Analysis on the motivation of financial investment and the influence of financial risk of listed enterprises: A case study of A-share manufacturing industry

Abstract

With the deepening of enterprise financialization, the trend of “moving away from reality to emptiness” has increased the difficulty of financial management in the manufacturing industry. This paper selects the data of A-share main board listed companies from 2012 to 2021 to study the motivation of financial investment in the manufacturing industry and its impact on financial risk. The research results show that the main motivation of listed companies’ financial investment in the manufacturing industry is “substitution” motivation. With the purpose of maximizing profits, the excessive allocation of monetary assets, especially long-term financial assets, increases financial risks of enterprises. Furthermore, the financial risk caused by the financial investment of state-owned enterprises is greater.

Keywords

Manufacturing financialization investment “substitution” motivation “reservoir” motivation

1. Introduction

Snce China’s economy has entered the new normal, the profitability of the real economy sector has declined. With the rapid development of the financial industry and real estate economy, a large number of manufacturing industries have shifted from the real economy to investment in the financial industry. This results in structural imbalances between the real and the virtual economy, and the rapid development of the real estate and financial sectors. The structural imbalance increases the cost of economic activities of real enterprises and leads them to be more inclined to carry out financial activities, resulting in the phenomenon of “moving from real to virtual”. The virtuous circle between industrial and financial capital is an important driving force for economic development. If non-financial enterprises are keen on financial investment, financial capital becomes the only form of capital. The development and innovation of their main business might be ignored, and the excessive allocation of financial assets will become a major hidden danger in China’s economic development. Industrial capital has lost its leading power and is controlled. The excessive allocation of financial assets means that the financial system has changed dramatically, and the assets of the country become “rich and vulnerable”. There are generally two possible motivations for financial investment in manufacturing. To regulate the flow of enterprise funds and alleviate financial difficulties, financial risks will be reduced [1]. Based on the profit-seeking motive of enterprises, financial income has increasingly become an important component of corporate profits in the context of insufficient growth power of the real economy. This may squeeze enterprise resources, decline the real economy, increase financial leverage, and aggravate the economic crisis. Does high financial investment income make manufacturing lose its dominant industry and turn from diversified investor to capital arbitrageur? This is the main research content of this paper. This paper mainly studies the relationship between the financialization and financial risk of the manufacturing industry, deduces its motivation, and analyzes its financial risk impact.

2. Theoretical mechanism analysis and research hypothesis

2.1 Influence mechanism of financialization on financial risk of the manufacturing enterprises

(1) The motivation of financial investment in manufacturing industry is “substitution”. “Substitution” mainly means that the main motivation of non-financial enterprises’ financial investment is profit maximization, and the main purpose is arbitrage. Because the financial return is higher than the return on physical investment, reducing the investment intensity of the main business and gradually developing into financial investment to replace physical investment, resulting in “substitution motivation”. Enterprises seek additional profits to make up for their main business operations. Excessive financial investment is bound to inhibit the development and innovation of the manufacturing industry, resulting in “crowding-out effect”. The “crowding-out effect” considers that the enterprise’s capital is limited. When the entity enterprises tend to invest in financial assets to obtain higher returns [2], financial investment squeezes out investment in the main industry. And the funds used for innovation in the main industry, product production, and research and development will decrease [3]. High returns in the financial sector will cause the enterprise management to change the priority of industrial investment and reduce the industrial investment rate [4]. With the increase of the total allocation of financial assets, the funds used by the enterprise for the main business will decrease, which will affect the future performance of the main business [5]. The financial investment of non-financial enterprises can reduce financial leverage and help reduce financial risks [6]. However, because it takes up company resources, it replaces the main business of enterprises, thus expanding the risks [7].

In addition, because the financial industry and the real estate industry have higher risks, the investment in these areas by the entity enterprises will spread the risks to their own enterprises, namely “the risk contagion effect”. Enterprises make a large amount of financial investment, resulting in the lack of cash flow buffer and possible financial problems. If external factors have brought crisis to the market, they will directly become the financial risks of the enterprise. Moreover, because the financial products themselves are affected by bank interest rates, government regulation, etc., the uncontrollability of risk is strong, resulting in uncertain returns and high risk of loss, which is likely to infect the entity enterprises. To sum up, the larger the total amount of financial assets allocated, the higher the financial risks faced by enterprises.

