Determinants of the Commodity Futures Market Performance: An Indian Perspective

Abstract

This article aims to find out interlinkages between equity and commodity markets through the channel of investors’ outlook in the equity market. The proxies used for gauging perception of investors are investor sentiment index and Advance–Decline ratio. The study also incorporates the introduction of Commodity Transaction Tax (CTT) and occurrence of National Spot Exchange Limited (NSEL) scam in the year 2013. Additionally, returns in commodity market are examined to be a function of equity returns. The empirical findings suggest that the liquidity of commodity futures is inversely related to investor sentiments in equity market, and commodity returns are also negatively related to equity returns. Therefore, equity and commodity markets are inversely related, as liquidity in both the markets reacts to the investor sentiments; contrarily, commodity returns experience a significantly negative impact from equity returns. Additionally, the results also provide evidence that investor sentiment in equity possesses the ability to predict liquidity in the commodity futures market. The study also suggests that the CTT and NSEL scam have significantly and positively affected the liquidity of the Indian commodity market.

Keywords

Advance–Decline ratio commodity futures commodity transaction tax investor sentiment Index NSEL scam

JEL: G12, Q02

Introduction

Futures trading in commodities commenced during late 2002 with the establishment of the National Multi-Commodity Exchange—MCX (Economic Times, 2019, November 26). Despite a long period of existence, commodity market has not been able to take off as rapidly as the stock market in India. Since the introduction of equity derivatives in 2001 and that of index derivatives in 2000, their derivatives stand at 15.5 times the cash segment in FY 2016–2017 (Lokeshwarri, 2019). On the other hand, in FY 2011, the derivatives to cash ratio in commodity market was only one-fifth (Rutten, 2011). Though the equity derivatives were launched only 1 year ahead of the launch of commodity derivatives in India, the former is on a rapid growth as compared to the latter, simply because people do not completely understand commodity derivatives, and awareness of equity products is augmented among investors. Hence, there is abundant scope of growth for commodity derivatives in India, and the same is also true for research in this segment. Various changes seen in commodity futures market are attributed to fundamentals of demand and supply. However, in recent developments since the 2008 food crisis,¹ it is observed that the changes witnessed are beyond the scope of fundamentals. There are many more factors at play, which are affecting the commodity market. One such factor is claimed to be the global equity market. In various arguments, it is stated that since the financialization of commodities, the traders have started treating commodities as an asset class. Due to this change, the funds from equity market have started shifting to the commodity market in the times of bust and distress. Therefore, we attempt to understand the nature of relationship between equity and commodity markets in India. We establish the question of whether these two markets are significantly interlinked or are independent of each other.

This study is aimed at examining the effect of equity market in the very first phase of information release in the market that is very well manifested by a change in investor perception. When any new information is revealed in the market, the first effect is seen in the investor outlook towards the market followed by changes in economic variables. Therefore, we examine if the commodity market performance can be predicted at the first stage of changes occurring in the equity market. This can prove extremely valuable for investors looking for profit opportunities as well as for policymakers aiming to manage commodity market volatility. The article also studies if there are any interlinkages between the two markets, which is examined by looking at the long-run relationship between the returns of both markets as well as by examining the effect of advance/decline (A/D) ratio in equity market on the liquidity of commodity futures. In addition to establishing the relationship between commodity and equity market via returns, liquidity and investors’ sentiments, the model used in the article controls for major events that took place in the Indian commodity market during the period of analysis, that is, introduction of the Commodity Transaction Tax (CTT) and the National Spot Exchange Limited (NSEL) scam, thus providing an advantage to get ahead in the portfolio decision-making as and when information is absorbed by the equity investors, and it is reflected in their outlook.

It is well known that the fundamental forces of commodities in the spot market, such as convenience yield, storage costs and inventory levels, affect the pricing and performance of commodity derivatives (Kaldor, 1939). There are several other factors beyond spot market that impact commodity market and should be acknowledged while making investment decisions. One such crucial factor, which is the focus of study in this article, is the equity market. We focus on the equity market’s impact on commodity market because both of these markets are held together by the channel of hedge funds and index funds and thus have begun to impact one another. Since a large part of the market is dominated by traders who are active in both markets, we test if a shift in investors’ outlook in one market leads to any changes in investment positions in another market. Investor’s sentiments towards equity market gauges the element of investor’s perception and expectation, which explains changes in liquidity of the equity market. Thus, we raise a question if investors’ sentiments towards one market affect the performance of the counter-market as well. The main aim of this study is to investigate the interlinkages between equity and commodity markets of India through the channel of investor’s sentiments, which has been measured via A/D ratio and investor sentiment index in the equity market. In order to deal with any significant changes brought into the commodity market during the period of analysis, this also investigates the effects of CTT and NSEL scam, both of which occurred in the year 2013. Moreover, the effect of the NSEL scam on market performance and investors’ confidence is not widely evaluated. Therefore, this article includes this scam for analysis to understand its role in market liquidity.

