Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic

Since the level of markets’ information efficiency is key to profiteering by strategic players, Shocks; such as the COVID-19 pandemic, can play a role in the nature of markets’ information efficiency. The martingale difference and conditional heteroscedasticity tests are used to evaluate the Adaptive form of market efficiency for four (4) major stock market indexes in the top four affected economies during the COVID-19 pandemic (USA, Brazil, India, and Russia). Generally, based on the martingale difference spectral test, there is no evidence of a substantial change in the levels of market efficiency for the US and Brazilian stock markets in the short, medium, and long term. However, in the long term, the Indian stock markets became more information inefficient after the coronavirus outbreak while the Russian stock markets become more information efficient. Intuitively, these affect the forecastability and predictability of these markets’ prices and/or returns. Thereby, informing the strategic and trading actions of stock investors (including arbitrageurs) towards profit optimization, portfolio asset selection, portfolio asset adjustment, etc. Similar policy implications are further discussed.     

Transaction taxes, greed and risk aversion in an agent-based financial market model

Recent agent-based financial market models came to the result that taxing financial transactions does not per se increase financial stability and that the response of volatility and misalignments to rising tax rates seem to be u-shaped. Moreover, greed and the risk appetite of traders are often blamed for financial instability and there is no evidence how greed and risk aversion affect the effectiveness of regulations in financial markets. We aim to add to this gap in the literature by analyzing how the effectiveness of transaction taxes depend on different behavioral patterns within an agent-based framework. Our simulations indicate that a tax rate of 0.1% demarcates the stabilizing tax regime from the destabilizing one. We figure out that transaction taxes are less effective, either when chartists trade more aggressively, fundamentalists trade less aggressively, agents switch more frequently between trading strategies or only have short memory in their fitness measures. Lower risk aversion of agents, however, makes higher tax rates more effective as indicated by a flatter volatility response curve. We conclude that additional regulations should concentrate on the traders’ responsibilities for their risk-exposure.     

Longshots,overconfidence and efficiency on the Iowa Electronic Market

We study the forecast accuracy and efficiency of popular “binary” prediction markets. Such markets forecast probabilities for future states of the world (e.g., election winners) by paying off $0 or $1 depending on the realized state (e.g., who actually wins). To assess accuracy, forecast probabilities must be compared to realization frequencies, not individual realizations. We use Iowa Electronic Market (IEM) data to test efficiency against two alternative propositions from behavioral finance: the longshot bias and the overconfidence bias (which yield opposing predictions). No longshot bias appears in IEM markets. Nor does overconfidence influence prices at short horizons. However, overconfident traders may bias prices at intermediate horizons. While the markets are efficient at short horizons, non-market data indicate some intermediate-horizon inefficiency. We calculate Sharpe ratios for static trading strategies and document returns for dynamic trading strategies to assess the economic content of the inefficiencies.     

Learning,information processing and order submission in limit order markets

By introducing a genetic algorithm learning with a classifier system into a limit order market, this paper provides a unified framework of microstructure and agent-based models of limit order markets that allows traders to determine their order submission endogenously according to market conditions. It examines how traders process and learn from market information and how the learning affects limit order markets. It is found that, measured by the average usage of different group of market information, trading rules under the learning become stationary in the long run. Also informed traders pay more attention to the last transaction sign while uninformed traders pay more attention to technical rules. Learning of uninformed traders improves market information efficiency, but not necessarily when informed traders learn. Opposite to the learning of informed traders, learning makes uninformed traders submit less aggressive limit orders and more market orders. Furthermore private values can have significant impact in the short run, but not in the long run. One implication is that the probability of informed trading (PIN) is positively related to the volatility and the bid-ask spread.     

