Introduction

Value Line was founded in 1931 by Arnold Berhard in response to the market crash of 1929. Value Line concentrated on developing objective measures of stocks’ value that would be independent of emotions. In 1950, Value Line Funds offered its first mutual fund. Currently, Value Line Funds offers 13 funds with a total fund size of more than $10 billion.¹

For decades, stocks that Value Line assigned a 1 or 2 on its Timeliness ranking system outperformed stocks that it assigned 3, 4, or 5. This effect has been consistent enough, for a long enough time, that it has been called the Value Line Effect, Value Line Enigma, and Value Line Anomaly, and the effect is cited as an exception to the Efficient Market Hypothesis.²

In recent years, as data has become increasingly available to institutional and retail investors, some research has found that the Value Line Effect has diminished or disappeared.

Index fund superiority

Many investment professionals, including Rain Dog, believe that most of a fund’s performance comes from asset allocation, i.e., most performance arises from the percentages allocated to broad categories of stocks in a portfolio, such as large cap growth, small cap value, and so on. Selection of the specific stocks in a portfolio usually adds little value, and in many cases subtracts value vs. what could be obtained by investing passively in the same asset classes. Index funds provide superior results because they allow investors to invest in the desired asset classes while eliminating the expense associated with active stock picking.

Rain Dog’s belief in the superiority of index funds was supported by the most recent SPIVA U.S. Scorecard from S&P Global, which reported that 79% of actively managed US equity funds underperformed their relevant benchmarks (aka indexes) in 2024.³ Even more significant, 93% of actively managed US equity funds under-performed their benchmarks over the 10-year period ending in 2024 on a risk-adjusted basis. This basic pattern has been true for decades.⁴

In short, the odds are overwhelmingly against active managers. That’s why identifying possible exceptions like Value Line is important.

Methodology

This analysis compares performance of Value Line Funds to comparable index funds.

Performance period

The time period used for this analysis was January 1, 2015, through December 31, 2024. Several Value Line funds were eliminated from consideration because they had inception dates later than January 1, 2015.

Value Line funds analyzed

These Value Line funds were evaluated to determine whether any of them outperformed comparable index funds:

• Value Line Core Bond (VAGIX)
• Value Line Capital Appreciation Investor (VALIX)
• Value Line Larger Companies Focused Investor (VALLX)
• Value Line Select Growth Fund (VALSX)
• Value Line Asset Allocation Investor (VLAAX)
• Value Line Small Cap Opportunities Inv (VLEOX)
• Value Line Mid Cap Focused (VLIFX)

Comparable index funds

The value of a comparison such as this depends on the comparison between each Value Line fund and its comparable index fund being accurate and fair.

Creating fair index-fund clones is a detailed process that is fully defined in the Appendix: Fund-Matching Method.

In brief, we built index “clones” for each Value Line fund using the well-known Fama & French five-factor model. This model evaluates investments on:

• Market return (excess returns)
• Size (small vs. large companies)
• Value vs. growth
• Profitability
• Investment aggressiveness

Using Portfolio Visualizer’s “Match Factor Exposure” tool, we created clones that matched each Value Line fund’s factor exposures as closely as possible with index funds.

This process allowed us to strip away the effect of factor exposure and see whether Value Line’s active management actually added—or subtracted—value.

Performance analysis of Value Line’s oldest, largest fund

Value Line Funds’ oldest fund is a mid-cap growth fund that was launched in 1950. Today, that fund is more than twice as large as Value Line Funds’ next largest fund.

Using the factor-exposure-match methodology just described, Value Line’s Mid Cap Focused fund (VLIFX) was cloned with index funds. The table below shows the closest factor match between VLIFX and index funds.

* Allocations may not total 100% due to rounding.

The performance of the Value Line fund and its index fund clone is shown in the table below.⁵

Fund performance 2015–2024

In this case, Value Line clearly added value—higher returns with lower volatility compared to its index clone.

Analysis of additional Value Line funds compared to index fund clones

This analysis was repeated for the other six Value Line funds that met the criteria for participating in this analysis. The results are shown in Table 1 below.

Of the seven funds analyzed, index clones were superior in five instances and Value Line funds were superior in two instances. Importantly, none of the differences were too close to call. In our judgment, all were meaningful.

Table 1. Summary of differences between Value Line funds and index fund clones

Value Line’s strengths and weaknesses

In this analysis, the strength of Value Line’s funds was in the areas of small cap growth and mid cap growth. This analysis suggests that investors who want exposure to those factors might benefit from investing in actively managed Value Line funds.

For other factors, index funds provided superior performance.

Will Value Line funds continue to outperform?

Value Line’s funds are actively managed, and most underperformed the market, according to this analysis.

Over time, 90–95% of actively managed funds underperform their benchmarks on a risk-adjusted basis. The failure of the vast majority of actively managed funds to outperform the market supports the Efficient Market Hypothesis, which states that it is not possible to beat the market except by luck.

For markets in which information flows most freely, such as large cap US stocks, Value Line has no special knowledge to leverage, and therefore performance of its funds has not surpassed index funds. This is consistent with the Efficient Market Hypothesis.

