Financial asset allocation duality and enterprise upgrading: empirical evidence from the Chinese A-share market

The study investigates how corporate financialization—specifically firms’ allocation to financial assets—affects enterprise upgrading among Chinese non-financial A-share listed companies during China’s economic transition. Motivated by the increasing financialization of the real economy and concerns about its implications for firm productivity and upgrading, the paper asks: (1) What effects does financial asset allocation exert on enterprise upgrading? (2) Through which micro-mechanisms do these effects operate? (3) Do these effects vary by firm characteristics? Drawing on the dual nature of financial assets, the authors hypothesize: H1: Short-term financial assets positively impact enterprise upgrading. H2: Long-term financial assets negatively impact enterprise upgrading. H3: Financial assets and enterprise upgrading exhibit an inverted U-shaped relationship, reflecting the superimposed effects of short- and long-term allocations. The study contributes by introducing enterprise upgrading (measured via TFP) into the analysis of financialization’s economic consequences, testing mechanisms (risk-taking capacity and earnings persistence), and exploring heterogeneity across debt status, ownership, and financing constraints.

The paper situates its inquiry within literature on corporate financialization. Prior work identifies determinants of firms’ financial asset allocation at macro and micro levels, including GDP cycles, money supply, policy uncertainty, environmental and industrial policy, taxes, institutional investors, operating income, financing constraints, executive characteristics, CSR, equity pledges, and performance commitments. Two primary consequence channels are emphasized: (1) the “reservoir effect,” whereby liquid, reversible financial assets buffer shocks, enhance liquidity, and broaden financing channels to support operations and investment; and (2) the “crowding-out effect,” whereby increased financial assets raise leverage and misallocation at the macro level and crowd out real investment and R&D, lower audit quality, raise debt costs, and reduce labor productivity at the micro level. Much past work examines linear relationships, with limited exploration of nonlinearity, mechanisms, and heterogeneity in the context of enterprise upgrading. This study addresses these gaps by positing and testing a nonlinear (inverted U-shaped) relation and examining mediators and heterogeneous effects.

Data and sample: The sample comprises Chinese non-financial A-share listed companies from 2012–2021. Financial data are sourced from CSMAR, Wind, and Juchao, cross-checked against annual reports. Exclusions: firms with missing key financial data; accounting insolvency (LEV>1); special statuses (ST/*ST/PT) during the period; IPO in listing year; cross-listing (A/H/N/B); accounting inconsistencies (e.g., total assets < net fixed assets or current assets). Continuous variables are winsorized at 1% and 99%. Variables: Financial asset allocation (FIN) = financial assets/total assets, including monetary funds, trading financial assets, available-for-sale financial assets, held-to-maturity investments, long-term equity investments, and investment property; post-2018, mapping to debt investments, other debt investments, and investments in other equity instruments. Short-term financial assets (FIN_S): monetary funds and trading financial assets; long-term financial assets (FIN_L): remaining categories. Enterprise upgrading: measured by firm total factor productivity using two approaches: Olley-Pakes (TFP_OP) and Levinsohn-Petrin (TFP_LP). OP production function includes lnK, lnL, age, ownership (state), export status, and fixed effects (year, region, industry); LP augments with intermediate inputs (cash paid for goods/services) as a proxy for unobservables. Controls: capital structure (LEV), cash flow (CFO), profitability (ROE), capital intensity (TAG), firm size (SIZE = ln total assets), growth opportunities (GROW = sales growth), labor intensity (LAB), and firm age/years on market (AGE). Empirical specifications: Baseline fixed-effects models with firm and time FE and cluster-robust SEs: (1) TFP_OP (or TFP_LP) = a + a1*FIN_S (or FIN_L) + γ*Controls + η_t + μ_i + ε. (2) Nonlinearity test: TFP_OP (or TFP_LP) = a + a1*FIN + a2*FIN^2 + γ*Controls + η_t + μ_i + ε; inverted U if a2<0, slopes change sign within observed FIN range, and the inflection point lies within FIN’s domain. Robustness: (i) Alternative TFP via OLS and lagging dependent variable by one period; (ii) One-period lag regressions; (iii) Adding province fixed effects. Mechanism tests: Mediators—risk-taking capacity (RISK; modified Altman Z-score; higher means lower actual financial risk) for short-term assets; earnings persistence (EP; sales revenue/average total assets) for long-term assets. Stepwise regressions: mediator on FIN component; TFP on FIN component and mediator, with FE and controls. Heterogeneity: Group analyses by (a) over-indebted vs. non-over-indebted (classification via deviation from Tobit-estimated target leverage by year-industry); (b) ownership (SOE vs. non-SOE); (c) financing constraints (high vs. low via KZ index relative to industry-year benchmark). Moderation: Interaction models with capital expenditure (CAP = ln cash paid for purchase/construction of fixed assets, intangibles, and other long-term assets) and financial channel profitability (RF = (investment income + fair value changes + net exchange gains/losses − investment income from associates/joint ventures − operating profit)/|operating profit|) to assess how CAP and RF alter the inverted U-shape (slope steepness and inflection point shifts).

