Value Investing in Quant Trading - Scan Undervaluation Investment Opportunities

In this article, I would like to walk through a quant trading strategy, which can scan thousands of undervalued investment opportunities in minutes. These opportunities are underpriced and can either create sustainable cash inflow for the long term or buy in cheaper stocks and sell at a higher price to generate capital gains. From this article, you can learn how to run a value investing strategy in quant trading and what things you need to be cautious of when implementing value investing tactics.

To whom:

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

Table of Contents: Value Investing in Quant Trading - Scan Undervaluation Investment Opportunities

• What is value investing?
• Why value investing is a sustainable approach for the long-term return
• How to implement value investing in quant trading
• Cons of Value trading
• AI Prompt Components to analyse investment opportunities using the value investing in quant trading
• AI Prompt sample and value investing strategy python script

What is the value investing in quant trading?

Value investing in quant trading is the application of systematic, data-driven methods to identify undervalued securities based on fundamentals, just like traditional value investing, but automated and scaled using algorithms. It focuses on buying stocks that appear undervalued based on financial metrics like low P/E, low P/B, or high dividend yield relative to their intrinsic value.

Different from DCF investing strategy, value investing focuses on the current and historical financial performance and uses simpler valuation ratios, such as P/E, P/B, and so on, to screen thousands of undervalued opportunities and identify relatively cheap companies based on various metrics. So, it’s a kind of broad investing style based on intrinsic value. The definition of intrinsic value compared to DCF is slightly similar, but it is different in terms of the standpoint. Here are the main core metrics as follows when talking about value investing

• P/E - Price-to-Earnings.
• P/B - Price-to-Book
• Dividend Yield

Why value investing is a sustainable approach for the long-term return

Compared to growth investing, momentum investing, value investing emphasises the company’s true worth and value as the key signal, where the historical and current price can not truly reflect its value. That means the current price is cheaper than its true company value. So even though the stock price momentum is trending down or being volatile in the short term due to many economic and external factors, value investing believes that in the end, the company's price will return to the right pricing point that can reflect its true value. It’s just a metaphor that it doesn’t make sense buying a 10KG of gold just costs US$1, although the current price is down due to some reasons.

For those potential capital gain opportunities, people can compare them to the peer or substitute companies in the market and scan similar companies with cheaper prices currently. If the sector and industry is trending up, investing in a lower price point that means it’s undervalued, it increases the chances to get a bigger capital gains, compared to investing those expensive ones.

For those companies with strong cash flow and earning capability, such as banks, utilities, oil, and so on, a higher dividend yield can offer you sustainable cash inflow quarterly or even monthly. In general, its ROI is higher than saving a time deposit in a bank as the dividend yield rate is higher

How to implement value investing in quant trading

First things first, people need to calibrate a specific market or a sector and prepare the full list of stock tickers from there, such as technology, finance, utility, energy, and so on.

Second, people need to develop a Python script or use an investment software to generate the value investing metric performance, such as P/E, P/B, and so on, programmatically from each stock ticker

Lastly, people need to set a threshold to scan those that can meet the criteria. For example, here is a general benchmark to filter stock tickers that are undervalued. That being said, it can vary based on different markets and sectors and bank time deposit rates.

• P/E < 20
• P/B < 2
• Dividend Yield > 0.02

Cons of value investing

No matter what kinds of value strategies, it can underperform for long periods. That implies people need to hold the stock financially for long periods without cash inflow, as the stock will not immediately become trending up or issue dividends to their stock account. So the money invested in the stocks must not be affecting people’s daily lives and work.

Furthermore, except for scanning the undervalued tickers, people need to further filter some tickers that might be value traps. That means they are cheap for some reasons. For example, we will advise people to further investigate the company’s liability, such as the debt/equity ratio, net debt, and so on. Some companies offer investors extremely high dividends, such as 15%, which is abnormal. Also, people can check the company’s liquidity, such as the quick ratio, free cash flow, and so on. These steps ensure you can avoid the value trap as much as possible

Lastly, buying timing matters as well because value investing requires investors to hold the stock for long periods. So buying in at a lower price point implies it may make you hold it shorter to get the capital gains, as it might rebound and reverse to be the mean pricing point very soon. So in terms of value strategies, people get useds to buying in in a trending down and set a target pricing point to buy in and wait for the stock rebounding.

AI Prompt Components to analyse investment opportunities using the value investing

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

• Full list of target market and sector stock tickers
• Value investing metric historical and TTM dataset from these stock tickers
• Latest interest rate and bank time deposit rate
• Set a target P/E, P/B and dividend rate for specific sectors and industries
• Liability metrics - debt/equity ratio, net debt
• liquidity metrics - quick ratio, free cash flow

AI Prompt sample and value investing strategy python script

def value_investing(ticker):
    nowTime_value = date.today()
    timeResult_value = str(nowTime_value)
    value_stocks=[]
    pe_ratio = ""
    pb_ratio = ""
    companyName = ""
    sectorv1 = ""
    industryv1 = ""
    dividend_yield = ""
    # Define value criteria/threshold (example: P/E < 20, P/B < 2, Dividend Yield > 2%)
    if pe_ratio and pe_ratio < 20 and pb_ratio and pb_ratio < 2 and dividend_yield and dividend_yield > 0.02:
        value_stocks.append({
            "Date": timeResult_value,
            "Ticker": ticker,
            "Company Name": companyName,
            "Sector": sectorv1,
            "Industry": industryv1,
            "P/E Ratio": pe_ratio,
            "P/B Ratio": pb_ratio,
            "Dividend Yield": dividend_yield,
            "Value_qualification": "Verified"
        })
    else:
        value_stocks.append({
            "Date": timeResult_value,
            "Ticker": ticker,
            "Company Name": companyName,
            "Sector": sectorv1,
            "Industry": industryv1,
            "P/E Ratio": pe_ratio,
            "P/B Ratio": pb_ratio,
            "Dividend Yield": dividend_yield,
            "Value_qualification": "Not Verified"
        })
    return value_stocks

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