1. An intelligent multi-model artificial intelligence-based system (100) for stock market volatility forecasting, the system (100) comprising: a user interface (104) integrated into a user device (102) and configured to collect market-related data inputs obtained from a plurality of data sources; a communication network (106) configured to establish a link for seamless data transmission within the system (100); a processing unit (108) operatively coupled to the user device (102) via the communication network (106) and configured to analyze the market-related data inputs to generate stock market volatility forecasts, wherein the processing unit (108) further comprises: a data acquisition module (112) configured to acquire the market-related data inputs from the user device (102); a preprocessing module (114) configured to perform preprocessing operations on the acquired data for subsequent analysis; a feature extraction module (116) configured to extract volatility-sensitive features from the preprocessed data; a hybrid volatility configuration module (118) configured to derive enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features; a volatility forecast generation module (120) configured to generate multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations; an evaluation module (122) configured to evaluate the generated multiple volatility prediction forecasts based on a plurality of performance metrics; and an output module (130) configured to transmit the generated and evaluated multiple volatility prediction forecasts to the user interface (104) of the user device (102).

2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (110) configured to store the market-related data inputs, trained model parameters and volatility forecasts to enable real-time retrieval, analysis and decision support within the system (100).

3. The system (100) as claimed in claim 1, wherein the hybrid volatility configuration module (118) employs a transformer-based self-attention mechanism to identify and model event-driven and sentiment-sensitive volatility dynamics in real-time.

4. The system (100) as claimed in claim 1, wherein the hybrid volatility configuration module (118) comprises a feature fusion layer mechanism to learn cross-model interactions among price-based features, macroeconomic indicators and sentiment-derived features for improving volatility forecasting accuracy.

5. The system (100) as claimed in claim 1, wherein the processing unit (108) comprises an explanatory module (124) configured to determine contributing factors influencing the generated multiple volatility prediction forecasts and provide interpretable, auditable and transparent outcomes suitable for analysis.

6. The system (100) as claimed in claim 1, wherein the processing unit (108) comprises a market regime adaptation module (126) configured to detect variations in market trends and adaptively recalibrate model parameters to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions.

7. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises a training and testing module (128) configured to split data into training and testing datasets and train the functional modules and predictive algorithms using training dataset.

8. The system (100) as claimed in claim 1, wherein the plurality of performance metrics include but not limited to root mean square error, mean square error, mean absolute error, coefficient of determination and directional accuracy metrics.

9. The system (100) as claimed in claim 1, wherein the market-related data inputs include but not limited to historical and real-time stock price data, market trading data, macroeconomic data and sentiment data.

10. A method (200) for an intelligent multi-model artificial intelligence-based system (100) for stock market volatility forecasting, the method (200) comprising: collecting market-related data inputs obtained from a plurality of data sources via a user interface (104) integrated into a user device (102); establishing a link for seamless data transmission within the system (100) via a communication network (106); analyzing the market-related data inputs to generate stock market volatility forecasts via a processing unit (108); acquiring the market-related data inputs from the user device (102) via a data acquisition module (112); performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module (114); extracting volatility-sensitive features from the preprocessed data via a feature extraction module (116); deriving enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features via a hybrid volatility configuration module (118); generating multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations via a volatility forecast generation module (120); evaluating the generated multiple volatility prediction forecasts based on a plurality of performance metrics via an evaluation module (122); and transmitting the generated and evaluated multiple volatility prediction forecasts to the user interface (104) of the user device (102) via an output module (130).

Description:FIELD OF DISCLOSURE
[0001] The present disclosure generally relates to the field of stock market analysis and artificial intelligence, and more particularly, to an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting using advanced data analytics techniques. The disclosure is particularly directed to hybrid predictive frameworks that integrate multiple analytical models to analyze market-related data inputs and generate accurate, adaptive and interpretable volatility forecasts, thereby supporting improved risk assessment and informed decision-making for investors, financial institutions and regulatory bodies.
