Past two and a half decades have seen conscious efforts on the part of several developing economies to liberalize their financial systems along with greater reliance on private corporate sector as a stimulant/driver of growth. Indian economy also witnessed series of liberalization, privatization and globalization measures post Industrial Policy 1991 partly to meet IMF conditionalities and partly in its own pursuit of recovery. One of the primary objectives of liberalization through Industrial Policy 1991 was to ensure that a market-oriented economy would infuse capital expenditures by corporate India to connect to the global economy and push the country’s growth rate. This paradigm shift was a result of the precarious situation faced by the economy in early 90s with respect to mounting fiscal and trade deficits and risk of being a defaulter at the international front. In the changed scenario, market forces largely govern the supply of funds and this may impact the pattern and pace of private corporate investment. As the capital expenditures involve huge investment, availability of funds is a prerequisite in undertaking them. This study confines itself to the consideration of business fixed investment (corporate capital expenditures). For better exposition, this paper has been organized into five sections. The first Section I introduce the topic followed by literature review in Section II. The next two sections, Section III and IV put forward methodology and empirical analysis. The next, Section V dwells upon the results followed by conclusion in Section VI.

LITERATURE REVIEW:

The landmark article of Modigliani and Miller (1958)¹ has served as a guiding light for a large pool of financial literature on independence of investment decisions from financing decisions in perfect capital market setting. Some others argue that market imperfection is a reality and it is necessary for the firms to consider financing options while making investment decisions. Donaldson (1961)² has shown that managers list retained earnings, debt and outside equity in decreasing order of priority for raising funds to finance investment decisions. This financing hierarchy is popularly known as pecking order of financing choices. McCabe (1979)³ has remarked that since the publication of the work of Modigliani and Miller (MM) in the late 1950s there has been a recurrent controversy in the finance and economic literature about the interdependence of investment and financing variables. Even MM recognized this interaction of decisions after incorporating taxes in their model. Researchers offer support to hierarchy in sources of finance – the pecking order theory (Myers and Majluf, 1984)⁴. Various other authors including, Mayer (1988)⁵, Fazzari et al. (1988)⁶ and Kaplan and Zingales (1997)⁷ have supported pecking order theory over independence of financing and investment decisions. Empirical support rests on the premise that asymmetric information makes external capital costly as compared to internal funds leading to underinvestment problem. This school of thought develops a strong favor towards a positive relationship between internal funds and corporate investment. Conversely, this relationship might also be an outcome of availability of plentiful retained earnings, which labels internal funds as too inexpensive and readily available. This may happen from the vantage point of management and agency costs in such instances may be noticeable (Jensen 1986⁸, Bernanke and Gertler 1989⁹). The pivotal piece of research by Fazarri, Hubbard, and Petersen (1988) regarding existence of financing constraints has drawn considerable attention from the research community and has garnered theoretical (asymmetric information and agency conflicts) as well as empirical support. In this framework, different sources of finance are expected to leave an impact on corporate investments and help better allocation of resources. Therefore, the primary aim of this paper is to empirically test the importance of various sources of finance used for the purpose of making capital expenditure decisions in public and private sector in India. The study covers manufacturing industries in both the sectors to draw comprehensive conclusions on this vital issue. The behavior of Indian firms 1990 onwards provides a unique opportunity to analyze the usage of internal as well as external sources along with the role of equity financing. Very few researches in the past have found equity to be a significant variable defining investment. However, the post liberalization period gives a unique dataset where usage of equity has been extensive. A review of relevant literature suggests that theories of investments primarily revolve around accelerator model, cash flow accelerator model and Q theory. Accelerator-cash flow model maintains that the quantum of investment spending is dependent on the amount of profit that a firm is earning. Studies by Grunfeld (1960)¹⁰ and Eisner (1967¹¹, 78¹²), Bagchi (1962)¹³ and Sarkar (1970)¹⁴ have used profit as a determinant of investment. Alternatively, Jorgenson (1963)¹⁵ in his neo-classical model of investment explains a process of capital stock adjustment to an optimal level with prominent influence of relative factor costs. He advanced Fisher’s analysis by using marginalist approach (where marginal productivity of capital and marginal cost are in balance) and also by inclusion of depreciation, expected capital value changes and interest rates into his concept of capital. Dixit (1962)¹⁶ has empirically tested this theory in Indian context. The present study plans to use accelerator- cash flow model as the basic model of analysis as output and internal funds seem to be the most significant factors in explaining investment in Indian corporate sector. A majority of the studies in India have used accelerator, accelerator cash flow models due to data availability constraints attached to user cost of capital concept of neoclassical model. Moreover, a large body of international literature also uses the Q-models to explain investment behavior. The Q-model claims that all required information to guide the firm’s investment decision is summarized in Q. In particular, the magnitude of internal funds known to the market should have no incremental explanatory power beyond Q. However, in an imperfect capital market, the Q model may not be a recommended framework for investment analysis. Due to the presence of asymmetric information, market expectations might not get truly reflected in the insiders’ valuations of investment opportunities. In such an environment, cash flow would be a better measure of investment opportunities than Q. Therefore, in the context of developing countries like India, where processing of information is inefficient, investment would be more sensitive to cash flow than to Q and the Q model might not add extra explanatory power over cash flow. Hence, markets in India are not efficient enough to echo the true value of firms, thereby restricting the usage of Tobin’s Q.

