Rational Speculators, Contrarians and Excess Volatility

The VAR approach for testing present value models is applied to a nonlinear asset pricing model with three types of agents, using historical US stock prices and dividends. Besides rational long-term investors, that value assets according to expected dividends, the model includes rational and contrarian speculators. Agents choose their regime based on evolutionary considerations. Supplementing the standard present value model with speculative agents dramatically improves the model's ability to replicate the observed market dynamics. In particular the existence of contrarians can explain some of the most volatile episodes including the 1990s bubble, suggesting this was not a rational bubble

Essays on expectations and the econometrics of asset pricing

The way in which market participants form expectations affects the dynamic properties of financial asset prices and therefore the appropriateness of different econometric tools used for empirical asset pricing. In addition to standard rational expectations models, this thesis studies a class of models in which boundedly rational agents may switch between various simple expectation rules. A well-known specific example features fundamentalists, who target the fundamental value of the asset, and chartists, who try to exploit recent trends in price movements. A crucial feature of these models is that not all agents have to follow the same expectation rule, but are allowed to form heterogeneous beliefs. Chapters 2 and 3 present empirical estimations of two specific heterogeneous agent models. Since the data generating processes are assumed to be nonlinear, due to the agents' switching between expectation rules, nonlinear regression models are applied. By framing the empirical results in a heterogeneous agent framework, these chapters provide an alternative view on important topics in asset pricing, such as the prevalence of excess volatility and the relation between financial markets and the macro-economy. The final two chapters deal with noncausal, or forward-looking, autoregressive models. Chapter 4 shows that US stock prices are better described by noncausal autoregressions than by their causal counterparts. This implies that agents' expectations are not revealed to an outside observer such as an econometrician observing only realized market data. Simulation results show that heterogeneous agent models are able to generate noncausal asset prices. Chapter 5 considers the estimation of a class of standard rational expectations models. It is shown that noncausality of the instrumental variables does not have an impact on the consistency of the generalized method of moments (GMM) estimator, as long as agents form rational expectations.Ei saatavill

Inter-pattern speculation: beyond minority, majority and $-games

A new model of financial market is proposed, based on the sequential and inter-temporal nature of trader-trader interaction. In this pattern- based speculation model, the traders open and close their positions explicitely. Information ecology can be precisely characterised, and is strikingly similar to that of the Minority Game. Naive and sophisticated agents are shown to give rise to very different phenomenology.Financial market, agent-based modelling, minority game, majority game, $-game, information, market efficiency

The behavior and performance of major types of futures traders

This article examines the behavior and performance of speculators and hedgers in 15 U.S. futures markets. We find that after controlling for market risk factors, speculators are contrarians, but respond positively to market sentiment. In contrast, hedgers engage in positive feedback trading and trade against market sentiment. We also find that trades of speculators (hedgers) are positively (negatively) correlated with subsequent abnormal returns; however, it does not appear that speculators possess superior forecasting power. Therefore, hedging pressure effects likely explain the negative relation between the performance of speculators and hedgers. The positive feedback trading by hedgers together with their negative performance suggests that hedgers have a destabilizing impact on futures prices.Trader behavior; perfromance; hedgers; speculators

Market ecologies: The effect of information on the interaction and profitability of technical trading strategies

Technical trading strategies make profits by identifying and exploiting patterns in market prices—patterns generated by the interaction of market participants. Using a model market populated by individuals using a range of trading rules we show that the presence of technical traders may be beneficial, in some cases reducing volatility and increasing price efficiency. In particular, contrarian traders who base their decisions on high frequency data have the largest positive effect. It is also found that if technical traders condition their actions using ‘real time’ information, they partially emulate arbitrageurs and make positive profits

Price Stability and Volatility in Markets with Positive and Negative Expectations Feedback: An Experimental Investigation

The evolution of many economic variables is affected by expectations that economic agents have with respect to the future development of these variables. Here we show, by means of laboratory experiments, that market behavior depends to a large extent on how the realized market price responds to an increase in average price expectations. If it responds by decreasing, as in commodity markets, prices converge quickly to their equilibrium value, confirming the rational expectations hypothesis. If the realized price increases after an increase of average expectations, as is typical for financial markets, large fluctuations in realized prices are likely.

