Legal

Legal Disclaimer

Terms of Use

Usage Requirements
Content/Limitations

Stock Coverage Policy

Research Process
Over the Counter Market

Research Policy

Copyright Permission Policy

MIT/FPA License/Rev/Additional Terms

General Terms

30-Day Money Back Guarantee

Legal Disclaimer

KappaSignal 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.

Artificial intelligence and machine learning are rapidly evolving fields of study. We are constantly working to improve our Services to make them more accurate, reliable, safe, and beneficial. However, due to the probabilistic nature of machine learning, there is always the possibility that our Services may produce incorrect output. As such, it is important to evaluate the accuracy of any output from our Services as appropriate for your use case, including by using human review. Information on this website has been obtained 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, KappaSignal 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 KappaSignal 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. KappaSignal’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 kappasignal.com website, mentioning KappaSignal 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.

Terms of use

KappaSignal Terms of Use govern the use of our services, which include our API, software, tools, developer services, data, documentation, and websites. The Terms also include our, Usage Policies, and other documentation, guidelines, or policies we may provide in writing. By using our services, you agree to these Terms. Our Privacy Policy explains how we collect and use personal information.

We have created these Terms to ensure that everyone who uses our services understands their rights and responsibilities. We believe that these Terms are fair and reasonable, and we hope that you will find them easy to understand.

A.Usage Requirements

A1: Use of Services

You are granted a non-exclusive right to use the Services in accordance with these Terms. You agree to comply with these Terms and all applicable laws when using the Services. KappaSignal and its affiliates own all rights, title, and interest in and to the Services.

A2: Feedback

Appreciates feedback, comments, ideas, proposals, and suggestions for improvements. If you provide any of these things, KappaSignal may use them without restriction or compensation to you.

A3: Restrictions

You may not:

Use the Services in a way that infringes, misappropriates, or violates any person's rights.

Reverse assemble, reverse compile, decompile, translate, or otherwise attempt to discover the source code or underlying components of models, algorithms, and systems of the Services.

Use output from the Services to develop models that compete with KappaSignal.

Use any automated or programmatic method to extract data or output from the Services, except as permitted through the API.

Represent that output from the Services was human-generated when it is not.

Buy, sell, or transfer API keys without KappaSignal prior consent.

Send KappaSignal any personal information of children under 13 or the applicable age of digital consent.

Use the Services in any geography that is not currently supported by KappaSignal.

A4: Third Party Services

Any third party software, services, or other products you use in connection with the Services are subject to their own terms, and KappaSignal is not responsible for third party products.

B.Content

B1.Limitations

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.

Stock Coverage Policy

The purpose of this policy is to establish guidelines for the coverage of stocks on the NASDAQ, NYSE, LSE, ASX, and TSX. This policy is intended to ensure that all stocks covered by the firm are adequately researched and that investment ratings are made in a responsible and informed manner.

KappaSignal will consider a stock for coverage if it meets the following criteria:

The stock is listed on one of the five exchanges listed above.
The stock has a sufficient trading volume to make it liquid.
The stock is of interest to the firm's clients.

A.Research Process

KappaSignal will conduct a thorough research of all stocks before making a decision to cover them. The research process will include the following steps:

Gathering financial information about the company, such as its financial statements, management team, and industry.
Analyzing the company's competitive landscape.
Evaluating the company's future prospects.

B.Over The Counter Markets

In addition to the criteria listed above, the firm will consider a stock for coverage on the OTC* markets if it meets the following criteria:

The stock has a sufficient trading volume to make it liquid.
The stock is of interest to the firm's clients.
The firm has the resources to research and track the stock.

The firm's research process for OTC markets stocks will be similar to the process for stocks listed on the major exchanges. However, the firm will take into account the following factors when researching OTC markets stocks:

The level of transparency and disclosure by the company.
The liquidity of the stock.
The risk of fraud or manipulation.

*OTC markets, also known as over-the-counter markets, are decentralized markets where securities are traded directly between two parties without the use of a central exchange. OTC markets are often used to trade securities that are not listed on a major exchange, such as penny stocks or stocks of small companies.

Research Policy

We recognize the importance of allowing the wider community to assess our research and products, particularly in identifying and addressing potential weaknesses, safety concerns, and biases in our models. Therefore, we encourage the publication of research papers related to the KappaSignal

If you have inquiries regarding research publications based on Algorithm or if you wish to inform us in advance about a publication (though this is not mandatory), please contact us at pr@kappasignal.com

Copyright Permission Policy-MIT License

KappaSignal is committed to advancing global knowledge and understanding through research, teaching, and scholarship. We simplify copyright permission to make it easier for people to use our research, and we encourage you to do so.

Permission is not required if your use is fair use under US copyright law.

Fair use exempts certain uses, including teaching, scholarship, research, from requiring copyright permission. Fair use is a flexible standard, which means it can adapt to new situations, but also that there are no black and white rules.

Fair use applies to this content, but you must still cite the original source of the content.

MIT/FPA License/Rev/Additional Terms

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Permission is hereby granted, free of charge, to any scholar research obtaining a copy of this algorithm and associated documentation files (the "Algorithm"), to deal in the Algorithm without limitation in the rights to use, copy, modify, merge, publish, of the Algorithm in an , scholarship or research context, subject to the following conditions:

-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Algorithm.

THE ALGORITHM IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE ALGORITHM OR THE USE OR OTHER DEALINGS IN THE ALGORITHM.

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General Terms

Relationship of the Parties: These Terms do not create a partnership, joint venture or agency relationship between you and KappaSignal or any of KappaSignal’s affiliates. KappaSignal and you are independent contractors and neither party will have the power to bind the other or to incur obligations on the other’s behalf without the other party’s prior written consent.

Use of Brands: You may not use KappaSignal’s or any of its affiliates’ names, logos, or trademarks, without our prior written consent.

Modifications: We may amend these Terms from time to time by posting a revised version on the website. All changes will be effective immediately. Your continued use of the Services after any change means you agree to such change.

Equitable Remedies: You acknowledge that if you violate or breach these Terms, it may cause irreparable harm to KappaSignal and its affiliates, and KappaSignal shall have the right to seek injunctive relief against you in addition to any other legal remedies.

Entire Agreement. These Terms and any policies incorporated in these Terms contain the entire agreement between you and KappaSignal regarding the use of the Services and, other than any Service specific terms of use or any applicable enterprise agreements, supersedes any prior or contemporaneous agreements, communications, or understandings between you and KappaSignal on that subject.

Jurisdiction, Venue and Choice of Law: These Terms will be governed by the laws of the Jersey, excluding Bailiwicks of Jersey’s conflicts of law rules or principles.

30-Day Money Back Guarantee

We offer a satisfaction guarantee on all new subscriptions. If you are not satisfied with our service within the first 30 days of your initial subscription purchase, you may be eligible for a full refund.

*This guarantee applies only to the initial purchase of a new subscription.

*Renewals or upgrades to existing subscriptions are not eligible.

*To qualify for a refund, you must request it within 30 days of your initial subscription purchase date.

To request a refund, please contact our customer support team by contact us.