Spread analysis can help improve analysts’ predictive ability

Kushankur Dey | Updated on January 24, 2018 Published on June 23, 2015


It can be used as an effective tool in investment analysis and portfolio management

Spread analysis for commodity futures can be a welcome departure from a conventional chartist theory that might improve the analysts’ predictive ability and make their recommendations consistent.

While the accuracy of technical analysis drawing on the pictorial presentation of price movement has long been contested, spread analysis with high frequency trade data could help the traders devise trading strategies and understand the market dynamics well.

As the motive for investment in commodities is quite distinct from that of financial asset, spread analysis may serve better to investment analysis and portfolio management for commodities.

Spread is the difference between buy (bid) and sell (ask) price as reflected in the electronic limit order book.

In other words, trade activation is subject to order matching exercise by considering a few competitive buy and sell orders in given trading horizon.


It may be noted that the spread is decomposed into adverse selection and order-processing components.

Both the component moves in opposite direction and their relative importance in determining the spread depends on the market liquidity, trade volume, and degree of participation.

For example, in case of a liquid market where it is not hard to find an intention-matching contract (consensus of the buyer and the seller for delivery), contribution of adverse selection to bid-ask spread could be higher as investors often get confused in selecting the right contract at right time.

This also implies the increased search costs for contract selection. However, in illiquid market, order processing may contribute more to bid-ask spread as the market participants wish to hold the stock until a fair value is realised.

Data and methods

Trading data of agricultural commodities are considered here. Commodities having a significant share in the futures trade with considerable trading frequency are chosen for analysis.

For example, daily closing futures prices of castor seed (2007-15); cumin/jeera (2008-15); wheat (2009-15); coriander (2010-15); rapeseed-mustard (2010-15); soyabean (2010-15) are extracted from a reliable data source. Cotton and guarseed are excluded as there is an issue of trading-synchronisation.

Spread calculation considers the foundational work of Roll (1984) and Kyle (1985) that appears to be reliable in most cases to spread determination. Spread analysis for selected commodities reveals that castor seed futures bid-ask spread lies between -2.15 (lower bound) and 6.6 per cent (upper bound) with an average spread of 0.58 per cent.


This implies that castorseed futures price may range between ₹3,914 and ₹4,264 a quintal with an average of ₹4,023 in the near-month contract if the current rate stands at ₹4,000. The spread dispersion from the mean could be 0.78 per cent.

In case of cumin, the average spread is 0.27 per cent with a lower bound of -2.08 per cent and upper bound of 5.68 per cent. It means that cumin futures can ideally be traded between ₹18,115 and ₹19,552 a quintal in near-month contract period assuming the current price of ₹18,500 a quintal. The spread dispersion is found to be 0.57 per cent that is relatively less than that of castorseed.

Wheat futures contract resumed in 2009 after the suspension on trading had revoked. So, the analysis considers only new contract data in that calculated average spread is 0.39 per cent with lower bound of zero and upper bound of 4.84 per cent.

The spread dispersion is 0.67 per cent. It is intuitive that wheat futures market is yet to buoy up the market sentiment with an improved liquidity. Coriander futures contract remains liquid, but the spread dispersion is relatively higher than that of castor seed and cumin futures.

With a range between -1.65 and 5.83 per cent, the estimated average spread is 0.78 per cent and the spread dispersion is 1.02 per cent. The deviation indicates the concentration of speculators from 2010 onward that could enhance the likelihood of adverse selection component in the spread.

While the average spread of rapeseed-mustard is 0.34 per cent with lower bound of -1.59 and upper bound of 5.5 per cent, soyabean futures spread varies between -0.95 and 5.51 per cent with an average of 0.41 per cent. However, the spread dispersion in both the contract falls between 0.56 and 0.62 per cent.


These findings could help market participants devise trading strategies and improve market timing ability. As futures trading in commodities is expected to offer a potential avenue to risk management, spread analysis remains imperative with implications for the trade environment and market efficiency.

First, spread impacts the liquidity that simultaneous buying and selling is possible with an incremental effect of transaction costs.

Second, it may serve as criteria for hedging as the higher spread (than expected) could induce the basis risk.

Third, as the spread is analogous to impact trading costs, it may help investors limit their position and motivate analysts to strengthen their recommendation.

The writer is Post-Doctoral Fellow at the Centre for Management in Agriculture of IIM, Ahmedabad. Views are personal.

Published on June 23, 2015
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