Abstract: The following research is to be conducted in laboratory asset markets. The proposal seeks to examine how the ‘concentration’ of information impacts upon asset prices and whether it is a factor in driving markets to crash.

 

Keywords: bubbles, crashes, laboratory experiments

1.    Introduction

Asset pricing bubbles and the associated crashes are not a new phenomenon. In the 1630’s it was a mania for Tulips, the 1720’s saw hysteria to invest in the remotely located Mississippi Company. More recent examples include; the internet stock bubble and the global financial crisis of 2008.   For a number of decades academics have placed the efficient market hypothesis (EMH (E. Fama, 1965) as cornerstone for describing market behaviour. The efficient market hypothesis follows an intuitively appealing logic and is derived from elegant mathematics[1] and in its crudest form describes the fact that the value of an asset is equal to its fundamental worth. The theory assumes that the existence of sufficiently many well-informed arbitrageurs guaranteed that any potential mispricing would be quickly corrected. An outcome of the EMH is such that asset pricing bubbles cannot form. However, the persistent occurrence of such asset pricing bubbles and the damage they inflict upon society has provided the impetus to re-examine the theoretical models that underlie finance.

Early research by notable economists such as Keynes (1936) , Fisher(1928) and Markowitz (1952)[2] recognised that financial agents were not ‘automatons’[3] but instead that psychology was an important factor in the development of financial theory. In recent years research has evolved substantially from the efficient market model and returned to re-incorporate the human side into theoretical models.  An area Pioneered by Vernon Smith [4]and his colleagues focuses on psychology and observable human behaviour in laboratory type environments.

In the seminal paper by Smith, Suchanek, & Williams (1988) it was found that in an experimental asset market that a particular class of asset generated asset pricing bubbles. A “bubble” is operationally defined as “trade in high volumes at prices that are considerably at variance from intrinsic values” (King, Smith, Williams, & Van Boening, 1993). The result has since been replicated and shown to be robust in several changes in experimental design. For example, the same result observed when short selling opportunities, margin buying opportunities, limit price-change rules, informed insider trading and increasing levels of subject experience are introduced(King et al., 1993).

Research to date has focussed on the causes of asset pricing bubbles in lab environments. The literature is vague on what causes prices to “crash” back to their fundamental values. Abreu & Brunnermeier (2003) developed a model based on cooperation and competition among traders and that at some critical point in time “synchronizations occurs” and the bubble bursts. Others such as C. Noussair et al, (2001) and C. Hommes et al.(2008) generalise that markets crash for the same reasons bubbles form.

This research proposes to examine how the ‘concentration’ of information impacts upon asset prices and whether it is a factor in creating market crashes in laboratory type environment.

The proposal is organised as follows; Section 2 examines the literature and is divided into two sections, part A examines laboratory asset markets and part B behavioural pricing. Section 3 is the proposed hypothesis. Section4 is the theoretical model. Section 5 discusses the experimental design. Section 6 discusses briefly the expected results. Section 7 is the research proposals contribution.

2.    Literature Review

The literature review is separated into two parts. The first section examines laboratory asset market while the second focuses upon behavioural pricing and framing effects.

·      Laboratory Asset Markets

Financial research has traditionally focussed on examining data bases and actual financial markets. The general reasoning is, “What can be learned from an experiment that cannot be observed and proven in an applied setting?” (Porter & Smith, 2003).However, in the economy, control over fundamental value and investor information is rarely possible. In a  laboratory, variables can be measured and controlled, making it an efficient environment to test  theory, (Henker & Owen, 2006).

In the late 1980’s after many years of being ignored by financial academics Smith, Suchanek, & Williams (1988) found that in an experimental asset market that a particular class of asset generated asset pricing bubbles. Markets were created for assets with a lifetime of a finite number of periods (typically 15-30 periods). The asset payed a dividend in each period, and the dividend (including a liquidation dividend) was the only source of intrinsic value. The dividend paid was identical for each trader and the dividend process was common knowledge to all traders. Rather than trading the fundamental value , the market price time series was usually characterised by a ‘boom’ phase, a period of time in which prices were higher than fundamental values, often followed by a ‘crash, a sudden drop in price (Smith et al., 1988) as shown in Figure 1.

 

Figure 1: Smith et al (1988) study.

