Ferreting out the Naïve One: Positive Feedback Trading and Commodity Equilibrium Prices. Jaap W. B. Bos Paulo Rodrigues Háng Sūn

Transcription

1 Ferreting out the Naïve One: Positive Feedback Trading and Commodity Equilibrium Prices Jaap W. B. Bos Paulo Rodrigues Háng Sūn

2 Extra large volatilities of commodity prices. Coincidence with Commodity Index Traders (CIT) positions, which are huge in scale and overwhelmingly long, raised suspicion. Proposed mechanisms include limit to arbitrage, agree to disagree, etc. Introduction

3 Blind men and an elephant Six blind men were asked to determine what an elephant looked like by feeling different parts of the elephant's body. The blind man who feels a leg says the elephant is like a pillar; the one who feels the tail says the elephant is like a rope; the one who feels the trunk says the elephant is like a tree branch; the one who feels the ear says the elephant is like a hand fan; the one who feels the belly says the elephant is like a wall; and the one who feels the tusk says the elephant is like a solid pipe. Introduction

4 Few study is on the behavior of investors, which is just like the blind men and elephant story. What will we find when the interaction between traders and the market is taken into consideration? Theory shows prices destablization can follow certain trading strategies: DeLong et al. (1990), Positive Feedback Investment Strategies and Destabilizing Rational Speculation (DSSW). We show that this story could happen in commodity markets as well. Introduction

5 Price evolution with positive feedback traders DSSW Assumed Investor Class Hypothesized Investing Strategy Theoretical Market Influence Positive Feedback Trader Positive feedback to past returns: Longing rising ones, and shorting falling ones Being exploited by rational investors and losing money Informed Rational Speculator They are rational: Utility maximization Earning money from PF traders by overraising or overdepressing prices A fundamental shock, e.g. good or bad news Price evolution without positive feedback traders Passive Investor Negative feedback to the overprice of assets Helping stabilizing market risk-bearing capacity Introduction

6 We have seen the story in the theory, but what about the actual commodity markets? CFTC (Commodity Futures Trading Commission of USA) has some data. Introduction

7 Two reports of CFTC: COT (Commitments of Traders), CIT (Commodity Index Trader Supplement). COT: Positions of commercial and non-commercial traders, spreading positions, and nonreportable positions. Nonreportable traders: traders other than clearing members, futures commission merchants, and foreign brokers, tend to be smaller, and more irrational. CIT: Index traders. Only for agricultural products, but the positions for other contracts can be estimated based on a series of algorithms. Introduction

8 DSSW Investor Class Hypothesized Investing Strategy Theoretical Market Influence Nonreportable traders tend to be Positive Feedback Positive feedback to Trader past returns Being exploited by rational investors and losing money CIT s Passive Investor Always there Negative feedback to the overprice Helping stabilizing market risk-bearing capacity Informed Rational Speculator Utility maximization Earning money from PF traders by overraising or overdepressing prices Introduction

9 Summary 1. Traders did behave as is assumed in the theoretical model: - CIT s traded in a negative feedback way, like the passive investors ; - Behaviors of CFTC non-reportable traders are evidences for the existence of positive feedback traders. 2. Traders behaviors can forecast commodity prices as is predicted in the theoretical model: - CIT s gained; - And positive feedback traders lost. 3. Conclusion: Recent commodity price volatilities can be attributed to the collaboration of three classes of traders, especially positive feedback traders; CIT s were necessary in the mechanism, but they influenced the market in a more complex way. Introduction

10 Data Our study covers 35 commodity futures contracts inside and outside S&P GSCI and DJ UBSCI. ENERGY Grains Softs WTI Crude Oil, Heating Oil, Unleaded Gas, Natural Gas, RBOB Gasoline, Brent Crude Oil, Gasoil Soybean Oil, Corn, Oats, Rough Rice, Soybeans, Soybean Meal, Chicago Wheat, Kansas Wheat, Minneapolis Wheat Cocoa, Cotton #2, Coffee, Orange Juice, Sugar #11, Lumber Livestock Metals Feeder Cattle, Lean Hogs, Live Cattle Gold, Silver, New York Copper, Palladium, Platinum, Aluminum, London Copper, Nickel, Lead, Zinc

11 Traders Strategies Hypothesis: CIT s respond negatively to commodity overprice. But we cannot directly regress CIT positions on individual contract prices because the commodity portfolios of index traders are fixed. If we cannot test a hypothesis, we can test a close corollary. Hypothesis: CIT s should respond more to the equal price change of commodities with larger weights in commodity indices, if they are negative feedback traders. Weights: Average percent weights in S&P GSCI and DJ UBSCI. Controlling CIT s capital capabilities in commodity markets.

12 Traders Strategies

13 Traders Strategies RBOB Gas Index weight=0.04 RBOB returns of 1 percent historically predicted: * The numbers are fictional and only serve for the demonstrative purpose. WTI CRUDE Index weight=0.13 coffee Index weight=0.02 Etc. Alternative definition of index weights: Taking natural price correlations into consideration. The adjusted weight of RBOB is therefore (-0.03) 0.02+

14 Traders Strategies

15 Traders Strategies Hypothesis: Nonreportable traders tend to be positive feedback traders. We cannot use nonreportable net positions as the indicator because this variable is prone to trader inflows and outflows instead of their willingness to take long positions. Use the long to total interests ratio instead.

16 Traders Strategies

17 Market Influences CIT net long positions are expected to predict returns positively They won. Non-reportable trader attitude is expected to predict returns negatively They lost.

18 Market Influences

19 Conclusion Recent price patterns of commodity futures markets could be partly attributed to the concurrence of trading strategies of positive feedback traders and commodity index traders. CFTC non-reportable long to total open interests ratios could be a decent proxy for the positive feedback trading as non-reportable traders prefer more long short positions. The expected price effects of positive feedback trading rely on the presence of passive investors who decide demands negatively related with prices.