BACKGROUND OF THE DISCLOSURE
[0002] Financial markets are inherently complex and subject to continuous fluctuations due to a variety of economic, political and behavioral factors. Market trends are influenced by the historical price movements, supply and demand conditions, macroeconomic indicators, investor activity, regulatory actions and unforeseen events. As a result, financial data often exhibits irregular patterns, uncertainty and rapid changes over time. Understanding and analyzing such market behavior is important for effective decision-making, risk assessment and financial planning across different market participants. These characteristics make it difficult to derive consistent insights from financial data using conventional analytical approaches. Market volatility and sudden shifts can obscure underlying trends and reduce the reliability of predictions. Additionally, financial data is often large in volume and generated at high frequency increasing analytical complexity. The presence of non-linear relationships limits the effectiveness of simplistic modeling techniques. Timely interpretation of such data is essential yet challenging due to the speed at which market conditions evolve. Consequently, market participants require more robust analytical methods to better interpret evolving market dynamics.
[0003] Conventional volatility forecasting approaches such as autoregressive models, GARCH-family models and other econometric techniques operate under fixed statistical assumptions and are often limited in their ability to capture complex non-linear relationships and sudden market changes. While these models are effective in estimating baseline volatility under stable conditions, they tend to perform poorly during periods of market stress, structural breaks or sentiment-driven volatility spikes. Isolated machine learning or deep learning models capable of modeling non-linear dynamics often suffer from issues such as overfitting, lack of interpretability and limited adaptability to evolving market conditions. Many existing systems function as static or batch processing tools and lack real-time data integration, automated recalibration or unified model coordination. Additionally, conventional platforms frequently fail to incorporate structured data sources such as news and social media sentiment, resulting in incomplete volatility assessment. These limitations can lead to delayed risk responses, unreliable forecasts and increased exposure to financial uncertainty. As a result, volatility estimates generated by such approaches may lack consistency across different market regimes and time horizons. Consequently, there is an increasing need for more comprehensive and adaptive volatility assessment methodologies.
[0004] The present invention overcomes these limitations by providing an integrated and adaptive analytical framework capable of handling complex and dynamic market conditions. Unlike conventional methods that rely on isolated models or static assumptions. The present invention combines multiple analytical techniques within a unified architecture to improve robustness and forecasting consistency. The invention enables continuous incorporation of new market information, allowing forecasts to remain relevant under changing market behavior and unexpected events. By supporting adaptive model updating and coordinated analysis across diverse data sources, the system reduces reliance on manual intervention and retrospective analysis. The present invention further enhances transparency and usability by producing interpretable forecasting outputs suitable for risk assessment and regulatory review. As a result, the system delivers more reliable, responsive and scalable volatility forecasts compared to conventional approaches. Additionally, the present invention supports scalable deployment across varying market environments and data volumes making it suitable for institutional and enterprise-level environments. The system operates in both historical and near real-time analytical modes, thereby improving responsiveness to evolving market conditions. By facilitating coordinated evaluation across multiple forecasting horizons, the invention enables more comprehensive market insight and improved risk awareness. The unified framework further supports consistent performance monitoring and evaluation contributing to sustained forecasting reliability over time.
[0005] Thus, in light of the above-stated discussion, there exists a need for an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting.
SUMMARY OF THE DISCLOSURE
[0006] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensures and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0007] According to illustrative embodiments, the present disclosure focuses on an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting which overcomes the above-mentioned disadvantages or provides the users with a useful or commercial choice.
[0008] An objective of the present disclosure is to provide an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting.
[0009] An objective of the present disclosure is to integrate a plurality of analytical and artificial intelligence models within a unified computational framework to enhance the accuracy and robustness of volatility forecasting.
[0010] An objective of the present disclosure is to enable adaptive volatility forecasting by continuously updating model parameters in response to evolving market trends.
[0011] An objective of the present disclosure is to improve the reliability of volatility predictions during periods of market instability, abrupt shocks and market regime transitions.
