Stock Forecast: Daily Stock Selection Based On a Self-Learning Algorithm (Press Release 9.13)

Press Release

FinTech company “I Know First” releases eight month forecast based off of new short-term trading strategies, providing investors with large premiums over the ‘market’.


I Know First

Press Release-Tel Aviv, Israel, September 13th, 2016– FinTech company “I Know First” developed a short-term algorithm to predict financial markets with up to 49.10% return in the last eight months.

I Know First, Ltd. is a financial technology company that provides daily investment forecasts based on an advanced, adaptable, self-learning algorithm. The company’s algorithm predicts over 3,000 securities (and growing) with capabilities to discover patterns in large sets of historical stock market data.

Throughout the past week, I Know First Research published an eight month return forecast, using the new short term trading strategies, for the algorithm. The time period for this press release is from January 7th, 2016 – August 31st, 2016. Additionally, the results of these strategies are shown below and involve a weighted average daily rebalancing the Top 20 ranked stocks by the algorithm. This allows the investors to greatly outperform well-known indices, such as SP500, and a more passive investment strategy. The short term trading, over the course of eight months, had yielded a superb alpha as high as 40.02% above the S&P500 Index.

Daily Trading Model: Stock Forecast

The short term signals of I Know First’s proprietary algorithm can be successfully utilized besides their application for better timing of the mid and long term investments. Variety of rules based on those can be developed for trades execution and rebalancing on a daily basis. A high predictability level and signal strength are key factors for the most intuitive approach of selecting the highest ranked stocks. However, there can be several ways of integrating a trend or mean reversion logic into the selection process to account for trader’s approach and/or market conditions. Below the back test results of five strategies in this context are given for the S&P 500 stocks universe since the begin of 2016. At most 20 highest ranked stocks per day (if available) are traded in each case, the equity lines represent the value of corresponding equally weighted and daily rebalanced baskets of stocks, setup to outperform the broader universe. For three of them, additionally to the predictability and signal level, the price and signal dynamics are taken into account for the respective selection processes. No technical analysis elements (indicators, oscillators) are part of the analysis below.

The signals are generated daily before the market opens and subsequently used to rank the stocks. For simplification purposes the simulation uses close-to-close price changes only and hence no limit orders or stop losses are considered for further performance enhancement. Both long and short positions can be taken, no leverage is applied.

The overall return in the period January 7th 2016 – August 31st 2016 ranges between 22.9% and 49.1% while the S&P 500 increased by 9.1%.

Press Release

The following table summarizes the overall results and the annualized figures (assuming 252 business days in a year) for each of the strategies.

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The table below breaks down the analysis into the respective trade statistics over the considered period:

Press Release

Overall, without the default predictability filter applied and without considering any specific strategy, below are the averages of the daily trade returns depending on the one-day signal strength vs. the realized average S&P500 stocks return:


Focusing on the trades with a higher level of predictability further improves the returns for stronger signals:



I Know First Research

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