Four decades of research say investor relations creates real enterprise value. Here is how to finally measure it… and the counterintuitive thing the data reveal about what separates the best IR programs from the rest 

Every quarter, in conference rooms from San Jose to Stamford, and in boardrooms around the world, a familiar conversation unfolds. The Chief Financial Officer (CFO) and Head of Investor Relations (IR) gather to answer a series of seemingly straightforward questions:

What messages and themes should we emphasize this quarter? Which investors should we target? How do we know whether our IR efforts are creating value for the company and its shareholders?

IR departments operate within a highly regulated environment, adhering to corporate governance requirements, securities laws, exchange rules, and industry best practices. Yet despite this responsibility, IR leaders face the same challenge as every other corporate function: they must justify their budgets, demonstrate effectiveness, and show that their activities contribute to organizational objectives.

To do so, IR teams rely on a familiar set of tactics: one-on-one investor meetings, earnings calls, investor conferences, non-deal roadshows, shareholder outreach, and analyst engagement. These activities are often described as essential to maintaining investor confidence and supporting fair market valuation.

Notice, however, what is missing from these discussions.

There is rarely a dollar figure attached to the outcome. There is no universally accepted benchmark against peers. There is no clear causal chain connecting specific IR activities to changes in investor behavior, market perception, or enterprise value.

As a result, Investor Relations remains one of the few executive-level functions that largely reports success through anecdotes, relationships, and qualitative assessments. Meanwhile, virtually every other corporate discipline, including marketing, operations, treasury, finance, and human resources, has increasingly adopted quantitative performance metrics and return-on-investment frameworks to justify resources and measure impact.

In an era defined by data-driven decision-making, IR remains one of the last major corporate functions operating without a standardized method for quantifying its effectiveness.

This is not because IR fails to create value. Four decades of academic research show that it does, and that the magnitude is large. Bushee and Miller (2012) estimated that professional IR programs contribute between 5% and 10% of long-run firm value through reduced information asymmetry, expanded analyst following, and broader institutional ownership. Kirk and Vincent (2014) found that moving from no IR officer to a dedicated function cut information asymmetry by 10–15%. Agarwal and colleagues (2016) tied IR programs to 8–12% higher institutional ownership and a lower cost of capital, with effects compounding over 12-24 months.

For a $10 billion enterprise, the lower bound of that research is $500 million in value. Yet most CFOs cannot tell you what fraction of that value their IR team is capturing, where the gap to best-in-class peers sits, or which lever to pull next. The discipline has lacked a metric. A real numerical value.

That’s the problem: IR departments cannot quantify whether their efforts are working.

That gap was tolerable when it was merely an academic annoyance. However, recent structural shifts have turned it into a live financial risk.

The audience for IR is no longer only human. Institutional investors now run earnings-call transcripts and filings through large language models (LLMs) that assess tone and word choice, and by late 2025, according to a Wall Street Journal report, roughly 44% of IR teams had integrated AI into their programs. When disclosure is parsed and priced by machines, its quality becomes both more consequential and, finally, more measurable.

The pool of investors who read the story is shrinking. Passive assets overtook active management in late 2024, reaching about $19.1 trillion by October 2025, compared with $16.2 trillion in active strategies. The 3 dominant index managers (BlackRock, Vanguard, and State Street) are now the largest registered shareholders in 88% of the S&P 500 and cast the votes on roughly ¼ of corporate America. Those holders do not trade on a company’s story, which leaves a smaller pool of fundamental investors to win and raises the value of every basis point of attention a disciplined IR function can capture.

The cost of getting it wrong has never been higher. 2025 set a record for shareholder activism, with 255 global campaigns, surpassing the prior peak of 249 in 2018, and 32 CEOs resigned within a year of an activist campaign… the most on record. A function that still reports its contribution in anecdotes is a liability the board can no longer afford, especially in today’s changing economy.

The Investor Relations Contribution Index (IRCI) rests on a simple premise: if IR creates value through measurable market mechanisms, then IR can be measured through those same mechanisms. There are 4 of them, each tied to a distinct strand of the empirical literature:

• Valuation. EV/EBITDA and PEG, percentile-ranked against peers. Captures whether the market is paying a fair multiple for the company’s fundamentals. Information asymmetry compresses multiples; transparency expands them.

• Liquidity. A composite of Amihud illiquidity, the Roll spread proxy, turnover, and institutional ownership. Captures how easily the stock trades without moving price: the most direct microstructural readout of IR quality.

• Coverage. Filing intensity and timeliness, plus a domain-weighted measure of media reach. Captures whether the company is being heard.

• Trust. Event-window calmness around SEC filings, residual volatility adjusted with the Fama-French five-factor model, media tone scored by a sentiment analysis tool, and social sentiment. Captures whether the market believes what the company says.

Each dial is scored on a peer-relative, median-anchored percentile scale (median = 50, top = 100, bottom = 0), then combined into a single 0–100 composite. For any given peer group, the weights are optimized using a bounded, constrained optimizer that selects the weights maximizing the explained variance (R²) between the composite and enterprise value, with each weight held between 5% and 60% so that no dial is ever dropped or allowed to dominate.

