On the Limits to Speculation in Centralized versus Decentralized Market Regimes

Speculation creates an adverse selection cost for utility traders, who will choose not to trade if this cost exceeds the benefits of using the asset market. However, if they do not participate, the market collapses, since private information alone is not sufficient to create a motive for trade. Therefore, there is a limit to the amount of speculative transactions that a given market can support. We compare this limit in decentralized versus centralized market regimes, finding that the centralized regime is more prone to speculation than the decentralized one: the transaction fees charged by an intermediary diminish the individual return to information, so that for a fixed value of trading, more speculative transactions can be supported. The analysis also suggests a reason for the existence of intermediaries in financial markets.Speculation, adverse selection, centralized markets

A Study of Neo-Austrian Economics using an Artificial Stock Market

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 for an active trading strategy is defined relative to a dynamic buy and hold benchmark. 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.Neoaustrian economics, Market efficiency, Artificial financial market, Learning, Adaptation

Bubbles and Experience: An Experiment on Speculation

We investigate experimentally how the share of experienced traders in double-auction asset markets affects trading, in particular the occurrence of bubble-crash pricing patterns. In each session, six subjects trade in three successive market rounds and gain experience. In a fourth round, depending on the treatment, two or four experienced subjects are replaced by inexperienced subjects. The results are compared to earlier findings when all traders were either inexperienced or experienced. We explore what can be learned by analogy between these laboratory findings and the performance of naturally occurring markets.Asset Market; Bubble; Crash; Experience; Experiment; Speculation

Order Submission Strategies and Information: Empirical Evidence from the NYSE

We investigate the role of asymmetric information in affecting order submission strategies. Order aggressiveness depends on the state of the order book and on the asset dynamics. We find that the most important determinants are the depth on the same side of the book and a momentum indicator. When we focus on specific situations characterized by higher probability of information-based trading, we find that orders are less aggressive, suggesting strategic behavior of informed traders. This conjecture is supported by a different response to changes in the investor's information set and by a stronger price impact of less aggressive orders.Order Aggressiveness, Informed Trading, Order Flow

VaR and Liquidity Risk.Impact on Market Behaviour and Measurement Issues.

Current trends in international banking supervision following the 1996 Amendment to the Basel Accord emphasise market risk control based upon internal Value-at-risk (VaR) models. This paper discusses the merits and drawbacks of VaR models in the light of their impact on market liquidity. After a preliminary review of basic concepts and measures regarding market risk, market friction and liquidity risk, the arguments supporting the internal models approach to supervision on market risk are discussed, in the light of the debate on the limitations and possible enhancements of VaR models. In particular, adverse systemic effects of widespread risk management practices are considered. Risk measurement models dealing with liquidity risk are then examined in detail, in order to verify their potential for application in the field. We conclude that VaR models are still far from effectively treating market and liquidity risk in their multi-faceted aspects. Regulatory guidelines are right in recognising the importance of internal risk control systems. Implementation of those guidelines might inadvertently encourage mechanic application of VaR models, with adverse systemic effects.

Do individual investors learn from their trading experience

This paper investigates whether individual investors adjust their stock trading according to their stock selection abilities, which can be inferred from their trading history. Fixed-effect panel regressions provide strong evidence that the ability to forecast future stock returns significantly affects investors’ trading activity: investors purchase more actively if they are more likely to have stock selection ability. Furthermore, trading experience – measured by the number of purchases, the number of different stocks purchased, and the variance of purchase dollar amounts – significantly helps improve investors’ portfolio performance. In addition, we find that learning behavior varies across investors, which corroborates the heterogeneity of individual investorsindividual investors, learning, rationality, trading

Trading Friction and Spread Decomposition in Indonesian Stock Exchange

We examine the intraday trading and price change for frequently traded stocks in Indonesian Stock Exchange. Using bid and ask price, trade price, number of trade, trade volume, we study how to measure and identify the source of trading friction and to infer what is the biggest component of trading friction. The result of 50 most frequently traded stocks in the Indonesian Stock Exchange using trading friction estimator conclude that the average trading friction of high market capitalization and the most relatively liquid stocks, scattered in various fractions price is equal to 1% per year, and the highest trading frictions derived from the information and it is consistent with spread decomposition estimator. Keywords: Trading friction; Spread decomposition; High market capitalization; Frequently traded stock; Liquid stock

Econometric analysis of financial trade processes by discrete mixture duration models

We propose a new framework for modelling the time dependence in duration processes being in force on financial markets. The pioneering ACD model introduced by Engle and Russell (1998) will be extended in a manner that the duration process will be accompanied by an unobservable stochastic process. The Discrete Mixture ACD framework provides us with a general methodology which puts the idea into practice. It is established by introducing a discrete-valued latent regime variable which can be justified in the light of recent market microstructure theories. The empirical application demonstrates its ability to capture specific characteristics of intraday transaction durations while alternative approaches fail. JEL classification: C41, C22, C25, C51, G14

Limit Orders, Trading Activity, and Transactions Costs in Equity Futures in an Electronic Trading Environment

The behaviour of limit order quotes and trading activity are studied using a unique and rich database that includes the identity of market participants from a fully automated derivatives market. The analysis is performed using transactions records for three aggregated trader types and three trade identifiers, with trades stamped in milliseconds for the SXF, the equity futures contract of the Montreal Exchange. The identifiers distinguish trades between principals; agency based trades, as well as transactions that are conducted for risk management as opposed to speculative purposes. Agency related trades are shown to represent the largest amount of trading activity relative to other account types. Over 90% of trades in this electronic market are limit orders. The limit order book, especially the depth 1 order, has a dominant role in providing liquidity and in explaining market participants’ trading behaviour. Participants in the SXF reference their trades to the best limit order depth. Hence, investors with large positions or investors who want to build a large position have to strategically split large orders to close/build their position, according to the depth of the best limit order, to ameliorate price impact and information leakage effects. In addition, the results show that traditionally measured spreads have no relationship with trading costs.Limit Orders, Trading Activity, Transactions Costs, Electronic Trading