Exploring issues of market inefficiency by the role of forecasting accuracy in survivability

Whether markets are efficient or not has been broadly discussed in the empirical literature since the efficient markets hypothesis was proposed by Fama and others in the 1960s. Unfortunately, they did not come to a consistent conclusion. Besides, while these studies show whether a specific market is efficient or not, little has been done to explore the issues regarding the degree of market inefficiency. This paper attempts to resolve the puzzle of the inconsistent conclusions in the empirical literature by adopting a bottom-up approach which takes market participants’ interactions and coordination into consideration. By simulating an agent-based artificial stock market, this paper concludes with three main findings. First, agents’ survivability is mainly decided by risk preference, and not forecasting accuracy. Survivors may have diverse forecasting accuracy. Second, because market prices are not decided by agents based on accurate predictions, markets can not be efficient. What may exist is only the difference of the degree of inefficiency between markets. Third, the more relevant to survivability the forecasting accuracy in a market is, the less inefficient the market will be. Therefore, this paper suggests that it may be better to view the divergent empirical results regarding market efficiency as a fact that markets are inefficient to a variety of degrees.     

Experimental evidence on trading behavior,market efficiency and price formation in double auctions with unknown trading duration

The reasons for the highly efficient market outcomes observed under the double auction remain unclear. This paper presents a series of experimental financial markets designed to investigate the importance of unknown trading period duration on trading behavior and the convergence tendencies of such markets. Using panel data techniques the results support the conclusions that individuals generally display more aggressive trading strategies, trading earlier in a period, and that markets exhibit reduced levels of informational efficiency when unknown duration is present. Markets with imperfect information structures are also studied and, in a unique result, are associated with significantly slower rates of trade, as traders become more cautious over their trading strategies. Investigation of the price formation process provides evidence that the pricing error varies over time and the estimation of a fixed effects model provides unique support that learning effects and unknown trading period duration influence the price formation process. Future refinement of theoretical models of the price formation process or institutions of exchange should recognize the effect of unknown trading period duration on market behavior, along with potential learning effects. Copyright © 2005 John Wiley & Sons, Ltd.     

Informational efficiency and behaviour within in-play prediction markets

Studies of financial market informational efficiency have proven burdensome in practice, because it is difficult to pinpoint when news breaks and is known by some or all the participants. We overcome this by designing a framework to detect mispricing, test informational efficiency and evaluate the behavioural biases within high-frequency prediction markets. We demonstrate this using betting exchange data for association football, exploiting the moment when the first goal is scored in a match as major news that breaks cleanly. There are pre-match and in-play mispricing and inefficiency in these markets, explained by reverse favourite-longshot bias (favourite bias). The mispricing tends to increase when the major news is a surprise, such as a goal scored by a longshot team late in a match, with the market underestimating their chances of going on to win These results suggest that, even in prediction markets with large crowds of participants trading state-contingent claims, significant informational inefficiency and behavioural biases can be reflected in prices.     

Do real estate investors trade on momentum?

Real estate markets are known to be less-than-efficient for many reasons, but what roles short-term trading plays are unclear. Do short-term investors bring additional risk to the market and cause prices to deviate from fundamental values? Based on an extensive dataset of property transactions and a policy shock that substantially raised the cost of short-term trading in Hong Kong, we estimate ‘real estate risk’ with and without short-term trading based on return predictability, return volatility, and price dispersion. Our results show that as short-term investors exit the market, market returns are less predictable and less volatile, while prices are less dispersed cross-sectionally. Consistent with herding models in behavioral finance, the findings suggest that short-term investors are momentum traders who do not enhance price efficiency.     

Stable processes of exchange

In this paper we address a long standing gap in economic theory—the gap between claims for the dynamic efficiency of trading in markets, and the findings of formal economic theory, which justify those claims only under restrictive assumptions. We use agent-based methods to study the dynamics of exchange with trading agents who are characterized by several different preference relations. We see that outcomes converge with high probability to Pareto optima in the cases studied, including the well-known example due to Scarf.     

The persistence of the small firm/January effect: Is it consistent with investors’ learning and arbitrage efforts?

Using improved methodology and an expanded research design, we examine whether the small firm/January effect (Keim, D. B. (1983). Size-related anomalies and stock return seasonality: further empirical evidence. Journal of Financial Economics 12:13–32), is declining over time due to market efficiency. First, we find that January returns are smaller after 1963–1979, but have simply reverted to levels that existed before that time. Second, we show that the January effect is not limited to mature markets but also appears in firms trading on the relatively new NASDAQ exchange in the 1970s. Third, trading volume for small firms in December and January is not different from other months, implying that traders are not actively arbitraging the anomaly. Together, our results suggest that this anomaly continues to defy rational explanation in an efficient market.     

