How to Use CAPM in Quant Trading: Anchor Your Portfolio to Market Returns

In this article, I would like to talk about CAPM, which is short for capital asset pricing model. It’s one of the most common metric to determine investment options quantitatively. From this article, you can learn what CAPM is, benefits on taking it into account in your investment decisions, its downside and how to implement quant trading in python script

To whom:

• People who are beginners in quant trading
• People who are using AI to analyze potential investment opportunities

Table of Contents: How to Use CAPM in Quant Trading: Anchor Your Portfolio to Market Returns

• What is CAPM investing strategy?
• Why CAPM investing is valuable in investment decision
• How to implement CAPM investing in quant trading
• Cons of CAPM investing
• AI Prompt Components to analyse investment opportunities using the CAPM investing in quant trading
• AI Prompt sample and CAPM investing strategy python script

What is CAPM investing strategy?

CAPM (Capital Asset Pricing Model) investing is an approach based on the CAPM formula, which helps investors assess whether a stock (or portfolio) offers a favorable expected return relative to its risk.

The formula of CAPM is Expected Return (E)=Rf​+β⋅(Rm​−Rf​)

• Rf​ = Risk-free rate (e.g., government bond yield)
• Rm​ = Expected market return
• β = Beta of the stock (how volatile the stock is compared to the market)
• Rm​−Rf​ = Market risk premium

So as you can see from the formula, CAPM is anchoring bond yield issued by government and the expected market return to justify if your stock investment return-wise is undervalued or not. For example, if you are investing a stock which expected to return is 10%, but the CAPM is 8%, that indicates the stock is undervalued. Conversely, the stock is overvalued, which is not a good trade-off.

Why CAPM investing is valuable in investment decision

CAPM focuses on risk-adjusted of investing, which means if you have many potential high return of investment options with same or similar level of risk, you definitely prefer to select those with higher return. This is the heart of the value of CAPM.

As it anchors the bond yield which represents the risk free investment options, so CAPM is a very good supplement in quant trading to quantitatively narrow down the investment options and determine for higher return with reasonable same level of risk

How to implement CAPM investing in quant trading

For devleoping a Python script in quant trading, people need to collect some fundamental market performance dataset based on the CAPM formula mentioned earlier. First things first, people need to get the risk free. In general, we get used to using the government bond yield as the benchmark. So if now you are investing in USA market, normally treasury bond yield 10 year is the common option, because there is no option else that can have lower risk than treasury bonds.

Second, people need to calculate the expected market return. It requires you to take one target market’s security index return as the threshold. For exampe, if you are investing in USA stock market, you can consider taking SP500 as the dataset source to calculate the current market return. Or if you are investing in technology related stocks, you can consider referring to Nasdaq index market return as the threshold.

Next, it’s the β calculation. β represents the volatility indicator, which the stock compares to the market. So people need to calculate the changes over a period of days between the stock and the market which the stock should compare to.

Next, it’s the β calculation. β represents the volatility indicator, which the stock compares to the market. So people need to calculate the changes over a period of days between the stock and the market which the stock should compare to.

Last but not least, it’s the risk premium which people use the expected market return (the market return where the stock belongs to) minus the risk free

Cons of CAPM investing

CAPM emphasises the investing context is an efficient market, which means the bond yield and market return totally make senses. Thus, to some degree, it ignores the real-world bias, political policy, user sentimental behavior and so on.

Also, CAPM does not care momentum and value. It focus on giving you a signal of risk-adjusted when you are selecting investing products and making decisions. It’s better for you to make a final decision with a narrowed down option list.

Therefor, we do not suggest to run CAPM investing strategies only in your quant trading. Conversely, CAPM is a supplementary in your investing strategy of quant trading. The main role of CAPM is to help develop a better portfolio to get return as high as possible with bearing same level of risk

AI Prompt Components to analyse investment opportunities using the CAPM investing

If you use generative AI to analyse investment opportunities, in terms of CAPM investing, here is a list of components you need to consider and include in the AI prompt as follows:

• Risk free rate
• Target market stock price over a period
• Target stock price over a period
• Lookback period - 3 years, 5 years, 8 years and so on

AI Prompt sample and CAPM investing strategy python script

def capm_investing(tickerv1, marketv1, start_datev1, end_datev1):
    ticker = tickerv1
    market_ticker = marketv1  
    start_date = start_datev1
    end_date = end_datev1
    # Fetch data
    stock_data, market_data, risk_free_rate = fetch_data(ticker, market_ticker, start_date, end_date)
    # Calculate returns
    stock_data = calculate_returns(stock_data)
    market_data = calculate_returns(market_data)
    # Align the data
    aligned_data = pd.concat([stock_data['Return'], market_data['Return']], axis=1).dropna()
    aligned_data.columns = ['Stock_Return', 'Market_Return']
    # Calculate beta: Beta of the stock (measures the stock's sensitivity to market movements).
    beta = calculate_beta(aligned_data['Stock_Return'], aligned_data['Market_Return'])
    # Calculate expected market return (average historical return of the market)
    market_return = aligned_data['Market_Return'].mean() * 252  # Annualized
    # Calculate expected return using CAPM
    expected_return = calculate_expected_return(risk_free_rate, beta, market_return)
  
    # Compare with actual annualized return
    actual_return = stock_data['Return'].mean() * 252
    risk_premium = expected_return - risk_free_rate
    # Evaluate the stock
    capmv1=[]
    if expected_return > actual_return:
        capmv1.append({
            "Ticker": tickerv1,
            "Risk-Free Rate": risk_free_rate,
            "Beta": beta,
            "Expected Market Return": market_return,
            "Expected Return (CAPM)": expected_return,
            "Actual Annualized Return": actual_return,
            "CAPM_Qualification": "The stock is OVERVALUED based on CAPM."
        })
    else:
        capmv1.append({
            "Ticker": tickerv1,
            "Risk-Free Rate": risk_free_rate,
            "Beta": beta,
            "Expected Market Return": market_return,
            "Expected Return (CAPM)": expected_return,
            "Actual Annualized Return": actual_return,
            "CAPM_Qualification": "The stock is UNDERVALUED based on CAPM."
        })
    return capmv1

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