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Speculative pricing in the Liverpool cotton futures market: a nonlinear tale of noise traders and fundamentalists from the 1920s

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Abstract

In the 1920s and 1930s, empirical studies of cotton futures pricing tend to attribute market fluctuations to shifts in fundamentals. In this paper, we qualify this view focusing on the role of speculation. Our research is based on a nonlinear heterogeneous agents model which posits the existence of two categories of speculators, feedback traders and fundamentalists, who react (differently) to deviations of market prices from their fundamental value. The analysis is based on original data drawn from the online archives of The Times and on an historical description of the working of a staple commodity market. The empirical findings allow us to conclude that whereas feedback traders tend to herd, fundamentalists are more affected by risk aversion and react but slowly to the underpricing/overpricing of the cotton contracts. As expected, the presence of fundamentalists stabilizes the market even if, at least in the time period under investigation, the behavior of feedback traders is the major driver of short-run price dynamics.

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Notes

  1. For a discussion of the effects of overenthusiasm and overtrading, both typical of noise trading, in the cotton market in the 1920s, see Hubbard (1923, pp. 435–441). On the relationship between noise trading, overconfidence and feedback trading, see the analysis of “investor sentiment” in Shleifer and Summers (1990).

  2. Source Chapman and McFarlane (1907). On early Liverpool cotton imports and the organization of the cotton market in the XVIII century, see Dumbell (1923).

  3. As Williams (1982, p. 306) recalls, although the year 1869 has been given as the earliest date for written rules in Liverpool, the minutes of the Liverpool Cotton Brokers Association for April 19 and June 17, 1864, mention the voting into force of rules for cotton “to arrive.” For an early account of the creation of the Liverpool Cotton Market and of the Liverpool Broker’s Association, see also Ellison (1886).

  4. Killough and Killough (1926, pp. 47–48) agree with Hubbard (and Emery) when they write “The speculator uses the futures market as a place to pit his judgment of supply, demand, and price movements against the judgments, better or worse, of other speculators. His function is to bring together all the available facts, to act upon them, and thus to turn the balance of influence toward the maintenance of a fair competitive price.”

  5. The list of supply and demand factors provided by Smith (1928, pp. 9–10) reads as follows: “(A)ctual supply of cotton in the USA at the beginning of the month. […] “(P)otential” supply, or estimated size of the crop. […] Accumulated domestic consumption, by months. Accumulated exports, for foreign consumption, by months. […] Accumulated rates of change in general price level. Average price of industrial stocks. […] Series representing the years from 1903 to 1924 and indicating yearly changes, or trend, in demand and other trend factors. Series representing the months of the crop year, beginning June, and indicating seasonal changes not otherwise taken care of.”

  6. Chapman and Knoop define inexpert speculators as follows: “the public apt to be influenced as a crowd, to give way to panic or become unduly sanguine […] and easily misguided by bulling and bearing operations” (Chapman and Knoop 1906, pp. 324–325).

  7. Assuming that the two groups of speculators partially overlap allows us to take into account the possibility that the same speculator shifts from one behavior to the other according to the size of market disequilibrium.

  8. Alternative switching rules, in which it is assumed either that the number of potential noise trader/fundamentalist speculators operating in the market varies from 0 to 100 percent, or that \(S_{2jt} = (1 - S_{1it} )\), i.e., that the number of fundamentalists rises as the number of active feedback traders declines, were tested and rejected in the empirical investigation.

  9. The choice of a weekly frequency can be justified on the basis of two considerations. First, the use of a daily frequency would imply a large and potentially confusing number of lags in the dynamic parameterization of the pricing equation, given the observed reduced informational efficiency of the cotton market. Second, we use the very frequency and dating patterns adopted by the International Institute of Agriculture in its “International Yearbook of Agricultural Statistics” (for more details, see Hynes et al. 2012). As pointed out by Hubbard (1923, p. 294), tenders on futures contracts in Liverpool were held on Mondays, Wednesdays, and Fridays.

  10. We use as spot price the price of the futures contract closer to delivery, which is with maturity in the current month.

  11. For a description of cotton futures contracts prior to the 1919 reform, see Chapman and Knoop (1904) and Hubbard (1923), Ch. XXV.

  12. On this, see also Rowe (1936, pp. 102–103) and the section devoted to cotton in The Special Memoranda on stocks of staple commodities, written by J.M. Keynes for the London and Cambridge Economic Service (Keynes 1983) Appendix 1.

  13. Howell (1934) identifies an analogous pattern for the New York cotton futures prices. His innovative empirical analysis, over the 1917 to 1933 time period, finds that the extent of the fluctuations in prices of cotton futures contracts, for various maturities, varied directly over time with the level of cotton prices.