The first question every executive asks is, "Which dial matters most?” The data refuse to answer it cleanly. Across peer-group runs spanning multiple market cycles, the dial that best separates leaders from laggards keeps shifting… by sector and over time. The lever moves.

What does not move is the structure of the winners. They share not a standout strength but the absence of a weakness. In peer group analysis, the companies that track the highest enterprise value aren't the ones that excel on a single dial; they're the ones with no dial in the bottom quartile. Balanced profiles consistently out-rank spiky ones, even when the spiky company leads on its best dial. The companies that win on IRCI are above the peer median on every dial at once, even if they lead on none.

That inverts how IR is usually run. The standard playbook staffs distinct channels (e.g., a media lead, an analyst lead, an events lead, a disclosure lead) and rewards excellence in whichever function a team is best at. But the four dials are not this way; they are one connected information-environment system, driven by the same underlying mechanism: reduced information asymmetry. Disclosure discipline narrows bid-ask spreads. Narrower spreads attract institutional investors. Institutional ownership expands analyst coverage. Broader coverage stabilizes returns through event windows. Stable event windows give the market the confidence to expand the multiple. Run that loop for 4-6 quarters, roughly the window over which research finds IR effects fully materialize, and all 4 dials  (valuation, liquidity, coverage, and trust) move together. Try to move them as 4 separate projects with 4 separate KPIs, and the loop never closes.

The implication for leaders is concrete. The marginal hour of IR effort belongs on whichever dial has the widest peer dispersion in your group, because that is where rank gains are cheapest. But the marginal year belongs to the underlying information-environment hygiene that lifts all four at once. Activity counts (e.g., press hits, roadshow days) are inputs. Market consequences such as spreads, depth, ownership, multiple, event-window volatility, are outputs. The companies that win measure themselves on the outputs, and they stop optimizing their loudest channel to fix their weakest dial first.

A score is interesting. A dollar figure changes the conversation. IRCI converts one into the other through a regression across the peer group:

Dollars per IRCI point = Enterprise Value × 0.05% × R²

The 0.05% coefficient is not arbitrary. It is derived from the 5–10% long-run IR contribution in the literature, spread across the typical 50-point leader-laggard IRCI gap, then conservatively halved. The R² term scales the figure by how tightly the composite tracks enterprise value in that specific peer group. One honest caveat accompanies it: because the dial weights can be tuned to maximize that R² on a small peer group, I treat it as the goodness-of-fit of an optimized model, not as independent evidence that the index explains value. It makes the dollar estimate more conservative in noisy groups; it does not, on its own, validate the framework.

Consider this real IRCI case study of semiconductor peers for the first quarter of 2026: NVIDIA, Broadcom, Micron, Qualcomm, Intel, TSMC, AMD, and ASML. The leader, NVIDIA, tops not a single one of the four dials. It leads because it has no weak one; its lowest dial sits at 55, the only company in the group with nothing below the peer median. Intel is the mirror image. It posts the highest valuation dial of any company in the group, an 82 that no rival approaches, and still ranks #5  of 8, because its liquidity, coverage, and trust dials are all below median. The single highest dial of any kind in the group (a 97 on trust) belongs to Broadcom, and Broadcom finishes 2nd, not 1st. Rank the 8 companies and the pattern is unambiguous: standing tracks a company's weakest dial far more than its strongest (a rank correlation of −0.79 against the low dial). No one wins by dominating one dial. The winners simply refuse to be bad at any.

Senior leaders care about two questions. First: Where do we stand? Second: What did this team do for me last quarter? The cross-sectional regression answers the first question. A separate quarterly attribution, scaled by an adjustable impact factor (10% by default) to distinguish marginal improvement from structural positioning, answers the second:

Quarterly IR contribution = (IRCI this quarter − IRCI last quarter) × dollars per point × 10%.  

The quarter-over-quarter view is where the index turns diagnostic. Take Intel (from the case study mentioned earlier) across the three quarters: its composite climbed from 54 to 59, then eased back to 56. What moved it is the useful part. Intel's valuation dial (its one genuine strength) never budged, holding at 82 the entire time. Every point of movement came from its two weakest dials: liquidity rose 14 percentage points in the fourth quarter, and trust climbed with it, lifting the composite nearly 6 points before both slipped back the following quarter and pulled it down again. The lesson the cross-section teaches shows up again in the time series: Intel's standing is governed by its weak dials, not its strong one. For a CFO, that turns the quarterly review from a recitation of activity into a single actionable read: the score lives or dies on liquidity and trust, the two dials the market keeps re-pricing, and that is exactly where next quarter's effort belongs. And IRCI also estimates quantifiable actions to add value in the areas of weakness 

The framework points to a 3-step playbook that any C-suite can apply this week.

1. Establish your baseline. Run an IRCI analysis against a tight, defensible peer group of 5-12 companies (IRCI can optimize this based on size, sector, coverage, liquidity, ownership, and geography) and choose quarters to analyze. The output is a composite score, four dial scores, and a dollar value per point… the first time most boards will have seen IR performance expressed as a benchmarked, dollar-denominated number.