For markets in which information is less available—including mid cap growth and small cap growth—Value Line appears to continue to be able to develop insights that allowed its funds to outperform comparable index funds. This could be an exception to the Efficient Market Hypothesis, consistent with the long history of the Value Line Effect.

That said, it is impossible to know how long this edge will last.

Rain Dog believes that the growth of AI will continue to shrink any advantage that Value Line’s successful actively managed funds may have had in the past. In effect, we believe that AI will continue to strengthen the Efficient Market Hypothesis and eventually eliminate the Value Line Effect.

Conclusions

For decades, the Value Line Effect has been cited as an exception to the Efficient Market Hypothesis. This analysis suggests that:

• For most investments, index funds remain the superior long-term choice.
• Value Line’s mid-cap growth and small-cap growth funds delivered strong outperformance over the last 10 years.
• There has been no apparent benefit for bonds or large-cap stocks.

For investors, the message is clear: build your portfolio on a solid index foundation. Evaluate actively managed possibilities with evidence rather than anecdotes. And proceed cautiously and selectively with any investments that include active management.

Appendix: Fund-Matching Method

Rain Dog believes that the vast majority of a fund’s performance comes from its asset allocation. We believe that little or no value is added through active stock picking.

At a coarse level, asset allocation consists of the portion of a portfolio invested in the categories of equities, fixed income, real estate, commodities, alternatives, and cash and cash equivalents.

Each of these broad asset classes can be further refined on the basis of investment factors. For equities, which is the primary focus of this analysis, we use the investment factors defined by Nobel prize winners Eugene Fama and Kenneth French in their five-factor model.⁷

The first three factors in the five-factor model come from the same authors’ earlier three-factor model:⁸

Market return (excess returns). This is the return calculated using the capital asset pricing model (CAPM).⁹ This factor establishes the base level of return before other factors are considered.

Small vs. big. This factor captures a fund’s mix of stocks based on market capitalization of “small minus big” (SMB).

Value vs. growth. This factor accounts for stocks with high book-to-market ratios (value) vs. those with low book-to-market ratios (growth) (high minus low, or HML).

The five factor model introduced two additional factors.

Profitability. This factor describes the level of operating profitability (robust minus weak, or RMW).

Investment. This factor describes the level of a firm’s investment (conservative minus aggressive, or CMA).

Collectively, these five factors explain 90–95% of a portfolio’s returns before active stock picking is considered.

The Fama-French five factor model is not the only model that has been proposed for evaluating investment-factor exposure. However it is widely known, and it is the work of two Nobel-prize winning economists, so we have used that model as the basis for this analysis.

Fund evaluation using the five-factor model

An objective method of evaluating an actively managed fund’s performance is to create a portfolio comprised of passively managed index funds that provide the same exposure to the five factors that the actively managed fund provides.

If the actively managed fund outperforms its factor-match clone, then it’s possible that active stock picking is adding value beyond passive exposure to the five factors.

If the actively managed fund underperforms its factor-match clone, then we assume that active stock picking is subtracting value from what could be achieved through passive exposure to the same five factors.

How we match the five factors

To match each fund’s factor exposure objectively, we use Portfolio Visualizer’s “Match Factor Exposures” tool¹⁰, which evaluates a fund’s exposure on each of the 5 factors. The tool creates a comparable fund with the closest possible factor exposures based on a set of candidate funds provided by the user.

For comparisons to US equity funds, Rain Dog uses the following candidate funds:

• Large cap blend index (VOO)
• Large cap value index (VTV)
• Large cap growth index (VUG)
• Mid cap blend index (VO)
• Mid cap growth index (VOT)
• Small cap blend index (VB)
• Small cap value index (VBR)
• Small cap growth index (VBK)

For comparisons to international funds, Rain Dog includes additional internationally-oriented index funds, however, international funds were not part of this analysis.

For comparison to US fixed income funds, Rain Dog uses the following candidate index funds, which provide access to government and corporate bonds of varying durations:

• US Bonds—Total Market (BND)
• Treasuries—Short Term (VFIRX)
• Treasuries—Intermediate Term (VFITX)
• Treasuries—Long Term (VUSUX)
• Treasuries—TIPS (VAIPX)
• GNMA (VFIIX)
• Corporate Bonds—Short-Term Investment Grade (VFSUX)
• Corporate Bonds—Intermediate-Term Investment Grade (VFICX)
• Corporate Bonds—Long-Term Investment Grade (VWETX)
• Corporate Bonds—High Yield (VWEAX)

For comparisons to international bond funds, Rain Dog includes additional internationally-oriented index funds, however that type of fund was not considered in this analysis.

Portfolio Visualizer options

Matching mode. Portfolio Visualizer’s Match Factor Exposures tool provides two matching portfolios: a Factor Exposure Clone and a Returns Based Clone. The goal of this analysis was to assess performance of index-funds with the closest possible match based on factor exposures, so this analysis used the Factor Exposure Clones.

Factor weighting. Portfolio Visualizer provides different options for weighting each of the five factors to assess the degree of match. This analysis used equal weighting for the five factors.

Number of funds in factor clones. Portfolio Visualizer supports matching using a maximum number of funds that can be set to a value from one to eight. This analysis used a maximum of five funds for each clone.

Footnotes