Non-commercial Traders Research Articles

Price Explosiveness, Speculation, and Grain Futures Prices

AbstractA recently developed testing procedure is used to detect and date‐stamp explosive episodes (“bubbles”) in corn, soybean, and wheat futures markets during 2004–2013. We find that the markets experienced price explosiveness only approximately two percent of the time and, when bubbles do occur, they are generally short‐lived and small in magnitude. The correspondence between observed price spikes and bubbles is rather low, with a large portion of the price explosiveness occurring during downward price movements. Commodity index trader positions do not significantly affect the probability of a positive bubble occurring in grain futures markets, which directly contradicts the argument (the “Masters Hypothesis”) that waves of index investment distorted underlying supply‐and‐demand relationships and led to a series of massive bubbles in agricultural futures markets. In addition, commodity index trader positions tend to reduce negative bubble occurrence, while general speculative activity as measured by Working's T reduces the probability of a positive bubble. There is some evidence that the positions of noncommercial traders have a direct effect on positive bubble occurrence, but the effect declines when accounting for the composition of other traders in the market. Overall, speculation has little effect or negative effects on price explosiveness. Finally, positive bubbles are more likely to occur in the presence of low inventories, strong exports, a weak U.S. dollar, and booming economic growth, whereas negative bubbles are more likely to occur with large inventories, weak exports, and stagnant economic growth.

Strategies Based on Momentum and Term Structure in Financialized Commodity Markets

The aim of this paper is to investigate the impact of the financialization of commodity markets on the profitability of strategies based on momentum and term structure. The performance of an array of portfolios from double-sorts on non-commercial traders’ participation, historical returns and term spreads are tested against a risk model. The analysis covers the listing of 26 commodities in the period 1986–2013. First and foremost, the paper provides a fresh evidence for the validity of strategies based on momentum and term structure investing in commodity markets. Secondly, it proves that term structure strategies generate significantly higher performance results in non-financialized markets. Moreover, it supports the thesis that market financialization adversely affects momentum profits. The results are important in terms of tactical and strategic asset allocation in commodity markets. They imply that investors who implement momentum or term structure based strategies should also consider the composition of market participants.

Modelling futures price volatility in energy markets: Is there a role for financial speculation?

This paper models volatility in four energy futures markets, adopting GARCH models. The variance equation is enriched with alternative measures of speculation, based on CFTC data: the market share of non-commercial traders, the Working's T index, and the percentage of net long positions of non-commercials over total open interest in future markets. It also includes a control for market liquidity. We consider four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) over the period 2000–2014, analysed at weekly frequency. We find that speculation presents a negative and significant sign. The robustness exercise shows that: i) results remain unchanged through different model specifications (GARCH-in-mean, EGARCH, and TARCH); ii) results are robust to different specifications of the mean and variance equation.

Investor Sentiment and Forecasting Ability: Evidence from COT Reports in Precious Metal Futures Markets

Exploring the information contained in Commodity Futures Trading Commission’s (CFTC) Commitments of Traders (COT) reports, this analysis investigates the forecasting ability of investor sentiment index in three major precious metal futures markets, namely, Gold, Silver, and Platinum. A strong contemporaneous relationship between investor sentiment and market returns has been found. In specific, commercial traders’ sentiment acts oppositely to the price changes while that of non-commercial and non-reporting traders positively correlated with the market returns. However, the bivariate Granger causality test shows little evidence that investor sentiment index could lead market returns. On the other hand, market returns generally lead investor sentiment. Particularly, commercial traders are trend counters while non-commercial and non-reporting traders are trend followers. Extreme traders’ position also fails to display significant forecasting ability of subsequent price movements. In general, the results do not support the use of investor sentiment as an indicator to predict price movements in precious metal futures markets. Nevertheless, we find that investor sentiment could play as a signal of buying or selling and the trading system is highly profitable compared to naive long position in all of three markets.

