Buying Financial Companies Based on Book Values: Regression Analysis Revisited
Some of my loyal viewers may recall that last year I had an idea to examine investment returns of banks and insurance companies by book value. Because, as some wise financier said, banks have assets that are impossible to value and insurance companies have both assets and liabilities that are impossible to value. I believe the author of this statement was talking about financial crises where valuations swing wildly, but even in a “normal” market situation changing asset prices and actuarial assumptions turn the value of a financial company’s equity into a “reasonably good” estimate. And this being the case, it may be that the book value may be in error, but the error is unbiased, i.e., it has the same odds of being too high as too low. Therefore, the further the market value of the company departs from the book value, the more likely the market value is to be wrong. And indeed I found the 2025 data as much more confirmatory of this hypothesis than in my 2024 study, as detailed below.
When I looked at returns from 2024 the results were mixed at best. Among banks there was no discernible effect at all, and in fact a high price/book ratio was a slight indicator of outperformance. The only confirmatory evidence was among life insurers, where I found an r-squared of .2 and a p value of about .1, meaning that there is roughly a 90% chance that about 20% of investment returns is explained by the price/book ratio. Which is a fairly thin thread to hang a portfolio construction system on, I admit.
However, I am unwilling to take “not statistically significant” for an answer, and I thought I should repeat the experiment in case 2024 was an unusual year. Again, I used the Value Line list of banks, regional banks (which are apparently distinct from other banks), life insurers, and property and casualty insurers, and this time I sought greater accuracy by, instead of using the one year returns from the day I looked at the particular company, I looked at when the company published its end of year book value, usually in the relevant 8-K but occasionally in the 10-K, and then looking at returns until the day before the end of year book value was revealed in the next year.
After some number crunching (and I wish banks would adopt a standard format for their 8-K to include the book value and tangible book value per share in every case in order to avoid some tedious calculation), the results were more encouraging. For the Value Line’s non-regional banks, when looking at book value I got a p value of .000009, indicating virtual certainty that I’m on to something. However, the r-squared was only .11, meaning the explanatory power of this test is highly limited. Eliminating outliers improved the r-squared to 15.8% and knocked another order of magnitude off of my p-value, making me 10 times even more virtually certain. In fact, you can see in the following graphic that for banks with an above average price/book ratio there were over three banks that had below average returns for every bank that had above average returns. The effect was more muted for banks with low price/book ratios, but it still means that the test worked for 107 banks and only failed for 61.

Among regional banks, which Value Line formerly identified as Midwestern, I obtained similar results, with a highly certain p value and a low r-squared. In this case tangible book value worked better than normal book value (r-squared of .17 as compared to .14, p value of .0011 instead of .0039), and eliminating outliers made the regression worse. And as we see below, when simply dividing the regional banks by below or above average book value, 40 banks complied with the test as compared to 18 that did not. Certainly encouraging news for one who intends to beat the sector index, low r squared or no low r squared.

For life insurers, which if you’ll recall was the shining star of this test last year, the results were likewise encouraging, with an r squared value of .35 based on ordinary book value and a p value of .071, meaning that this could yet be an unreliable correlation, but when using tangible book value we got an r-squared of .41 and a p value of .047, implying that the book value effect was both large enough to seriously consider and likely enough to be reliable at least as far as inviting further inquiry. Curiously, going by book value as adjusted by the insurance company (for changes in actuarial assumptions, unrealized losses, etc.), the r-squared diminished to .275 and the p value to .12. As I have noted, it seems suspicious and not at all surprising that every insurance company in the Value Line universe improves its book value by these “adjustments;” not a single insurance company has net unrealized gains or actuarial assumptions that worked out in their favor.
Among property insurers, tangible book value was again the most reliable (p value of .032), but rather weak (r-squared of .13). Turning to the quadrant method, we see that the above average price/tangible book value are again reliably more likely to underperform, but curiously the lower price/book ratio companies were in fact more likely to underperform. So even though the formal regression revealed a small r squared, the heuristic of avoiding property insurance companies with high price/book ratios seems to be worthy of consideration.

And overall, although the book value effect is small, it seems fairly reliable if 2025 was a typical year, and if any significant part of returns can be reliably explained by a single and readily available metric, it seems advisable to take it into account in portfolio construction.

