Finding the invisible hand: an objective model of financial markets (original) (raw)
Related papers
Rethinking the Efficient Markets Hypothesis
SSRN Electronic Journal, 2012
We develop an adaptive learning game to rethink the efficient markets hypothesis using the stochastically stable state of this game to characterize a richer set of market states than those suggested by the hypothesis. In particular, the model predicts that the economy may follow a path leading to bubbles.
Learning about Risk and Return: A Simple Model of Bubbles and Crashes
American Economic Journal: Macroeconomics, 2011
This paper demonstrates that an asset pricing model with least-squares learning can lead to bubbles and crashes as endogenous responses to the fundamentals driving asset prices. When agents are risk-averse they generate forecasts of the conditional variance of a stock's return. Recursive updating of the conditional variance and expected return implies two mechanisms through which learning impacts stock prices: occasional shocks may lead agents to lower their risk estimate and increase their expected return, thereby triggering a bubble; along a bubble path recursive estimates of risk will increase and crash the bubble. JEL Classifications: G12; G14; D82; D83
Toward a General Model of Financial Markets
SSRN Electronic Journal
This paper aims to discuss the possibilities of capturing efficient market hypothesis and behavioral finance under a general framework using the literature of decision theories and information sciences. The focus is centered on the broad definition of rationality, the imprecision and reliability of information. The main thesis advanced is that the root of behavioral anomalies comes from the imprecision and reliability of information. Modeling on basis of imprecision and reliability of information within the broad definition of rationality will lead us to a more general model of financial markets.
Efficient Markets and Financial Bubbles
2017
When it comes to money and investing, the individual portfolio investor is not always as rational as he believes he is – which is why there's a whole field of study that explains an individual‟s sometimes irrational and strange behavior. This research paper mainly deals with the insight into the theory and findings of behavioral finance and the financial bubbles in history. The paper will also assist individual investors to avoid these “mental mistakes and errors” by recommending some important investment strategies for those who invest in stocks and mutual funds.
2013
We construct an agent-based computer simulated financial market. Trading in this market is not continuous. The market price is formed using a limit-order book. The modelled investors receive biased information and they attempt to maximize their wealth. Different traders, from noise to chartist and informed, coexist in the same market. We show how stylized facts can be formed by the presence of chartists or a simple lag in investor information. Price bubbles can arise when market prices are dominated by technical traders. Interestingly we show that well informed investors can earn more if the adopt, in special situations, a technical strategy. Using our results we propose a new theorem for market dynamics called “sometimes efficient markets”.
Journal of Economic Behavior & Organization, 2002
By studying two well known hypotheses in economics, this paper illustrates how emergent properties can be shown in an agent-based artificial stock market. The two hypotheses considered are the efficient market hypothesis and the rational expectations hypothesis. We inquire whether the macrobehavior depicted by these two hypotheses is consistent with our understanding of the microbehavior. In this agent-based model, genetic programming is applied to evolving a population of traders learning over time. We first apply a series of econometric tests to show that the EMH and the REH can be satisfied with some portions of the artificial time series. Then, by analyzing traders' behavior, we show that these aggregate results cannot be interpreted as a simple scaling-up of individual behavior. A conjecture based on sunspot-like signals is proposed to explain why macrobehavior can be very different from microbehavior. We assert that the huge search space attributable to genetic programming can induce sunspot-like signals, and we use simulated evolved complexity of forecasting rules and Granger causality tests to examine this assertion.
An Inherent Instability of Efficient Markets
Scientific Reports, 2013
Speculative markets are often described as “informationally efficient” such that predictable price changes are eliminated by traders exploiting them, leaving only residual unpredictable fluctuations. This classical view of markets operating close to an equilibrium is challenged by extreme price fluctuations which occur far more frequently than can be accounted for by external news. Here we show that speculative markets which absorb self-generated information can exhibit both: evolution towards efficient equilibrium states as well as their subsequent destabilisation. We introduce a minimal agent-based market model where the impacts of trading strategies naturally adapt according to their success. This implements a learning rule for the whole market minimising predictable price changes and an extreme succeptibility at the point of perfect balance. The model quantitatively reproduces real heavy-tailed log return distributions and volatility clusters. Our results demonstrate that market...
Journal of Empirical Finance, 2010
An agent-based artificial financial market (AFM) is used to study market efficiency and learning in the context of the Neo-Austrian economic paradigm. Efficiency is defined in terms of the "excess" profits associated with different trading strategies, where excess is defined relative to a dynamic buy and hold benchmark in order to make a clean separation between trading gains and market gains. We define an Inefficiency matrix that takes into account the difference in excess profits of one trading strategy versus another (signal) relative to the standard error of those profits (noise) and use this statistical measure to gauge the degree of market efficiency. A one-parameter family of trading strategies is considered, the value of the parameter measuring the relative informational advantage of one strategy versus another. Efficiency is then investigated in terms of the composition of the market defined in terms of the relative proportions of traders using a particular strategy and the parameter values associated with the strategies. We show that markets are more efficient when informational advantages are small (small signal) and when there are many coexisting signals. Learning is introduced by considering "copycat" traders that learn the relative values of the different strategies in the market and copy the most successful one. We show how such learning leads to a more informationally efficient market but can also lead to a less efficient market as measured in terms of excess profits. It is also shown how the presence of exogeneous information shocks that change trader expectations increases efficiency and complicates the inference problem of copycats.