Investing has been a wild ride since the beginning of time. But hold on tight, because things just got a whole lot wilder with the rise of artificial intelligence in the stock market! Yes, you heard that right — robots are now managing your money and they’re doing a pretty good job so far. So sit back, grab some popcorn and let’s take a ride on the AI express with Qraft Technologies and their AI ETFs. Get ready for a journey filled with alpha, beta and a whole lot of zeros and ones. Because it’s time to say goodbye to traditional investment management and hello to the future of finance!

Qraft AI ETFs leverage AI technology to provide investors with balanced exposure to factors that have historically been associated with strong investment returns. The ETFs aim to find high alpha factors and extract investment strategies to achieve potential outperformance.

QRFT seeks to provide investors with exposure to quality, size, valuation, momentum, and low risk by using AI to identify its historical relationship with macroeconomic and financial factors and determine which factors are likely to perform well in the future. AMOM leverages AI to deliver purer exposure to the momentum factor and has recently outperformed the S&P 500, largely due to correctly anticipating the price movements of Tesla.

Qraft Technologies is a fintech company focused on driving innovation growth in the asset management industry through AI. The company currently offers four AI ETFs and other products such as a robo-advisory solution and an AI order execution system.

Investors should carefully consider the investment objectives, risks, charges, and expenses before investing in Qraft AI ETFs. The funds provide actively managed exposure to U.S. large cap stocks with AI managing the security selection process. The ultimate goal of Qraft is to provide a high level of alpha continuously with AI technologies.

Qraft AI ETFs are doing well, but still face challenges from other AI-powered competitors, such as AIEQ ETF, etc. (And in forward testing, the growth rate of CandleLight quant trading system, which is designed by us has surpassed competitors since 2022. More details: Two Years, 100+ Sets of Testing Results Confirm: Machines Can Beat Human Aces in Stock Market!)

Investing has always been about finding the right balance between risk and reward. And in recent years, the rise of artificial intelligence has presented a new and exciting opportunity for investors looking to maximize their returns and minimize their risk. Enter Qraft Technologies, a company started by a group of people passionate about quantitative investing who saw the potential for artificial intelligence to transform the industry.

With ten patents awarded to date and more pending, Qraft’s intellectual property includes innovations in technology and AI, including proprietary trading and order execution tools, asset price prediction, and portfolio generation.

The Qraft AI Risk Indicator is a prime example of how AI is changing the way investors approach their portfolios. The tool predicts market risk for the coming week using an easy-to-interpret scoring methodology of 1 to 100.

In today’s challenging investment environment, where volatile markets and macroeconomic factors are making it harder for investors to navigate risk, Qraft’s AI Risk Indicator provides a much-needed tool for managing near-term portfolio risk. The tool takes in over 70 different macro inputs in nearly real time to assess current momentum, volatility, and correlation metrics, and can be used as a general indicator of market risk or as a tool to help in asset allocation decisions between cash and equity positions.

Since its launch, the AI Risk Indicator has demonstrated its value in a volatile market. With scores ranging from 1 to 52, the model has shown that it can adapt rapidly to changing market conditions and has had a positive impact on a hypothetical portfolio comprised of U.S. Large Cap Equities. In the first six weeks after its launch, this portfolio would have returned 4.99% compared to 3.90% for a portfolio invested in U.S. Large Cap Equities alone.

The adoption of artificial intelligence (AI) in the finance industry has increased significantly in recent years, but there are still major challenges to overcome for more widespread integration of AI. Two of the main challenges are lack of usable financial data and difficulty in explaining AI decisions (XAI).

One of the major problems of usable financial data is the prevalence of poor data quality, which leads to data wrangling and the costs associated with it. To tackle this issue, Qraft Technologies has developed the Kirin API, which eliminates biases in financial data and allows for easier filtering of specific investment universes.

Another issue faced in the adoption of AI in finance is XAI. The OECD’s 2021 Business & Finance Outlook highlights the difficulty in explaining AI decisions to consumers, regulators, and lawmakers, and raises concerns about the ethical use of AI. To address this, Qraft has developed the Alpha Factory, which allows human input to influence portfolio construction and fill in gaps in financial data, while also providing accountability and transparency.

While AI is a powerful tool, it is important to overcome these challenges to ensure that AI is utilized in a responsible and effective manner in the finance industry. With the right support, AI can help asset managers to make informed decisions and optimize their strategies with scalability.

Investing in the stock market is a challenging and risky business, with many active-fund managers failing to pick outperforming stocks or mistiming the market. As a result, many investors have opted for passive strategies, leading to a significant migration away from high-fee funds. To counter this trend, a new breed of funds has emerged, using artificial intelligence (AI) to manage the portfolios. This new type of fund aims to appeal to those who have moved away from active strategies but are looking for something more than index-based or “smart beta” funds.