The article is divided into six sections. The second section summarizes the theoretical background of the concepts used in the article. The third section explains the review of literature on the impact of investor sentiment index in equity market on financial markets and the linkage between equity and commodity markets. In the fourth section, we shall be enlisting the data and methodology used for the empirical analysis of the article. In the fifth and sixth sections, we explain the empirical results and then mention concluding remarks for the results so acquired, respectively. Ending the article with the seventh section, we provide policy implications of the study and its findings.

Theoretical Background

NSEL was a commodity exchange where one can execute spot trade, that is, buy or sell the commodity in the current period by paying or receiving cash as soon as the quality checks and assurance receipts are issued by the exchange. These contracts were completed in (T + 2) days, settling the contract in 2 days from when contract is entered into. It provided an online platform for buyers and sellers to execute trade with no middlemen and assured settlement. NSEL scam was unveiled in FY 2013–2014 when NSEL went bankrupt and led to the payment default of ₹56 billion to its clients. The scam began with the exchange issuing paired contracts, which were beyond the time period specified by Forward Contract Regulation Act (FCRA), which was 11 days. Any contract which was settled beyond this time period was to be considered a forward contract and thus came under the regulation of Forward Markets Commission (FMC). NSEL was violating the definition of spot contracts because their paired contracts had a time period of settlement ranging up to 36 days (beyond 11 days). Investors were buying these paired contracts under which they were supposed to buy the same commodity that they sold a few days earlier and vice versa. Investors were engaged in these contracts at such intensity that instead of settling their contracts, they were rolling them over and not making the payments. The frequency of trading was so large that the exchange was not able to keep a track of warehouse receipts in addition to the fact that none of the investors were actually delivering the goods in the warehouse. This mechanism came to an end when NSEL was ordered to settle all their contracts in July 2013. Since the order was abrupt and sudden, all the investors who were rolling over their contracts rather than making payments defaulted to make payment in the end because there was no demand for T + 2 or T + 11 contracts as investors were not interested in spot trading. Therefore, they failed to sell their contracts and could not make the payments. While investors defaulted, NSEL could also not make the payments by selling the goods in the warehouse as there were no physical goods, and the receipts were bogus. Thus, the scam burst into the open, and it was reported that NSEL defaulted a payment of ₹56 billion. All the activities of NSEL were thus suspended.²

During the same time, that is, July 2013, CTT was introduced under Finance Act, 2013. CTT is to be levied on transactions of non-agricultural commodity derivatives traded on commodity derivatives exchanges. When it was first proposed to be introduced in the 2008–2009 Union budget, the idea was opposed and declined because it was considered to be a hindrance in the growth of commodity derivatives. However, it was considered to be a mandatory requirement in 2013, given that MCX had become the sixth largest commodity derivatives exchange globally in 2010 (Economic Times, 2010). Therefore, as equity trading is levied with Securities Transaction Tax (STT), trading of commodities, except agricultural commodities, fall under the liability of CTT. It is a separate story if levitation of CTT proved beneficial for the liquidity of commodity trading or not, but in 2017, the MCX and National Commodity and Derivatives Exchange Limited (NCDEX) fell from sixth to seventh place and ninth to tenth place, respectively, in the rankings carried out by Futures Industry Association (Bhayani, 2017).