[0012] An objective of the present disclosure is to generate multi-horizon volatility forecasts suitable for both short-term and long-term risk assessment and analysis.
[0013] An objective of the present disclosure is to enhance the interpretability and transparency of volatility forecasts to support informed decision-making and regulatory evaluation.
[0014] An objective of the present disclosure is to facilitate real-time volatility forecasting through automated data ingestion, processing and model execution.
[0015] An objective of the present disclosure is to improve system scalability and deployment feasibility for institutional, enterprise and large-scale financial environments.
[0016] An objective of the present disclosure is to minimize manual intervention in volatility analysis by employing automated learning, evaluation and model recalibration mechanisms.
[0017] An objective of the present disclosure is to support enhanced risk management, portfolio optimization and strategic financial planning in volatile and dynamic market environments.
[0018] An objective of the present disclosure is to improve forecasting consistency and stability by leveraging ensemble learning and coordinated multi-model inference techniques.
[0019] An objective of the present disclosure is to enable continuous monitoring of market dynamics for improved identification of emerging trends, anomalies and volatility patterns.
[0020] An objective of the present disclosure is to provide actionable forecasting insights capable of supporting both short-term trading strategies and long-term investment planning.
[0021] In light of the above, in one aspect of the present disclosure an intelligent time series-based profit prediction system for stock market forecasting is disclosed herein. The system comprises a user interface integrated into a user device and configured to collect market-related data inputs obtained from a plurality of data sources. The system includes a communication network configured to establish a link for seamless data transmission within the system. The system also includes a processing unit operatively coupled to the user device via the communication network and configured to analyze the market-related data inputs to generate stock market volatility forecasts, wherein the processing unit further comprises a data acquisition module configured to acquire the market-related data inputs from the user device, a preprocessing module configured to perform preprocessing operations on the acquired data for subsequent analysis, a feature extraction module configured to extract volatility-sensitive features from the preprocessed data, a hybrid volatility configuration module configured to derive enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features, a volatility forecast generation module configured to generate multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations, an evaluation module configured to evaluate the generated multiple volatility prediction forecasts based on a plurality of performance metrics and an output module configured to transmit the generated and evaluated multiple volatility prediction forecasts to the user interface of the user device.
[0022] In one embodiment, the system further comprises a cloud database configured to store the market-related data inputs, trained model parameters and volatility forecasts to enable real-time retrieval, analysis and decision support within the system.
[0023] In one embodiment, the hybrid volatility configuration module employs a transformer-based self-attention mechanism to identify and model event-driven and sentiment-sensitive volatility dynamics in real-time.
[0024] In one embodiment, the hybrid volatility configuration module comprises a feature fusion layer mechanism to learn cross-model interactions among price-based features, macroeconomic indicators and sentiment-derived features for improving volatility forecasting accuracy.
[0025] In one embodiment, the processing unit comprises an explanatory module configured to determine contributing factors influencing the generated multiple volatility prediction forecasts and provide interpretable, auditable and transparent outcomes suitable for analysis.
[0026] In one embodiment, the processing unit comprises a market regime adaptation module configured to detect variations in market trends and adaptively recalibrate model parameters to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions.
[0027] In one embodiment, the processing unit further comprises a training and testing module configured to split data into training and testing datasets and train the functional modules and predictive algorithms using training dataset.
[0028] In one embodiment, the plurality of performance metrics include but not limited to root mean square error, mean square error, mean absolute error, coefficient of determination and directional accuracy metrics.
[0029] In one embodiment, the market-related data inputs include but not limited to historical and real-time stock price data, market trading data, macroeconomic data and sentiment data.
