Stock Forecast: Daily Stock Selection Based On a Self-Learning Algorithm September 11, 2016
Stock Forecast: Short-term Trading
Daily Stock Selection Based On a Self-Learning Algorithm
Time Period: January 7th, 2016 – August 31st, 2016
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%.
The following table summarizes the overall results and the annualized figures (assuming 252 business days in a year) for each of the strategies.
The table below breaks down the analysis into the respective trade statistics over the considered period:
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