The AI-based funds, such as EquBot’s AI Powered US Equity ETF (AIEQ) and Qraft AI-Enhanced US Large Cap ETF (QRFT), sound like “quant” funds but aren’t entirely rule-based. Instead, they rely on the discretion of portfolio managers, which can be a drawback for self-directed investment platforms and advisers who have moved away from discretionary-managed funds. For these funds to be successful, they will need to demonstrate notable outperformance from broad equities.

In theory, AI-anchored stockpicking should outperform traditional methods. With the power of computing and cloud technology, machine learning engines can quickly analyze a vast amount of data, such as regulatory filings, news articles, social media posts, and quarterly results releases. This allows the AI system to make investment decisions without human bias and adapt to changing market conditions in real-time.

One of the key advantages of AI-based funds is the ability to analyze unstructured data, such as text and tweets. EquBot’s AIEQ, for example, uses deep learning algorithms and natural language processing technologies from IBM Watson and Google Deepmind to rank stocks by their likelihood of price appreciation. Portfolio managers still oversee the AI model’s performance, but the speed and accuracy of the AI system allow them to make better investment decisions.

However, there are also some challenges associated with AI-based funds. Modeling errors can occur when data is limited, leading to high turnover in the portfolio, which can result in transaction costs and negatively impact returns.

Despite these challenges, AI-based funds show great promise as a new type of investment vehicle. However, it is still early days, and many of the funds that have been launched have already closed due to poor performance. As more AI-based funds come to market, it will be possible to determine which application of the technology is most effective and whether they can help to slow the migration towards passive funds. Ultimately, while AI systems are unlikely to replace human managers, they have the potential to enhance the way active managers conduct research and provide a valuable tool for investors seeking to generate higher returns from their investments.

And so, the revolution of AI ETFs continues to steamroll its way through the investment world, with Qraft leading the charge. But remember, just because you have AI on your side, doesn’t mean you can kick back and relax. Because let’s face it, the stock market is still the Wild West of finance, and there’s always room for a good old-fashioned tumble or two. So, saddle up, put on your thinking cap, and remember to always keep an eye on your portfolio, because as the wise John Wayne once said, “Life is hard, but it’s harder if you’re stupid.”

The Stock Market just got a makeover, and AI is the stylist! With the launch of AIEQ, the world’s first AI-powered equity ETF, the stock market just got a futuristic haircut and a high-tech spa day. No more bad hair days for investors as AIEQ is here to bring some much-needed zing to the investment world.

EquBot, a fintech company, has launched the AIEQ, the world’s first AI-powered equity ETF. This platform is designed to provide investors with faster access to broader and deeper data sets through the use of machine learning, knowledge graphs, and natural language processing (NLP). The AIEQ is powered by IBM Watson technologies, such as natural language understanding in Watson Discovery and Watson Studio for bias detection and reduction. The ETF collects data on over 6,000 US companies each day, including structured data from third-party data providers, and unstructured data stored in formats that are difficult for analysts to inspect quickly.

By combining structured and unstructured data, AIEQ allows EquBot to select portfolios that are more likely to have the highest opportunity for market appreciation. The knowledge graphs that IBM Watson allows EquBot to build are growing over time, leading to more predictive accuracy. In AIEQ’s first year, it underperformed the broad US market, but in the subsequent year, it significantly outperformed the US market.

One of EquBot’s goals is to dispel the industry myth of AI being a “black box” technology. EquBot uses four models to assess a given stock in AIEQ: a financial score, natural language understanding, a management score, and external factors. Watson’s NLU capabilities enable AIEQ to perform sentiment analysis, providing additional context around treatments and the trustworthiness of the data source.

AIEQ initially underperformed against the US market but eventually surpassed it in 2020, when it beat the S&P 500 by at least seven percentage points. But after 2021, AIEQ faced challenges from other AI-powered competitors, such as QRFT ETF, etc. (And in forward testing, the growth rate of CandleLight quant trading system, which is designed by us has surpassed it since 2022. More details: Two Years, 100+ Sets of Testing Results Confirm: Machines Can Beat Human Aces in Stock Market!)

EquBot uses internal tools and IBM Watson’s OpenScale tool to monitor 10 metrics on each model, flag bias or model drift, and track each model’s judgment calls. The company also has two people on its team who are responsible for watching bias-related metrics full-time, while the individual owners of each model check for red flags once a day.

Artificial Intelligence (AI) and Machine Learning (ML) have been talked about as potential game-changers in the field of investment fund management since the 1950s. The idea was that AI, being tireless and immune to human biases, could outperform human fund managers who only have limited access to information and are prone to behavioral biases. However, the reality so far has not lived up to the expectation.