[0030] In light of the above, in one aspect of the present disclosure, a method for an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting is disclosed herein. The method comprises collecting market-related data inputs obtained from a plurality of data sources via a user interface integrated into a user device. The method includes establishing a link for seamless data transmission within the system via a communication network. The method also includes analyzing the market-related data inputs to generate stock market volatility forecasts via a processing unit. The method also includes acquiring the market-related data inputs from the user device via a data acquisition module. The method also includes performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module. The method also includes extracting volatility-sensitive features from the preprocessed data via a feature extraction module. The method also includes deriving enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features via a hybrid volatility configuration module. The method also includes generating multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations via a volatility forecast generation module. The method also includes evaluating the generated multiple volatility prediction forecasts based on a plurality of performance metrics via an evaluation module. The method also includes transmitting the generated and evaluated multiple volatility prediction forecasts to the user interface of the user device via an output module.
[0031] These and other advantages will be apparent from the present application of the embodiments described herein.
[0032] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0033] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0034] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0035] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0036] FIG. 1 illustrates a block diagram of an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting, in accordance with an exemplary embodiment of the present disclosure.
[0037] FIG. 2 illustrates a method for an intelligent multi-model artificial intelligence-based system for stock market volatility forecasting, in accordance with an exemplary embodiment of the present disclosure.
[0038] Like reference, numerals refer to like parts throughout the description of several views of the drawing.
[0039] An intelligent multi-model artificial intelligence-based system for stock market volatility forecasting is illustrated in the accompanying drawings, which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0040] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0041] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0042] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0043] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0044] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0045] Referring now to FIG. 1 and FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a block diagram of an intelligent multi-model artificial intelligence-based system 100 for stock market volatility forecasting, in accordance with an exemplary embodiment of the present disclosure.
[0046] The system 100 may include a user device 102, a user interface 104, communication network 106 and a processing unit 108.
[0047] In one embodiment of the present invention, the system 100 is designed to provide an intelligent, adaptive and multi-model artificial intelligence-based framework for combining multiple analytical models to process market-related data and generate adaptive volatility forecasts across varying time horizons.
[0048] In one embodiment of the present invention, the system 100 incorporates a cloud-native and modular architecture to support real-time inference, horizontal scalability across multiple assets and exchanges and integration with external trading platforms and risk management systems to enable large-scale institutional deployment and seamless system expansion.
[0049] The user interface 104 integrated into a user device 102 and is configured to collect market-related data inputs obtained from a plurality of data sources.
[0050] In one embodiment of the present invention, the user device 104 may include but not limited to smart-phone, tablet, laptop, computer and many other.
[0051] In one embodiment of the present invention, the plurality of data sources include but not limited to stock exchanges including the national stock exchange of india (NSE) and the Bombay stock exchange (BSE), exchange-provided live market feeds, index providers, financial data vendors, broker and trading platform feeds, order book and transaction systems, governmental and regulatory databases, macroeconomic repositories, financial news portals, corporate disclosure platforms, analyst reports and digital sentiment sources including social media and online information platforms.
[0052] In one embodiment of the present invention, the market-related data inputs include but not limited to historical and real-time stock price data, market trading data, macroeconomic data and sentiment data.
[0053] In one embodiment of the present invention, the historical and real-time stock price data include open, high, low, close and adjusted closing price values to identify long-term volatility patterns, regime persistence and statistical characteristics of price movements, the market trading data include real-time market information representing ongoing trading activity including transaction prices, traded volumes, bid-ask spreads, market depth and intraday price movements to enable short-term and high-frequency volatility forecasting, the macroeconomic data include interest rates, inflation indicators, gross domestic product (GDP) growth rates, monetary policy signals, exchange rates and fiscal indicators and the sentiment data include information derived from financial news, policy statements and social media sources.
[0054] In one embodiment of the present invention, the market-related data inputs further include technical volatility indicator data including but not limited to realized volatility measures, average true range (ATR), bollinger band width, momentum indicators, liquidity metrics and other technical signals computed from price and volume data.
[0055] In one embodiment of the present invention, the user interface 104 enhances usability of the system 100 and facilitates intuitive decision-making by the user without requiring advanced technical expertise.
[0056] The communication network 106 is configured to establish a link for seamless data transmission within the system 100.
