Our Investment signals with a 3-month time horizon forecast the ability of a stock or other instrument or asset class to deliver returns over a 3-month period. They are suited to both Short and Longer-term traders and Investors, to see the current and bigger evolving picture in the markets over time.

We provide real-time signals on all securities via our proprietary model. Each signal is assigned a score on 10, and the signal scores are further supported by various metrics as below:

Fundamental = Score out of 10 for the intrinsic value of the asset using various economic, accounting, operational and market data sources.

Technical = Score out of 10 for the predicted future price action based on historical price data, volume and expected statistical trends.

Premium Trading Forums Sentiment = Score out of 10 for the recommendations and optimism in the particular asset or security as surveyed with a select list of exclusive stock market trading forums, private discord servers, and sophisticated trader networks.

Quant Trading Signals Index = Score out of 10 for the optimism and bullish sentiment in the particular asset as gauged from the aggregate ratings from major quantitative machine-learning based trading signal providers.

Rumour Pulse Index = Score out of 10 for the bullish sentiment in a particular asset based on NLP/ML algorithms to provide insights into positive or negative rumours spreading on social platforms, digital media, and other publications.

Top-Rated Analyst Consensus = A consensus rating of some of the top analysts, investors and experts from their holdings and recommendations.

Insider Trades = The level of bullish activity in the asset determined from analysis of holding changes of corporate insiders.

Politician Trades = The level of bullish activity in the asset determined from analysis of holding changes of government servants and political figures.

Hedge Fund Activity = The level of bullish activity in the asset determined from analysis of holdings of top hedge funds and quantitative investment funds.

Dark Pool Activity = The level of bullish activity in the asset determined from analysis of the dark pool market for the stock or stocks related to the asset. Dark pools are private asset exchanges designed to provide anonymity for trading large blocks of securities away from the public eye and give an indication of Institutional sentiment.

Overall AI = AI generated and proprietary formulated score out of 10 for investment rating of the asset considering all data and analysis.

Risk = Level of Risk in the asset compared to the general market.

The Methodology

Stage 6: Real-Time Signal Generation

We take it a step further...

The final step in the signal generation is where we run the signal generated from the main model above through our proprietary model overlay modules called the:

PowerPlay AccuBoost Matrix©

This module adjusts the model outputs for any pattern-learning biases in the data and to calibrate for cross-sectional sensitivity in global markets.

This is a crucial final step in the process that is estimated to increase the accuracy of our model by a further 5-10% and is what helps us compete with some of the most sophisticated forecasting models in existence.

Again, in keeping with our uncompromising approach to extracting maximum value from our forecasts, we add-on another proprietary model overlay module called the:

PowerBull Return Accelerator©

This module enables the prediction of market movements with short-term forecasts using quantitative strategies and technical indicators. It ensures confidence in results and allows for consistent returns independent of market momentum. It accelerates returns in a portfolio with supplementary returns from exploiting short-to-medium term pricing inefficiencies by improving market timing.

Stages 1 - 5: How Signal AI score is calculated

Data collection: Our model analyses thousands of features of each security per day, based on more than 500 technical, 100 fundamental, 100 sentiment and other risk indicators. It also considers alternative data sources, such as hedge fund insider transactions, market volatility, news sentiment, economic indicators, etc.

Prompts, Training & Validation: We choose appropriate machine learning algorithms such as regression, decision trees, support vector machines, or neural networks. Our model is trained on historical data, optimizing for certain parameters which deliver higher performance. It is then validated using out-of-sample data to assess efficacy and fine-tuned based on back testing results. Our exclusive algorithmic script employs intuitive reasoning and mathematical equations to forecast lucrative trading opportunities, alerting you precisely when to execute buy or sell actions. Using Predictive AI, we initially forecast the asset prices a few days ahead of time using the above indicators. We then forecast the predicted values of the indicators themselves using a simulated probabilistic model to account for the elements of uncertainty in the markets. The output forecasted indicators are then fed back into the model as inputs and are used to reverse engineer the computation of the current price to reconcile the current and future model price predictions using Recurrent reinforcement learning.

Back testing: Our model is continuously back tested with historical data. During this process, the model's predictions and decisions are compared against actual outcomes, allowing for the assessment of its accuracy and reliability. The recurrent nature of the model enables it to capture temporal dependencies and patterns in sequential data, making it particularly suitable for tasks involving time-series information.

Deep Learning: We repeat the same process with deep learning algorithms in an iterative manner till there is a significant degree of 'model fit' or internal synchronization between the current and predicted values of the asset prices and their underlying indicators. The higher the degree of model fit, the higher the confidence of the target price being achieved. This is reflected in a higher Overall AI score. The signal strength also indicates a probability of positive returns so investors can make a risk-informed decision.

Stage 7: Expert Analysis

The generated signals are reviewed on a continuous time scale by our team of various investment and financial analysts comprising our Investment Committee. They are reviewed using Explainable AI from the same source model to understand the logic behind the signals. Using our domain knowledge, this is then used to sense-check and fine tune model prompts and outputs going forward to maintain our edge on the markets and continue to achieve a higher than usual predictive success rate.

Stage 8: Risk management & Performance Optimisation

There is a Risk Indicator for every signal which could be Very High to Very Low, depending on the underlying asset and the predictive power of the model. Therefore, this impacts the Overall AI score.

We believe in minimizing risks by relying on our relatively high degree of predictability over a large sample size of trades to make sustainable profits.

Finally, we provide model probabilities of the expected performance of our trading signals for every signal so investors and traders can make risk-informed decisions.

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