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I Know First Evaluation Report for UK Stocks Package

Stock Forecast Based On a Predictive Algorithm. 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I Know First Evaluation Report for UK Stocks PackageJuly 15, 2019

Executive SummaryIn this forecast evaluation report, we will examine the performance of the forecasts generated by the I Know First AI Algorithm for short term and long term stocks from the London Stock Exchange which is sent to our customers on a daily basis. Our analysis covers the time period from 1 January 2019 to 30 June 2019. We will start with an introduction to our asset picking and benchmarking methods and then apply it to the UK stock universe as covered by the I Know First’s “UK Stocks” package. We will then compare returns based on our algorithm with the benchmark performance over the same period. The following report provides extensive explanation on our methodology and detailed analysis of the performance metrics that we obtained during the evaluation. This report is a new I Know First evaluation series illustrating the ability to provide successful long term and flexible forecasting for UK stocks.

About the I Know First Algorithm

The I custodia iphone 6 piquadro Know First self learning algorithm analyzes, models, and predicts the capital market, including stocks, bonds, currencies, commodities and interest rates markets. The algorithm is based on Artificial Intelligence (AI) and Machine Learning (ML), and incorporates elements of Artificial Neural Networks and Genetic Algorithms.

The system outputs the predicted trend as a number, positive or negative, along with a wave chart that predicts how the waves will overlap the trend. This helps the trader to decide which direction to trade, at what point to enter the trade, and when to exit. Since the model is 100% empirical, the results are based only on factual data, thereby avoiding any biases or emotions that may accompany human derived assumptions. The human factor is only involved in building the mathematical framework and providing the initial set of inputs and outputs to the system. The algorithm produces a forecast with a signal and a predictability indicator. The signal is the number in the middle of the box. The predictability is the number at the bottom of the box. At the top, a specific asset is identified. This format is consistent across all predictions.

Our algorithm provides two independent indicators for each asset signal and predictability.

The signal is the predicted strength and direction of movement of the asset. This is measured frominf to +inf.

The predictability indicates our confidence in that result. It is a Pearson correlation coefficient between past algorithmic performance and actual market movement. This is measured from1 to 1.

You can find a detailed description of our heatmap here.

The Asset Picking Method

The method in this evaluation is as follows:

To fully utilise information provided by custodia per samsung tab 4 10.1 our forecast, we filter out the top X most predictable assets and rank them according to their predictability value. Thereafter, from them, we pick the top Y highest signals and re adjust the rankings accordingly.

By doing so we focus on the most predictable assets on the one hand, while capturing the ones with the highest signal on the other.

For example, a cover custodia iphone 1 top 30 predictability filter with a top 10 signal filter means that on each day we take only the 30 most predictable assets from our asset universe, and then we pick from them the top 10 assets with the highest absolute signals. On the other hand, a top custodia cover samsung note 8 30 predictability filter with a top 30 signal custodia cell samsung s6 filter would imply that we are solely filtering based on predictability, since we are selecting all assets in this particular set which have already been filtered by predictability.

We use absolute signals since these strategies are long and short ones. open long position and, if negative, we open short position on such asset. This is to help us to identify the assets with the maximum magnitude of change, which is indiscriminate as to whether one adopts a short or long position.

The Performance Evaluation Method

We perform evaluations on the individual forecast level. This means that we calculate the return of each forecast we have issued for each horizon in the testing period. We then take the average of those results based on our positions on different assets and forecast horizon.

For example, to evaluate the performance of our 1 month forecasts, we calculate the return of each trade by using this formula:

This simulates a client purchasing the asset on the day we issue our prediction and selling it exactly 1 month in the future from that day.

We iterate this calculation for all trading days in the analyzed period and average the results.

Note that this evaluation does not take a set portfolio and follow it. This is a different evaluation method at the individual forecast level.

The Hit Ratio Calculation

The hit ratio helps us to identify the accuracy of our algorithm’s predictions.

Using our asset filtering method based on predictability and signal, we predict the direction of movement of different assets. Our predictions are then compared against actual movements of these assets within the same time horizon…

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