Returns to individual traders in agricultural futures markets: skill or luck?

Using individual trader data from the Commodity Futures Trading Commission's (CFTC) Large Trader Reporting System (LTRS) for the period January 2000 to September 2009, this article investigates whether noncommercial traders in the corn, live cattle and coffee futures markets persist in making profits. Two out-of-sample measures of skill – a winner and a loser ranking test and a top and a bottom decile test – are used to analyse the ability of traders to consistently perform well at monthly, quarterly and annual time horizons. The findings identify significant persistence in rankings – traders in the top half of the profit distribution in a time period tend to stay in the top half in the next period. Differences in magnitude of profitability between the top and bottom deciles also provide support that persistent skill exists among the top 10% of traders. Detailed examination of annual rankings for those traders who were most continuously in the markets further reveals persistence in profits for a smaller subset of traders, as well as an indication of persistence in the face of losses.

Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation

This paper evaluates how different types of speculation affect the volatility of commodities’ futures prices. We adopt four indexes of speculation: Working’s T, the market share of non-commercial traders, the percentage of net long speculators over total open interest in future markets, which proxy for long term speculation, and scalping, which proxies for short term speculation. We consider four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) and seven non-energy commodities (cocoa, coffee, corn, oats, soybean oil, soybeans and wheat) over the period 1986-2010 analyzed at weekly frequency. Using GARCH models we find that speculation significantly affects volatility of returns: short term speculation has a positive and significant impact on volatility, while long term speculation generally has a negative effect. The robustness exercise shows that: i) scalping is positive and significant also at higher and lower data frequencies; ii) results remain unchanged through different model specifications (GARCH-in-mean, EGARCH, and TARCH); iii) results are robust to different specifications of the mean equation.

Futures Price Volatility in Commodities Markets: The Role of Short Term vs Long Term Speculation

This paper evaluates how different types of speculation affect the volatility of commodities’ futures prices. We adopt four indexes of speculation: Working’s T, the market share of non-commercial traders, the percentage of net long speculators over total open interest in future markets, which proxy for long term speculation, and scalping, which proxies for short term speculation. We consider four energy commodities (light sweet crude oil, heating oil, gasoline and natural gas) and seven non-energy commodities (cocoa, coffee, corn, oats, soybean oil, soybeans and wheat) over the period 1986-2010 analyzed at weekly frequency. Using GARCH models we find that speculation significantly affects volatility of returns: short term speculation has a positive and significant impact on volatility, while long term speculation generally has a negative effect. The robustness exercise shows that: i) scalping is positive and significant also at higher and lower data frequencies; ii) results remain unchanged through different model specifications (GARCH-in-mean, EGARCH, and TARCH); iii) results are robust to different specifications of the mean equation.

Financialization of commodities

The basic theory of price formation tells us how the price of a particular asset will change based on the adjustment to its supply and demand. However, values of assets are also determined by other business fundamentals, company’s and world events, human psychology, and investors’ belief about the possible future profit. In recent history that lead to an increase of individual and institutional investors’ interest in allocating their resources in commodity markets. With a large inflow of capital commodities’ prices started to rise making them attractive components to effective investment portfolios. The presented paper addresses the issue of so called commodities ‘financialization’ process. It looks at the main factors standing behind commodities’ price movements and to what extent financial market participants contributed to commodities price volatility in recent years. Based on the data examined it distinguishes the involvement of both commercial and non-commercial traders in short and long term periods of time. As well as explaining the impact of growing investors’ interest in commodity markets it defines other market forces - like currency appreciations and emerging markets - as being part of increased volatility in raw and soft commodity markets. Along with market examination the paper focuses on possible future outcomes in attempts to regulate commodities derivatives markets and potential effects of those efforts.