Literature Review

Equity and commodity markets are components of the same economy and hold some relationship (Nguyen et al., 2014; Thuraisamy et al., 2013). Few attempts have been made to study interlinkages between the two markets. Buyuksahin and Robe (2012) suggest a significant relationship between both the markets via the link of hedge funds. A study conducted by Gorton et al. (2007) states that commodity futures returns are the same as that of equity instruments, and the returns are negatively related to the equity and bond returns. There is a two-way asymmetric causality between the US equity and commodity futures market (Nguyen et al., 2014). Mo et al. (2017) lists stock index as one of the macroeconomic variables, which affect returns in commodity futures market of India. Tang and Zhu (2015) observe a structural break in commodity market, which is caused by the inflow of index traders. Although Nissanke (2012) reported that the commodity market is now more synchronized with the financial markets, a significant portion of its performance in terms of volatility and other factors is explained by the performance of the financial sector. This article, therefore, suggests that the financial crisis has been transmitted to the commodity segment because of increased cross-linkages between these two markets. Yamori (2011) reports that during the crisis period, equity and commodity returns showed positive correlation, which was not the case in pre-crisis period. As per his results, commodity market is no different from financial markets now, and financialization of commodities is on the rise in the Japanese market; these results make commodity products a redundant tool to diversify the investment portfolio. Buyuksahin and Robe (2012) have found increased cross-market trading, and the link of cross-market relation is explained by the participation of hedge funds in both the markets. Therefore, hedge fund is the most important channel through which the stress in equity market is transmitted to the commodity market (Buyuksahin & Robe, 2012). Thuraisamy et al. (2013) examined the volatility spillover effects of the equity market on the commodity futures market in 14 Asian economies. Their results proved that in all 14 economies, there exists a volatility spillover in the commodity market from their respective equity markets. Interestingly, in three of the countries, including India, there is a bidirectional spillover between the oil and equity markets. However, in cases of spillover from the gold market, the event occurs in the pre-crisis period, whereas, in cases of spillover from the oil market, the event occurs during the crisis period.

Many of the papers linking equity and commodity markets have restricted the time period of analysis and discussion to the 2008 food crisis. Researchers have found that one of the causes of 2008 food crisis was excessive speculation in the commodity futures market (Masters, 2008; Pradhananga, 2014; Wahl, 2009). As per some researchers, this crisis was a consequence of the 2008 financial crisis along with other fundamental forces in the commodity market—hedge funds, index funds and speculators diverted their money from the financial instruments as soon as the collapse of Lehman Brothers took place and found refuge in the commodity market instead (Braun, 2008; Wahl, 2009). Gilbert (2010) empirically reports that index-based investment in the commodity market played a role in creating a bubble-like situation. A study by Buyuksahin and Robe (2012) has suggested through the help of empirical results that the crisis period plays a role in determining the relationship between equity and commodity markets. Studies dedicating to the crisis period have mixed results where one set of findings imply no relationship between both the markets in the time of crisis, while, on the other hand, others suggest that the crisis in the commodity market was a consequence of crisis in the equity market, and, thus, the two markets are linked.

One of the factors which plays a significant role in the stock market is the confidence of investors or investor’s sentiments towards the stock market in the economy. Investor sentiment index in equity is found to affect the stock returns positively in the market (Anusakumar et al., 2017; Bolaman & Mandaci, 2014; Oprea & Brad, 2014). However, some studies has also shown a negative relationship between the two (Schmeling, 2008). Chang et al. (2009) in their semantic paper have examined and found a significant role of individual investor sentiment index in predicting the stock returns in developed as well as developing countries. Bolaman and Mandaci (2014) have studied and found a positive long-term relationship between investors’ sentiments and Turkish stock market while taking financial crisis period into account. Anusakumar et al. (2017) have worked on the sentiments of investors and its impact on the equity stock returns by categorizing the sentiments as stock-specific and market-specific. Its results suggest that there is a positive relationship between both kinds of investor sentiments and equity returns. However, stock-specific sentiments play a more powerful role in influencing the returns. Using the New York Stock Exchange (NYSE) ARMS index³ as one of the proxies for investor sentiment, a significantly negative correlation is found between ARMS and stock index returns (Wang et al., 2006). A/D ratio has also been used by Naik and Padhi (2016) to construct a composite sentiment index, which is found to have a significant impact on market excess returns. Investors’ confidence in the equity market and the returns in the same market have been researched worldwide, but there is negligible proof of the same holding true for two different markets, that is, investors’ confidence or sentiments towards one market affecting the other market.