(2) The motivation of financial investment in manufacturing industry is “reservoir”. If the enterprise’s investment in financial assets squeezes the original main business investment, the former has a “substitution” effect on the latter, and it may be in the “preventive” or “reservoir” [8]. “Reservoir” refers to the use of well-developed income channels to reserve funds in enterprises, which can avoid the business risks caused by the uncertainty of macroeconomic policy. It is also a way to meet the capital needs of other projects or fields, balance the capital through diversification strategy, and reduce financial risks by holding financial assets. Through the reservoir, enterprises can avoid risks in an uncertain environment, which is the performance of diversified development of enterprises, and improve profitability in economic prosperity. When the economy is depressed, the diversification improves the ability of enterprises to resist risks [9]. When the enterprise faces financing constraints or main business losses, the income brought by financial assets can make up for the main business losses, meet the capital demand, and improve the financing ability, solvency, and operating capacity of the enterprise. “Reservoir” enables enterprises to reduce the cost of financial distress by selling financial assets. With the increase of the price of financial assets, it is conducive to improving the balance sheet and realizing corporate refinancing [10]. In addition, the holding of financial assets by physical enterprises can reduce the financial leverage ratio, thus reducing financial risks. From the perspective of the “reservoir” effect, the total allocation of financial assets is inversely proportional to the leverage ratio of enterprises, which is conducive to reducing financial risks and is a proactive activity.

In short, if the manufacturing industry holds financial assets moderately due to the “reservoir” motivation, it will idle funds and capital reserve, and then quickly realize the financial assets when the assets are needed. That is, holding finance can maintain certain liquidity and liquidity and reduce financial risk. If the enterprise is in a preference for financialization, over-financialization will directly affect the financial situation of the enterprise, and the allocation of financial assets is likely to aggravate the financial risk of the enterprise. The supervision should be strengthened, and financial investments that cannot solve the development problems of enterprises in a positive way should be cleaned up to prevent the possible adverse consequences.

2.2 Related works

In recent years, research on investment motivation has gradually deepened. Bachtiar Y et al. used quantitative research methods to explore the impact of investment knowledge, investment motivation, and financial literacy on the investment interest of the millennial generation in the Madhyan region of Indonesia. How investment interest served as a mediating variable to influence investment decisions was studied. The study surveyed 384 participants and found that investment knowledge, motivation, and financial literacy had a significant direct impact on investment interest. And investment interest significantly affected their investment decisions. Ultimately, it was emphasized that investment knowledge, motivation, and financial literacy indirectly played an important role in investment decisions through investment interest [11]. Christensen et al. conducted a study on the strategic motivations, processes, and expectations of institutional investors in sustainable building investment. It revealed the focus on green technology and certification in the investment process through 33 industry high-level interviews. Research found that the values of ecological labels were widely recognized when financial expectations dominate sustainability decisions, and institutional managers and investors generally used data collection and benchmarking methods to monitor energy and water usage. Ultimately, the study identified a series of localized and company level policies [12]. Lei et al. focused on the motivation of Chinese listed companies to hold cash and examined the impact of property rights. Research found that private enterprises were forced to hold high amounts of cash due to financing restrictions, while state-owned enterprises actively accumulated cash to increase financial flexibility. This study pointed out that credit discrimination was still an urgent issue that needs to be addressed in China’s financial system reform [13]. Mattke et al. studied people’s motivations for investing in Bitcoin using a hybrid approach. Combining qualitative and quantitative research, as well as Fuzzy Set Qualitative Comparative Analysis (fsQCA), seven specific investment motivations were identified for Bitcoin and how these motivational configurations explain Bitcoin investment behavior was analyzed. The research findings revealed that the motivation for investing in Bitcoin not only included profit expectations, but some people invested in Bitcoin without profit expectations, providing a new foundation for understanding the role of cryptocurrency in society [14].

In addition, research on financial risks is gradually increasing. Ahmad et al. explored the dynamic correlation between financial risk, renewable energy technology budget, and ecological footprint. They found, based on annual data from Organisation for Economic Co-operation and Development (OECD) countries from 1984 to 2018, that improvements in financial risk significantly reduced environmental footprint, and investments in renewable energy technologies promoted environmental sustainability. This study also confirmed the validity of the Environmental Kuznets Curve (EKC) hypothesis in OECD countries. And there was a one-way causal relationship from financial risk to renewable energy technology budget, and a two-way causal relationship between financial risk, ecological footprint, and economic growth [15]. Tobisova et al. proposed a new method for evaluating the financial risks and investment development of Slovak enterprises. This method integrated modern and traditional economic statistical methods, helped improve the sustainability of enterprises, and provided auxiliary tools for enterprise management in the investment decision-making process. They used commercial software tools to model the cash flow indicators of enterprises to calculate Net Present Value (NPV). Various statistical methods such as One-Way Analysis of Variance (ANOVA) and Shapiro Wilk test were applied to generate mathematical model algorithms among different Slovak enterprises [16]. Tariq et al. focused on the impact of green process innovation on corporate financial risk, exploring the moderating effect of organizational slack resources and competition intensity. Through a questionnaire survey and financial statement analysis of 202 listed manufacturing companies in Thailand, the research showed that green process innovation performance could significantly reduce the financial risk of enterprises. And this negative impact was more pronounced in situations of high competition intensity. However, companies with sufficient organizational slack resources were unlikely to reduce their financial risks by increasing green process innovation performance [17].