Information dissemination in an experimentally based agent-based stock market

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.     

WHAT DO WE KNOW ABOUT THE PROFITABILITY OF TECHNICAL ANALYSIS?

Abstract.  The purpose of this paper is to review the evidence on the profitability of technical analysis. The empirical literature is categorized into two groups, 'early' and 'modern' studies, according to the characteristics of testing procedures. Early studies indicate that technical trading strategies are profitable in foreign exchange markets and futures markets, but not in stock markets. Modern studies indicate that technical trading strategies consistently generate economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 95 modern studies, 56 studies find positive results regarding technical trading strategies, 20 studies obtain negative results, and 19 studies indicate mixed results. Despite the positive evidence on the profitability of technical trading strategies, most empirical studies are subject to various problems in their testing procedures, e.g. data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.     

Efficiency of online football betting markets

This paper evaluates the efficiency of online betting markets for European (association) football leagues. The existing literature shows mixed empirical evidence regarding the degree to which betting markets are efficient. We propose a forecast-based approach for formally testing the efficiency of online betting markets. By considering the odds proposed by 41 bookmakers on 11 European major leagues over the last 11 years, we find evidence of differing degrees of efficiency among markets. We show that, if the best odds are selected across bookmakers, eight markets are efficient while three show inefficiencies that imply profit opportunities for bettors. In particular, our approach allows the estimation of the odds thresholds that could be used to set profitable betting strategies both ex post and ex ante.     

Estimation of financial agent-based models with simulated maximum likelihood

This paper proposes a general computational framework for empirical estimation of financial agent-based models, for which criterion functions have unknown analytical form. For this purpose, we adapt a recently developed nonparametric simulated maximum likelihood estimation based on kernel methods. In combination with the model developed by Brock and Hommes (1998), which is one of the most widely analysed heterogeneous agent models in the literature, we extensively test the properties and behaviour of the estimation framework, as well as its ability to recover parameters consistently and efficiently using simulations. Key empirical findings indicate the statistical insignificance of the switching coefficient but markedly significant belief parameters that define heterogeneous trading regimes with a predominance of trend following over contrarian strategies. In addition, we document a slight proportional dominance of fundamentalists over trend-following chartists in major world markets.     

Volatility clustering and herding agents: does it matter what they observe?

Recent agent-based models have demonstrated that agents’ herding behavior causes volatility clustering in stock markets. We examine economies where agents herd on others, yet they have limited sets of information on other agents to imitate. In particular, we conduct experiments on economies with agents with different levels of information sharing where agents can imitate: (1) the strategies of others but with an error, (2) the strategies of only a fraction of agents, or (3) the strategies of others, but update their parameters only by a proportion. In each experiment we change the likelihood that agents make errors to copy the strategy of others, the fraction of agents to herd, or the proportion of the parameter that agents update, in order to examine the effect of the different degrees of information sharing on volatility clustering. We show that volatility clustering tends to disappear when agents have limited information on the strategies of others, and agents need to imitate the strategy details of others in order to generate the clustered volatility.     

Nonlinear causality testing with stepwise multivariate filtering: Evidence from stock and currency markets

We examine the spillovers of the US subprime crisis to Asian and European economies and in particular to what extent currency and stock markets have been affected by the crisis. Linear and nonlinear dependencies are detected after pairwise and system-wise causality analysis. A new stepwise multivariate filtering approach is implemented after controlling for conditional heteroskedasticity in the raw data and in VAR/VECM residuals using multivariate GARCH models. Significant nonlinear causal linkages persisted even after the application of GARCH-BEKK, CCC-GARCH and DCC-GARCH modelling. This indicates that volatility effects might partly induce nonlinear causality. Perhaps new short-term asset-pricing models could be developed to explain this stylized fact. These results might also have important implications for hedging, trading strategies and financial market regulation.     

Risk spillovers and hedge strategies between global crude oil markets and stock markets: Do regime switching processes combining long memory and asymmetry matter?