METHODOLOGY:

a. Data and Sample Selection:

This study is based on the analysis of firm level data. The objective of the study as stated earlier requires the sample to have companies actively involved in capital expenditures. The study is based on the 500 largest companies of India on the premise that large companies are actively involved in capital (investment) expenditures. Inclusion of the largest Indian companies makes the sample more representative of Indian corporate sector rendering authenticity to the results thereby. The scope of investigation has been confined to listed companies because the financial and accounting information related to unlisted companies is not available in detail. An attempt has been made to include government companies which are run as commercial enterprises. In the initial heydays of socialism, the public sector enterprises were not envisaged as purely commercial entity. Rather these were entrusted with a large number of non commercial objectives, such as, equitable growth, generating employment, balanced regional development and also as a stimulating agent for other private sector initiatives intended to benefit from backward and forward linkage. As a result, a large number of public sector enterprises’ turned in to loss making units and budgetary support used to be provided to such public sector enterprises’ to keep them in operation. With the onset of the New Economic Policy, 1991 and economic liberalization, an increasing number of public sector firms/ government owned companies have started operating on commercial lines. This is also reflected in corporatization of many departmental undertakings. The economic reforms have changed the ideology ruling the management of these government companies. Divestment of shareholding and autonomy in management has brought these companies at a common platform in terms of their investment policies and expansion plans. Statutory corporations have not been included in the present study due to the different set of objectives governing their decision making process. Further, trading, banking and finance companies have been excluded at the outset due to the different nature of services offered by them. The following criterion has been applied to select companies in the study:

(i) Continuity of Operations:

The companies with uninterrupted operating activity have been considered.

(ii) Consistent Data Availability:

Companies with necessary financial data for the whole period have been included in the study.

(i) Common and Consistent Accounting Year:

The companies having a common accounting year starting from 1^st April to 31^st March have been incorporated in the study to maintain a consistent basis of comparison.

Even the companies that have changed the accounting year during the study period have been kept outside the scope of analysis. The financial data pertaining to these companies was imbalanced for two accounting years, with one year having less than twelve months data and succeeding year having more than twelve months data. While furthering the analysis of this sample, the companies beyond manufacturing sector had to be dropped due to unavailability of a suitable deflator, Wholesale Price Index (WPI). As this study is covering a period of fourteen financial years, it is necessary to conduct the analysis on constant prices so as to avoid the influence of price level changes.

b. Study Period:

The study period was planned to be 1990-91 to 2008-09, to virtually cover the growth of Indian corporate sector after the structural adjustments, industrial policy and opening up of Indian economy since 1991. However, the source of data collection (i.e. PROWESS) had some limitations on this count. The coverage of the companies and the relevant information about the sample companies was insufficient for the initial years. The data pertaining to sample companies for the sample period of 1994-95 to 2008-09 has been sourced primarily from a firm-level micro database; PROWESS administered by the Centre for Monitoring Indian Economy (CMIE). Since this is a study of investment behaviour of Indian corporate sector requiring a cross-section comprising of companies over fourteen years (1994-95 to 2008-09), panel data models are used for regression and estimation. Balanced panel has been chosen for two reasons:

1. In case of unbalanced panel, the results have to be interpreted with caution and there will be necessity of dropping the groups (firms) with many missing points; and

2. If firms with incomplete data for the entire period under study are included, it will lead to an extremely large number of groups (firms) for analysis.