Based on the above literature, this study will focus on the relationship between financial investment motivation and financial risk of listed companies and attempt to explore it in the specific context of A-share manufacturing industry. The innovations of this study lie in expanding the previous research perspective on individuals or non-listed companies to a systematic analysis of listed companies, especially the manufacturing industry, and further combining this specific market and industry background. Unlike previous studies, this article will not be limited to exploring the factors that affect investment interest, but rather deepen the understanding of how financial investment motivation directly or indirectly affects financial risk. By focusing on the key industry of manufacturing, this study aims to provide more specific and customized recommendations for enterprise risk management and financial decision-making, providing a new perspective for theoretical and practical interfaces.

2.3 Research hypothesis

Hypothesis 1: The motivation for the financial investment in manufacturing industry is “substitution motivation”, and the financial risk faced by enterprises is directly proportional to the holding of financial assets.

Hypothesis 2: The motivation for the financial investment in manufacturing industry is “storage pool”, and the financial risk faced by enterprises is inversely proportional to the holding of financial assets.

3. Measurement model and data description

3.1 Sample selection

A total of 1199 manufacturing listed companies in China’s Shanghai and Shenzhen A-share main board market from 2012 to 2021 are selected as research samples. This paper has deleted the following information: (1) The samples with missing data; (2) The ST listed companies; (3) The enterprises with asset-liability ratio greater than 1; (4) The outliers. Finally, 647 companies are obtained. All the data in this paper are from Wind Information.

3.2 Explained variable

Enterprise financial risks: Financial risks mainly arise from the following aspects: enterprise debt management, investment mistakes, lack of liquidity of funds, and unreasonable financial management system. The index to measure financial risk was first proposed by Altman as the Z-core index [18], which was [19] revised by Altman. This paper adopts the revised Z-core to measure the financial risk of enterprises, and its calculation formula is: (0.717 * working capital $+$ 0.847 * retained earnings $+$ 3.107 EBIT $+$ 0.42 * total market value of shares $+$ 0.998 * sales income)/total assets. The higher the value of Z-score, the smaller the financial risk.

3.3 Explanatory variables

Financial investment: It is generally measured by financial assets/total assets. Because the effects of long-term financial assets and short-term financial assets may be inconsistent, holding long-term financial assets shows a substitution effect and can reduce financial risks. Contrary to the effects of long-term assets, holding long-term financial assets shows a reservoir effect [20]. Therefore, the explanatory variable financial investment is divided into long-term and short-term financial investments. The details are as follows:

(1)
Before the revision of the new standard in 2018

Financial investment (Fa): Financial assets/total assets $=$ (trading financial assets $+$ net available-for-sale financial assets $+$ net held-to-maturity investment $+$ net loans and advances issued $+$ derivative financial instruments $+$ long-term equity investment $+$ net investment real estate)/total assets.

Long-term financial investment (Long): Long-term financial assets/total assets $=$ (net available-for-sale financial assets $+$ net held-to-maturity investment $+$ net loans and advances issued $+$ derivative financial instruments $+$ long-term equity investment $+$ net investment real estate)/total assets. It refers to Bachmann et al. [21].

Short-term financial investment (Short): Short-term financial assets/total assets $=$ trading financial assets/total assets.
(2)
After the revision of the new guidelines in 2018:

According to the newly revised Accounting Standards for Business Enterprises No. 22 – Recognition and Measurement of Financial Instruments, “debt investment” replaces “held-to-maturity investment”.

“Other debt investment” replaces the debt investment in the “available-for-sale financial assets” title.

“Other debt investment” replaces the stock investment in the subject of “available-for-sale financial assets”.

Financial investment (Fa): Financial assets/total assets $=$ (trading financial assets $+$ other debt investment $+$ other equity investment instruments $+$ debt investment $+$ net loans and advances issued $+$ derivative financial instruments $+$ long-term equity investment $+$ net investment real estate)/total assets.