The NSEL scam has been explained by various research papers (Rachuri & Aurora, 2017; Satish & Kumar 2015; Singh et al., 2017). Many papers talk about the governance aspect related to this scam like merger of FMC with Securities Exchange Board of India (SEBI) (Shamsher & Gadia, 2015). Gulati et al. (2017) in their study have listed NSEL scam and introduction of CTT as the possible causes for reducing liquidity in the agricultural futures market. Although CTT is applicable only on non- agricultural commodities, frequent changes in the list of commodities exempt from CTT would have possibly impacted the investor confidence in the market. In order to assess the effects of the introduction of CTT, various papers have studied its impact on the commodity market in terms of liquidity and have found a negative relationship, signifying that due to introduction of CTT in the market, the commodity market has become less liquid (Pattanaik & Thomas, 2017; Ray & Malik, 2014; Sehgal & Agrawal, 2019). A study by Sinha and Mathur (2015) in their study, have found negative relationship between the introduction of CTT and return on commodity index. Some researchers argue that the imposition of tax is to avoid speculation and increase the welfare in the market (Tobin, 1978). These results are not only valid in India but is also valid in other countries such as Tokyo (Hayashida & Ono, 2011), China (Baltagi et al., 2006) and many more. The transaction tax is supposed to make markets more efficient and reduce volatility (Stiglitz, 1989). The information flow of imposition of transaction tax is bound to have some effect on the perception of an investor by weakening or strengthening the confidence in markets (Hayashida & Ono 2011; Sinha & Mathur, 2015; Stiglitz, 1989). Roll (1989) rules out the possibility of any relationship between the imposition of transaction tax and stock market volatility. Hence, the direction and magnitude of effects of transaction tax in equity as well as commodity markets stand in a grey area.

Data and Methodology

Logged volume of futures contracts traded on MCX is used as a proxy for the liquidity of the commodity futures market in India (Gilbert, 2008; Raizada & Sahi, 2006). We have used non-agricultural commodity futures because CTT is only levied on these commodities. For capturing investors’ sentiments in the Indian equity market, two measures of investor sentiment have been used, namely investor sentiment index by American Association of Individual Investors’ (AAII) sentiment survey (Anusakumar et al., 2017; Oprea & Brad, 2014) and A/D Ratio (Brown & Cliff, 2001; Naik & Padhi, 2016; Wang et al., 2006). While the Indian equity market is co-integrated with the global equity markets (Ahmad et al., 2005; Kumar & Pandey, 2011; Mukherjee & Bose, 2008; Mukhopadhyay, 2009; Srivastava, 2007; Wheatley, 1988); it is assumed that the investor sentiments of global investors will also be co-integrated, and thus, we use AAII sentiment index as a proxy for Indian investors’ sentiments. In order to capture a net effect of the market perception, we use bull-bear spread of the investor sentiments. The data are collected on monthly frequency, and the time period of study is from November 2003 to August 2019. Another variable to gauge investor outlook in the equity market is the A/D ratio in NIFTY 50 used from time period November 2003 to April 2020. A/D is ratio of the number of advancing stocks to the number of declining stocks.⁴ It reflects the direction and sentiment of the stock market (Economic Times, 2019, March 29). AAII sentiment index has been sourced from AAII website, while the A/D ratio is sourced from National Stock Exchange (NSE) website.

The introduction of CTT is used as a proxy for policy/regulation change and the existence of NSEL scam is used for scam occurrences. To impose the existence of NSEL scam and CTT, a dummy variable is introduced in the analysis. Since both of these events took place in the same time period, July 2013, one dummy variable (D1) is introduced. The control variables added to the model are change in money supply (M3), oil prices and consumer price inflation (CPI).

To check for the stationarity of all the time series, Augmented Dicky–Fuller test is applied. Thereafter, following Du et al. (2015), Oprea and Brad (2014) and Schmeling (2008), who apply ordinary least squares (OLS) regression to investigate the potential role of investor sentiment in the determination of oil prices, this study applies OLS regression to explore the significance of investor sentiment in the equity market on the commodity market. The relationship between the dependent and the independent variables has been defined with a constant and slope values (coefficients) in such a way that the coefficients are a linear function of the dependent variable. In other words, the linearity assumption is about the parameters. Though other methods for regression estimation like generalized method of moments (GMM) and maximum likelihood (ML) estimation can also be applied, they are relatively more complex. Moreover, these methods may require additional conditions, for instance, in order to apply ML, the form of distribution of the error needs to be known. OLS minimizes the variance and produces the best estimates of the parameters when all its assumptions are satisfied. The parameters thus estimated are best linear unbiased estimator (BLUE). Equations (1) and (2) are the OLS equations for finding the existence of long-run relationship between the variables:

\begin{array}{l} \ln Q_{t} = β_{1} + β_{2} \times {ISI}_{t} + β_{3} \times {ISI}_{t - 1} + β_{4} \times D 1 + β_{5} \times ΔM 3_{t} + \\ β_{6} \times {ΔOil}_{t} + β_{7} \times {ΔCPI}_{t} + e_{t} \end{array}