- Descriptive statistics (N=9640): mean FIN=0.221 (min 0.020, max 0.728), FIN_S=0.193, FIN_L=0.027, indicating short-term assets dominate financialization; TFP_OP mean 6.673 and TFP_LP mean 6.854. - Baseline effects: FIN_S is positively associated with TFP_OP and TFP_LP (promotes upgrading), supporting H1. FIN_L is negatively associated with TFP_OP and TFP_LP at the 1% level (impedes upgrading), supporting H2. - Nonlinearity: FIN enters positively and FIN^2 negatively at the 1% level; inverted U-shape confirmed by U-tests (t≈1.99 and 2.13, 5% significance). Inflection points are approximately 0.278 (TFP_OP) and 0.288 (TFP_LP), both within the observed FIN range. The sample mean FIN (0.221) is near but below/around the threshold; authors note many firms are already on or near the right side where marginal increases hinder upgrading. - Robustness: Results persist when (i) re-estimating TFP via OLS with lagging; (ii) using one-period-lagged dependent variables; (iii) adding province fixed effects. - Mechanisms: Short-term assets increase risk-taking capacity (RISK), which positively relates to TFP; including RISK renders FIN_S effects insignificant, indicating mediation. Long-term assets reduce earnings persistence (EP); including EP diminishes FIN_L effects to insignificance, indicating mediation. - Heterogeneity: • Over-indebted firms: FIN^2 significantly negative; inverted U pronounced. Non-over-indebted: FIN^2 not significant. • Ownership: Non-SOEs exhibit significant inverted U (FIN^2 negative at 1%); SOEs do not show significant nonlinearity. • Financing constraints: High-constraint firms show significant inverted U (FIN^2 negative at 1%); low-constraint firms do not. - Moderation: • Capital expenditure (CAP) flattens the inverted U (interaction FIN^2*CAP positive), shifting the inflection point left; CAP itself is negatively associated with TFP, but it mitigates the marginal disincentive of higher FIN. • Financial channel profitability (RF) steepens the inverted U (interaction FIN^2*RF negative), shifting the inflection point right, exacerbating the marginal negative effect of higher FIN. Overall, short-term financial assets act as liquidity buffers facilitating upgrading, whereas long-term financial assets crowd out real investment and reduce performance persistence, producing an inverted U-shaped relation between total financialization and enterprise upgrading.

The findings address the central question of how financial asset allocation affects enterprise upgrading by demonstrating a dual effect: liquidity and flexibility of short-term financial assets facilitate investment, operations, and R&D continuity, enhancing risk-bearing capacity and productivity; conversely, long-term financial assets tie up capital, create maturity mismatches, crowd out real investment and R&D, and reduce earnings persistence, thereby impeding upgrading. The inverted U-shaped relationship integrates these opposing effects, indicating that moderate financialization can be beneficial but excessive levels are detrimental. Mechanism tests confirm the roles of risk-taking capacity (for short-term assets) and earnings persistence (for long-term assets). Heterogeneity analyses reveal stronger adverse nonlinear effects among over-indebted, non-SOE, and highly finance-constrained firms, consistent with their greater sensitivity to liquidity pressures and higher external financing costs. Moderation results show that real capital expenditure can attenuate the adverse marginal effects of financialization, while high profitability from financial activities can intensify them. These insights inform how firms and policymakers can balance financial and real investments to support upgrading during economic transition.

This study contributes micro-level evidence on the dual effects of corporate financial asset allocation on enterprise upgrading in China. It establishes an inverted U-shaped relationship between financialization and upgrading, clarifies mechanisms via risk-taking capacity and earnings persistence, and documents heterogeneity by leverage, ownership, and financing constraints. Policy implications include: (1) optimizing capital allocation and dynamically managing investment structures to leverage short-term financial assets while avoiding overinvestment in long-term financial assets; (2) strengthening securities regulation and disclosure to curb speculation and information asymmetry; (3) expanding financing channels for real assets to reduce reliance on long-term financial investments and speculative behavior. Future research could test generalizability across countries, refine upgrading measures (including field and case studies), enlarge samples, and incorporate additional contextual controls. Further mechanism work could include R&D investment and profit-to-cost ratios, and verification using alternative datasets such as the Chinese industrial enterprise database.

- External validity: Results are based on Chinese listed firms; cross-national studies are needed to generalize findings. - Measurement: Enterprise upgrading via TFP may not capture all qualitative and quantitative aspects; further refinement and validation are warranted. - Sample size and estimation: Despite a large panel, parameter estimation for mediation and regression could benefit from larger samples and broader coverage. - Controls and context: Although age, size, and industry are controlled, other contextual factors may influence relationships; future work should incorporate additional relevant controls.