Long-term financial investment (Long): Long-term financial assets/total assets $=$ (other debt investment $+$ other equity investment instruments $+$ debt investment $+$ net loans and advances issued $+$ derivative financial instruments $+$ long-term equity investment $+$ net investment real estate)/total assets.

3.4 Control variables

This paper controls the characteristics and environmental factors of listed companies. It mainly includes enterprise scale, financial leverage, equity concentration, market return, nature of enterprise property rights, capital expenditure of listed companies, and cash flow. The details are as follows:

Enterprise size (Size): It is the natural logarithm of the company’s total assets at the end of the year. Financial leverage (Lev): Asset-liability ratio $=$ total liabilities/total assets. Equity concentration (Holding): It is the sum of the shareholding ratio of the top five shareholders. Market yield (market): It is the yield of CSI 300 index. Nature of enterprise property right (SOE): 0 stands for state-owned enterprises and 1 stands for non-state enterprises. Capital expenditure of listed companies (capital): ratio of fixed assets to total assets. Cash flow (cashflow): net operating cash flow/total assets.

3.5 Model construction

To alleviate the possible endogenous problems, this paper adopts the double-fixed (individual fixed and year fixed) effect model as follows:

$\displaystyle\textit{risks\_it}=\beta 0+\beta\textit{1Fa\_it}+\beta\textit{2% Size\_it}+\beta\textit{3Lev\_it}+\beta\textit{4Holding\_it}+\beta\textit{5SOE% \_it}+\beta\textit{6Market\_it}+\beta\textit{7Capital\_it}+\beta\textit{8% Cashflow\_it}+\alpha_{i}+\gamma_{t}+\varepsilon_{\textit{it}}$

risks_it represents the financial risk of a specific enterprise in a specific year. The higher the risk, the worse the business condition may be, thereby reducing the investment return for investors. Fa_it is an explanatory variable that represents the financial investment of a specific enterprise in a specific year, expressed as the proportion of investment in financial assets in the enterprise’s assets. Size_it is a control variable that represents the size of a specific enterprise in a specific year, usually expressed in logarithmic form of the total assets of the enterprise. The size of the enterprise may affect its risk level. Lev_it is a control variable that represents the financial leverage of a specific enterprise in a specific year, usually expressed as the ratio of the total liabilities of the enterprise to the total assets of the enterprise. The level of financial leverage has a direct impact on a company’s financial risk. Holding_it is a control variable that represents the concentration of equity in a specific enterprise in a specific year, usually expressed as the sum of the proportion of shares held by the largest five shareholders. The concentration of equity may affect the size of a company’s financial risk. SOE_it is a control variable that represents the nature of corporate rights of a specific enterprise in a specific year, mainly manifested as whether the enterprise is a state-owned enterprise, commonly represented by 0,1. Market_it is a control variable that represents the market return of a specific enterprise in a specific year, which is generally represented by the rise or fall of a stock price index. Market returns may have an impact on a company’s financial risk. Capital_it is a control variable that represents the capital expenditure of a specific enterprise in a specific year, generally expressed as the ratio of enterprise capital expenditure to total assets. Cashflow_it is a control variable that represents the cash flow of a specific enterprise in a specific year, expressed as the ratio of the net cash flow generated from operating activities to the total assets of the enterprise. $\alpha_{i}$ is an individual fixed effect, which can be roughly understood as some inherent and unchanging characteristics of each enterprise, such as the management team and technical capabilities of certain enterprises. $\gamma_{t}$ is a fixed time effect, which refers to a specific effect that a certain year has, which can control the common impact that all enterprises are affected by in the same year, such as macroeconomic conditions, policy environment, etc. $\varepsilon_{it}$ is a random error term that represents other factors that affect enterprise risk that have not been considered in the model. A to B are parameters.

4. Analysis of research results

4.1 Descriptive statistics

Table 1 shows the descriptive statistics of samples. The maximum value of risk indicators is 6.234, the minimum value is 0.403, and the average value is 1.878. There is a large gap in the investment of listed manufacturing companies in financial assets. Compared with short-term financial assets, listed manufacturing companies prefer long-term financial assets and real estate investments, which are more than 7 times of short-term financial assets. In terms of the nature of enterprises, there are more state-owned enterprises, with non-state enterprises accounting for only 41.9%.