[0057] In one embodiment of the present invention, the communication network 106 is further configured to facilitate reliable data exchange within the system 100 by implementing data integrity mechanisms and managing network traffic to maintain stable and uninterrupted communication.
[0058] In one embodiment of the present invention, the communication network 106 includes but not limited to a wired network, a wireless network, a cellular communication network, a bluetooth-based network and an internet-based communication network.
[0059] The processing unit 108 operatively coupled to the user device 102 via the communication network 106 and is configured to analyze the market-related data inputs to generate stock market volatility forecasts. The processing unit 108 further comprises several modules including a data acquisition module 112, a preprocessing module 114, a feature extraction module 116, a hybrid volatility configuration module 118, a volatility forecast generation module 120, an evaluation module 122 and an output module 130.
[0060] In one embodiment of the present invention, the processing unit 108 is further configured to support adaptive learning by dynamically updating model parameters, recalibrating forecasting logic and managing interactions among multiple analytical and artificial intelligence-based models in response to variations in market conditions, volatility regimes and underlying data characteristics.
[0061] In one embodiment of the present invention, the processing unit 108 may include, but not limited to a microcontroller, a microprocessor, a computing device, a development board, an application-specific integrated circuit, a system-on-chip.
[0062] In one embodiment of the present invention, the system 100 further comprises a cloud database 110 configured to store the market-related data inputs, trained model parameters and volatility forecasts to enable real-time retrieval, analysis and decision support within the system 100.
[0063] The data acquisition module 112 is configured to acquire the market-related data inputs from the user device 102.
[0064] In one embodiment of the present invention, the data acquisition module 112 obtains diverse market-relate data from the user device 102 and transmits the acquired data to the processing unit 108 for subsequent processing and analysis to serve as the input for generating stock market volatility forecasts.
[0065] The preprocessing module 114 is configured to perform preprocessing operations on the acquired data for subsequent analysis.
[0066] In one embodiment of the present invention, the preprocessing operations include but not limited to noise filtering, outlier detection and removal, handling of missing values, data imputation, temporal alignment across the plurality of data sources, scaling and data normalization to ensure compatibility with subsequent analytical and artificial intelligence-based models.
[0067] In one embodiment of the present invention, the preprocessing operations are performed to reduce data inconsistencies and generate high-quality datasets to facilitate accurate model training and reliable volatility prediction across multiple time horizons.
[0068] The feature extraction module 116 is configured to extract volatility-sensitive features from the preprocessed data.
[0069] In one embodiment of the present invention, the volatility-sensitive features include but not limited to technical indicators including average true range (ATR), bollinger bandwidth, realized variance, statistical volatility signals derived from generalized autoregressive conditional heteroskedasticity-based residuals, momentum indicators, trend indicators, sentiment polarity and intensity scores.
[0070] In one embodiment of the present invention, the feature extraction module 116 is configured to enable detection of non-linear dependencies, volatility clustering and market regime shifts and transform the processed data into meaningful and high-impact feature representations that enhance predictive performance of the system 100.
[0071] The hybrid volatility configuration module 118 is configured to derive enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features.
[0072] In one embodiment of the present invention, the hybrid volatility configuration module 118 employs generalized autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models as baseline econometric volatility modeling techniques to capture volatility clustering and leverage effects observed in financial time series data and further employs artificial intelligence-based temporal learning models including long short-term memory (LSTM) networks and gated recurrent unit (GRU) networks to learn long-term temporal dependencies and non-linear volatility patterns to generate sequential representations that capture regime persistence, delayed market reactions and cyclical volatility behavior.
[0073] In one embodiment of the present invention, the hybrid volatility configuration module 118 employs a transformer-based self-attention mechanism to identify and model event-driven and sentiment-sensitive volatility dynamics in real-time.
[0074] In one embodiment of the present invention, the hybrid volatility configuration module 118 comprises a feature fusion layer mechanism to learn cross-model interactions among price-based features, macroeconomic indicators and sentiment-derived features for improving volatility forecasting accuracy.