The result has since been replicated and shown to be robust to several changes in experimental design. King et al (1993) explored the robustness of this phenomenon to short selling opportunities, margin buying opportunities, limit price-change rules, informed insider trading and increasing levels of subject experience. It was found that the only reliable way to generate prices that approximately reflect the instrinsic value of an asset share is to bring the same group of traders back for a series of three 15 round markets. In the first two rounds, prices tended to bubble above instrinsic value and then crash back. Prices in the third market tended to track the intrinsic value much more closely (Williams & Charles, 2008).

This finding is in keeping with research by Marimon, Spear, & Sunder (1993) in which bubbles were generated if subjects were preconditioned by past experience to form expectations of bubbles. The idea that human judgement of future events shows systematic biases is reflected in research by Tversky & Kahneman (1981) in prospect theory.

Porter & Smith (2003) extended upon their earlier research and summarized a variety of laboratory experiments, finding that the bubble and crash behaviour is robust to variations in a number of variables, including liquidity, short selling, certainty or uncertainty of dividend payments, brokerage fees and others.

C. Noussair et al (2001) also found that asset bubbles formed even when the fundamental price of an asset is constant over the experiments life.

Krahnen, Rieck, & Theissen[5] (2000) argue, however, that the experimental design, in particular the fundamental value which steadily declines in the course of the experiment, facilitates the emergence of overpricing. Krahnen et al argue that they employ a more “realistic” fundamental value process in their experimental design. Interestingly, their results do not confirm the previous findings: positive and negative price deviations are equally likely and similar in magnitude. Mispricing seems to be more pronounced in call markets than in continuous auctions. Surprisingly, price and value expectations noted by experimental subjects do not help to explain the price deviations.

Empirical evidence suggests that prices do not always reflect fundamental values and individual behaviour is often inconsistent with rational expectations theory. Ackert & Church (1998) examined whether the interactive effect of subject pool and design experience tempers price bubbles and improves forecasting ability. Their main findings were: (i) price run-ups are modest and dissipate quickly when traders are knowledgeable about financial markets and have design experience; (ii) price bubbles moderate quickly when only a subset of traders are knowledgeable and experienced; and (iii) individual forecasts of price are not consistent with the predictions of the rational expectations model in any market(Ackert & Church, 1998).

Smith et al., (1988) offered the following conjecture about the origin of the bubble phenomenon. ‘What we learn from the particular experiment is that a common dividend, and common knowledge thereof, is insufficient to induce initial common expectations. As we interpret it, this is due to agent uncertainty about the behaviour of others”. Although the experimenter can control much about the underlying structure and parameters of the market, the beliefs and actions of participants cannot be (Charles Noussair, Plott, & Charles, 2008).

Lei et al.(2001) refer to the conjecture by Smith et al (1988) as a Speculative Hypothesis. Whereby, bubbles can occur when traders are uncertain that future prices will track the fundamental value, because they doubt the rationality of the other traders, and therefore speculate in the belief that there are opportunities for future capital gains (This is a similar argument as proposed by Plott (1991). The Speculative Hypothesis is outlined as follows. Consider a rational trader who believes that there may be irrational traders in the market place who are willing to trade at prices that deviate from a securities intrinsic value. Thus trading will occur at values that differ from the fundamental value when the end of the time horizon is sufficiently far in the future, even when all agents are rational. However, as the end of the time horizon approaches, the probability of realizing a capital gain declines and the incentive for speculation is reduced.

Many of the studies that have previously been mentioned conjecture that expectations play an important role in generating bubbles or more specifically a lack of common expectations that drives the emergence of bubbles. That is, although every participant has the same information, a participant engages in trade at a higher price than the intrinsic value of the stock, because he or she speculates to be able to sell to somebody later at an even higher price (C. Hommes et al., 2008).

However, in the Lei et al (2001) paper, they showed, that even if speculation is prohibited (that is, a subject can only buy or only sell the asset, but subjects are not able to do both in order to reap capital gains), bubbles occur. They claim that this points to irrationality of participants instead of a lack of common expectations.

It need not be the case that irrational traders actually exist, but only that their existence be believed to be possible(Lei et al., 2001). Lei et al.(2001) conclude that bubbles and crashes are not caused by attempts to buy and to resell at higher prices. It is the actual presence of ‘irrational’ behaviour and not the lack of common knowledge of rationality that causes the bubbles that were observed in their experiments.

