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AC Investment Research currently does not act as an equities executing broker, credit rating agency or route orders containing equities securities. In our Machine Learning experiment*, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making.The rating information provided is for informational, non-commercial purposes only, does not constitute investment advice and is subject to conditions available in our Legal Disclaimer. Usage as a credit rating or as a benchmark is not permitted.

Information on this website has been obtained by AC Investment Research from the machine learning*, neural network*, support vector machines* and other sources believed to be accurate and reliable. However, because of the possibility of human, machine learning or mechanical error as well as other factors, AC Investment Research makes no representation or warranty, express or implied, as to accuracy, results, adequacy, timeliness, completeness or merchantability, or fitness for any particular purpose, with respect to any such information, and is not responsible for any errors or omissions, or for results obtained from the use of such information. Under no circumstances will AC Investment Research be liable for any special, indirect, incidental or consequential damages of any kind caused by the use of any such information, including but not limited to, lost opportunity or lost money, whether in contract, tort, strict liability or otherwise, and whether such damages are foreseeable or unforeseeable. AC Invest’s machine learning* based forecast, ratings and credit assessments are statements of opinion, and not statements of fact as to forecast, credit risk decisions or recommendations regarding decisions to purchase, sell or hold any securities such as individual bonds or commercial paper. The forecast, ratings and credit assessments may be changed, suspended or withdrawn as a result of changes in or unavailability of information as well as other factors. 

Usage as a credit rating, investment advice or as a benchmark is not permitted. Unless otherwise explicitly agreed in writing, usage for products and services, index creation, derivative work, portfolio or fund management, or any other usage are not permitted. By way of exception, usage is permitted only to the rated company, limited to a single reference of its own information in annual reporting and website, mentioning AC Investment Research as a source.

*In our experiment, we focus on an approach known as Decision making using game theory. We apply principles from game theory to model the relationships between rating actions, news, market signals and decision making. 

*Neural networks are made up of collections of information-processing units that work as a team, passing information between them similar to the way neurons do inside the brain. Together, these networks are able to take on greater challenges with more complexity and detail than traditional programming can handle.AI design teams can assign each piece of a network to recognizing one of many characteristics. The sections of the network then work as one to build an understanding of the relationships and correlations between those elements — working out how they typically fit together and influence each other. 

*In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.The Support Vector Machine (SVM) algorithm is a popular machine learning tool that offers solutions for both classification and regression problems.


There are several limitations of using machine learning for stock prediction, including:

Limited Data: The quality and quantity of data available for training a machine learning model can be limited, especially for smaller companies. This can result in less accurate predictions.

Non-stationarity: Financial markets are dynamic and constantly changing, which means that historical trends may not necessarily repeat themselves in the future. Therefore, the predictive power of historical data may be limited.

Complexity of Financial Markets: The financial markets are complex, with a variety of factors that can influence stock prices. Machine learning models may not be able to capture all of these factors and their interactions accurately.

Overfitting: Machine learning models may sometimes overfit the data, which means that they perform well on the training data but fail to generalize to new data. This can result in inaccurate predictions.

Black Swans: Unpredictable events like natural disasters, pandemics, and political events can have a significant impact on stock prices. Machine learning models may not be able to account for such unpredictable events, leading to inaccurate predictions.

Data Preprocessing: Data preprocessing plays a crucial role in machine learning models, and it can be challenging to preprocess financial data, which is often noisy and non-linear.

Overall, while machine learning can be a useful tool for predicting stock prices, it is important to be aware of its limitations and use it in conjunction with other methods and expert analysis.