What is Driving Oil Futures Prices? Fundamentals Versus Speculation

In this paper we analyse the relative importance of fundamental and speculative demand on oil futures price levels and volatility. In a first step, we present a theoretical heterogeneous agent model of the oil futures market based on noise trading. We use the model to study the interaction between the oil futures price, volatility, developments in underlying fundamentals and the presence of different types of agents. We distinguish between commercial traders (who are physically involved in oil) and non-commercial traders (who are not involved physically with oil). Based on the theoretical model we find that a multiplicity of equilibria can exist. More specifically, on the one hand, if we have high fundamental volatility, high uncertainty about future oil demand, and the oil price deviation from fundamentals or the price trend is small, we will only have commercial traders entering the market. On the other hand, if a large unexpected shock to the oil spot price occurs then all traders will enter the market. In a next step, we empirically test the model by estimating a markov-switching model with time-varying transition probabilities. We estimate the model over the period January 1992 - April 2011. We find that up to 2004, movements in oil futures prices are best explained by underlying fundamentals. However, since 2004 regime switching has become more frequent and the chartist regime has been the most prominent. JEL Classification: D84, Q33, Q41, G15

Smart Money: The Forecasting Ability of CFTC Large Traders in Agricultural Futures Markets

The forecasting content of the Commodity Futures Trading Commission’s Commitments of Traders (COT) report is investigated. Bivariate Granger causality tests show very little evidence that traders’ positions are useful in forecasting (leading) returns in 10 agricultural futures markets. However, there is substantial evidence that traders respond to price changes. In particular, noncommercial traders display a tendency for trend following. The other trader classifications display mixed styles, perhaps indicating those trader categories capture a variety of traders. The results generally do not support use of the COT data in predicting price movements in agricultural futures markets.

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Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports

The Commodity Futures Trading Commission (CFTC)'s Commitments of Traders (COT) data are examined for crude oil, unleaded gasoline, heating oil, and natural gas futures contracts. The collection procedures for the COT data are first examined, followed by Granger causality tests to determine if relationships between trader positions and market prices exist. A positive correlation between returns and positions held by noncommercial traders, and a negative correlation between commercial positions and market returns, are found. Furthermore, positive returns result in an increase in noncommercial net positions in the following week, whereas the net long positions held by commercial hedgers decline following price increases. However, traders' net positions do not lead market returns in general.

Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports

The Commodity Futures Trading Commission (CFTC)'s Commitments of Traders (COT) data are examined for crude oil, unleaded gasoline, heating oil, and natural gas futures contracts. The collection procedures for the COT data are first examined, followed by Granger causality tests to determine if relationships between trader positions and market prices exist. A positive correlation between returns and positions held by noncommercial traders, and a negative correlation between commercial positions and market returns, are found. Furthermore, positive returns result in an increase in noncommercial net positions in the following week, whereas the net long positions held by commercial hedgers decline following price increases. However, traders' net positions do not lead market returns in general.

Who Trades Futures and How: Evidence from the Heating Oil Futures Market

We document the trading activities over the period from June 1993 through March 1997 of the 223 largest traders of heating oil futures - traders who together account for 58% of the open interest in this market. Dividing these traders into eleven different groupings: refiners, marketers/distributors, commercial banks, investment banks, end users, energy traders, other energy firms, commodity pools, commodity trading advisors, floor brokers, and other non-commercial traders, we explore how their trading activities differ and compare. While past studies have documented the trading of a single trader, a single type of trader, or traders in general, this is the first dis-aggregated study of all large traders in any derivatives market. We find substantial and significant differences between the eleven trader types in: their propensity to take long or short positions, whether they hold outright (naked) or spread positions, the term-to-maturity of the contracts they hold, how long they hold a position, and the size of their positions. We also find that both trading volume and open interest positions are dominated by potential hedgers, rather than speculators, where potential hedgers are defined as traders with substantial positions in the cash and/or forward heating oil market. Finally, we find that it is not appropriate to treat traders which the CFTC identifies as commercials as hedgers as is common practice in the finance literature.