(1)

\ln Q_{t} = β_{1} + β_{2} \times A / D_{t} + β_{3} \times D 1 + e_{t}

(2)

In Equations (1) and (2), Ln Q_t is the logged quantity of total futures contracts traded; ISI_t is investor sentiment index; ISI_t−1 is the 1-year lagged investor sentiment index; A/D_t is advance/decline ratio; D1 is the dummy variable for existence of CTT and occurrence of NSEL scam in the month of July 2013; M3 is the money supply; Oil represents the oil prices; and CPI is the consumer price index. βs are the partial coefficient of their corresponding variables in the equation.

This article also employs the difference-in-differences (DID) estimation approach to assess the impact of introduction of CTT because the dummy variable in our regression equation does not isolate its effect due to its simultaneous representation of NSEL scam as well. Therefore, we use the time period from November 2003 to June 2013 as pre-CTT period and the time period from July 2013 to April 2020 as post-CTT period. The control group is the agricultural commodity futures traded on NCDEX as they are exempt from CTT, and the treatment group is non-agricultural commodity futures traded on MCX since CTT is applicable on its trading. We have chosen NCDEX as the control group because majority of its trade is in agricultural commodities (Dutta, 2011). Therefore, by using the DID approach, we estimate the treatment (CTT) effect on the treated group (MCX) by estimating the following equation:

\ln Q_{t} = β_{1} + β_{2} \times D 1 + β_{3} \times {CG}_{t} + β_{4} \times D 1 \times {CG}_{t} + e_{t}

(3)

To confirm interlinkages between Indian equity and commodity market, we have also used the SENSEX returns, NIFTY returns and COMDEX returns from May 2006 to October 2019. We sourced SENSEX and NIFTY returns from BSE and NSE websites, respectively. We regressed COMDEX returns on the equity index returns separately in two OLS regression equations.

{COMDEX}_{-} {RET}_{t} = β_{1} + β_{2} \times {NIFTY}_{-} {RET}_{t} + e_{t}

(4)

{COMDEX}_{-} {RET}_{t} = β_{1} + β_{2} \times {SENSEX}_{-} {RET}_{t} + e_{t}

(5)

In Equations (4) and (5), COMDEX_RET is used for COMDEX returns, NIFTY_RET is NIFTY returns and SENSEX_RET is SENSEX returns. βs are partial coefficients for corresponding variables.

Empirical Findings

As per the augmented Dicky–Fuller test, we have found our time series of all the variables stationary at level. The results of stationarity are depicted in Table 1.

After verifying the stationarity of all variables, we conduct an OLS regression to understand the nature of relationship among the variables. By conducting regression as in Equation (1), that is, regressing the volume of commodity futures traded on the investor sentiment index in the equity market, money supply, oil prices, CPI and dummy variable, we establish the findings summarized in Table 2.

Table 1

. ADF Results

Variables	t-Statistic	p-Value
Logged volume of commodity futures (Ln Q)	−12.6921	0.0000*
Investor sentiment index (ISI)	−10.0066	0.0000*
Investor sentiment index (ISI) Lag 1	−10.10986	0.0000*
Change in Money Supply (∆M3)	−4.2941	0.0006*
Change in oil prices (∆Oil)	−6.5243	0.0000*
Change in consumer price index (∆CPI)	−13.8058	0.0000*
COMDEX returns (COMDEX_RET)	−11.4137	0.0000*
NIFTY returns (NIFTY_RET)	−12.0945	0.0000*
SENSEX returns (SENSEX_RET)	−11.6208	0.0000*
Advance decline ratio (A/D)	−12.9415	0.0000*

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

Table 2.