2. Find your weakest dial and your widest-dispersion dial… and resist the urge to fix the loudest one. The weakest dial shows where the composite is being dragged down; the widest-dispersion dial shows where rank gains are cheapest. They are often the same; when they are not, fix the weak one first. If liquidity is the gap, the high-leverage moves are technical: market-maker engagement, float and lock-up management, and institutional outreach. If valuation is the gap, work on the analyst day, the long-range model, and segment disclosure. If coverage is the gap, combine targeted analyst outreach with a disciplined filing cadence. If trust is the gap, the work is in the event windows, reducing volatility around earnings and filings through guidance discipline and consistent messaging.

3. Report quarterly attribution to the board. Replace the activity list with 2 numbers: the IRCI change and its dollar translation. The conversation about IR will change in a single quarter.

IRCI is not a causal model. It is a peer-relative composite calibrated against academic point estimates of IR’s contribution to firm value, and its dollar translations should be read as defensible orders of magnitude rather than point predictions. Its credibility rests less on any single in-sample R² than on out-of-sample behavior tested across market cycles: directional tests, in which higher dial scores precede the expected next-quarter outcomes, and ablation tests, in which removing any single dial degrades the signal… evidence that all 4 do carry independent information. The framework is most useful when the peer group is genuinely comparable, the horizon is at least 2-4 quarters, and leaders use the dials to direct attention rather than to keep score.

What the index does is end the era of IR-by-anecdote. For the first time, the function that has spent 40 years arguing for its seat at the table has the same kind of measurement instrument that finance, operations, and marketing have long taken for granted. Whether a company uses IRCI or builds its own version on the same four dials, the underlying point holds: investor relations is too valuable to keep managing on instinct.

About the author. Bonnie Rushing is the creator of IRCI. Her expertise is in quantitatively measuring information environments, building and validating frameworks that translate diffuse communication effects into decision-grade numbers. She is a U.S. Air Force Senior Master Sergeant (sixteen years as an intelligence analyst, special-operations airborne linguist, and flight instructor), served as Course Director and Senior Instructor in Military and Strategic Studies at the U.S. Air Force Academy (2021–2024), and is a PhD candidate in cybersecurity at the University of Colorado Colorado Springs. IRCI applies that measurement discipline to the capital markets.

Methodology. IRCI is a quantitative, archival-data study. Comparable US-listed companies are assembled into peer groups of roughly 5-12 names via an optimized matching algorithm, drawing on SEC EDGAR (filings, 13F holdings, transcripts), Financial Modeling Prep, Alpha Vantage, and Yahoo Finance for pricing and fundamentals, and a range of news and social feeds for media and sentiment. Dial weights are re-optimized per peer group using a bounded, constrained optimizer (sequential least-squares programming) that maximizes the R² between the weighted composite and enterprise value, with each weight held between 5% and 60%. The complete methodology, formulas, and academic citations are available on request.

External review. The framework was reviewed by Jim Wilkinson, who is a seasoned global communications professional, former Chief of Staff at the U.S. Department of the Treasury and a veteran of multiple public-IR programs. He is one of many who identified the IR measurement gap that the index addresses and characterized it as a significant unsolved challenge for the profession.

Financial disclosure. The author has no financial relationship with any of the companies cited in the worked examples. IRCI is an independent framework.

AI disclosure. Generative-AI tools were used to brainstorm framing, summarize prior literature, and accelerate development of the codebase that powers the index. All factual claims, citations, and recommendations have been independently verified by the author, who is solely accountable for the article’s accuracy, integrity, sourcing, and originality.

Full citations available on request; these are the load-bearing empirical sources for the claims above.

Agarwal, V., Taffler, R. J., Bellotti, X., & Nash, E. A. (2016). Investor Relations, Information Asymmetry and Market Value. Accounting and Business Research, 46(1), 31–50.

Amihud, Y. (2002). Illiquidity and Stock Returns: Cross-Section and Time-Series Effects. Journal of Financial Markets, 5(1), 31–56.

Botosan, C. A. (1997). Disclosure Level and the Cost of Equity Capital. The Accounting Review, 72(3), 323–349.

Bushee, B. J., & Miller, G. S. (2012). Investor Relations, Firm Visibility, and Investor Following. The Accounting Review, 87(3), 867–897.

Diamond, D. W., & Verrecchia, R. E. (1991). Disclosure, Liquidity, and the Cost of Capital. The Journal of Finance, 46(4), 1325–1359.

Fama, E. F., & French, K. R. (2015). A Five-Factor Asset Pricing Model. Journal of Financial Economics, 116(1), 1–22.

Healy, P. M., & Palepu, K. G. (2001). Information Asymmetry, Corporate Disclosure, and the Capital Markets. Journal of Accounting and Economics, 31(1–3), 405–440.

Kirk, M. P., & Vincent, J. D. (2014). Professional Investor Relations within the Firm. The Accounting Review, 89(4), 1421–1452.

Lang, M. H., & Lundholm, R. J. (1996). Corporate Disclosure Policy and Analyst Behavior. The Accounting Review, 71(4), 467–492.

Roll, R. (1984). A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market. The Journal of Finance, 39(4), 1127–1139.