Embarking further into the realm of stock market analysis, this article stands as a continuation of our previous exploration. In our last discussion, we delved into the intricacies of constructing a robust risk model.
Building on that foundation, we now focus on Alpha Factors — pivotal concept in predictive financial analytics. Alpha Factors aim to exploit market inefficiencies and forecast future stock movements, offering investors refined strategies for market engagement.
As we delve deeper, let’s connect the dots from our risk model insights to the nuanced world of Alpha Factor creation and evaluation.
If you want to access the notebook we will cover in this article, you can find it on my GitHub (“04_AlphaResearch_FactorModeling.ipynb”).
Creating Alpha Factors
The journey into Alpha Factors begins with the fetch_sector_data function. This function is key for sector categorization, crucial for our analysis. By identifying the sector of each stock in our ticker list, we set the stage for more targeted Alpha Factor calculations, especially for those requiring sector-based comparisons. Here's the function's implementation:
# Function to fetch sector information for a list of tickers
def fetch_sector_data(ticker_list):
sector = {}…