OLS Regression Results (Equation [1])

Variables	Coefficient	p-Value
ISI	−1.613637	0.0114*
ISI (Lag 1)	−2.068234	0.0012*
D1	0.741332	0.0002*
∆M3	3.69E-05	0.6288
∆Oil	0.000299	0.2728
∆CPI	−0.044788	0.6558
Intercept	15.57868	0.0000*
R-squared	0.1580

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

The regression results imply that there is a significant relationship between our interest variable, investor sentiment index and the dependent variable—volume of commodity futures. The dummy variable also significantly impacts the volume of futures, which signifies that the introduction of CTT and NSEL scam has had an impact on the liquidity levels of the commodity futures market. As per results, a unit increase in investor sentiment index in the equity market significantly reduces the volume of commodity futures traded by 1.61 per cent. Interestingly, 1-year lag of the investor sentiment index has a significantly negative impact on the liquidity of commodity futures. A unit increase in the lagged value of investor sentiment, decreases the volume traded of commodity futures by 2.07 per cent. As the results suggest, when investors have a positive outlook in the equity market, the liquidity in commodity market reduces as the investors who are active in both the markets, like hedge funds, are more inclined to invest in the equity market rather than switching their funds to the commodity market. Additionally, the analysis also suggests that the introduction of CTT in July 2013 has significantly affected the liquidity in the commodity futures market. The effect seen in the results is positive, which implies that since CTT has been introduced in the market, the liquidity of commodity futures market has improved. Our dummy variable, D1, also shows the occurrence of NSEL scam in the commodity market because the scam, as well, took place in July 2013. A positive coefficient of D1 suggests that the liquidity has improved after the scam took place. It may be so because the effects of NSEL scam and CTT have not been isolated in the model developed and, therefore, it reflects a mixed effect. The DID analysis, thus applied to isolate the effect of CTT, finds that the introduction of CTT increased the trading volume in the market. As the positive coefficient of interaction term implies, the trading volume in treatment group, that is, MCX increased after the CTT introduction (Table 3).

All control variables in the model are found to be insignificant and do not pose any effect on the volume of commodity futures contracts traded on exchanges in India. Therefore, to understand the effect of investor sentiment index and the regulations and scams in Indian commodity futures market, we further improve the model by eliminating all the insignificant variables, namely money supply, oil prices and CPI.

Table 3.

Difference in Difference Result (Equation [3])

Variable	Coefficient	p-Value
D1	−0.09981	0.0000*
CG (control group)	5.0863	0.0000*
D1*CG	1.6047	0.0000*
Constant	17.3067	0.0000*

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

Ln Q_{t} = β_{1} + β_{2} \times {ISI}_{t} + β_{3} \times {ISI}_{t - 1} + β_{4} \times D 1 + e_{t}

(6)

The improved regression model for our analysis gives consistent results as that in Equation (1). Results of second regression model (Equation [6]) with only investment sentiment index, 1-year lag of the sentiment index and dummy variable are summarized in Table 4. The findings suggest that even after getting rid of additional variables, investor sentiment index along with its lag and the dummy variable remain significant. The magnitude of the coefficient of ISI changes to −1.56, which implies that as investor sentiments in the equity market improve by one unit, the quantity traded in the commodity futures market reduces by 1.56 per cent and vice versa. The lag coefficient of ISI becomes −1.95, suggesting a reduction of 1.95 per cent in the commodity futures trading quantity in year t + 1 when the value of ISI increases by a unit in the current year (t). The effect of dummy variable stays approximately the same on the volume of futures contract traded. The findings suggest that after the disclosure of NSEL scam in July 2013, the trading volume in commodity futures increased as the investors switched their funds from spot to futures contracts. The analysis by Gulati et al. (2017) also shows that the NSEL scam resulted in decreased trading volume in agricultural commodities, thereby suggesting that the effect was seen in agricultural commodities, which form a very minute part of the MCX trade. Since agricultural commodities’ trade declined, the rise in trading volume on MCX seems to make sense because majority of its trade is in non-agricultural commodities (Dutta, 2011). It seems plausible that the investors switched their funds from agricultural futures to non-agricultural futures.

Second, after running regression as in Equation (2), we find that the A/D ratio at NSE along with our dummy variable significantly affects the volume of total contracts traded on the MCX. The results are presented in Table 5, which suggest that coefficient of A/D Ratio is −1.16, implying that the improvement in investors’ outlook in the equity market decreases the volume of futures in the MCX. These results remain consistent with the results found from examining the effect of investor sentiment index. Also, since both investor sentiment index and A/D ratio have the same sign of the coefficient, the findings become more robust.

Table 4.

OLS Regression Results (Equation [6])

Variables	Coefficient	p-Value
ISI	−1.556193	0.0137*
ISI (Lag 1)	−1.952505	0.0019*
D1	0.708574	0.0002*
Intercept	15.58903	0.0000*
R-squared	0.1502

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

Table 5.