[0075] In one embodiment of the present invention, the feature fusion layer mechanism includes ensemble learning techniques including random forest models, support vector machine (SVM) models and meta-learning mechanisms to combine, weigh and reconcile the outcomes generated by the plurality of artificial intelligence-based models to produce volatility forecast that are more stable, accurate and robust than the forecasts generated by individual model.
[0076] In one embodiment of the present invention, the hybrid volatility configuration module 118 is configured to leverage complementary strengths of statistical volatility models and artificial intelligence-based learning models to enhance volatility representation and improve overall forecasting performance.
[0077] The volatility forecast generation module 120 is configured to generate multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations.
[0078] In one embodiment of the present invention, the multiple volatility prediction forecasts include but not limited to continuous volatility forecast values, projected volatility index metrics and risk classification indicators categorized as low, moderate and high volatility to enable real-time decision-making for hedging strategies, portfolio allocation, risk management and regulatory compliance and monitoring.
[0079] The evaluation module 122 is configured to evaluate the generated multiple volatility prediction forecasts based on a plurality of performance metrics.
[0080] In one embodiment of the present invention, the plurality of performance metrics include but not limited to root mean square error, mean square error, mean absolute error, coefficient of determination and directional accuracy metrics.
[0081] In one embodiment of the present invention, the root mean square error (RMSE), the mean square error (MSE) and the mean absolute error (MAE) is utilized for assessing prediction accuracy, the coefficient of determination (R2 score) is utilized for evaluating explanatory power and the directional accuracy metrics for validating volatility trend direction.
[0082] In one embodiment of the present invention, the evaluation module 122 is further configured to ensure consistent forecasting performance across calm, volatile and crisis market conditions and maintain predictive stability, reliability, compliance with regulatory and risk management requirements.
[0083] In one embodiment of the present invention, the processing unit 108 comprises an explanatory module 124 configured to determine contributing factors influencing the generated multiple volatility prediction forecasts and provide interpretable, auditable and transparent outcomes suitable for analysis.
[0084] In one embodiment of the present invention, the explanatory module 124 is configured to incorporate model explainability techniques including but not limited to shapley additive explanations (SHAP), local interpretable model-agnostic explanations (LIME) and integrated gradients to determine and interpret key factors contributing to volatility predictions including macroeconomic variations, foreign investment activity and market sentiment fluctuations.
[0085] In one embodiment of the present invention, the processing unit 108 comprises an market regime adaptation module 126 configured to detect variations in market trends and adaptively recalibrate model parameters to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions.
[0086] In one embodiment of the present invention, the market regime adaptation module 126 is configured to detect concept drift and structural breaks in market behavior, initiate automated model retraining and recalibration process, dynamically adjust model parameters through online learning mechanisms and support transfer learning across different financial assets and market regimes to maintain forecasting accuracy and robustness.
[0087] In one embodiment of the present invention, the processing unit 108 further comprises a training and testing module 128 configured to split data into training and testing datasets and train the functional modules and predictive algorithms using training dataset.
[0088] The output module 128 is configured to transmit the generated and evaluated multiple volatility prediction forecasts to the user interface 104 of the user device 102.
[0089] In one embodiment of the present invention, the system 100 provides a transition from conventional static and assumption-driven volatility modeling approaches to a dynamic, data-driven and artificial intelligence-based volatility prediction system 100 capable of adapting to changing market conditions.
[0090] FIG. 2 illustrates a method for an intelligent multi-model artificial intelligence-based system 100 for stock market volatility forecasting.
[0091] The method 200 may include the following steps:
[0092] At step 202, market-related data inputs obtained from a plurality of data sources are collected via a user interface 104 integrated into a user device 102.
[0093] At step 204, a link for seamless data transmission is established within the system 100 via a communication network 106.
[0094] At step 206, the market-related data inputs are analyzed to generate stock market volatility forecasts via a processing unit 108.
[0095] At step 208, the market-related data inputs are acquired from the user device 102 via a data acquisition module 112.
