It is shown that AI is not valuable in isolation; rather, value emerges when AI is combined with firm-specific complementary assets, and the market constantly undervalues organizational change that is difficult and depends on the path.
Abstract
The 'AI Valuation Paradox' refers to the persistent gap between how much companies invest in artificial intelligence and how those investments are reflected in market valuations for S&P 500 firms. This study helps explain this paradox by showing that AI is not valuable in isolation; rather, value emerges when AI is combined with firm-specific complementary assets. When you invest in AI, you get negative marginal gains that are not important. The main new idea is to formalize and evaluate an integrated valuation method that divides firm value into three parts: basic value, real-option premium, and behavior-happiness adjustment. This model can explain 53% of the changes in Tobin's q. A study of dynamic events talks about a predicted "integration dip," which shows that the initial mood, which is lifted by story-driven hype, sharply corrects itself during operational implementation, which causes systemic instability. The results show that this difference exists because we are using tools from the Industrial Age to value assets from the Information Age, and the market constantly undervalues organizational change that is difficult and depends on the path. The results can be used and are important for two things: planning plans and looking at money.
Financial markets are commonly described as information-efficient, yet the phrase conceals more complexity than it clarifies. Between the arrival of information and the adjustment of prices lies a process that is neither fully observable nor mechanically uniform. Interpretation mediates how signals are received, credibility shapes their perceived relevance, and responses unfold unevenly across participants. From a knowledge management perspective, this raises a central question: how does dispersed information consolidate into knowledge that meaningfully informs decisions? The framework we developed does not seek to model the entire market mechanism. We concentrate instead on a measurable segment of that transformation. Media sentiment is examined using a hybrid analytical approach that combines the lexicon-based VADER model with the domain-specific transformer model FinBERT. The two models do not always converge, and this divergence provides additional insight rather than methodological weakness. Their integration enables us to capture both surface polarity and contextual financial nuance within a single structure. Because informational inputs differ in reliability, sources are weighted by credibility and filtered through an entity-based relevance mechanism. Sentiment scores are aggregated across adjustable time windows, enabling us to detect whether informational pressure accumulates before a price adjustment. Using selected S&P 500 firms characterised by sustained media exposure and high liquidity as the empirical setting, we conduct structured backtesting to evaluate directional alignment and interval coherence across multiple horizons. The main objective of this paper is to assess whether consistent relational patterns can be identified. Our results indicate that such patterns are present, although their strength varies across horizons and informational density. In some instances, aggregated sentiment precedes adjustment, while in others it dissipates without measurable impact. This unevenness suggests that sentiment analysis may be more appropriately understood as a mechanism for tracing knowledge flows within financial ecosystems rather than as a deterministic forecasting device. From this perspective, our study contributes to knowledge management by offering a computational approach for examining how dispersed information becomes actionable knowledge in high-velocity environments.
Péter Németh, Mária Bohdalová· European Conference on Knowl...· 0 citations
The widespread diffusion of artificial intelligence (AI) has delivered a systemic shock to the global labor market, making it a central concern in macroeconomic policy and social governance. This paper systematically reviews 180 representative articles published in leading journals between 2000 and March 2026, with the aim of clarifying the transmission mechanisms and economic consequences of AI‐induced structural changes in employment. Using bibliometric and qualitative analyses, we show that research output in this field is strongly shaped by major technological breakthroughs—especially generative AI—and is geographically concentrated in economies such as the United States and China. We document a shift from static, aggregate measures of technological exposure toward dynamic, multidimensional vulnerability frameworks that integrate task content and micro‐level worker characteristics. We further synthesize evidence that AI's labor‐market impacts arise from interactions among technological attributes, individual endowments, organizational heterogeneity, and institutional environments. The resulting economic effects are asymmetric and mixed: productivity gains coexist with factor‐income polarization, while new skill demands are accompanied by heightened displacement risks, skill mismatches, and occupational insecurity. We conclude by outlining a future research agenda that addresses data limitations, identification challenges, and cross‐country comparability, and that supports dynamic, context‐sensitive policy responses.
Yiqiang Feng, Lan Qiu, Xin-Wen Zhang et al.· Journal of economic surveys...· 0 citations
A co-creative framework in which the final decision combines AI-generated signals with human input is proposed, which shows that the allocation of decision authority between humans and AI can lead to different return–risk outcomes.
L. Bohrinovtseva, Olha Kliuchka, I. Chunytska et al.· Artificial Intelligence and...· 0 citations
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