  14. As suggested by Teräsvirta (1994, p. 211), using the SBIC order selection criterion in this context sometimes leads “to too parsimonious a model in the sense that the estimated residuals of the selected model are not free from serial correlation.”

  15. The LR tests set out in the LRT row of Table 2 show that, with the exception of the three months to maturity contract, the joint hypothesis that the noise trading and fundamentalist parameters are nil is rejected at the 5 percent level of significance.

  16. As the maturity of the cotton contracts rises, the absolute value of γ 1 declines, the degree of synchronization of the response of feedback traders to price deviations from their normal value declines, and the persistence and (first order) autocorrelation of the S 10t–1 time series rises. ter Ellen and Zwinkels (2010) follow De Jong et al. (2009) and attribute strategy persistence to a relevant status quo bias.

  17. The local regressions are performed on a subsample selected according to the Cleveland (1993) procedure and involve about 100 evaluation points. Tricube weights are to be found in the weighted regressions used to minimize the weighted sum of squared residuals. The bandwidth span of each local regression is set to 0.3, which implies a sample of 140 observations for each evaluation point.

References

  • Baffes J, Kaltsas I (2004) Cotton futures exchanges: their past, their present and their future. Quarterly Journal of International Agriculture 43:153–176

    Google Scholar 

  • Bollerslev T, Wooldridge JM (1992) Quasi-maximum likelihood estimation and dynamic models with time-varying covariances. Econometric Reviews 11:143–172

    Article  Google Scholar 

  • Brock WA, Hommes CH (1997) A rational route to randomness. Econometrica 65:1059–1095

    Article  Google Scholar 

  • Brock WA, Hommes CH (1998) Heterogeneous beliefs and route to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control 22:1235–1274

    Article  Google Scholar 

  • Chapman SJ, Knoop D (1904) Anticipation in the cotton market. Economic Journal 14:541–554

    Article  Google Scholar 

  • Chapman SJ, Knoop D (1906) Dealing in futures on the cotton market. Economic Journal 16:321–373

    Google Scholar 

  • Chapman SJ, McFarlane J (1907) Cotton supplies. Economic Journal 17:57–65

    Article  Google Scholar 

  • Cifarelli G, Paesani P (2012) An assessment of the theory of storage: has the relationship between commodity price volatility and market fundamentals changed over time? Working paper n. 12/2012, Università di Firenze, Dipartimento di Scienze per l’Economia e l’Impresa

  • Cleveland WS (1993) Visualizing data. Hobart Press, Summit, NJ

    Google Scholar 

  • Cox AB (1927) Cotton futures markets in Europe. Journal of Farm Economics 9:176–191

    Article  Google Scholar 

  • Cristiano C, Naldi N (2012) Keynes’s activity on the cotton market and the theory of the ‘Normal Backwardation’: 1921–1939. In Marcuzzo MC (ed) Speculation and regulation in commodity markets: the Keynesian approach in theory and practice, Rapporto Tecnico n. 21, Sapienza Università di Roma, Dipartimento di Scienze Statistiche, pp 25–56

  • Cutler DM, Poterba JM, Summers LH (1990) Speculative dynamics and the role of feedback traders. Am Econ Rev 80:63–68

    Google Scholar 

  • Day R, Huang W (1990) Bulls, bears and market sheep. J Econ Behav Organ 14:299–329

    Article  Google Scholar 

  • De Jong E, Vershoor WFC, Zwinkels RCJ (2009) Behavioural heterogeneity and shift contagion: evidence from the Asian crisis. Journal of Economic Dynamics and Control 33:1929–1944

    Article  Google Scholar 

  • De Long JB, Shleifer A, Summers LH, Waldmann RJ (1990) Positive feedback investment strategies and destabilizing rational speculation. Journal of Finance 45:379–395

    Article  Google Scholar 

  • Dumbell S (1923) Early Liverpool cotton imports and the organisation of the cotton market in the eighteenth century. Economic Journal 33:362–373

    Article  Google Scholar 

  • Eitrheim Ø, Teräsvirta T (1996) Testing the adequacy of smooth transition autoregressive models. Journal of Econometrics 74:59–75

    Article  Google Scholar 

  • Ellison T (1886) The cotton trade of Great Britain: including a history of the Liverpool cotton market and of the Liverpool Broker’s Association, Effingham Wilson: London. Reprinted by Kessinger Publishing

  • Emery HC (1896) Speculation on the stock and produce exchanges of the United States, University of Columbia. Reprinted by Kessinger Publishing, New York