Table 1
Descriptive statistics of variables

Stats	Mean	Sd	p5	p25	p50	p75	p95	Min	Max
Z-core	1.878	1.029	0.65	1.163	1.654	2.32	3.994	0.403	6.234
Fa	0.078	0.116	0	0.009	0.035	0.096	0.304	0	0.953
Short_term	0.010	0.036	0	0	0	0.00048	0.0627	0	0.845
Long_term	0.069	0.1096	0	0.007	0.029	0.081	0.279	0	0.953
Size	4.195	1.324	2.116	3.32	4.084	5.004	6.529	$-$ 0.766	9.126
Lev	0.472	0.191	0.158	0.329	0.477	0.616	0.776	0.008	0.999
Holding	49.138	15.266	26.26	38.02	48.45	58.68	76.66	9.590	157.75
Soe	0.419	0.493	0	0	0	1	1	0	1
Market	0.101	0.229	$-$ 0.253	$-$ 0.077	0.056	0.272	0.517	$-$ 0.253	0.5166
Capital	0.248	0.156	0.049	0.125	0.215	0.345	0.552	0	0.836
Cashflow	0.047	0.076	$-$ 0.060	0.008	0.044	0.087	0.17	$-$ 0.657	0.920

4.2 Correlation analysis

Table 2 shows the results of the correlation analysis of each variable. The correlation coefficient coefficients between the explanatory variable and the control variable are less than 0.5, that is, the model does not have multicollinearity.

Table 2
Correlation analysis

	Z-core	Fa	Short_term	Long_term	Size	Lev	Holding	Nfb	Market	Capital	Cashflow
Z-core	1.000
Fa	$-$ 0.062***	1.000
Short_term	0.044***	0.334***	1.000
Long_term	$-$ 0.080***	0.951***	0.028**	1.000
Size	$-$ 0.201***	$-$ 0.057***	$-$ 0.00300	$-$ 0.059***	1.000
Lev	$-$ 0.360***	$-$ 0.193***	$-$ 0.192***	$-$ 0.142***	0.335***	1.000
Holding	0.057***	$-$ 0.081***	0.011	$-$ 0.090***	0.350***	0.024*	1.000
Nfb	0.079***	0.112***	0.074***	0.095***	$-$ 0.152***	$-$ 0.149***	$-$ 0.133***	1.000
Market	$-$ 0.027**	0.014	0.063***	$-$ 0.00600	$-$ 0.001	0.007	0.002	0.000	1.000
Capital	$-$ 0.143***	$-$ 0.289***	$-$ 0.112***	$-$ 0.270***	0.099***	0.082***	0.009	$-$ 0.058***	0.007	1.000
Cashflow	0.213***	$-$ 0.082***	0.044***	$-$ 0.102***	0.184***	$-$ 0.126***	0.123***	0.035***	0.004	0.218***	1.000

4.3 Analysis of basic regression results

The three models in Table 3 are the regression of total financial investment, long-term financial investment, short-term financial investment, and financial risk of manufacturing enterprises. The results of model (1) show that the coefficient of total financial assets held by the manufacturing industry is significantly negative at the level of 1%, indicating that the financial investment will increase the financial risk of the manufacturing industry. This is not consistent with the motivation of “reservoir”, indicating that enterprises are mainly “substitution” effect, hoping to obtain high returns through financial investment, resulting in increased financial risk. Financial assets occupy the development funds of the main business, and the funds used by enterprises for innovation investment and financial response decrease. In addition, the risk of increased financial investment itself spreads to enterprises, leading to an increase in the financial risk of the manufacturing industry. From the regression results of models (2) and (3), the short-term financial investment and the financial regression results of manufacturing enterprises are not significant. According to, the regression results of model (3) are significantly negative at 1%, mainly due to the poor profitability of manufacturing owners’ business and the excessive increase of long-term financial investment by enterprises. The income and loss of long-term financial investment are uncertain, and the liquidity is weak, thus the enterprise faces higher risks.

Table 3
Basic regression results of manufacturing industry

	Model (1)	Model (2)	Model (3)
Fa	$-$ 0.674^*** ( $-$ 3.263)
Short_term		$-$ 0.050 ( $-$ 0.141)
Long_term			$-$ 0.818^*** ( $-$ 3.527)
Control variable	Control	Control	Control
_cons	3.270^*** (16.487)	3.113^*** (15.732)	3.280^*** (16.683)
N	5809	5809	5809
${\mathbb{R}}^{2}$	0.288	0.285	0.288
Adjusted ${\mathbb{R}}^{2}$	0.286	0.283	0.286
F	61.033	59.551	61.479

$t$ statistics in parentheses, $*p<$ 0.1, ^**p< 0.05, ^*** p< 0.01.