OLS Regression Results (Equation [2])

Variables	Coefficient	p-Value
A/D	−1.1673	0.0302*
D1	0.6897	0.0018*
Intercept	16.4806	0.0000*
R-squared	0.0706

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

Finally, it is found that the returns in the equity market significantly affect the returns in the commodity market as can be seen by the regression results presented in Tables 6 and 7. These results imply that as the returns of SENSEX move up, that of COMDEX fall down and vice versa. The findings also remain consistent with the NIFTY returns as the sign of coefficient of both indices of equity market is negative. Therefore, all tests conducted imply that both the markets—equity and commodity—in India are contrary to each other. Their performances are negatively related, which suggest that when one performs better, the other downgrades. These results also seem to be consistent with the above analysis because, as investors become bullish for equity, the demand for commodity futures falls, and thus the returns in commodity markets should also fall, thus confirming the negative relationship.

Table 6.

OLS Regression Results (Equation [4])

Variables	Coefficient	p-Value
NIFTY_RET	−0.1386	0.0243*
Intercept	0.0055	0.1472
R-squared	0.0314

Source: Authors’ calculations and test results.

Note: *Significant at 0.05 level.

Table 7.

OLS Regression Results (Equation [5])

Variables	Coefficient	p-Value
SENSEX_RET	−0.1504	0.0162*
Intercept	0.0056	0.1413
R-squared	0.0358

Source: Authors’ calculations and test results.

Note: * Significant at 0.05 level.

Conclusion

The analysis implies that the volume of commodity futures is significantly impacted by the investor perception in equity market as well as the lagged value of the investor sentiment index besides the introduction of CTT and the NSEL scam. The commodity market returns are also significantly affected by the equity market returns. The findings clearly suggest that both the markets are interlinked and are inversely related to each other, while also highlighting the predictive ability of the investor sentiment in equity. Hedge funds and index funds are found to be traded in both the markets simultaneously, and they are the channel through which information effects pass from one financial market to another. Any kind of information dissemination that affects liquidity in equity market also affects the commodity derivatives market liquidity. The effect in both the markets is opposite because it is the same category of traders who are engaging in both the markets. When the equity market sees a negative sentiment prevailing, the investors switch their funds to the commodity market, whereas, when the sentiments are positive, traders indulge in equity market to reap higher gains in the immediate future. In order to earn supernormal profits from the market, traders keep an eye on which market is performing better and provide better short-term gains. As traders keep or switch their funds from one market to another, it fluctuates liquidity and returns in both the markets. Hence, outlook of investors in the equity market not only impacts the equity market but also the commodity market. Literature suggests that the investor perceptions positively affect the equity market, and our findings reveal that investment sentiment index affects the commodity futures market negatively. Therefore, the change of direction in liquidity due to change in the investor sentiment index and A/D ratio in the market is contrary as one market gains liquidity, the other loses it. If investor sentiment index and A/D ratio in the equity market drops (gains), the liquidity in the commodity futures market increases (decreases). Similarly, as one market performs better in returns, the other’s performance of returns goes down, owing to dynamics playing between liquidity and investor perceptions. Hence, the Indian commodity derivatives market is sensitive to Investor Sentiment Index and A/D ratio in the equity market and reacts to its changes. It is also sensitive to the returns prevailing in the equity market.

This study helps us understand the interlinkages between equity and commodity markets in India so that one can exploit the opportunity in the commodity market by first looking at the investor outlook in the equity market. The article finds that the Indian commodity market is significantly impacted by the equity market via its returns and investor sentiments, which are measured by average returns and Investor Sentiment Index besides A/D ratio, respectively. The increased trading in the commodity market in recent times is often said to be a spillover of negative equity performance. Since commodities are a good hedge against inflation, index and hedge funds have begun diverting their funds to the commodity market in times of distress. Thus, interlinkages between the two markets can also help us understand these existing as well as new phenomena like financialization of commodities, increased speculative trading, etc. Therefore, this article contributes to the argument that the Indian commodity market is not independent of the equity market performance and is significantly impacted by the same. Also, in order to show that the Indian commodity and equity markets, in our period of analysis, are related to each other, we have regressed the returns of the commodity market on the returns of the equity market. The results suggest that both the markets share a significant long-run relationship, wherein when one market witnesses increased returns, the other goes through decreased returns, and vice versa. Additionally, this article has also tested the effect of A/D ratio in the equity market on the liquidity in the commodity futures market, which tells us about the market sentiment but a little further ahead in time as compared to the sentiments seen from Investor Sentiment Index. Therefore, it establishes a relationship between the equity and commodity market through A/D ratio as well and finds a significant relationship.

