Introduction to Quant Strategies

Factor Investment Strategies (Smart Beta Strategies) – A Smarter Approach to Markets

Traditional index investing is like taking what the market gives you – buying everything in proportion to its market cap, no questions asked. We know this works, but is this the most efficient way to invest? Factor investing, usually called smart beta, takes a more strategic approach. Instead of accepting market returns as they come, it tilts a portfolio towards factors that have historically delivered higher risk adjusted returns.

These factors, for example, value, momentum and low volatility aren’t just theoretical. There has been decades of academic research and real-world performance show that certain stock attributes tend to outperform over time. By systematically targeting these factors, investors can aim for better returns or reduced risk without resorting to stock picking or high fee active management.

In this post, we’ll break down the key factors, how they work, and why factor investing has become a core strategy for both institutions and individual investors. If you’re looking for a way to refine your portfolio without overcomplicating things, factor investing might be worth considering.

Idea: Certain security (e.g. stock) characteristics or factors predict long-term returns.

Example: Buying stocks with low valuations (Value Factor) or high profitability (Quality Factor).

Why this works: Academic researchers (Fama-French among others) has shown that these factors outperform over time.

Risk: Factors go through cycles – sometimes they underperform for years.

Real world application: Firms like AQR Capital and BlackRock run multi-factor funds.

Python Example: Factor Based Stock Selection

This is slightly more complex than momentum and reversal strategies as this incorporates multiple characteristics – in this example momentum, value and quality and provides a signal regarding the comparative results of these regarding multiple stocks. Here is a simplified example looking at Apple, Microsoft, Google, Amazon and Tesla.

import pandas as pd
import numpy as np
import yfinance as yf
from scipy.stats import zscore

#List of Stocks 
stocks = ["AAPL", "MSFT", "GOOGL", "AMZN", "TSLA "]

#Download Historical Data
data = yf.download(stocks, start= "2020-01-01", end= "2024-01-01")
prices = data["Close"]

#Calculus Daily Return
returns = prices.pct_change()

momentum = prices.pct_change(252).iloc[-1]  #252 trading days in a year

#Fetch Fundamental Data(example: P/E ratio)
pe_ratios = {ticker: yf.Ticker(ticker).info["forwardPE"] for ticker in stocks}
earnings_yield= {ticker: 1/pe if pe > 0 else np.nan for ticker, pe in pe_ratios.items()}


#Convert to DataFrame
value= pd.Series(earnings_yield)

roe = {ticker: yf.Ticker(ticker).info.get("returnOnEquity", np.nan) for ticker in stocks}

quality = pd.Series(roe)
factor_df = pd.DataFrame({"Momentum": momentum, "Value": value, "Quality": quality})
factor_df = factor.df.apply(zscore) #Standardise Factor for Ranking

factor.df["Final Score"] = factor_df.mean(axis=1) #Equal-weighted score
ranked_stocks = factor_df.sort_values("Final Score", ascending=False)

Essentially this code fetches historical stock data and fundamental metrics (momentum, value and quality) for our 5 selected stocks, standardises these factors and ranks them based on equal-weighted composite score.

Interpretation of results

- The higher the final score, the better the stock ranks based on Momentum, Value and Quality factors.

- A long-only strategy would by the top ranked stocks.

- A long-short strategy would buy the top ranked and short the bottom ranked stocks.

Key Observations

Best Ranked Stock: Apple (AAPL)

Final score: 0.432044 (highest)

Strong Quality score (1.964376) boosting its ranking significantly.

Decent Value (0.284786) but negative Momentum (-0.953031).

Worst Ranked Stock: Tesla (TSLA)

Final score: -0.227969 (Lowest)

Poor Quality score (-0.824052) and Value (-1.655803).

Strongest Momentum (1.795949) but not enough to offset the poor quality and value scores.

Trading Strategy Implications

Long-Only: AAPL and AMZN as they have the highest final scores.

Long-Short: Buy AAPL, short TSLA, capitalising on Tesla’s low scores.

Momentum vs Quality Trade-Off: Stocks with strong momentum (TSLA) tend to have poor fundamentals (e.g. Quality/Value, reflecting business health and intrinsic value) whilst high quality stocks like Apple, may have weaker momentum.

This Weeks Quant News Stories

Investors have more choice – but are the new offers any good?

This article examines how the investment landscape has shifted from high-fee mutual funds to low-cost passive investing, prompting fund managers to introduce new products like active EFTs and smart beta (factor investing). While these strategies aim to increase returns by targeting specific factors, like momentum, they often come with higher fees (~0.4% annually) and can underperform for long periods. Despite scepticism, ETFs continue to grow rapidly, surpassing $14 trillion in global assets. Read the article here (for FT subscribers).

More choices can be beneficial, but investors should be wary of higher fees and inconsistent performance in factor-based funds. While smart beta strategies can be useful, they require patience and a strong understanding of their risks. For most investors, low-cost passive investing remains the most reliable long-term approach.

Key Terms

Beta: A numerical value that represents the sensitivity of a stocks returns to movements in the overall market. It is calculated as the slope of the stocks returns regressed against the markets returns in a linear regression.

Long-Only: A trading approach that involves buying and holding stocks expected to appreciate, without short selling.

Long-Short: A trading strategy that buys (long) high-scoring stocks and sells (shorts) low scoring stocks to profit from relative performance.

Quality: A measure of a company’s financial health, often based on profitability, stability and efficiency metrics like return on equity (ROE).

Value: A factor assessing how cheap, or expensive a stock is relative to fundamentals, often using metrics like P/E ratio or earnings yield.