Lei et al (2001) suggested that agents may systematically make unprofitable transactions due to  some particular aspect of the methodology of the experiment that encourages such behaviour. They proposed the Active Participation Hypothesis, a hypothesis that much of the trading activity in the asset market is due to that fact that the protocol of the experiment encourages subjects to participate actively in some manner. Since no activity is available other than to participate in the asset market, excess trading occurs.

C. Hommes et al (2008) proposed the so-called positive feedback expectations, that is, participants seem to extrapolate trends in realised asset prices into the future. This expected price change (i.e. increase or decrease) is self-fulfilling and leads to a further price change in the expected direction. Therefore co-ordination on a common prediction strategy occurs, which contrasts the conjecture of lack of common expectations by Smith et al.(Smith et al., 1988). Interacting agents in finance represent a behavioural, agent-based approach in which financial markets are viewed as complex adaptive systems consisting of many rational agents interacting through simple heterogeneous investment strategies, constantly adapting their behaviour in response to new information, strategy performance and through social interactions. An interacting agent system acts as a noise filter, transforming and amplifying purely random news about economic fundamentals into an aggregate market outcome exhibiting important stylized facts such as unpredictable asset prices and returns, excess volatility, temporary bubbles and sudden crashes, large and persistent trading volume, clustered volatility and long memory.(Cars Hommes, 2006)

Although not in an experimental type environment there is a substantial body of research that finds that people try to extrapolate trends when forecasting the price of a stock (Hirshleifer, 2001). For example, Shiller (2003) proposed the price-to-price feedback theory. When speculative prices go up, creating success for some investors, this promotes enthusiasm and heighten expectations for further price increases. If the feedback is not interrupted, it may produce many rounds of successive price increases and leads to a ‘bubble’. The feedback that propelled the bubble carries the seeds of its own destructions, and so the end of the bubble may be unrelated to news stories about fundamentals(Shiller, 2003).

Abreu & Brunnermeier (2003) argue that bubbles can survive despite the presence of rational arbitrageurs who are collectively well informed and have sufficient capital. The backdrop for the analysis is that certain agents are subject to ‘animal spirits’, behavioural tendencies. They suppose that rational arbitrageurs understand that the market will eventually collapse but meanwhile attempt to ride the bubble as it continues to grow and generate high returns. These investors are attempting to as Keynes (1936) suggested “beat the gun”. Arriving at an optimal exit strategy is not clear especially when the fundamental value of the security is already known. There is likely to be a dispersion of exit strategies and the consequent lack of synchronization are precisely what permit the bubble to grow, despite the fact that the bubble bursts as soon as a sufficient mass of traders exit. Abreu & Brunnermeier (2003) present a model that formalises the synchronisation problem which focuses upon the dispersion of opinion among rational arbitrageurs and the need for coordination. In this model it is assumed that the price surpasses the fundamental value at a random point. Thereafter, rational arbitrageurs become aware that the price has departed from its fundamental value. The coordination element in their model is that selling pressure only bursts the bubble when sufficient mass of arbitrageurs have sold out. Overall, the idea that the bursting of a bubble requires synchronized action by rational arbitrageurs, who might lack the incentive and ability to act in a coordinated way has important implications. It provides a theoretical argument for the existence and persistence of bubbles. It undermines the central presumption of the efficient market perspective that not all agents need to be rational for prices to be efficient, and hence provides further support for behavioural finance models that do not explicitly model rational arbitrageurs.

·      Behavioural Effects

Imagine that you are a CEO and your company has an unexpected cash surplus.  You have decided to return this cash to your shareholders in the form of a dividend payment but want to maximise the positive effect as perceived by investors and the market. The company historically has not payed dividends. Is it best to make one off dividend payment of $6 or three equal dividends of $2  spread over the year[6] ?If financial agents are rational the manner in which the situation is framed should not influence the choice since both are equivalent (Tversky & Kahneman, 1986).

However, research indicates that the manner in which a situation is presented influences the way in which individuals behave which cannot be explained by traditional economics and choice theories. Much of the literature in this section is drawn from the area of ‘behavioural pricing’ which lies largely within the marketing domain. The term behavioural pricing is used to describe how price presentation influences perceived value and choices (Haugtvedt, Herr, & Kardes, 2008).

Thaler & Shefrin (1988) proposed that individuals follow a cognitive version of cost accounting to organise and interpret information as the basis for making a decision. Thaler (1985) described this as a mental accounting system. Three components underlie the mental accounting system: Firstly, the manner in which the outcome is framed and evaluated i.e. this is grounded in prospect theory as developed by Tversky and Kahneman, 1981. Secondly, the breadth of the mental account, including time, and finally, the currency used in mental accounting.

