural network, identifying key risk factors and opportunity indicators. Key risk factors include quantitative indicators such as abnormal volatility, liquidity depletion, and excessive financial leverage, while opportunity indicators cover positive signals such as valuation troughs, earnings growth momentum, and upward revisions of analyst expectations. The report content layout is optimized based on responsive design principles to automatically adapt to the display sizes of different terminal devices, and the arrangement of key visualization elements uses the optimal visual flow verified by eye-tracking experiments.

[0048] The steps to optimize the report content layout are as follows: The stock valuation indicator set and trend pattern classification results are updated based on real-time market data. Real-time market data is obtained through the stock exchange's real-time data interface, providing the latest trading prices and volumes. The update triggers for the stock valuation indicator set include company events such as IPOs, financial report releases, and major asset restructurings. The recalculation of trend pattern classification results is based on the latest data sequence within a sliding time window. Stock selection recommendations and risk warning outputs are dynamically adjusted. Adjustments to stock selection recommendations are based on the relative deviation of the latest valuation indicators from the industry average. The trigger threshold for risk warning outputs dynamically fluctuates with market volatility. The implementation of the visualized report structure adopts a modular design concept, establishing a data linkage mechanism between various visualization components. When users interact with the stock risk distribution map, the trend comparison visualization component synchronously displays the detailed time series of the corresponding stocks. The deep feature extraction process of the large model includes two stages: forward propagation and feature importance calculation. Forward propagation inputs stock data into the neural network to obtain high-dimensional feature vectors. Feature importance calculation uses the SHAP value method to quantify the contribution of each input variable to the output result. The identification results of key risk factors are presented intuitively in the visualization interface through a color-coding system. High-risk factors are marked with red tones, while opportunity indicators are highlighted with green tones. The optimization algorithm for the report content layout is based on a genetic algorithm that continuously iterates to find the optimal component arrangement scheme, ensuring that important information modules are always kept in the visible area of ​​the first screen.

[0049] The real-time market data update mechanism establishes a multi-level caching structure. The first-level cache stores the latest transaction snapshot data, while the second-level cache retains historical time-series data. The update process of the stock valuation indicator set is implemented as an atomic operation, ensuring the consistency of query results during data updates. The dynamic update of trend pattern classification results adopts an incremental calculation mode, performing pattern recognition only on newly added data points without recalculating the entire historical data. The dynamic adjustment logic of stock selection suggestions integrates a multi-factor scoring model, with factor weights automatically adjusted according to market conditions. The optimization of risk warning output introduces a machine learning classifier, reducing the false positive rate while improving the ability to capture tail risks.

[0050] In a specific application scenario, a technology sector stock releases a disappointing quarterly earnings report after market close. The real-time market data update module detects abnormal after-hours price fluctuations and immediately recalculates valuation metrics such as the price-to-earnings ratio (P / E ratio) and price-to-book ratio (P / B ratio). The trend pattern classification result update module detects that the stock's valuation metric sequence has broken through its historical fluctuation range, and the large-scale model's deep feature extraction identifies a cash flow deterioration risk factor. The visualization report system automatically adjusts the stock's position on the risk distribution map to a high-risk area, and the trend comparison visualization component highlights the degree of deviation between the stock's valuation and the industry average. The stock selection suggestion generation module removes the stock from the recommendation list, and the risk warning output system generates a major risk warning announcement. The interactive functionality of the visualization report structure allows users to drill down to view detailed analysis of any high-risk stock, and clicking on a specific data point on the stock risk distribution map brings up the complete risk assessment report for that stock. The feature importance ranking generated by the large-scale model's deep feature extraction supports users' understanding of the logical basis for risk judgment, and the historical trends of key risk factors are displayed through dynamic line charts. The report's layout allows for personalized settings, enabling users to customize key indicators and alert thresholds. Investors with different risk appetites can configure monitoring panels to suit their individual needs. Real-time market data updates support multiple granularities, from minutes to seconds, with streaming processing ensuring timeliness during high-frequency trading periods. Version management of stock valuation indicator sets allows for retrospective analysis of valuation levels at any historical point in time, and the visualization of trend pattern classification results includes pattern similarity clustering analysis. Dynamic adjustments to stock selection recommendations are recorded and a complete decision log is maintained, while historical records of risk alert outputs support retrospective analysis.