The CTT is also found to positively affect the liquidity in the commodity derivatives market, which is a winning situation for the regulators like Forward Market Commission as their purpose of introducing CTT did not prove to be redundant in making both the markets fair and equal. CTT is levied at 0.01 per cent on all non-agricultural commodities in India, while STT is levied at 0.1 per cent on all transactions. This variation in the tax rate could be a huge motivation for the investors to invest in commodity market, thereby increasing the liquidity of commodity futures contracts.

In addition to the CTT, the commodity futures market has seen a positive change in the liquidity after the occurrence of the NSEL scam, which can only be explained by the fact that we modelled the effects of CTT and the NSEL scam in a single variable instead of separate variables, which amounted the aggregated effect of both the events thus portraying a positive coefficient for the NSEL scam too. The scam took almost 2 years to unfold and come on the surface. This tells us the inefficiency of regulations in place as well as the slow reaction of the FMC. Ideally, such events not only shake the faith of traders in regulatory authority but also put a dent in the performance of the market in the form of low interest taken by investors. This scam unfolded in such a manner that approximately 13,000 investors lost ₹56 billion, which left traders’ outlook towards commodity spot market very sour. However, one can also argue that the commodity futures market remained resilient to the negative spillover as per the results found in our article. One of the reasons for this could be that investors always look out for new asset classes in times of crisis and switch their funds to a more profitable option. In this case, when spot trading became a point of distress for most traders, many others might have taken refuge in futures trading. Traders familiar in commodity market who were previously engaged in spot trading could have helped increase liquidity in the futures market when the NSEL scam unfolded. Despite that, the article does not clearly showcase the effects of CTT and NSEL, but a net effect of both events have been portrayed in the results, which is positive for the commodity futures liquidity.

The results in the article are India-centric. Having said that, the findings can be generalized as well because the Indian commodity exchanges show a co-movement with global exchanges, which open the possibility of common factors affecting the exchanges. One of the measures of investors’ sentiment is Investor Sentiment Index, which takes into account investors from all across the world rather than from one single country or continent. Moreover, Bombay Stock Exchange being one of the top 10 stock markets in the world (Business Insider, 2020), India has been a hotspot for investors, especially those willing to invest in emerging nations.

Policy Implications

The article establishes a strong link between the commodity futures market and the equity market through the channel of investor sentiments and returns. Investor Sentiment Index as well as A/D ratio in the equity market show a negative effect on commodity market liquidity, and similar results are witnessed when sentiment index is lagged by one period. Commodity market return is also affected by equity returns such that rising equity returns cause a decline in commodity market return. Since the findings are robust to different measures of explanatory variables, this provides investors with additional information, which can improve their forecasting models while making investment decisions. As the Indian commodity futures market is not mature as yet, there are several factors, which still remain unknown in order to make better and informed decisions. Therefore, along with fundamental information, one can also use the information available in the equity market up to a certain extent to take position in the commodity market. Informed buy and sell moves by the investors can further improve the overall market performance. Policymakers can rely on this significant relationship between the two markets to account for investor sentiment and equity returns while framing policies to control return volatility or liquidity shortages in the commodity market.

The results highlight a positive impact of CTT on commodity futures liquidity, which can prove to be an interesting insight for policymakers for future taxation policies, as it clearly reflects that CTT has been successful in improving market liquidity.

The findings of the study stand significant for the investors who intend to diversify their portfolio in order to minimize risk of behavioural shifts in the financial markets. The analysis further provides investors with an additional factor/variable, which can prove useful in forecasting commodity futures liquidity. Since equity and commodity futures market are found to be complementary, they are bound to hedge each other’s risk. The commodity market is known to hedge inflation and is an efficient tool to hedge against risk in the equity market. It provides an insight into the fact that both markets are complementary to each other and respond to similar economic information in different fashions. The participants who are engaged in both markets can utilize information on investor sentiments in the equity market to take decisions for positions held in both markets. Even if an investor is trading in the commodities market only, they can utilize the information of investors’ sentiments in the equity market to take decisions.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

Notes

ORCID iD

Priti Dubey

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