The literature identifies a number of mental accounting effects, including; loss aversion, transaction utility and what this research draws upon the concept of multiple discounts/price changes.

Cheema & Soman (2008) demonstrate that economic agents conduct mental partitioning. Their findings indicate that partitioning an aggregate quantity of a resource into smaller units reduces the consumed quantity or rate of consumption. This effect of partitioning is demonstrated for consumption of chocolates, gambles and accuracy in forecasting time. In this context partitioning controls consumption to a greater extent. It is speculated that this same mental partitioning can be extended to how financial agents view asset prices. For example, when dividend policy changes incrementally, the equivalent of ‘small packages’, investors are likely to partition each event separately and thus not react as quickly.

When faced with multiple price/discounts changes instead of a single change of an equal amount, financial agents are generally believed to segregate gains and integrate losses based upon the mental accounting principles[7]. For example, Büyükkurt (1986)stated that a large number of noticeable discounts could lead to a higher perceived value than a smaller number of extreme discounts.  Mazumdar & Jun (1993) also found that multiple price increases were evaluated more favourably than a single price increase.  This research proposal seeks to extend this finding to a financial market domain and how changes in pricing variables, for example, discount rates, dividends or news events may impact upon the size asset price changes.

 

1. 3.    Research Question

Hypothesis:

Ho: X = ∑ Y

Ha: X ≠ ∑ Y

Let Y = minor informational event

Let X = major informational event

X = ∑ Y in terms of true fundamental impact

 

4.    Theoretical Model

 

Figure 2: Illustration of Price Path

The theoretical model is based on Abreu & Brunnermeier (2003) which is based on the idea that bubbles and crashes occur due to a ‘synchronisation’ problem.

• Prior to t = 0 the asset price coincides with its fundamental value, which grows at the risk free interest rate r and rational arbitrageurs are fully invested in the asset.
• From t=0 onwards the asset prices P grows at a rate of g > r, that is ,𝑝-𝑡.= ,𝑒-𝑔𝑡.. This higher growth rate is justified by the assets fundamentals
• Until some random time t, the higher price increase is justified by fundamental development.
• We assume that t is exponentially distributed on [0, ∞] with the cumulative distribution function ∅,,𝑡-0..= 1− ,𝑒-−𝛾𝑡0..
• Nevertheless, the price continues to increase at the faster rate g after t.
• Hence from t onwards, only some fraction (1 – β (.)) of the price is justified by the fundamentals while the fraction β (.) reflects the “bubble component”.
• The price, ,𝑝-𝑡.= ,𝑒-𝑔𝑡. is kept above its fundamental value by “irrational exuberant” traders.  The think that the price will grow at the rate of g  into perpetuity
• The mispricing is correct only when a sufficient mass of rational arbitrageurs collectively correct it

5.    Experimental Design

Experimental markets are intended to provide observations on the interaction between financial agents in a controlled situation that is isolated from outside interferences (i.e. “real markets”). These experiments are designed to control for any variable that is not itself the object of the investigation (Bühler, Hax, & Schmidt, 1999).

The focus of these experiments will be on the stock market. There are a number of challenges faced when trying to model equity securities in an experimental asset market (Please see Figure 3 for a general model of the stock market process). Consider the following, stock market contains a strong speculative element i.e. individual valuations underlying today’s bids, asks and prices are to a large extent determined by expectations of future prices, which in turn depend on expectations of other prices in the distant future. If the “value” of a stock is given by the present value of its future payoff. When individuals time horizons are shorter than the lifetime of the asset, expected prices become more important than expected payoffs and information on difference between value and price maybe worthless (Keynes, 1936).

Figure 3: A general model of the market process (Bühler et al., 1999)

 

The long lifetime of stocks, compared to individuals’ time horizons, has a number of important implications. Prices become endogenous, i.e. exogenous determinants of value exert their influence on prices only through the decisions of present and future market participants. There is no predetermined point in time at which price and the fundamental value of a stock must converge, because nobody lives long enough to realize the fundamental value without reselling the stock. While the payment of dividends partially decreases the uncertainty of future payoffs before the dividend date, new uncertainty about other dividends still further in the future restores the uncertainty immediately thereafter.