In model (1), the total amount of manufacturing financial assets held has a significant positive relationship with corporate financial risk, which proves that excessive financial investment increases the financial risk of the enterprise. The empirical results in model (2) are not significant, which may indicate that the scale of short-term financial investment does not have a significant impact on the overall financial risk of the enterprise. It indicates that the enterprise may prefer short-term financial assets with strong liquidity to cope with potential liquidity needs. Focus on the results of model (3), there is a significant negative correlation between long-term financial investment and corporate financial risk. This means that long-term financial investment more reflects an increase in financial risk, especially when the main business operations are not ideal. Long-term financial investment places more emphasis on long-term returns, and the instability and liquidity risk in changing market environments are significantly higher than short-term financial assets. If a company lacks sufficient understanding and management of long-term investment, it is likely to increase the market risk it faces. In the results of model 3 (the relationship between long-term financial investment and corporate financial risk), there is a significant negative correlation between long-term financial investment and corporate financial risk. It indicates that the higher the long-term financial investment, the greater the corporate financial risk. In theory, long-term financial investments may bring higher returns, but also correspondingly higher risks. On the one hand, due to the large time span involved in long-term financial investment, its profitability and liquidity are often inferior to short-term financial assets. On the other hand, long-term financial investment often involves a large amount of money. Once investment problems occur, they may cause significant pressure on the financial situation of the enterprise, thereby increasing the financial risk of the enterprise. Analyzing empirical results, in practical operations, enterprises may not fully consider risk control issues while pursuing high returns on long-term financial investments. Once the investment fails, it may lead to a significant loss of funds and even affect the normal operation of the enterprise, thereby significantly increasing the financial risk of the enterprise, which is consistent with our empirical results. In addition, it should be noted that due to the weak profitability of the manufacturing owner’s business, enterprises overly rely on long-term financial investments to obtain returns. The returns and losses of long-term financial investments are uncertain, and the liquidity is weak. Therefore, enterprises face higher risks, increasing their financial risks. Overall, the empirical results of model 3 reveal that long-term financial investment activities in the manufacturing industry may increase a company’s financial risk. So companies should consider and balance investment returns and risks more strategically when making long-term financial investment decisions.

4.4 Further study

According to Bachmann et al. [21], non-state-owned enterprises are more difficult to finance and more prudent to invest. So enterprises are divided into state-owned enterprises and non-state-owned enterprises for analysis.

Table 4 and 5 show the regression results of state-owned manufacturing and non-state manufacturing, respectively. Model (4) is significantly negative at 1%, and model (7) is significantly negative at 10%. Both state-owned enterprises and non-state-owned enterprises are motivated by “substitution”, and they are motivated by long-term financial investment to pursue high returns. Moreover, state-owned enterprises are more risk-averse, and their awareness of risk diversification is weaker, and their financial risks are greater. Models (5) and (8) show that the regression results of short-term financial investment and financial risk are not obvious, and it is difficult to confirm their investment motivation.

Table 4
Basic regression results of state-owned manufacturing industry

State-owned enterprise group
	Model (4)	Model (5)	Model (6)
	$-$ 1.019^*** ( $-$ 3.158)
Short_term		$-$ 0.280 ( $-$ 0.482)
Long_term			$-$ 1.107^*** ( $-$ 3.299)
Control variable	Control	Control	Control
_cons	3.820^*** (14.765)	3.613^*** (14.340)	3.804^*** (14.776)
N	3373	3373	3373
$R^{2}$	0.265	0.260	0.265
Adjusted ${\mathbb{R}}^{2}$	0.262	0.257	0.262
F	42.608	40.884	42.870

Table 5

Basic regression results of non-state manufacturing industry

Non-state-owned enterprise group
	Model (7)	Model (8)	Model (9)
Fa	$-$ 0.469^* ( $-$ 1.659)
Short_term		0.213 (0.463)
Long_term			$-$ 0.692^** ( $-$ 2.104)
Control variable	Control	Control	Control
_cons	2.941^*** (9.394)	2.809^*** (8.886)	2.975^*** (9.550)
N	2436	2436	2436
${\mathbb{R}}^{2}$	0.329	0.328	0.330
Adjusted ${\mathbb{R}}^{2}$	0.325	0.324	0.326
F	31.943	31.367	32.328

In general, the financial investment of manufacturing enterprises will increase their financial risk. From the perspective of stage limit, it is mainly due to the long-term financial product investment of enterprises. From the perspective of macro-economy, the financialization of physical enterprises is due to the high profits of the financial industry and the real estate industry, followed by the overcapacity of the traditional manufacturing industry, the lack of effective supply, the inability of products to match the market demand, the decline of the overall profit margin. To obtain more income, enterprises choose financial investment.