This paper investigates risk spillovers and hedge strategies between global crude oil markets and stock markets. In the paper, we propose a multivariate long memory and asymmetry GARCH framework that integrates state-dependent regime switching in the mean process with multivariate long memory and asymmetry GARCH in the variance process. Our results first show that there are linear risk spillovers running from the US stock markets to the WTI oil market in the short term. However, the linear risk spillover effect running from the oil market to the US stock market can only exist in the long term. In addition, there is a bidirectional linear risk spillover effect between the European stock markets and the Brent oil market in the short and long terms. Furthermore, there is no linear risk spillover effect between the Dubai oil market and the Chinese stock market. Second, the nonlinear risk spillovers running from the WTI oil market to the US stock market can be found in the tranquil regime. Moreover, there is also a nonlinear risk spillover effect running from the European stock markets to the Brent oil market in the tranquil regime. In addition, the nonlinear risk spillover effect running from the Brent oil markets to the European stock market can be found in the crisis regime. Furthermore, there is bidirectional nonlinear Granger causality between the Dubai crude oil market and the Chinese stock market in the tranquil regime. Finally, dynamic hedge effectiveness shows that the regime switching process combined with long memory and asymmetry behavior seems to be a plausible and feasible way to conduct hedge strategies between the global crude oil markets and stock markets.     

The endogenous formation of cartels

We discuss large but finite linear market games which are represented as minima of finitely many measures. These games describe markets in which the agents decompose into finitely many disjoint groups each of which holds a corner of the market. Most solution concepts like the core, the Shapley value, or the Walrasian equilibrium tend to favor the short side of such market excessively. That is, in the replicated limit or in the continuum version, the short side is awarded all the possible profits even though cooperation within the grand coalition is required. We show that vNM-Stable Sets differ markedly. For large but finite player sets we exhibit vNM solutions that assign wealth to the long side of the market. It turns out that the shape of the generic vNM-Stable Set suggests cartelization of the market.Received: 29 March 2000, Accepted: 26 October 2001, JEL Classification: C71, D70     

COMPETITION BETWEEN EQUITY MARKETS: A REVIEW OF THE CONSOLIDATION VERSUS FRAGMENTATION DEBATE

Technological advances and regulatory initiatives have led to the emergence of a competitive, but fragmented, equity trading landscape in several markets around the world. While these changes have coincided with benefits like reduced transaction costs, advancements in trading technology, and access to a diverse array of execution venues, regulators and market participants have also raised concerns about the welfare implications of innovations like dark pools as well as the resulting increase in execution complexity. Exchanges are often viewed as natural monopolies due to the presence of network externalities and economies of scale. However, heterogeneity in traders' preferences means that no single venue can serve the interests of all investors. Fragmentation of the marketplace can be seen as a direct outcome of this heterogeneity. In this paper, we review the theoretical and empirical literature examining the economic arguments and motivations underlying market fragmentation, the resulting implications for liquidity and price efficiency, and the role for public policy. Beyond the concerns for equity markets, the lessons from this literature are relevant for other asset classes experiencing an increase in competition between trading venues.     

Existence of an equilibrium for infinite horizon economies with and without complete information

This work proves the existence of an equilibrium for an infinite horizon economy where trade takes place sequentially over time. There exist two types of agents: the first correctly anticipates all future contingent endogenous variables with complete information as in Radner [Radner, R. (1972). Existence of equilibrium of plans, prices and price expectations in a sequence of markets. Econometrica, 289–303] and the second has exogenous expectations about the future environment as in Grandmont [Grandmont, J. M. (1977). Temporary general equilibrium theory. Econometrica, 535–572] and information based on the current and past aggregate variables including those which are private knowledge. Agents with exogenous expectations may have inconsistent optimal plans but have predictive beliefs in the context of Blackwell and Dubbins [Blackwell, D., Dubins, L. (1962). Merging of opinions with increasing information. The Annals of Mathematical Statistics, 882–886] with probability transition rules based on all observed variables. We provide examples of this framework applied to models of differential information and environments exhibiting results of market selection and convergence of an equilibrium. The existence result can be used to conclude that, by adding the continuity assumption on the probability transition rules, we obtain the existence of an equilibrium for some models of differential information and incomplete markets.