The asset market experiments would be conducted using the Macquarie Trading Room at Bond University (See Figure 4) using the Financial Trading System (FTS) platform (See Figure 5) developed by John O’Brien at Carnegie Mellon University.

 

Figure 4: Macquarie Trading Room

 

Figure 5: Screenshot of FTS Platform

The design of the experiments is described in the next section.

• The dependent variable is the asset value of the risky asset
• The experiment would be run as a 2 x 2

Endogenous Event (e.g.  Dividend Change) Incremental Event	Endogenous Event (e.g.  Dividend Change) Concentrated Event
Exogenous Event (e.g. Change in external stock market) Incremental Event	Exogenous Event (e.g. Change in external stock market) Concentrated Events

 

• An infinite time horizon cannot be implemented in a finite experiment. However, to try and approximate the essence the experiment stocks pay risky dividends over an indefinite lifetime.
• The experiments run for an indefinite number of consecutive 15-minute periods and dividends influence the market value of stocks through the prices of transactions between participants.
• There is revolving uncertainty about fundamental values and price formation process is driven exclusively by the orders of the market participants (i.e. endogenous price formation).
• At the start of the experiment, each participant is endowed with an equal share of the stock as well as cash.
• The market has two investment options available; a risk free asset and a risky asset. The risk free investment is putting all money in an Australian Government Treasury Bill paying a fixed and known interest rate. The alternate risk asset is an investment in a stock. The inclusion of the risk free asset is designed to address the active participation hypothesis proposed by Lei et al., 2001(2001) that trading activity occurs because there is no other activity.
• Dividends are derived from earnings. Dividend would follow a normal distribution and the fall within defined probabilities which are announced to all market participants. Figure 6 is an example of an FTS outcome table which is displayed on each participants trading platform.

 

 

Figure 6: An example of an FTS outcome table

 

 

a.    Information for Participants

This has been included to only provide a very brief illustrative example of the information that would be provided. This section would be developed and tested as part of the experimental design section.

General Information

You are a trader at for a leading investment house. The market has two investment options available; a risk free asset and a risky asset. The risk free investment is putting all money in an Australian Government Treasury Bill paying a fixed and known interest rate. The alternate risk asset is an investment in a stock.

In each period you as the trader have to decide what fraction of your limited capital is invested in each asset class.

Your earnings during the experiment depend on your forecasting accuracy.

6.    Expected Results

The research assumes that the findings of Smith et al (1988) and others[8] holds and that asset pricing bubbles would emerge in the proposed experiments.

It is speculated that the greater the concentration of the change in the independent variable the greater the likelihood of the ‘crash’ occurring (i.e. the dependent variable price) and the asset returning to its fundamental value.

 

 

 

1. 7.    Contribution

The research seeks to examine how the ‘concentration’ of information impacts upon the behaviour of financial agents. The study would extend upon the existing literature of laboratory asset bubbles and instead focus on the factors that may potentially cause the resulting crashes.  Specifically, the research seeks to examine how the ‘concentration’ of information impacts upon the behaviour of financial agents.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1. 8.    Bibliography

Abreu, D., & Brunnermeier, M. K. (2003). Bubbles and Crashes. Econometrica, 71(1), 173-204.

Ackert, L. F., & Church, B. K. (1998). The Effects of Subject Pool and Design Experience on Rationality in Experimental Asset Markets. SSRN eLibrary.

Bühler, W., Hax, H., & Schmidt, R. (1999). Empirical research on the German capital market: Physica-Verlag, New York.

Büyükkurt, B. K. (1986). Integration of serially sampled price infromation: Modeling and some findings. Journal of Consumer Research, 13(3), 357-373.

Caginalp, G., Porter, D., & Smith, V. L. Overreactions, momentum, liquidity, and price bubbles in laboratory and field asset markets.

Cheema, A., & Soman, D. The Effect of Partitions on Controlling Consumption. Journal of Marketing Research, Vol. 44, 2008.

Fama, E. (1965). THE BEHAVIOR OF STOCK-MARKET PRICES. Journal of Business, 38(1), 34-105.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of finance, 383-417.

Haugtvedt, C. P., Herr, P., & Kardes, F. R. (2008). Handbook of consumer psychology: Routledge.

Henker, J., & Owen, S. A. (2006). Bursting Bubbles: Linking Experimental Financial Market Results to Field Market Data. SSRN eLibrary.

Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. The Journal of Finance, 56(4), 1533-1597.

Hommes, C. (2006). Interacting Agents in Finance. SSRN eLibrary.

Hommes, C., Sonnemans, J., Tuinstra, J., & van de Velden, H. (2008). Expectations and bubbles in asset pricing experiments. Journal of Economic Behavior and Organization, 67(1), 116-133.

Irving, F. (1928). The money illusion. New York: Adelphi.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.

Keynes, J. M. (1936). The general theory. London, New York.

King, R. R., Smith, V. L., Williams, A. W., & Van Boening, M. (1993). The robustness of bubbles and crashes in experimental stock markets. Nonlinear dynamics and evolutionary economics, 183-200.

Lei, V., Noussair, C. N., & Plott, C. R. (2001). Nonspeculative bubbles in experimental asset markets: Lack of common knowledge of rationality vs. actual irrationality. Econometrica, 831-859.

Marimon, R., Spear, S. E., & Sunder, S. (1993). Expectationally driven market volatility: an experimental study. Journal of Economic Theory, 61, 74-74.

Markowitz, H. (1952). The utility of wealth. The Journal of Political Economy, 60(2), 151-158.

Mazumdar, T., & Jun, S. Y. (1993). Consumer evaluations of multiple versus single price change. Journal of Consumer Research, 20(3), 441-450.

Nobel Prize. (2010).   Retrieved 20 March 2010, 2010, from www.nobelprize.org

Noussair, C., Plott, C., & Charles, R. P. a. V. L. S. (2008). Chapter 32 Bubbles and Crashes in Experimental Asset Markets: Common Knowledge Failure? In Handbook of Experimental Economics Results (Vol. Volume 1, pp. 260-263): Elsevier.

Noussair, C., Robin, S., & Ruffieux, B. (2001). Price bubbles in laboratory asset markets with constant fundamental values. Experimental Economics, 4(1), 87-105.

Plott, C. R. (1991). Will economics become an experimental science? Southern Economic Journal, 57(4), 901-919.

Porter, D. P., & Smith, V. L. (2003). Stock market bubbles in the laboratory. Journal of Behavioral Finance, 4(1), 7-20.

Shiller, R. J. (2003). From Efficient Markets Theory to Behavioral Finance. The Journal of Economic Perspectives, 17(1), 83-104.

Smith, V. L., Suchanek, G. L., & Williams, A. W. (1988). Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica, 56(5), 1119-1151.

Thaler, R. (1985). Mental accounting and consumer choice. Marketing science, 4(3), 199-214.

Thaler, R. H., & Shefrin, H. M. (1988). The behavioral life-cycle hypothesis. Economic Inquiry, 26(4), 609-643.

Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453.

Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. Journal of business, 59(S4).

Williams, A. W., & Charles, R. P. a. V. L. S. (2008). Chapter 29 Price Bubbles in Large Financial Asset Markets. In Handbook of Experimental Economics Results (Vol. Volume 1, pp. 242-246): Elsevier.

 

 

 

Table 3 reports the performance of the simulations over the in-sample data period (January 2006 – December 2003)[8].  The table illustrates two key points. Firstly, changing the values of the system parameters has a significant impact on the overall performance of the system, despite the core basis of components of the system remaining unchanged. Secondly, a risk-averse set of parameters does not necessarily imply the lowest loss. System 1 was the most risk-averse of the system tested and it occurred the highest loss (-$1,290.46) as compared to system 2, 3 and 4 which resulted in profitable outcomes. The loss achieved by system 1 indicates that despite potentially profitable trading opportunities, as shown by systems 2, 3 and 4, the restrictive nature of the parameters prevents profits from being captured. Based on table 3 system 4 had the highest performance over the in-sample data set.

Table 4 reports the profit distribution for the in-sample simulations. The key reason for the profit distribution is to provide information on the nature of the trades that make up the overall performance of the system. This adds additional information as compared to table 3 which shows overall performance over time. For example, the reason for the overall loss incurred by System 1 (see table 3 and appendix 1) becomes is evident from table 4 which shows that losses were too frequent (72.00 percent) and too great (average loss 8.00 percent) relative to favourable trades to result in a profitable system. In terms of overall performance based on table 4 system 4 had the highest success.

From a trading system perspective system; 2, 3 and 4 were able to generate positive in-sample profits. This finding indicates that a potential exploitable trading pattern exist in GBP/USD data set following the release of US nonfarm employment data. Based on the results of the optimisation and in-sample simulations trading system 4 was applied to the out-of-sample data set.