[0096] At step 210, preprocessing operations are performed on the acquired data for subsequent analysis via a preprocessing module 114.
[0097] At step 212, volatility-sensitive features are extracted from the preprocessed data via a feature extraction module 116.
[0098] At step 214, enhanced volatility representations are derived by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features via a hybrid volatility configuration module 118.
[0099] At step 216, multiple volatility prediction forecasts are generated over a predefined forecasting horizon based on the enhanced volatility representations via a volatility forecast generation module 120.
[0100] At step 218, the generated multiple volatility prediction forecasts are evaluated based on a plurality of performance metrics via an evaluation module 122.
[0101] In one embodiment of the present invention, contributing factors influencing the generated multiple volatility prediction forecasts are determined and interpretable, auditable and transparent outcomes are provided suitable for analysis via an explanatory module 124.
[0102] In one embodiment of the present invention, variations in market trends are detected and adaptively model parameters are recalibrated to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions via a market regime adaptation module 126.
[0103] In one embodiment of the present invention, the data is splitted into training and testing datasets and the functional modules and predictive algorithms are trained using training dataset via a training and testing module 128.
[0104] At step 220, the generated and evaluated multiple volatility prediction forecasts are transmitted to the user interface 104 of the user device 102 via an output module 130.
[0105] In the best mode of operation, the market-related data inputs are collected from a plurality of data sources via the user interface 104 integrated into the user device 102 and transmitted to the processing unit 108 via the communication network 106. The market-related data inputs are acquired from the user device 102 via the data acquisition module 112 and preprocessing operations are performed on to the acquired data via the preprocessing module 114. Volatility-sensitive features are extracted from the preprocessed data via the feature extraction module 116. The extracted volatility-sensitive features are subsequently provided to the hybrid volatility configuration module 118 to learn baseline volatility behavior, volatility clustering, regime persistence, non-linear patterns, long-term temporal dependencies and dynamic volatility structures present in the market-related data inputs. The multiple volatility outcomes generated by the hybrid volatility configuration module 118 are further processed through the feature fusion layer mechanism to generate multiple volatility prediction forecasts over a predefined forecasting horizon via the volatility forecast generation module 120. The generated multiple volatility prediction forecasts are evaluated using the plurality of performance metrics via the evaluation module 122. The contributing factors influencing the generated multiple volatility prediction forecasts are determined and interpretable, auditable and transparent outcomes suitable for analysis are provided via the explanatory module 124 and variations in market trends are detected and adaptively model parameters are recalibrated to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions via the market regime adaptation module 126. The validated volatility forecasts, risk classification signals and explanatory insights are then transmitted to the user interface 104 of the user device 102 via the output module 130 to support informed decision-making for risk management, portfolio optimization, hedging strategies and regulatory assessment.
[0106] The system 100 offers significant advantages by providing an artificial intelligence-based, data-centric framework for accurate and adaptive stock market volatility forecasting. The system 100 integrates statistical econometric models and advanced artificial intelligence-based learning techniques within a unified and coordinated architecture to effectively analyze complex, non-linear and non-stationary financial market data. By employing hybrid multi-model volatility configuration forecasting the system 100 delivers reliable and consistent volatility prediction forecasts across varying market regimes and time horizons. Further, the system 100 supports continuous performance evaluation, adaptive recalibration and feature fusion-based inference to enhance forecasting robustness and reduce model bias during periods of market instability. The system 100 improves interpretability and transparency of volatility forecasts through explainability mechanisms, thereby facilitating regulatory assessment and informed risk evaluation. Additionally, the scalable, modular and cloud-compatible architecture of the system 100 facilitates deployment across multiple assets, exchanges and institutional environments to support enhanced risk assessment, optimized portfolio allocation, implementation of risk mitigation strategies and data-driven long-term financial planning.
