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“True value” or “intrinsic value” often refers to a company’s underlying worth as determined by fundamental analysis and is independent of its market value. “True value” can be estimated using various methods. The study conducted by Dr Leong Ken Yien and colleagues explores the validity of selected commonly used models, i.e. whether these models do explain and forecast banking stocks. Popular models like the Dividend Discount Model (DDM), Price-to-Earnings (P/E) ratios, Residual Income Model (RIM), Free Cash Flow (FCF) and the Capital Asset Pricing Model (CAPM) are used to estimate the pricing of a stock. As banks operate on complex business models and are heavily regulated, the effectiveness of these models becomes even more critical and questionable.

The research examines how these models perform in the banking sector. Unlike tech companies or retail firms, banks have a distinct balance sheet, and their earnings are heavily influenced by interest rates, credit risks, and government regulations. Traditional models, while useful, often oversimplify the bank’s operations. Complexity arises from banks’ financials, including loan-loss provisions, capital adequacy ratios, and exposure to systemic risk are vaguely captured in risk models. This gap can lead to mispricing, where the market value of a bank’s share deviates from its “true value”.

One key finding is that these models do explain the bank prices well, with the explanatory power ranging from 82% to 90%. RIM outperforms the other models. Meanwhile, DDM underperforms on its explanatory power as dividends may not be stable, and banks struggle during financial uncertainty or economic downturns. Hence, DDM may not hold true for banks due to external shocks. Similarly, the CAPM model may need to adjust for the risk premium for the country studied.

The study also evaluates how accurately these models forecast future share prices. The forecast tests suggest that while these models have predictive powers, P/E has superior predictive power, followed by the RIM, DDM and FCF models (up to 5-year ahead).

The research highlights the need for more adaptive, sector- and country-specific adjusted valuation models. It suggests that integrating macroeconomic indicators, stress test outcomes, and regulatory trends is informative of a bank’s “true value”. These existing models need to be used with caution and address the complexity of financial institutions due to their business environment and regulations.

The key implications of the research call for a more cautious and informed use of share valuation models in the banking sector. Investors should recognise that these models serve as guides and understand their underlying assumptions to avoid costly errors. Traditional models need to be adapted to reflect the financials and regulatory landscape of banks. Financial analysts are encouraged to integrate policy shifts and central bank trends into their assessments. Relying solely on numerical outputs is insufficient, as investors must combine model insights with a broader understanding of economic dynamics, especially during periods of uncertainty. More advanced, hybrid valuation models that incorporate tools like AI and big data can be explored.

The practical application of this research lies in improving how investors evaluate and make decisions about banking stocks via banking-specific approaches. The research benefits retail investors, financial analysts, and leaders from the banking and investment sectors. It equips these stakeholders with more realistic understanding of valuation models to make informed and less risky financial decisions.

The research also offers insights to the general investing public, including small-scale or novice investors who may rely on simplified models without fully understanding their limitations. By encouraging better valuation practices, the research contributes to a more stable financial market with informed investors (retail and institutional), which ultimately benefits wider communities, especially those who are financially vulnerable and more likely to be affected by mispriced banking stocks. This makes the research academically relevant and practically significant for fostering financial literacy, banking stability and information efficiency in the financial market and financial institutions.

The research promotes financial stability, financial inclusion for informed investment practices, which are essential for sustained, inclusive economic growth (SDG 8). Accurate and reliable valuation models support better decision-making for investors, reduce systemic risk, and contribute to a more stable and resilient financial system as the foundations for fostering productive economies.

Additionally, valuation models can be innovated with the integration of advanced technologies like AI and big data (SDG 9), hence fostering innovation and building resilient infrastructure. Financial institutions and analysts are encouraged to modernise traditional models in support of the development of a robust and forward-looking financial ecosystem under the evolving economic landscape. Together, these alignments demonstrate how academic research in finance can deepen theoretical knowledge and contribute towards sustainable and equitable development goals.

Dr Leong Ken Yien
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Email: @email