1. Empirical Findings (Out-of-sample)

The developed trading system was applied to the out-of-sample data set (Jan 2004 – June 2010).

It was found that the system when tested on unseen data generate a negative result (-$1,020.32). Therefore, the alternate hypothesis is rejected. Table 3 reports the out-of-sample results. The first observation is that the system is unable to replicate a positive return as per the in-sample simulation. The results indicate the that ratio of winning trades to losing decreased when tested out of sample (56.00 percent out-of-sample versus 80.00 percent in-sample).

	Long + Short
Net Profit	-$1,020.32
Profit per Bar	-$3.37
Number of Trades	43
Avg Profit/Loss	-$23.73
Avg Profit/Loss %	-0.01%
Avg Bars Held	7.05
Winning Trades	24
Winning %	55.81%
Gross Profit	$8,949.84
Avg Profit	$372.91
Avg Profit %	0.21%
Avg Bars Held	5.71
Max Consecutive	6
Losing Trades	19
Losing %	44.19%
Gross Loss	-$9,970.16
Avg Loss	-$524.75
Avg Loss %	-0.30%
Avg Bars Held	8.74
Max Consecutive	4
Max Drawdown	-$2,630.15
Max Drawdown Date	4/12/2009

Table 3: Out-of-sample performance

Figure 4 are the systems results across time. What was observed is that the system was unprofitable for the majority of the tested period (Jan 2004 – Aug 2009). This differs from the in-sample result which found the system was able to generate positive returns over the entire data set.Figure 4: Profit curve (out-of-sample)

Figure 5 reports the breakdown of the trade. What is found provides an explanation for the different outcome between the in-sample and out-of-sample data sets. A majority of the trades were profitable (58.81 percent). Despite the higher frequency of favourable outcomes the greater weight of the losses ultimately leads to the diminished performance. The average loss for the system was -$542.75 as compared to the average profit $372.91. This is reflected in figure 5 with the ‘fatter’ left tail.

Figure 5: Profit distribution (out-of-sample)

1. Future Work

The first possible avenue for future research is to examine whether the trading performance of this system can be improved by feeding other information than price data into the system. In earlier work (Love & Payne, 2009) demonstrated that order book or order flow information is able  to enhance the predictive performance. Additionally, future studies will investigate a wider range of economic data releases across additional currency pairs.

1. Conclusions

In this paper we developed an automated trading system based on the publication of US Department of Labour non-farm payrolls data. The study was undertaken to contribute to the literature examining market microstructure in foreign exchange markets and how market participants impound information into prices.

Traditional asset market models of exchange rates, based on rational expectations and efficient markets, imply that public announcements are directly impounded into prices with no role for trading. Recent research has found that ‘trading’ is an important factor in the price formation process and that the behaviour of financial agents is not always consistent with rational expectations.

We developed a trading system that was developed on the basis of a formal methodology previously applied to equity markets. The paper departs from previous approaches that have applied a linear econometric approach to provide a more applied interpretation of the results.

The system examined whether a rule based trading system based on a predictable event could generate profits. The currency pair examined was the GBP/USD and the specific event was the US non-farm payrolls, which is published monthly. The study examined intraday data from 1996-2010.

The system attempted to predict the reaction of market participants following an event not the actual announcement itself. It was found that the developed system was unable to generate a positive return on out-of sample data. The results indicates the following; Based on the system parameters used in this paper there are no predictable behaviour  or exploitable trading patterns following US nonfarm payrolls data in the GBP/USD currency pair.

References

Andersen, T. G., Bollerslev, T., Diebold, F. X., & Vega, C. (2003). Micro effects of macro announcements: Real-time price discovery in foreign exchange. American Economic Review, 93(1), 38-62.

Azoff, E. M. (1994). Neural network time series forecasting of financial markets: John Wiley & Sons, Inc. New York, NY, USA.

Balsara, N. J. (1992). Money management strategies for futures traders: John Wiley & Sons Inc.

Bank of International Settlements. (2007). Triennial Central Bank Survey of Foreign Exchange and Derivatives Markets Activity: Bank of International Settlements.

Berger, D. W., Chaboud, A. P., Chernenko, S. V., Howorka, E., & Wright, J. H. (2008). Order flow and exchange rate dynamics in electronic brokerage system data. Journal of International Economics, 75(1), 93-109.