In models (4) and (7), the financial risks faced by state-owned and non-state-owned manufacturing industries in the process of financialization are analyzed, and their empirical results were similar. This indicates that increasing the holding of financial assets will increase the financial risk of the enterprise. Whether for state-owned or non-state-owned enterprises, excessive financial investment (“substitution motivation”) will bring greater financial risks. This trend reflects that state-owned and non-state-owned enterprises may not have fully considered risk factors when pursuing high returns in the financial sector. However, further research shows that the financial risk of non-state-owned manufacturing is significantly higher than that of state-owned manufacturing. This may be due to the higher risk preference and tolerance of non-state-owned manufacturing in financial investment decision-making compared to state-owned manufacturing. However, in models (5) and (8), the relationship between short-term financial investment and corporate financial risk is not significant, and their investment motives cannot be confirmed. This result may suggest that compared to long-term financial investments, companies have relatively better risk control and management of short-term financial investments. In other words, because the returns and losses of short-term financial investments are relatively easy to predict, companies are more likely to make appropriate risk management strategies to control potential financial risks. The regression results of model (6) show that long-term financial investment has a significant negative impact on the financial risk of state-owned manufacturing enterprises. Due to the fact that the investment behavior of state-owned enterprises is often influenced by policy orientation, there is stronger financial support and policy bias behind it. This may make them more cautious when making long-term investment decisions. However, when these companies still choose a higher proportion of long-term financial investments, it may be based on long-term strategic layout rather than short-term returns considerations. Model (9) explores the situation of non-state-owned manufacturing enterprises, and these results show a significant negative impact of long-term financial investment on financial risk. Compared with state-owned enterprises, non-state-owned enterprises usually face a more severe financing environment and market competition pressure. They may be more inclined to seek additional income through long-term financial investment to alleviate operational and financing pressures. Combining the analysis of models (6) and (9), the long-term financial investment has a significant negative impact on the financial risks of both types of enterprises. This emphasizes the importance of risk management and the necessity of long-term risk assessment in financial investment decisions. It also reveals that state-owned and non-state-owned enterprises may need to adopt differentiated management strategies when facing financial challenges. Future research can further consider the robustness of these results in different market environments and explore specific measures to improve corporate financial risk management.

5. Conclusions

The environment for the development of China’s manufacturing industry is increasingly severe. Affected by the overall environment, there are contradictions such as shrinking demand, overcapacity, rising costs, insufficient technological innovation, and sharp decline in industry profits. At present, the idle rate of the manufacturing industry has reached 28%, and about 35% of the manufacturing capacity utilization rate is lower than 75%. China’s manufacturing industry has strong volatility and weak profitability. The financial industry has developed rapidly in recent years. The financial investment income of the manufacturing industry is about three times of the main business income. The expansion of financial investment has become an important source of expansion and increase of profits in the manufacturing industry. And the manufacturing industry has begun to replace the main industry with financial investment. The financialization of manufacturing industry will bring short-term prosperity, but it is extremely fragile. Excessive increase in financial investment leads to the flow of funds to non-real economic sectors, increases the financial risk of enterprises, weakens the development capacity of manufacturing industry, and further aggravates the operational risk of the whole economy.

After summarizing the research results, the financial risk of the manufacturing industry is mainly affected by the financialization of enterprises. When manufacturing enterprises excessively invest in financial assets, it may lead to a squeeze on the investment and development of their main business, thereby increasing the financial risk of the enterprise. This phenomenon is mainly reflected in the “substitution effect”, which means that enterprises choose to invest in high-risk financial assets and real estate to achieve higher returns. And this behavior often has adverse effects on the main business of the enterprise and may even lead to financial difficulties for the enterprise. There are significant differences in risk attitudes and strategies among enterprises of different natures in the process of financialization. In the empirical analysis, both state-owned and non-state-owned enterprises exhibit a “substitution” motivation. However, due to their weaker resistance to risk, their financial risks are often higher. Long-term financial investment is the main source of financial risk for manufacturing enterprises. Among all financial investments, long-term financial product investments have the highest risk, mainly because the returns and losses of long-term financial products are uncertain, and the liquidity is poor, so enterprises face greater risks. In summary, it should be vigilant about the financial risks that excessive financialization in the manufacturing industry may bring. Especially in the current macroeconomic environment, manufacturing enterprises need to make moderate financial investments, ensuring both their own operating cash flow and the normal operation and development of their main business. And for financial regulatory authorities, it is also necessary to strengthen the supervision of the financialization process of manufacturing enterprises to prevent potential risks caused by excessive investment in financial assets.