[0107] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0108] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0109] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0110] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0111] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. An intelligent multi-model artificial intelligence-based system (100) for stock market volatility forecasting, the system (100) comprising:
a user interface (104) integrated into a user device (102) and configured to collect market-related data inputs obtained from a plurality of data sources;
a communication network (106) configured to establish a link for seamless data transmission within the system (100);
a processing unit (108) operatively coupled to the user device (102) via the communication network (106) and configured to analyze the market-related data inputs to generate stock market volatility forecasts, wherein the processing unit (108) further comprises:
a data acquisition module (112) configured to acquire the market-related data inputs from the user device (102);
a preprocessing module (114) configured to perform preprocessing operations on the acquired data for subsequent analysis;
a feature extraction module (116) configured to extract volatility-sensitive features from the preprocessed data;
a hybrid volatility configuration module (118) configured to derive enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features;
a volatility forecast generation module (120) configured to generate multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations;
an evaluation module (122) configured to evaluate the generated multiple volatility prediction forecasts based on a plurality of performance metrics; and
an output module (130) configured to transmit the generated and evaluated multiple volatility prediction forecasts to the user interface (104) of the user device (102).
2. The system (100) as claimed in claim 1, wherein the system (100) further comprises a cloud database (110) configured to store the market-related data inputs, trained model parameters and volatility forecasts to enable real-time retrieval, analysis and decision support within the system (100).
3. The system (100) as claimed in claim 1, wherein the hybrid volatility configuration module (118) employs a transformer-based self-attention mechanism to identify and model event-driven and sentiment-sensitive volatility dynamics in real-time.
4. The system (100) as claimed in claim 1, wherein the hybrid volatility configuration module (118) comprises a feature fusion layer mechanism to learn cross-model interactions among price-based features, macroeconomic indicators and sentiment-derived features for improving volatility forecasting accuracy.
5. The system (100) as claimed in claim 1, wherein the processing unit (108) comprises an explanatory module (124) configured to determine contributing factors influencing the generated multiple volatility prediction forecasts and provide interpretable, auditable and transparent outcomes suitable for analysis.
6. The system (100) as claimed in claim 1, wherein the processing unit (108) comprises a market regime adaptation module (126) configured to detect variations in market trends and adaptively recalibrate model parameters to preserve forecasting accuracy and reliability under market shock conditions and market regime transitions.
7. The system (100) as claimed in claim 1, wherein the processing unit (108) further comprises a training and testing module (128) configured to split data into training and testing datasets and train the functional modules and predictive algorithms using training dataset.
8. The system (100) as claimed in claim 1, wherein the plurality of performance metrics include but not limited to root mean square error, mean square error, mean absolute error, coefficient of determination and directional accuracy metrics.
9. The system (100) as claimed in claim 1, wherein the market-related data inputs include but not limited to historical and real-time stock price data, market trading data, macroeconomic data and sentiment data.
10. A method (200) for an intelligent multi-model artificial intelligence-based system (100) for stock market volatility forecasting, the method (200) comprising:
collecting market-related data inputs obtained from a plurality of data sources via a user interface (104) integrated into a user device (102);
establishing a link for seamless data transmission within the system (100) via a communication network (106);
analyzing the market-related data inputs to generate stock market volatility forecasts via a processing unit (108);
acquiring the market-related data inputs from the user device (102) via a data acquisition module (112);
performing preprocessing operations on the acquired data for subsequent analysis via a preprocessing module (114);
extracting volatility-sensitive features from the preprocessed data via a feature extraction module (116);
deriving enhanced volatility representations by employing econometric volatility modeling techniques and at least one artificial intelligence-based temporal learning model on to the extracted volatility-sensitive features via a hybrid volatility configuration module (118);
generating multiple volatility prediction forecasts over a predefined forecasting horizon based on the enhanced volatility representations via a volatility forecast generation module (120);
evaluating the generated multiple volatility prediction forecasts based on a plurality of performance metrics via an evaluation module (122); and
transmitting the generated and evaluated multiple volatility prediction forecasts to the user interface (104) of the user device (102) via an output module (130).