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Published in: Asia-Pacific Financial Markets 1/2021

21-07-2020 | Original Research

Predicting Wheat Futures Prices in India

Author: Raushan Kumar

Published in: Asia-Pacific Financial Markets | Issue 1/2021

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Abstract

Futures markets perform their economic roles of price discovery and hedging only when they are efficient. One of the important features of efficient market is that one cannot make abnormal profits from the futures markets by trading in it. This paper addresses the question of whether Indian wheat futures prices can be forecast. This would add to our knowledge whether wheat futures market is efficient, and would enable brokers, traders and speculators to develop profitable trading strategy. We employ the economic variable model to predict the wheat futures prices, and employ out of sample point forecasts. We also evaluate the robustness of our results by employing several alternative specifications, viz. ARMA process and artificial neural network technique. We then test the statistical significance of point forecast using the Diebold and Mariano test. We consider random walk orecast as the bench mark. In order to predict the evolution of wheat futures prices, we use traders’ expectations about the futures prices, a number of economic variables and futures prices (lagged) of wheat. The study finds that the futures price of wheat cannot be forecast, and the wheat futures market is efficient.

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Appendix
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Footnotes
1
In Indian context, wheat is the staple food grain crop, and a significant commodity beside rice. The volume of wheat futures contract has gone through a roller coaster ride since its inception on June 10, 2005 at the National Commodity and Derivative Exchange (NCDEX), Mumbai. The government of India banned futures trading in wheat in February 2007 and again lifted the ban in May 2009 (Government of India 2011). The present study has taken the data for the period between May 2009 to August 2014. The average daily volume of wheat futures traded on national exchange decreased by more than 50 percent over the sample period. In 2009, more than 250 futures contracts were traded on average each day, while below 100 contracts were traded averagely on a daily basis in 2014 (calculation based on data from the NCDEX website). This might be because government by interfering (through minimum support price, public distribution system, etc.) in the market, controls its demand and supply so that prices do not inflate.
 
2
Statistical technique means that we make a forecast based on the past prices only. However, economic metric technique implies that we require the data for the various type of traders who actually trade in the market. We look at the prices they receive, costs they incur, opportunity cost of investing, then we calculate the overall profit of these investors in the market. If they make abnormal profits, we conclude that markets can be predicted.
 
3
Financialization of commodity markets implies that the flows of the financial investors have impact on the agricultural commodity futures prices.
 
4
Real rate of interest is the nominal interest (call money) rate minus inflation. Monthly data for whole sale price index is available, so we have calculated inflation for the month and used it for all days in that particular month.
 
5
This manuscript is part of my Ph.D. dissertation. The work was started in 2015, so data employed in this study is till August 2014. Over the years, we have developed the model and incorporated latest econometric techniques for the study. Though the data is relatively old, but the relevance of the study is quite significant. In addition, we have taken the data from May 21, 2009 because futures trading in wheat started in 2005, and Government of India stopped trading in wheat from August 20, 2007 to May 20, 2009 (Government of India 2008).
 
6
In case of wheat futures, warehouse stocks plays a dominant role. Despite that we have not selected the variable for predicting grain futures. We have taken the daily data for futures prices in our study. Warehouse stocks data is announced for a year, then it remains constant for a given year. It won’t have any impact as warehouse stocks variable is not changing on daily basis.
 
7
The results of two tests, i.e., ADF and DF-GLS tests to validate the presence of unit root in the data series have been reported.
 
8
Wheat and gram have substitutability in production relationship because these two crops are grown in the same season in a given state.
 
9
Based on the models that we have selected.
 
10
The terms neurons and nodes are used interchangeably here.
 
11
The summation formula at a neuron is
$$I_{j} = \mathop \sum \limits_{j = f}^{s} W_{ij} O_{i}$$
where \(I_{j}\) is the inputs (weighted) received by neuron \(j\), \(W_{ij}\) is the weight from neuron \(i\). to neuron \(j\), \(O_{i}\) is the signal given by neuron \(i\), \(j\) is the neuron that receives signal, \(i\) the neuron that sends signal and \(f, s\) are the first and last neurons that send signals, respectively.
 
12
The transfer function is nonlinear. After we calculate \(I_{j}\), we send the strength of the incoming signals to the receiving neuron. They are then transformed and become the outgoing signal to the next layer of neurons. We employ sigmoid function to transform the signal.
$$O_{j} = \frac{1}{{\left( {1 + e^{{ - I_{j} }} } \right)}}$$
Since the output of the sigmoid transfer function lies between 0 and 1, \(O_{j}\) lies between 0 and 1. High value of \(I_{j}\) implies that \(O_{j}\) will approach to 1. If \(I_{j}\) is very very negative, the \(O_{j}\) will tend to 0.
 
13
There is convergence of the training data set, and we get the global minimum point of the RMS value.
 
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Metadata
Title
Predicting Wheat Futures Prices in India
Author
Raushan Kumar
Publication date
21-07-2020
Publisher
Springer Japan
Published in
Asia-Pacific Financial Markets / Issue 1/2021
Print ISSN: 1387-2834
Electronic ISSN: 1573-6946
DOI
https://doi.org/10.1007/s10690-020-09320-6

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