The main contribution of this study is to reveal the impact of financial investment on financial risk in the financialization of manufacturing enterprises. This discovery has guiding significance for risk assessment and management of enterprises when making financial investment decisions. And it also provides policy recommendations for policymakers to regulate corporate financial behavior and promote stable development of the real economy. However, the conclusions of this study also have some limitations. Firstly, the samples are limited to manufacturing companies listed on the A-share markets in Shanghai and Shenzhen, China, and may not represent the financialization behavior of all types of enterprises. Future research can consider a wider range of samples, including small and medium-sized enterprises and non-listed enterprises, to obtain a more comprehensive perspective. On the basis of considering these limitations, the conclusion of this study still provides a valuable perspective for understanding the drivers of financial risks in manufacturing enterprises, providing policy makers and business managers with countermeasures.

Footnotes

Suggestions

To address the financial risks brought about by the financialization of the manufacturing industry, a series of specific policy recommendations are proposed below.

Establish a financial risk assessment system for enterprises. Encourage enterprises to establish a financial risk assessment and early warning system based on their own scale and business characteristics, and regularly conduct risk assessments on their financial investment behavior. Government departments can provide professional guidance and technical support to encourage enterprises to scientifically evaluate and control risks.

Promote financial investment transparency. Improve the information disclosure system and require enterprises to disclose their financial investment structure and related risk information to enhance market supervision and provide transparent information for investor decision-making.

Implement differentiated financing support policies: Develop targeted financial and tax preferential policies to provide tax incentives and financing support for enterprises that invest more funds in their main business, especially in research and innovation.

Conduct multi-dimensional risk management training. Organize periodic financial risk management training and seminar activities to enhance the risk management awareness and ability of manufacturing entrepreneurs and executives, and help enterprises optimize financial decisions.

Restrict financial investment activities in non-core businesses. Regulatory authorities can set upper limits on the financial investment amount and proportion of enterprises in non-core businesses, and dynamically adjust them based on industry characteristics, setting thresholds for financial investment of enterprises with frequency restrictions and capital adequacy requirements.

Encourage innovation-driven development. Advocate manufacturing enterprises to invest a certain proportion of their profits in research and innovation capacity building, fundamentally improving the profitability of their main business and avoiding relying on financial investment to supplement business income.

Provide diversified financial services. Innovate financial service products to provide diversified financial support for the manufacturing industry, such as offering more flexible financial derivative products such as futures and options, helping enterprises hedge risks and mitigate the adverse effects of market fluctuations.

Intensify the crackdown on illegal financial activities. Regulatory authorities need to strengthen supervision, crack down on high-risk, low transparency, and regulatory avoidance financial activities, purify the financial environment, and ensure that corporate financial investment behavior is conducted within compliance and supervision.

Encourage the healthy development of industry finance integration. Guide banking and financial institutions to deepen cooperation with manufacturing enterprises, promote the integration of industry and finance, help enterprises optimize resource allocation through financial means, and support the sustainable development of the real economy.

Based on the above suggestions, by strengthening supervision, guidance, and support, manufacturing enterprises will be able to find a balance between financial investment and main business growth, establish stable business models, and achieve long-term sustainable development.

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Analysis on the motivation of financial investment and the influence of financial risk of listed enterprises: A case study of A-share manufacturing industry

Abstract

Keywords

1. Introduction

2. Theoretical mechanism analysis and research hypothesis

2.1 Influence mechanism of financialization on financial risk of the manufacturing enterprises

2.2 Related works

2.3 Research hypothesis

3. Measurement model and data description

3.1 Sample selection

3.2 Explained variable

3.3 Explanatory variables

3.5 Model construction

4. Analysis of research results

4.1 Descriptive statistics

Table 1 Descriptive statistics of variables

Table 2 Correlation analysis

Table 3 Basic regression results of manufacturing industry

Table 4 Basic regression results of state-owned manufacturing industry

Footnotes

Suggestions

References

Table 1
Descriptive statistics of variables

Table 2
Correlation analysis

Table 3
Basic regression results of manufacturing industry

Table 4
Basic regression results of state-owned manufacturing industry