By Pratik Patel
We generally measure the accuracy/quality of an alpha’s predictions by metrics such as information ratio (IR) and information coefficient (IC). The IR is the ratio of excess returns above a benchmark to the variability of those returns. It suggests that an alpha with high excess returns and low variability consistently predicts future returns over a given time period. The IC measures the relationship between the predicted and actual values using correlation, and a value of 1.0 suggests great forecasting ability.
While high IR and IC are great, we must not forget that they measure return prediction irrespective of real world constraints; they assume liquidity is endless, trading is free, and there are no other market participants but ourselves. But actual trading strategies must abide by certain constraints, and an alpha that makes predictions correctly and often, but does so with reasonable assumptions about market conditions, will be more easily leveraged.
Predictions change as new information becomes available. Whether a stock moved one tick, an analyst revised his recommendation, or a company released earnings, this change in information is a catalyst for trading activity. We measure this trading via turnover: the total value traded divided by the total value held. A company’s stock price changes much more often than does a company’s earnings per share, and so it follows that an alpha based on price movements (e.g. price reversion) ...