3. Results and Interpretations

EMPIRICAL ANALYSIS:

To understand the significance of financing patterns in corporate investment, the model used by Clark et al (1979)¹⁷ with U.S. based data and by Gangopadhyay, Lensink and Molen (2001)¹⁸ with Indian dataset has been used in the study. In the model Clark et al integrated accelerator and internal funds as follows:

I = f (output, internal liquidity) Equation 1

Where “I” is investment, output represents accelerator and cash flow from operating activities have been used as a proxy for internal funds.

The essential feature of this model is that it controls firms’ investment opportunities through output and studies the sensitivity of investment to internal liquidity thereby highlighting the respective importance of internal and external funds in investment decisions. This model has been chosen considering its aptness in current Indian context. First, there is a need for integrating capital market imperfections in the analysis consequent to the period of financial sector reforms. Second, various Indian studies have stressed on the influence of capital market imperfections on financing patterns. An attempt has been made to encompass major relevant variables in Indian context influencing corporate investment and hence the choice of the model. This model will help to understand the differential impacts of the mode of financing on investment. Considering these features, the model (Equation 1) is specified as follows for the purpose of estimation in this study for a large panel of 176 firms for the period of 1994-95 to 2008-09. Thus, for firm i at time t (measured in years), investment function is postulated as:

∆F’_it= α + β₁∆Y’_it+ β₂∆I’_it+ β₃CFO’_it+ β₄FEQ’_it+ β₅FB’_it+ β₆TC’_it+ β₇LAG∆F’_it+ β₈LAG∆Y’_it+

ν_it; ν_it~IID (0, σ_ν²) Equation 2

Where

∆F’_it =Change in net fixed assets of firm I in period t

∆Y’ = Change in output

∆I’ = Change in inventory

CFO’ = Cash flow from operating activities

FEQ’ = Flow of equity

FB’ = Flow of borrowings

TC’ = Trade credit and Acceptances

LAG ∆F’= Change in net fixed assets in the period t-1

LAG ∆Y’= Change in output in the period t-1

V = Error term

The single equation model [Other authors that use such a single equation model to study corporate investment behavior include Eisner (1978), Fazzari et al. (1988), Hoshi et al. (1991)¹⁹, Oliner and Rudebusch (1992)²⁰, and Athey and Laumas (1994)²¹] expresses investment as a function of change in output (∆Y’), change in inventory (∆I’), cash flow from operating activities (CFO’), flow of equity (FEQ’), flow of borrowings (FB’), trade credit and acceptances (TC’), change in net fixed assets in the previous year (LAG ∆F’) and change in output in the previous year (LAG ∆Y’) for the period under consideration. In the present study, autocorrelation has been estimated through D-W statistic for both composite sample and industry groups. Further, all the variables have been divided by capital stock at the beginning of the year (K). This has been done to remove the scale effects from the data and tackle the common problem of heteroskedasticity in such a heterogeneous sample. A similar practice of scaling down the variables has been adopted by various other researchers like Fazzari et al (1988), Athey and Laumas (1994), Gangopadhyay, Lensink and Molen (2001) and so on. Multicollinearity has been analyzed through correlation matrix. Moreover, as the study encompasses a dataset dealing with more than a decade, the inflation impact is bound to creep into the data thereby lowering the reliability of the results. This issue has been suitably addressed by adjusting all the nominal variables by 1993-1994 Wholesale Price Index (WPI) for manufacturing industries. The data for the same has been sourced from the Office of the Economic Adviser, Ministry of Commerce and Industry, Government of India.

Hence the estimation is based on the following model:

Equation 3

The same has been reproduced as,

∆F_it= α + β₁∆Y_it+ β₂∆I_it+ β₃CFO_it+ β₄FEQ_it+ β₅FB_it+ β₆TC_it+ β₇LAG∆F_it+

β₈LAG∆Y_it+ ν_it Equation 4

Equation 4 pertains to classical regression (Panel Ordinary Least Squares). Further, Lagrange Multiplier test statistic favors fixed/random effect model over the OLS, FEM/REM has been applied later in the analysis. Moreover, later the Hausman test statistic favors fixed effects model over random effects model. A major advantage of FEM is that it largely corrects for any potential selectivity bias arising out of sample selection. Time period effects have also been introduced along with group dummy variables in FEM to verify if group dummy results hold good even after adding time/ period effects. The equation with inclusion of period effects is as follows:

Equation 5

The firm level data used for the study have both cross-section and time dimensions and therefore, the technique of panel data analysis has been found as the appropriate choice of methodology.

a. Investment:

The investment variable is denoted by F_it i.e. investment (F) of the firm (i) in period t. A similar definition of net investment has been preferred over gross investment by various other researchers such as Clark et al (1979), Cleary (1991)²², Gangopadhyay, Lensink and Molen (2001), Athukorola and Sen (2002)²³, Bhattacharya (2007)²⁴ and various other studies. Three of the above mentioned studies have been conducted with a sample dataset from Indian companies.

b. Change in Output:

Change in level of output is considered as a measure of accelerator. This study has taken current (t) and a year (t-1) lagged value of change in output to represent accelerator as earlier considered by Athey and Laumas (1994). The lagged values have been considered to the extent of past one year as current year less two (t-2) and current year less three (t-3) lags did not show much impact on the results. Additionally, larger lags would also imply loss of degree of freedom. The value of output has been defined as sales plus changes in inventory of finished goods as supported by Jorgenson and Siebert (1968)²⁵. Various researchers have used accelerator theory including Clark (1979), Whited (1992)²⁶, Krishnamurty and Sastry (1975)²⁷, Athey and Laumas (1994), Kumar, Sen and Vaidya (2001)²⁸, Gangopadhyay, Lensink and Molen (2001), Nair (2004)²⁹, and Bhattacharyya (2007) to explain corporate investment. A positive change in output by a firm is intended towards serving a larger market through higher production levels. This extension of the scale of production paves the way for capital expenditures i.e. investment decisions. Moreover, the accelerator theory holds that if output is growing, an increase in capital stock is required. Though the accelerator theory initially had certain limitations like full utilization of capital stock, ignorance of lags and expectations formations, the theory was later modified and presented as flexible accelerator theory. Further the introduction of cash flow as a variable in the model gained prominence firstly, due to the signaling effect of cash flows as an indicator of robust future profitability and secondly, creation of financing hierarchy owing to market imperfections. Primarily, a significant and positive relationship of change in output (current and lagged) with investment (change in net fixed assets) has been hypothesized.

c. Change in Inventory:

A company’s expansion plans are bound to show impact on two kinds of investment, namely, fixed (to increase the production capacities) and inventory (to support the increase in production capacities) investment. Both these kinds of investment are theoretically expected to have a competitive relationship. Moreover, investment may be required in work in progress in the products with long gestation periods and those involving huge amount of funds. Finally the finished goods inventory surely needs investment to facilitate the working of sales and marketing departments and it is furthermore important for products with seasonal demand. Moreover, the expansion plans of the company may push both capital and inventory investment simultaneously if the funds allow. Both the types of investment are therefore expected to be complementary in nature but are subject to the same pool of funds. Therefore, at various instances the capital expenditure plans of a company may be delayed due to blockage of funds in inventory or vice versa. It may hence be anticipated that an increase of investment in fixed assets may require a restriction or decrease in inventory investment. To obtain a precise analysis of flow of funds (internal as well as external) it is important to include change in inventory as an explanatory variable in the fixed investment equation. Even if flow of borrowings and cash flows are included in the equation, there is a need to include inventory investment because the same funds may be diverted towards inventory investment instead of total allocation towards fixed investment. In this study a negative sign for change in inventory has been postulated for the reasons stated above. This variable has been explicitly included in various studies conducted in India such as Krishnamurty and Sastry (1975), Swamy and Rao (1974)³⁰ and Rao and Mishra (1976)³¹. Krishnamurty and Sastry (1975) also postulated as well as concluded a negative sign for this variable in their fixed investment equation.

d. Cash flow from Operating Activities:

Cash flows can be defined as flow of cash in a firm over a period of one year. This variable has been incorporated in the investment equation by a large number of Indian and international studies with slight variations in its definition depending on data availability. Its significance is evident from the fact that investment requires funds, and the same can be arranged either externally or through internal funds. Cash flows serve as a strong proxy for internally generated funds. The rationale behind inclusion of internal funds in the fixed investment equation relies on the fact that supply of funds dictates the investment plans of a company. Internal funds are considerably more convenient to deploy and do not pose the risk of control dilution. Cost is another important factor for raising the significance of internal funds. External funds have floatation and maintenance costs (dividend or interest) associated with them as compared to implicit cost of internal funds. Additionally, the internally generated funds are less risky than externally raised funds. Various studies conducted in India and abroad have considered cash flows as an explanatory variable in the fixed investment equation including Clark et al (1979), McCabe (1979), Fazzari, Hubbard and Peterson (1988), Devereux and Schiantarelli (1989)³², Whited (1992), Calomiris and Hubbard (1995)³³, Cleary (1999), Goergen and Renneboog (2000)³⁴, Galizia and Brien (2002)³⁵, Bruinshoofd (2004)³⁶, Cava (2005)³⁷, Gangopadhyay, Lensink and Molen (2001) and Nair (2004). Most of the studies have defined cash flows as a sum of profit after tax and depreciation or retained earnings. In this study, cash flows have been defined as cash flows from operating activities that in turn has been elaborated as change in cash and cash equivalents due to operating activities of the firm. In this study a positive and significant impact of cash flows on change in net fixed assets has been hypothesized.

e. Flow of Equity:

Though prima facie, it may sound to be a favorable source of finance for the company, but it has its own share of complexities involved like control dilution and reduction in earnings per share. These factors, therefore, render equity to be a non-preferred source for regularly raising funds. Moreover, mobilizing funds from public at large requires goodwill, competitive results and positive future prospects making it even tougher for all and sundry to tap funds through this route. Most of the previous studies revolving around investment and sources of finance have not postulated or concluded a significant role of equity in investment decisions [McCabe (1979), Pruitt and Gitman (1991)³⁸, Cleary (1999) and so on]. However, the present study cover the period of economic and structural reforms combined with a flourishing capital market. It is therefore hypothesized that flow of equity might play a significant and positive role in fixed asset financing. The study period has witnessed a booming new issue market along with milestone changes in rules and regulations governing investor safety thereby boosting the investor confidence. A large volume of funds were mobilized by the corporate sector through equity route. The positive sign of equity is postulated on the premise that any addition to the firms’ total source of funds is bound to impact the fixed investment plans in a positive way.

f. Flow of Borrowing:

This variable has captured the change in level of short-term and long-term borrowings over a period of twelve months and has been used a proxy for external funds. Various similar international and Indian studies in the past have considered debt as a proxy for external funds and liquidity ratio separately. However the present study plans to club both long and short-term borrowings as none of the previous studies have clearly separated the use of long-term funds to finance fixed investments and short-term funds for inventory investment. Nonetheless, external source of financing has been a significant part of investment related studies in various Indian and international works including Clark et al (1979), Fazzari, Hubbard and Peterson (1987), Whited (1992), Oliner and Rudebusch (1992), Calomiris and Hubbard (1995), Cleary (1999), Estrada and Valles (1998)³⁹, Cava (2005), Krishnamurty and Sastry (1975) and Gangopadhyay, Lensink and Molen (2001). Most of these studies have concluded the existence of hierarchy in sources of finance favoring the use of internal funds over external funds. The present study postulates a significantly positive impact of external funds on change in net fixed assets. The major premise behind the hypothesis is the existence of well developed financing institutions and a strong banking infrastructure making debt a preferred source of finance for those firms who either lack internally generated funds or are relatively new to raise funds from public at large on the basis of brand value.

RESULTS:

The panel data regression estimation has been conducted for set of sample observations. The correlation matrix presented in Table 1 does not indicate high correlation amongst any of the independent variables with a cut-off correlation coefficient of 0.7 as per Bhattacharya (2007).

In OLS results, Durbin - Watson statistic have indicated absence of autocorrelation amongst the explanatory variables. Lagrange Multiplier favored use of using fixed/random effects model over ordinary least squares or classical regression. As a next step to choose between fixed and random effects model, Hausman’s test has been conducted for the overall data to determine which model is appropriate for the panel data employed in the study. The test results show that fixed effect model would better serve the purpose for Indian sample as the Hausman Test statistic has been found to be highly significant. Moreover, when the effects and regressors may be correlated, a fixed effects model would generate consistent results while the random effects model would generate inconsistent results. The fixed effects results with ‘group dummy’ and ‘group dummy and period effects’ have been discussed in the following paragraphs.