Investors' behaviour and dynamics of ship prices: a heterogeneous agent model

Distinguishing investors into speculators and operators, and classifying the former group into momentum and contrarian investors, we develop a heterogeneous agent model (HAM) to examine the dynamics of price of second-hand dry bulk ships. The results suggest that momentum strategies based on short-term measures of earnings perform significantly better than the contrarian or passive (buy-and-hold) strategies. The HAM seems to capture the dynamics of vessel prices and the investors’ behavior in the market for ships very well. Finally, an increase in participation of momentum investors tends to increase price volatility, whereas higher demand from contrarian investors seems to lower price variability

Behaviour and performance of key market players in the US futures markets

This study gives an insight into the behaviour and performance of large speculators and large hedgers in 29 US futures markets. Using a trading determinant model and priced risk factors such as net positions and sentiment index, results suggest hedgers (speculators) exhibit significant positive feedback trading in 15 (7) markets. Information variables like the S&P500 index dividend yield, corporate yield spread and the three months treasury bill rate were mostly unimportant in large players’ trading decisions. Hedgers had better market timing abilities than speculators in judging the direction of the market in one month. The poor market timing abilities and poor significance of positive feedback results suggest higher trading frequency intervals for speculators. Hedging pressures, which measure the presence of risk premium in futures markets, were insignificant mostly in agricultural markets. As a robust test of hedging pressures, price pressure tests found risk premium to be still significant for silver, crude oil and live cattle. The positive feedback behaviour and negative market timing abilities suggest hedgers in heating oil and Japanese yen destabilize futures prices, and points to a need to check CFTC’s (Commodity Futures Trading Commission) position limits regulation in these markets. In fact, large hedgers in these two markets are more likely to be leading behaviour, in that they have more absolute net positions than speculators. Alternatively stated, positive feedback hedgers in these two markets are more likely to lead institutions and investors to buy (sell) overpriced (underpriced) contracts, eventually leading to divergence of prices away from fundamentals.Atlhought hedgers in crude oil had significant positive feedback behaviour and negative market timing skills, they would not have much of a destabilizing effect over remaining players because the mean net positions of hedgers and speculators were not far apart. While the results are statistically significant, it is suggested these could be economically significant, in that there have been no regulation on position limits at all for hedgers compared to speculators who are imposed with strict limits from the CFTC. Further, mean equations were regressed against decomposed variables, to see how much of the futures returns are attributed to expected components of variables such as net positions, sentiment and information variables. While the expected components of variables are derived by ensuring there are enough ARMA (autoregressive and moving average) terms to make them statistically and economically reliable, the unexpected components of variables measure the residual on differences of the series from its mean. When decomposing net positions against returns, it was found expected net positions to be negatively related to hedgers’ returns in mostly agricultural markets. Speculators’ expected (unexpected) positions were less (more) significant in explaining actual returns, suggesting hedgers are more prone in setting an expected net position at the start of the trading month to determine actual returns rather than readjusting their net positions frequently all throughout the remaining days of the month. While it important to see how futures returns are determined by expected and unexpected values, it is also essential to see how volatility is affected as well.In an attempt to cover three broad types of volatility measures, idiosyncratic volatility, GARCH based volatility (variance based), and PARCH based volatility (standard deviation) are used. Net positions of hedgers (expected and unexpected) tend to have less effect on idiosyncratic volatility than speculators that tended to add to volatility, reinforcing that hedgers trading activity hardly affect the volatility in their returns. This suggest they are better informed by having a better control over their risk (volatility) measures. The GARCH model showed more reliance of news of volatility from previous month in speculators’ volatility. Hedgers’ and speculators’ volatility had a tendency to decay over time except for hedgers’ volatility in Treasury bonds and coffee, and gold and S&P500 for speculators’ volatility. The PARCH model exhibited more negative components in explaining current volatility. Only in crude oil, heating oil and wheat (Chicago) were idiosyncratic volatility positively related to return, reinforcing the suggestion for stringent regulation in the heating oil market. Expected idiosyncratic volatility was lower (higher) for hedgers (speculators) as expected under portfolio theory. Markets where variance or standard deviation are smaller than those of speculators support the price insurance theory where hedging enables traders to insure against the risk of price fluctuations. Where variance or standard deviation of hedgers is greater than speculators, this suggest the motivation to use futures contracts not primarily to reduce risk, but by institutional characteristics of the futures exchanges like regulation ensuring liquidity.Results were also supportive that there was higher fluctuations in currency and financial markets due to the higher number of contracts traded and players present. Further, the four models (GARCH normal, GARCH t, PARCH normal and PARCH t) showed returns were leptokurtic. The PARCH model, under normal distribution, produced the best forecast of one-month return in ten markets. Standard deviation and variance for both hedgers’ and speculators’ results were mixed, explained by a desire to reduce risk or other institutional characteristics like regulation ensuring liquidity. Moreover, idiosyncratic volatility failed to accurately forecast the risk (standard deviation or variance based) that provided a good forecast of one-month return. This supports not only the superiority of ARCH based models over models that assume equally weighted average of past squared residuals, but also the presence of time varying volatility in futures prices time series. The last section of the study involved a stability and events analysis, using recursive estimation methods. The trading determinant model, mean equation model , return and risk model, trading activity model and volatility models were all found to be stable following the effect of major global economic events of the 1990s. Models with risk being proxied as standard deviation showed more structural breaks than where variance was used. Overall, major macroeconomic events didn’t have any significant effect upon the large hedgers’ and speculators’ behaviour and performance over the last decade