Informed arbitrage with speculative noise trading

We consider speculative noise trading when some naïve speculators trade on noise as if it were information [Black, F., 1986. Noise. Journal of Finance 41, 529–543]. We examine the optimal trading strategy of an informed investor who faces such naïve speculators in the market. We find that the informed investor trades aggressively on her information and takes large, opposite positions against the naïve speculators. The trading volume is thereby drastically magnified. While such speculative noise trading enhances liquidity, it makes prices less efficient. The overall dynamic patterns that emerge from our model are most consistent with the evidence for interday variations in volume, volatility, and transaction costs.     

Does information drive trading in option strategies?

We study trading in option strategies in the FTSE-100 index market. Trades in option strategies represent around 37% of the total number of trades and over 75% of the total trading volume in our sample. We find some evidence that order flow in volatility–sensitive option strategies contains information about future realized volatility. We do not find evidence that order flow in directionally–sensitive option strategies contains information about future returns. Overall, our evidence suggests that option strategies are used both by traders who possess non-public information about future volatility and by uninformed speculators who appear to follow unprofitable trend chasing strategies.     

Fundamentalists,chartists and asset pricing anomalies

This paper investigates whether excess volatility of asset prices and serial correlations of stock monthly returns may be explained by the interactions between fundamentalists and chartists. Fundamentalists forecast future prices cum dividends through an adaptive learning rule. In contrast, chartists forecast future prices based on the observation of past price movements. Numerical simulations reveal that the interplay of fundamentalists and chartists robustly generates excess volatility of asset prices, volatility clustering, trends in prices (i.e. positive serial correlations of returns) over short horizons and oscillations in prices (i.e. negative serial correlations of returns) over long horizons, often observed in financial data. Moreover, we find that the memory of the learning rule plays a key role in explaining the above-mentioned stylized facts. In particular, we establish that excess volatility of asset prices; volatility clustering and autocorrelation of returns at different horizons emerge when fundamentalists have short memory. However, volatility clustering as well as short-run and long-run dependencies, observed in financial time series, are more pronounced when fundamentalists have longer memory.     

Capturing the risk premium of commodity futures: The role of hedging pressure

We construct long–short factor mimicking portfolios that capture the hedging pressure risk premium of commodity futures. We consider single sorts based on the open interests of hedgers or speculators, as well as double sorts based on both positions. The long–short hedging pressure portfolios are priced cross-sectionally and present Sharpe ratios that systematically exceed those of long-only benchmarks. Further tests show that the hedging pressure risk premiums rise with the volatility of commodity futures markets and that the predictive power of hedging pressure over cross-sectional commodity futures returns is different from the previously documented forecasting power of past returns and the slope of the term structure.     

Trading behavior in S&P 500 index futures

This article examines the determinants of trading decisions and the performance of trader types, in the context of the E-Mini S&;P 500 futures and S&;P 500 futures markets. Speculators and small traders tend to follow positive feedback strategies while hedgers dynamically adjust positions in response to market returns. Such strategies apparently reverse during the 2008–09 financial crisis. Investor sentiment and market volatility play an important role in determining the net trading position of traders across the sample period. While all trader types are better at foreseeing market upturns, an out-of-sample test suggests that speculators and small traders have some predictive ability for short-term market returns.     

The relationship between futures trading activity and exchange rate volatility,revisited

This paper examines the relationship between trading activity in currency futures and exchange rate volatility. In order to measure trading activity, the paper uses both volume and open interest to distinguish between speculators/day traders and hedgers. The study uses three different measures of volatility: (1) the extreme value estimator that measures intra-day volatility; (2) historical volatility; and (3) conditional volatility from the GARCH (1, 1) process. Main finding is that speculators and day traders destabilize the market for futures. Whether hedgers stabilize or destabilize the market is inconclusive. The results suggest that speculators’ demand for futures goes down in response to increased volatility. Meanwhile, the demand from hedgers shows mixed results, depending on the method used to measure volatility.     

Positive Feedback Investment Strategies and Destabilizing Rational Speculation

Analyses of rational speculation usually presume that it dampens fluctuations caused by “noise” traders. This is not necessarily the case if noise traders follow positive-feedback strategies—buy when prices rise and sell when prices fall. It may pay to jump on the bandwagon and purchase ahead of noise demand. If rational speculators' early buying triggers positive-feedback trading, then an increase in the number of forward-looking speculators can increase volatility about fundamentals. This model is consistent with a number of empirical observations about the correlation of asset returns, the overreaction of prices to news, price bubbles, and expectations.     

