✨_Originally forked from WenqiAngieWu's implementation._

This codebase is based upon the paper: Frazzini, A. & Pedersen, L. (2014). Betting against beta, available online here, which was covered by the Hudson & Thames quantitative finance research reading group. It seeks to implement the work discussed in the paper.

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• Data folder stores the fetched data and Data.py.

• Data.py consists of 2 parts: save tickers, get data. Tickers are processed through website information, data are fetched using 'pandas-datareader'.

• main.py contains all the functions.

• figure.py is for drawing plots.

The strategy was back-tested on SP500 stocks and TSX (Toronto Stock Exchange) stocks and compared with two other similar factors presented in the Fama French 3-factor model: one is the SMB (small minus big), the other is the HML (high minus low). (SMB and HML data are collected from Ken French’s data library

Cumulative Return with $1 invested in the beginning in the SP500 (shown as US) equity market (in comparison with the SMB and HML factors)

Cumulative Return with $1 invested in the beginning in the TSX (shown as CAN) equity market (in comparison with the SMB and HML factors)

• Portfolio construction

• Hedging

• Trading cost: Looking at the actual weights the strategy puts on stocks with different market cap, we find out small-cap stocks are overweighted, causing significant implementation issues because the smallest stocks usually have limited capacity and are expensive to trade.

• Set some threshold regarding the market capitalization when assigning weights

• Mitigate risk using diversification

• Explore the relationship between the strategy and market states, and refine it by incorporating the judgment of market trends into the strategy

1. Andrea Frazzini and Lasse Heje Pedersen. Betting against beta. Journal of Financial Economics, 111(1):1–25, 2014