AlphaLab is a one-of-a-kind stock research tool that helps you assess the predictive power of any alpha (also referred to as a factor). It helps you assess the factor’s ability to predict future returns – the most important thing in factor investing.
Before we move further, we need to understand what alpha is.
The traditional definition of alpha is risk-adjusted excess return over a benchmark. But in this context, we use the word “alpha” slightly differently. Also, note that we will use 'alpha' and 'factor' interchangeably.
Alpha is something that can be used to explain the stock returns, and it is used to predict future stock returns.
Investors can then use this alpha to construct a portfolio. Very simply put, alpha is a value associated with each stock at a particular point in time. For example, suppose we are constructing a value portfolio using the P/E ratio. Then the alpha for RELIANCE on 31st March 2025 is its PE Ratio on that day. Some commonly used alphas (factors) include Value (PE Ratio, PB Ratio), Momentum (1 year return), and Technical (RSI). Note that in each case, each stock will have a particular value associated with it at a particular point in time.
Typically, a good alpha (factor) has a high predictive power – its ability to assess future return. There are 2 ways to assess factor predictivity:
1. Forward return analysis – Here, you try to assess forward returns on stocks for different values of alpha, typically by bucketing into quantiles and bins (We will discuss in more detail later). If alpha is predictive, we should see certain buckets exhibiting higher returns than other buckets.
2. Alpha values should exhibit high correlation (Information Coefficient) with future returns.
3. Back test with portfolios constructed at different values of Alpha. This is step 3. Once Step 1 and Step 2 show that the alpha indeed has predictive power, the final obvious step is to create portfolios that take exposure to stocks in different buckets (bucketed by alpha values) and compare their performance.
The first step towards building a robust quant investing strategy is to have in place a strong signal (which in our context we call alpha or factor). So, the first step is to assess the quality of alpha itself. Back testing will not help you assess this.
If back-tested results are good, you cannot conclusively prove that your alpha has predictive power. Good back testing results could be because of other parameters like position sizing, rebalancing frequency (and luck). While in most cases, a good back-tested result will imply a good alpha, you would still want to start with analysing the alpha.
Further, a detailed analysis of alpha will help you optimise other parameters in your strategy. Our alpha analysis will help you answer these questions:
1. Is your alpha predictive, and if yes, how much is the predictive power?
2. Is the alpha consistent across the cross-section of the universe and across time?
3. What is the alpha decay, or in other words, what is the optimal holding period for this alpha and hence, how often should I rebalance?
4. Does the alpha perform well across all sectors, or is it good only for some sectors?
With this introduction, let’s jump into building our first alpha.