[0051] During system implementation, a complete data pipeline was established, encompassing multiple stages from raw data acquisition to final visualization, including data verification, quality control, and anomaly handling. The large-scale model's deep feature extraction module is periodically retrained to maintain adaptability to market changes, and the definitions of key risk factors and opportunity indicators are continuously updated based on regulatory policies and market developments. The A / B testing mechanism for report content layout continuously optimizes the user experience, and display adaptation across different device types is achieved through a cross-platform framework. The real-time market data update module implements load balancing and failover mechanisms to ensure stable system operation in highly volatile market environments. The calculation of stock valuation indicator sets optimizes numerical precision processing, avoiding the impact of floating-point arithmetic errors on analysis results. The storage of trend pattern classification results utilizes time-series database technology to support fast range queries, and the dynamic adjustment algorithm for stock selection recommendations has been validated through historical backtesting. Risk warning outputs are distributed through multiple channels, including email, SMS, and mobile push notifications.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent valuation analysis and stock selection based on a large model, characterized in that, The method includes: Basic information data and real-time market data of stocks are collected from multiple data sources, and data fusion processing is performed through a large model to generate initial valuation indicators. Timestamps and stock codes are associated to form a set of stock valuation indicators. The valuation indicators in the stock valuation indicator set are sorted by time series, and segments with similar and different trend changes in the indicators in the series are identified to obtain trend pattern classification results. For stocks in the trend similarity segment in the trend pattern classification results, time series data of macroeconomic variables and market sentiment index are extracted, and the adjustment magnitude is calculated by combining the fluctuation of valuation indicators. The strength of valuation correction under market dynamics is assessed, and comprehensive valuation correction analysis data is generated. Based on the comprehensive analysis data of the valuation correction, outliers in stocks whose correction values ​​exceed the benchmark level and are located in the trend difference range are screened out, and a set of stock outliers is formed. Risk attribute analysis is performed on all stocks in the set of stock anomalies, stocks with high risk are marked, and intelligent stock selection and risk assessment outputs are generated.

2. The intelligent valuation analysis and stock selection method based on a large model according to claim 1, characterized in that, The set of stock valuation indicators includes valuation indicator values, stock code identifiers, and standardized financial data. The trend pattern classification results specifically include trend similarity segment markings, trend difference segment markings, and the change rate of valuation indicators for adjacent stocks. The comprehensive valuation correction analysis data includes the impact of the rate of change of economic variables on valuation, the impact of the rate of change of sentiment index on valuation, and a comparison of indicator correction responses under each market dynamic condition. The set of stock anomalies includes anomaly stock codes, anomaly market sentiment fluctuation characteristics, and the ratio of anomaly indicators to trading volume changes. The intelligent stock selection and risk assessment output includes a list of high-risk stocks and multi-dimensional judgment tags for high-risk stocks.

3. The intelligent valuation analysis and stock selection method based on a large model according to claim 1, characterized in that, The steps of collecting basic stock information data and real-time market data from multiple data sources, and then performing data fusion processing through a large model to generate initial valuation indicators include: The process involves acquiring historical price sequences, financial statement data, and industry classification information for stocks; collecting market capitalization data and liquidity indicators corresponding to the stocks; and recording the collected results as two types of data elements: market capitalization factor and liquidity factor, forming a stock data element group. Based on the market capitalization factor and liquidity factor data in the stock data element group, normalization processing is performed on each of them. The normalized results are then mapped to the stock code and timestamp. The weighted average of the normalized market capitalization value and the normalized liquidity value is calculated as the initial valuation indicator, generating a set of stock valuation indicators.

4. The intelligent valuation analysis and stock selection method based on a large model according to claim 3, characterized in that, The step of sorting the valuation indicators in the stock valuation indicator set by time series and identifying segments with similar and different trends in the indicator changes within the series includes: Extract the valuation indicator values ​​and corresponding stock code data from the stock valuation indicator set. Sort all stock data points according to timestamp information. Call the time series sequence and, based on adjacent time intervals, perform sliding window calculation and sorting on the data points that are continuous in time to generate a time series of adjacent data points. Based on the time series of adjacent data points, calculate the rate of change of valuation indicators between every two adjacent data points and integrate them to generate a sequence of changes in valuation indicators. According to the sequence of changes in valuation indicators, analyze the direction of market value change and liquidity change between adjacent data points. Classify and label each pair of data points according to whether the change patterns in the two directions are consistent. Record and group segments with similar trends and different trends respectively to obtain the trend pattern classification results.