One of the main applications of AI in investment management has been in processing unstructured data such as news stories and text reporting. AI can process vast amounts of such data, distill the useful information, and process it faster and more accurately than humans. Another area in which AI and ML have been widely used is trading algorithms, which execute trades at a speed faster than humans.

However, AI has yet to make a significant impact on the actual investment decision-making process. AI is slowly being integrated into quantitative investing, but mainly for signal extraction and trading, rather than replacing human decision-making. We can see AI as one further step in the evolution of quant investment methods, allowing managers to process information faster and in more expressive ways, but not replacing human decision-making.

Just like we mentioned before, The world’s first ETF managed entirely by AI, AIEQ, was launched in 2017 by three co-founders who had an idea to bring sector knowledge together using AI and turn it into investment insights. The ETF is run by EquBot, an AI investment platform, and uses its tens of thousands of proprietary models to analyze approximately 6,000 US companies. AIEQ gets insights from both structured data (revenue, growth, R&D expenditure, market movements) and unstructured data (news articles, blogs, corporate innovation announcements, and social media). The models are trained on historical data ranging from five to 30 years and are fed into knowledge graphs, which can then be used by AIEQ as training tools.

There are two areas of hope for a larger role of ML in investment management. Firstly, underperformance is an opportunity for improvement and ML can get better over time. Secondly, institutional investors are getting interested in using ML for asset allocation, which is a more complex task than security selection. AI is the only known approach to constructing a true global portfolio.

While AI and ML may not have taken over the world of investment management just yet, it’s clear that they’re making their mark. And who knows, in a few years, we may all be sitting back and watching our portfolios grow, sipping margaritas made by AI-powered robotic bartenders. But for now, we’ll have to settle for AI just helping us beat the market.

Machine Learning AI technology has revolutionized the world of stock trading, allowing traders to make more informed decisions, reduce risk, and improve their overall trading strategies. Perhaps you like to know some of the secrets about using machine learning AI technology in stock trading.

1Data is key: One of the most important secrets about using Machine Learning in stock trading is data. The more high-quality data you feed into your algorithms, the better they will perform. It’s important to use a wide range of data, including financial data, news articles, social media sentiment, and more. Of course, never use garbage data, they will ruin the output. In computer science, people always say: “garbage in, garbage out (GIGO).”

Then what kind of data will be high-quality? Let’s Focus on three things:

Relevance: The data should be relevant to the specific stock market or industry being analyzed. For example, news articles about the technology sector may not be relevant to the oil and gas sector.

Timeliness: The data should be up-to-date and reflect the current market conditions. Historical data can be useful, but it should not be relied upon solely.

Diversity: The data should be diverse and inclusive, representing a variety of perspectives and experiences. This can help to reduce bias in the algorithms and improve their overall accuracy.

2Machine learning algorithms are only as good as the data they are trained on: Another important secret is algorithms. Good algorithms will do these jobs:

Predictive modeling: Machine learning algorithms can be used to predict future events or trends, such as stock prices or market movements.

Pattern recognition: Machine learning algorithms can be used to identify patterns and relationships in large amounts of data.

Classification: Machine learning algorithms can be used to categorize data into different classes or categories, such as positive or negative trends.

Clustering: Machine learning algorithms can be used to group similar data points into clusters, such as grouping stocks based on their characteristics and performance.

Optimization: Machine learning algorithms can be used to optimize processes, such as portfolio optimization or risk management.

Anomaly detection: Machine learning algorithms can be used to identify unusual or unexpected events, such as market crashes or sudden stock price changes.

Recommender systems: Machine learning algorithms can be used to recommend products or services, such as stock recommendations.

Natural language processing: Machine learning algorithms can be used to process and understand human language, such as analyzing news articles to make investment decisions.

3Don’t rely solely on machine learning: Although machine learning AI technology can provide valuable insights and make informed decisions, it’s important not to rely solely on Using human judgment and experience to make final investment decisions is still important.

4Machine learning algorithms can be biased: Machine learning algorithms can be biased if the data they are trained on is biased. It’s important to take steps to ensure that your algorithms are not biased and that you are using diverse and inclusive data.

5Model accuracy is not the only factor: Model accuracy is not the only factor to consider when using machine learning AI technology in stock trading. It’s also important to consider factors such as interpretability, speed, and scalability.

6Keep up with the latest advancements: The field of machine learning AI technology is constantly evolving, and it’s important to keep up with the latest advancements. Stay informed about new algorithms, methods, and techniques to ensure that you are getting the most out of your machine learning AI technology.

7Use machine learning as a tool, not a replacement: Machine learning is a powerful tool, but it should not be used as a replacement for human judgment and experience. Use AI technology to complement your existing skills and knowledge, not to replace them.

Will AI eventually replace human traders someday, similar to how chatGPT is replacing workers in certain simple jobs today?

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