Volatility risk and the value premium: Evidence from the French stock market

This paper documents that systematic volatility risk is an important factor that drives the value premium observed in the French stock market. Using returns on at-the-money straddles written on the CAC 40 index as a proxy for systematic volatility risk, I document significant differences between volatility factor loadings of value and growth stocks. Furthermore, when markets are classified into expected booms and recessions, volatility factor loadings are also time-varying. When expected market risk premium is above its average, i.e. during expected recessions, value stocks are seen riskier than their growth counterparts. This implies in bad times, investors shift their preferences away from value firms. Instead they use growth stocks as hedges against deteriorations in their wealth during those times. The findings are in line with the predictions of rational asset pricing theory and support a “flight-to-quality” explanation.     

Asymmetric stock market volatility and the cyclical behavior of expected returns

Recent explanations of aggregate stock market fluctuations suggest that countercyclical stock market volatility is consistent with rational asset evaluations. In this paper, I develop a framework to study the causes of countercyclical stock market volatility. I find that countercyclical risk premia do not imply countercyclical return volatility. Instead, countercyclical stock volatility occurs if risk premia increase more in bad times than they decrease in good times, thereby inducing price–dividend ratios to fluctuate more in bad times than in good. The business cycle asymmetry in the investors’ attitude toward discounting future cash flows plays a novel and critical role in many rational explanations of asset price fluctuations.     

Asymmetric risk premium in value and growth stocks

There are two competing explanations for the existence of a value premium, a rational market risk explanation, whereby value stocks are inherently more risky than growth stocks, and a market over-reaction hypothesis, where agents overstate future returns on growth stock. Using asymmetric GARCH-M models this paper tests the predictions of the two hypotheses. Specifically, examining whether returns exhibit a positive (negative) risk premium resulting from a negative (positive) shock and the relative size of any premium. The results of the paper suggest that following a shock, volatility and expected future volatility are heightened, leading to a rise in required rates of return which depresses current prices. Further, these effects are heightened for value stock over growth stock and for negative shocks over positive shocks. Thus, in support of the rational risk interpretation, with a volatility feedback explanation for predictive volatility asymmetry.     

Does more information in stock price lead to greater or smaller idiosyncratic return volatility?

We investigate the relation between price informativeness and idiosyncratic return volatility in a multi-asset, multi-period noisy rational expectations equilibrium. We show that the relation between price informativeness and idiosyncratic return volatility is either U-shaped or negative. Using several price informativeness measures, we empirically document a U-shaped relation between price informativeness and idiosyncratic return volatility. Our study therefore reconciles the opposing views in the following two strands of literature: (1) the growing body of research showing that firms with more informative stock prices have greater idiosyncratic return volatility, and (2) the studies arguing that more information in price reduces idiosyncratic return volatility.     

Risk A version and Information Structure: An Experimental Study of Price Variability in the Securities Markets

This study investigates the differences in the behaviors between the speculative investors and the conservative investors in two separate experimental markets. Although the market for speculators shows greater price volatility in both bid/ask spread within a trade as well as with intraperiod variances, it exhibits several desirable properties. Specifically, the price patterns tend to converge closer, and at a greater speed to either the prior information equilibrium price or the rational expectation equilibrium price. It also achieves better allocational efficiency. And, it is also less likely to be misled by potentially “false” price information.     

Futures Commitments and Exchange Rate Volatility

It is commonly suggested that certain groups of futures traders, such as speculators and small traders, exacerbate cash market volatility. Empirical research on the subject has been conducted in context of the relationship between price volatility and futures volume or open interest and fails to satisfactorily resolve such an issue. This paper examines the relationship between exchange rate variability and futures trading activity in the context of disaggregated open interest. The data and techniques employed allow for more specific inferences regarding which group of traders contribute to exchange volatility. The results suggest that while 'typical' levels of futures commitments are not destabilizing, surges in the level of commitments of large speculators and small traders causes exchange rate volatility. The actual release of the commitment-of-traders data, however, has no impact on spot prices.     

What is going on in the oil market?

Conclusion In this article, we propose a consistent view on the recent oil-price history based on fundamental data and economic theory. We sum up: After the turn of the century three major stylized shocks have hit market. First, the demand curve has shifted fight outwards, mainly driven, as extensively reported in the media, by sustained growth in China and other Asian Countries. Second, supply disruptions in countries with low extraction costs (Iraq and Venezuela) have shifted the supply curve to the left. Third, we show that speculators adjust their inventories in order to take advantage of predictable price fluctuations and play themselves a major role in the price formation. Optimal storage theory implies that aggregate inventories are negatively related to the oil price and positively to the volatility of supply and demand shocks. We provide evidence that the political events in the last years have increased volatility and induced the inventory curve to shift right outwards. We analyze in a graphical framework the interaction of all these shocks and conclude that speculators have caused the oil price to overshoot in the short run its long-run fundamental value. However, this is not at all attributable to market failure or the harmfulness of speculators. In fact, the opposite is true. Speculators have in general a dampening effect on the oil price. The record oil price in the very recent history is partly a consequence of speculators maintaining or building-up inventories to cope with the supply and demand shocks to come. Hence, high prices represent a short-term toll for future price stability. It follows from our analysis that the oil price is expected to fall towards its long-term mean, provided that no further shocks hit the economy and, critically, the oil supply. As we saw, this prediction is consistent with the observed prices in the futures markets. Also in terms of future price volatility, the outlook is rather upbeat. The increased inventory levels held by speculators will cushion the spot market against fluctuations in natural supply and demand and limit the degree to which the currently high underlying volatility will translate into higher price volatility. We would like to thank Manuel Ammann, Bernd Brommundt, Stephan Kessler, Ralf Seiz, Michael Verhofen, Hemrich von Wyss, and two anonymous referees for helpful comments We are particularly indebted to Sergej Peisotchenko at United Energy System (UES) of Russia and Jan Gjerde at Shell for clearing us up on technical aspects of oil production     