5. The intelligent valuation analysis and stock selection method based on a large model according to claim 4, characterized in that, The steps for extracting time series data of macroeconomic variables and market sentiment indices from stocks in the trend pattern classification results include: Based on the trend pattern classification results, segments marked as having similar trends are selected. Economic growth rate data and sentiment fluctuation data within each stock trading cycle are detected and arranged chronologically to form economic variable time series and sentiment index time series, generating a market dynamic time series set. Based on this market dynamic time series set, the rate of change of economic variables and the rate of change of sentiment index between consecutive time points in each stock's time series are calculated. The rates of change of economic variables and the rate of change of sentiment index are compared in parallel under the same valuation conditions. By jointly analyzing the two types of speed indicators, the adjustment magnitude under the indicator values ​​is quantitatively identified, and the correction value series of each stock is integrated to establish a comprehensive valuation correction analysis data set.

6. The intelligent valuation analysis and stock selection method based on a large model according to claim 5, characterized in that, The step of screening outliers among stocks whose adjusted values ​​exceed the benchmark level and are located in the trend difference range based on the comprehensive analysis data of the valuation correction includes: Based on the comprehensive analysis data of valuation correction, stocks with correction values ​​exceeding the benchmark level and stocks in the trend difference range are selected. Continuous trading records of these stocks are extracted in chronological order, and valuation indicator values ​​and trading volume data corresponding to each time point are collected to generate a continuous trading record set. The continuous trading record set is then used to extract indicator values ​​and trading volumes for two consecutive time points, and the indicator change ratio and trading volume change ratio are calculated respectively. These are integrated into an indicator change ratio sequence and a trading volume change ratio sequence to establish a trading fluctuation dataset. Based on the trading fluctuation dataset, market sentiment fluctuation data and liquidity change data for the corresponding time period are extracted. It is determined whether both the indicator change ratio and the trading volume change ratio exceed the set anomaly identification threshold, and whether both sentiment fluctuation and liquidity change exceed the anomaly judgment threshold simultaneously. Time points that meet the conditions are marked as anomalies, generating a set of stock anomaly points.

7. The intelligent valuation analysis and stock selection method based on a large model according to claim 6, characterized in that, The step of performing risk attribute analysis on all stocks in the set of stock anomalies and marking stocks with high risk includes: Obtain all stocks and their corresponding codes and attribute information from the set of stock anomalies, calculate the comprehensive risk assessment value for each stock, and establish a comprehensive risk assessment value sequence. Based on the comprehensive risk assessment value sequence, screen stocks whose correction values ​​exceed the risk threshold, whose valuation index values ​​are lower than the safety benchmark value, and whose labels in the trend pattern classification results are in the difference segment, extract the corresponding stock codes, name identifiers, and industry classifications, and mark them as high-risk stocks. Sort and output the stocks that meet the conditions according to the classification format to generate intelligent stock selection and risk assessment output.

8. The intelligent valuation analysis and stock selection method based on a large model according to claim 7, characterized in that, The steps for generating intelligent stock selection and risk assessment outputs also include: integrating the high-risk stock list and multiple judgment tags to generate a visual report structure, including a stock risk distribution chart and a trend comparison visualization component.

9. The intelligent valuation analysis and stock selection method based on a large model according to claim 8, characterized in that, The steps for generating a visual report structure include: using a large model to perform in-depth feature extraction on high-risk stock data, identifying key risk factors and opportunity indicators, and optimizing the layout of the report content.

10. The intelligent valuation analysis and stock selection method based on a large model according to claim 9, characterized in that, The steps for optimizing the report content layout include: updating the set of stock valuation indicators and trend pattern classification results based on real-time market data, and dynamically adjusting stock selection recommendations and risk warning outputs.

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