Table: 2: Empirical Findings with Fixed Effects Model (t-statistic in parentheses)

Variables	Fixed Effects
Variables	Group Dummy	Group Dummy and Period Effects
Constant		-0.10
		(-3.553)*
Y	0.02	0.02
	(5.379)*	(5.349)*
CHG_I	-0.12	-0.12
	(-2.712)*	(-2.677)*
CFO	0.09	0.09
	(2.971)*	(3.059)*
FEQ	1.62	1.60
	(12.263)*	(12.052)*
FB	0.45	0.45
	(17.595)*	(17.342)*
TC	0.099	0.11
	(3.159)*	(3.459)*
LAGF	-0.004	-0.01
	(-0.244)	(-0.694)
LAGY	-0.006	-0.004
	(-1.726)***	(-1.343)
R²	0.29	0.29

*, ** and *** indicate the coefficient is statistically significant at 1%, 5% and 10% significance level.

Table 2 displays the sample results as per fixed effects with ‘group dummy’ and ‘group dummy with time effects’ model. Though fixed effects results with ‘group dummy’ are sufficient to meet the requirements Lagrange Multiplier Statistic and Hausman Test statistic, ‘group dummy with time effects’ model have been presented just to re-affirm that group dummy results hold good even after adding time/ period effects. All major variables including change in output (Y), change in inventory (CHG_I), and cash flow from operating activities (CFO), flow of equity (FEQ), flow of borrowing (FB) and trade credit (TC) have been found highly significant at 1 percent level of significance. The signs of the coefficients are largely in line with the signs hypothesized.

CONCLUSION:

The investment in fixed assets has tremendously increased over the study period which primarily captures the economic reforms era of Indian economy. The results have been robust and majorly in line with the pre-established hypothesis. Accelerator (change in output), cash flow from operating activities, flow of equity, flow of borrowings and trade credit have been found as highly and positively significant as postulated. At the same time as stipulated initially, change in inventory has been found as negatively significant. The analysis showed that corporate investment has exhibited impressive growth rate during the study period. As for the financing patterns from 1994-95 to 2008-09, some changes have been observed from the past studies. Both internal and external sources of finance have been found to be dynamically affecting the change in net fixed assets. The analysis reveals that flow of borrowings is highly significant variable. This might be due to the predominant role of banks and other development financial institutions in the debt market in India apart from debentures where the money is raised from public at large. The banks, in common parlances are considered to be better off in terms of access to information than common investors in the securities market. This helps in reducing the disadvantages associated with informational problems so that the companies that rely on debt do not suffer from capital market imperfections. As far as the significant impact of flow of equity is concerned, the financial sector reforms post nineties was aimed at various measures to foster economic growth. A greater emphasis was placed on development, strengthening and improving transparency in capital markets. To be precise, broadening of equity market was a primary concern by removing control over capital issues and pricing. In a response to this, changes in corporate financing pattern have been observed with a movement towards greater equity financing. Additionally, a notable contribution of equity financing in total funds raised has been observed even during the analysis of panel data results. The analysis has brought out some interesting results on the relationships between financing patterns and changes in capital expenditures and concluded with a positive role of accelerator and both internal and external sources of finance.

ACKNOWLEDGEMENT:

The author conveys a special thanks to Prof. Jawahar Lal for his valuable inputs and constant guidance and to University of Delhi for the facilities.

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Received on 08.04.2017 Modified on 16.05.2017

Asian J. Management; 2017; 8(3):471-478.

DOI: 10.5958/2321-5763.2017.00075.0

	F	Y	CHG_I	CFO	FEQ	FB	TC
F	1	0.15202	0.17156	-0.0261	0.2821	0.3943	0.14165
Y	0.15202	1	0.24478	0.24052	0.09885	0.06432	0.68675
CHG_I	0.17156	0.24478	1	-0.0774	0.11004	0.40016	0.26156
CFO	-0.0261	0.24052	-0.0774	1	-0.1775	-0.2527	0.28029
FEQ	0.2821	0.09885	0.11004	-0.1775	1	0.11413	0.0274
FB	0.3943	0.06432	0.40016	-0.2527	0.11413	1	0.03933
TC	0.14165	0.68675	0.26156	0.28029	0.0274	0.03933	1