Cross‐Asset Speculation in Stock Markets

In practice, heterogeneously informed speculators combine private information about multiple stocks with information in prices, taking into account how their trades influence the inferences of other speculators via prices. We show how this speculation causes prices to be more correlated than asset fundamentals, raising price volatility. The covariance structure of asset fundamentals drives that of prices, while the covariance structure of liquidity trade drives that of order flows. We characterize how speculator profits vary with the distributions of information and liquidity trade across assets and speculators, and relate the cross‐asset factor structure of order flows to that of returns.     

Futures Commitments and Commodity Price Jumps

We examine the relationship between the commitments of three of the largest groups of futures traders and the abnormal price movements in five agricultural commodities. The general evidence suggests that the commitments of futures traders have been increasing over time, whereas the frequency of price jumps have not. Regression results indicate a negative relationship between price jumps and the commitments of speculators and small traders. There is also evidence of a negative relationship between the number of speculators and cash market volatility, consistent with a host of speculation-based theories.     

SENSITIVITY OF INVESTOR REACTION TO MARKET DIRECTION AND VOLATILITY: DIVIDEND CHANGE ANNOUNCEMENTS

We examine whether investor reactions are sensitive to the recent direction or volatility of underlying market movements. We find that dividend change announcements elicit a greater change in stock price when the nature of the news (good or bad) goes against the grain of the recent market direction during volatile times. For example, announcements to lower dividends elicit a significantly greater decrease in stock price when market returns have been up and more volatile. Similarly, announcements to raise dividends tends to elicit a greater increase in stock price when market returns have been normal or down and more volatile, although this latter tendency lacks statistical significance. We suggest an explanation for these results that combines the implications of a dynamic rational expectations equilibrium model with behavioral considerations that link the responsiveness of investors to market direction and volatility.     

Retail banking and behavioral financial engineering: The case of structured products

We apply cumulative prospect theory and hedonic framing to evaluate discount reverse convertibles (DRCs) and reverse convertible bonds (RCBs) as important examples of structured products from a boundedly rational investor’s point of view. While common expected utility theory would also conclude that DRCs and RCBs are of interest to investors with moderate return expectations and underestimated stock return volatility, that theory would overestimate the market success of DRCs and underestimate that of RCBs in comparison to a situation with bounded rationality. Hedonic framing and relatively low subjectively felt competence levels of investors are decisive for the demand for RCBs.     

Information, Trading Demand, and Futures Price Volatility

We examine the relation between futures price volatility and trading demand by type of trader in the Standard & Poor's (S&P) 500-stock index futures market. We find that volatility covaries negatively with signed speculative demand shocks but is positively related to signed hedging demand shocks. No significant relation between volatility and demand shocks for small traders is found. Our results suggest that changes in positions of large hedgers destabilize the market, whereas changes in positions of large speculators stabilize volatility. Consistent with models with asymmetrically informed traders, we find that large speculators are likely to possess superior forecasting ability, large hedgers behave like positive feedback traders, and small traders are liquidity traders.     

Disagreement and asset prices

How do differences of opinion affect asset prices? Do investors earn a risk premium when disagreement arises in the market? Despite their fundamental importance, these questions are among the most controversial issues in finance. In this paper, we use a novel data set that allows us to directly measure the level of disagreement among Wall Street mortgage dealers about prepayment speeds. We examine how disagreement evolves over time and study its effects on expected returns, return volatility, and trading volume in the mortgage-backed security market. We find that increased disagreement is associated with higher expected returns, higher return volatility, and larger trading volume. These results imply that there is a positive risk premium for disagreement in asset prices. We also show that volatility in and of itself does not lead to higher trading volume. Instead, only when disagreement arises in the market is higher uncertainty associated with more trading. Finally, we are able to distinguish empirically between two competing hypotheses regarding how information in markets gets incorporated into asset prices. We find that sophisticated investors appear to update their beliefs through a rational expectations mechanism when disagreement arises.