Machine-assisted Stock Price Pattern Analysis

Can machine learning software discover predictive stock price patterns? In their December 2020 paper entitled “(Re-)Imag(in)ing Price Trends”, Jingwen Jiang, Bryan Kelly and Dacheng Xiu apply convolutional neural network machine learning software to analyze stock price series images (depicting daily open, high, low, close and moving average prices and trading volume) in search of the patterns most predictive of future returns. Their model standardizes price series scales, recursively smooths and accentuates certain shape elements of images of the last 5, 20 and 60 days trading to isolate patterns that predict returns over the next 5, 20 and 60 days. They translate predictions into hedge portfolio performance by each month going long (short) the tenth, or decile, of stocks with the strongest (weakest) return forecasts. They benchmark performance against hedge portfolios for conventional momentum (return from 12 months ago to one month ago), 1-month short-term reversal and 1-week short-term reversal. Using daily price and volume series for all listed U.S. stocks during January 1993 through December 2019, they find that: (more…)