ENHANCED MACHINE LEARNING ALGORITHM FOR FINANCIAL FORECASTING

Abstract

The present invention relates to an enhanced machine learning algorithm designed for financial forecasting, integrating dynamic feature selection, adaptive learning rates, and ensemble learning techniques to improve the accuracy, efficiency, and scalability of financial predictions. By seamlessly combining both structured and unstructured data sources—such as historical market data, economic indicators, news articles, and social media sentiment—the algorithm adapts in real-time to market conditions, adjusting its learning rate during periods of volatility. The system automatically selects relevant features, reduces overfitting, and delivers robust, real-time financial insights, making it ideal for applications such as stock price prediction, risk assessment, and portfolio optimization.

Specification

Description:In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.

Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The present invention pertains to an advanced machine learning algorithm for financial forecasting, which integrates several key features aimed at improving the accuracy, adaptability, and scalability of financial prediction models. The algorithm is designed to work with diverse types of data, including both structured data (such as historical stock prices, trading volumes, and economic indicators) and unstructured data (such as sentiment analysis from news articles and social media). The goal of this invention is to provide a robust solution that can predict financial outcomes in real-time, adapt to market conditions, and improve over time with minimal human intervention.

One key aspect of the invention is dynamic feature selection. Traditional machine learning models often require manual selection of features before training, which can be time-consuming and prone to errors. This invention automates the feature selection process by dynamically choosing the most relevant features based on the input data. By doing so, it eliminates the need for human intervention and ensures that the model is continuously adapting to the most significant factors affecting financial outcomes. The feature selection algorithm can leverage statistical techniques, such as correlation analysis or information gain, to identify and prioritize the features that have the highest predictive value.

Another major innovation is the incorporation of adaptive learning rates. Financial markets are inherently volatile, with rapid fluctuations in stock prices and economic indicators. A model that does not adjust its learning rate during periods of high volatility may struggle to learn new patterns and adapt to shifting market conditions. The algorithm of this invention adjusts its learning rate in real-time, depending on the level of market volatility. When the market is stable, the learning rate is set to a lower value, focusing on long-term trends. Conversely, during periods of high volatility, the learning rate is increased to enable faster adaptation to the new, rapidly changing market data.

The invention also incorporates ensemble learning techniques to improve predictive accuracy. Instead of relying on a single model to make predictions, the algorithm combines the outputs of multiple models, such as decision trees, neural networks, and support vector machines. The results of each model are weighted and aggregated, with weights adjusted dynamically based on each model's past performance. This ensemble approach helps to reduce the risk of overfitting and ensures that the final prediction is more robust and reliable.

To ensure that the model can handle large datasets efficiently, the algorithm employs data fusion techniques, allowing it to integrate and process both structured and unstructured data seamlessly. Structured data sources, such as historical price data, are combined with unstructured data, such as sentiment analysis from news articles, social media, and market reports, to capture a broader range of influencing factors. This fusion of diverse data sources helps the model provide more accurate predictions by capturing the underlying sentiment and emerging trends that could impact financial outcomes.

The invention also features an automated data preprocessing pipeline that handles common challenges in financial data processing, such as data normalization, missing values, and outlier detection. By automating these processes, the invention reduces the chances of errors and ensures that the data is clean and consistent before being input into the model for training.

Finally, the invention provides a real-time prediction engine that can deliver forecasting insights in a timely manner. This capability is crucial for applications like high-frequency trading, risk assessment, and portfolio optimization, where decisions must be made rapidly based on up-to-date information.

In the first embodiment of the invention, the system operates with a dataset consisting of both historical market data (such as stock prices, trading volumes, and economic indicators) and unstructured data derived from financial news articles and social media sentiment. The preprocessing module automatically cleanses the structured data, performs normalization, and handles missing values. Simultaneously, the unstructured data is processed through a natural language processing (NLP) module that extracts sentiment and key information that may influence financial markets. The system then applies the dynamic feature selection algorithm to identify the most relevant features for predicting stock prices in a specific market segment, such as technology or healthcare stocks.

The ensemble learning module uses multiple machine learning models, such as random forests, neural networks, and support vector machines, to generate predictions. Each model is evaluated based on its past performance in similar market conditions, and the weights assigned to the models are adjusted accordingly. When the market experiences high volatility, the system increases the learning rate and focuses on short-term patterns. Conversely, during stable market conditions, the system uses a lower learning rate to detect long-term trends. This embodiment of the invention is capable of predicting stock price movements with a high degree of accuracy and providing real-time forecasts for investors.

In the second embodiment, the invention is applied to a financial risk assessment tool used by banks and investment firms to evaluate the risk of investment portfolios. The system integrates both structured data (e.g., asset performance, interest rates, and bond yields) and unstructured data (e.g., market reports, news articles, and political events). The data preprocessing module cleans the structured data and integrates the unstructured data into a unified dataset. The dynamic feature selection process identifies key risk factors that have the greatest impact on portfolio performance, such as changes in interest rates or political instability.

The system's adaptive learning rates allow it to adjust the speed at which it learns based on changing market conditions. During periods of high market uncertainty, the system increases the learning rate to respond quickly to sudden market shifts, while during stable periods, it learns at a slower pace to capture long-term trends. The ensemble learning approach combines predictions from multiple models to provide a more accurate risk assessment. The final output is a risk score for each portfolio, with recommendations for rebalancing to mitigate potential losses. This embodiment provides investment firms with a powerful tool to assess and manage financial risks in real time.
While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1.A method for enhancing machine learning-based financial forecasting, comprising the steps of:
Collecting historical financial data, economic indicators, and unstructured data sources;
Preprocessing the collected data to identify relevant features;
Dynamically selecting features based on their relevance to current market conditions;
Training a machine learning model using a hybrid framework that combines supervised and unsupervised learning techniques;
Adjusting the learning rate of the model based on market volatility;
Providing financial predictions based on the trained model.

2.The method of claim 1, wherein the model further comprises an ensemble learning mechanism that combines predictions from multiple models, with weights adjusted based on real-time performance.

3.The method of claim 1, wherein the feature selection process utilizes a genetic algorithm or other optimization techniques to identify the most relevant features.

4.The method of claim 1, wherein the algorithm integrates unstructured data such as sentiment analysis from social media and news feeds to improve forecasting accuracy.

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