    Google Scholar 

  • Engle RF, Ng VK (1993) Measuring and testing the impact of news on volatility. Journal of Finance 48:1749–1778

    Article  Google Scholar 

  • Fama E (1970) Efficient capital markets: a review of theory and empirical work. Journal of Finance 25:383–417

    Article  Google Scholar 

  • Frankel JA, Froot KA (1986) Understanding the US Dollar in the eighties: the expectations of chartists and fundamentalists. The Economic Record 62:24–38

    Google Scholar 

  • Howell LD (1934) Fluctuations of prices in cotton futures contracts, United States Department of Agriculture Technical Bulletin 423

  • Howell LD (1939) Cotton prices in spot and futures markets, United States Department of Agriculture Technical Bulletin 685

  • Hubbard WH (1923) Cotton and the cotton market. D. Appleton and Company, New York

    Google Scholar 

  • Hynes W, Jacks DS, O’Rourke KH (2012) Commodity market disintegration in the interwar period. European Review of Economic History 16:119–143

    Article  Google Scholar 

  • Keynes JM (1983) The collected writings of John Maynard Keynes, Vol. XII economic articles and correspondence. Investment and editorial, London: MacMillan

  • Killough HB, Killough LW (1926) Price making forces in cotton markets. Journal of the American Statistical Association 21:47–54

    Article  Google Scholar 

  • Lundberg S, Teräsvirta T (1998) Modeling economic high frequency time series with STAR GARCH models, Stockholm School of Economics, Working Paper no. 291, available at: http://swapec.hhs.se/hastef/papers/hastef0291.pdf

  • Luukkonen R, Saikkonen P, Teräsvirta T (1988) Testing linearity against smooth transition autoregressive models. Biometrika 75:491–499

    Article  Google Scholar 

  • Lux T, Marchesi M (2000) Volatility clustering in financial markets: a micro-simulation of interacting agents. International Journal of Theoretical and Applied Finance 3:675–702

    Article  Google Scholar 

  • Reitz S, Slopek U (2009) Non linear oil price dynamics: a tale of heterogeneous speculators? German Economic Review 10:270–283

    Article  Google Scholar 

  • Reitz S, Westerhoff FH (2003) Nonlinearities and cyclical behavior, the role of chartists and fundamentalists. Studies in Nonlinear Dynamics and Econometrics 7(4), Article 3

  • Reitz S, Westerhoff FH (2007) Commodity price cycles and heterogeneous speculators: a STAR-GARCH model. Empirical Economics 33:231–244

    Article  Google Scholar 

  • Rowe JWF (1936) Markets and men. Cambridge University Press, Cambridge

    Google Scholar 

  • Schwartz E, Smith JE (2000) Short-term variation and long-term dynamics in commodity prices. Manage Sci 46:893–911

    Article  Google Scholar 

  • Shleifer A, Summers LH (1990) The noise trader approach to finance. Journal of Economic Perspectives 4:19–33

    Article  Google Scholar 

  • Smith BB (1928) Factors affecting the price of cotton. United States Department of Agriculture Technical Bulletin 50

  • ter Ellen S, Zwinkels RCJ (2010) Oil price dynamics: a behavioral finance approach with heterogeneous agents. Energy Economics 32:1427–1434

    Article  Google Scholar 

  • Teräsvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association 89:208–218

    Google Scholar 

  • Teräsvirta T, Anderson H (1992) Characterizing nonlinearities in business cycles using smooth transition autoregressive models. Journal of Applied Econometrics 7:119–139

    Article  Google Scholar 

  • Wan J-Y, Kao C-W (2009) Evidence on the contrarian trading in the foreign exchange markets. Econ Model 26:1420–1431

    Article  Google Scholar 

  • Westerhoff FH (2004) Multiasset market dynamics. Macroeconomic Dynamics 8:596–616

    Google Scholar 

  • Westerhoff FH, Reitz S (2005) Commodity price dynamics and the non linear market impact of technical traders: empirical evidence for the US corn market. Phys A 349:641–648

    Article  Google Scholar 

  • Williams JC (1982) The origins of futures markets. Agric Hist 56:306–316

    Google Scholar 

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Authors and Affiliations

  1. University of Florence, Florence, Italy

    Giulio Cifarelli

  2. University of Rome Tor Vergata, Rome, Italy

    Paolo Paesani

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  1. Giulio Cifarelli
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  2. Paolo Paesani
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Cifarelli, G., Paesani, P. Speculative pricing in the Liverpool cotton futures market: a nonlinear tale of noise traders and fundamentalists from the 1920s. Cliometrica 10, 31–54 (2016). https://doi.org/10.1007/s11698-014-0121-y

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