Berkowitz, J., & Giorgianni, L. (2001). Long-horizon exchange rate predictability? Review of Economics and Statistics, 83(1), 81-91.

CFA Institute. (2010). CFA^(R) Program Curriculum : Equity (Vol. 4): Pearson.

Chaboud, A., Chernenko, S., Howorka, E., Iyer, R. S., Liu, D., & Wright, J. H. (2004). The High-Frequency Effects of U.S. Macroeconomic Data Releases on Prices and Trading Activity in the Global Interdealer Foreign Exchange Market. SSRN eLibrary.

Chaboud, A. P., Chernenko, S. V., & Wright, J. H. (2008). Trading activity and macroeconomic announcements in high-frequency exchange rate data. Journal of the European Economic Association, 6(2-3), 589-596.

Chande, T. S. (2001). Beyond technical analysis : how to develop and implement a winning trading system (2nd ed.). New York: Wiley.

Chande, T. S. (2001). Beyond Technical Analysis: how to develop and implement a winning trading system: John Wiley & Sons Inc.

Cheung, Y. W., & Chinn, M. D. (2001). Currency traders and exchange rate dynamics: a survey of the US market. Journal of International Money and Finance, 20(4), 439-471.

Ehrmann, M., & Fratzscher, M. (2005). Exchange rates and fundamentals: new evidence from real-time data. Journal of International Money and Finance, 24(2), 317-341.

Evans, M. D. D. (2002). FX trading and exchange rate dynamics. The Journal of Finance, 57(6), 2405-2447.

Evans, M. D. D., & Lyons, R. K. (2005a). Do currency markets absorb news quickly? Journal of International Money and Finance, 24(2), 197-217.

Evans, M. D. D., & Lyons, R. K. (2005b). Meese-Rogoff redux: Micro-based exchange-rate forecasting. American Economic Review, 95(2), 405-414.

Evans, M. D. D., & Lyons, R. K. (2008). How is macro news transmitted to exchange rates? Journal of Financial Economics, 88(1), 26-50.

Faust, J., & Rogers, J. H. (2003). Exchange rate forecasting: the errors we’ve really made. Journal of International Economics, 60(1), 35-59.

Froot, K. A., & Ramadorai, T. (2005). Currency returns, intrinsic value, and institutional-investor flows. The Journal of Finance, 60(3), 1535-1566.

Graham, M., Nikkinen, J., & Sahlström, P. (2003). Relative importance of scheduled macroeconomic news for stock market investors. Journal of Economics and Finance, 27(2), 153-165.

Guillaume, D. M., Dacorogna, M. M., Davé, R. R., Müller, U. A., Olsen, R. B., & Pictet, O. V. (1997). From the bird’s eye to the microscope: A survey of new stylized facts of the intra-daily foreign exchange markets. Finance and stochastics, 1(2), 95-129.

Jegadeesh, N., & Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. The Journal of Finance, 56(2), 699-720.

Kim, S., McKenzie, M., & Faff, R. (2004). Macroeconomic News Announcements and the Role of Expectations: Evidence for US Bond. Stock and Foreign Exchange MarketsV Journal of Multinational Financial Management, 14.

Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and. Journal of Finance, 55(4), 1705-1765.

Love, R., & Payne, R. (2009). Macroeconomic news, order flows, and exchange rates. Journal of Financial and Quantitative Analysis, 43(02), 467-488.

Meese, R. A., & Rogoff, K. (1983). Empirical exchange rate models of the seventies:: Do they fit out of sample? Journal of international economics, 14(1-2), 3-24.

Osler, C. L. (2003). Currency orders and exchange rate dynamics: an explanation for the predictive success of technical analysis. The Journal of Finance, 58(5), 1791-1820.

Pardo, R. (1992). Design, testing, and optimization of trading systems: John Wiley & Sons Inc.

Thawornwong, S., & Enke, D. (2004). The adaptive selection of financial and economic variables for use with artificial neural networks. Neurocomputing, 56, 205-232.

Vanstone, B., & Finnie, G. (2009). An empirical methodology for developing stockmarket trading systems using artificial neural networks. Expert Systems with Applications, 36(3, Part 2), 6668-6680.

 

 

 

Appendix 1: In-sample simulations

Fixed Stop Loss: 10,      Profit Target: 20,           Trailing Stop: 20,20		Fixed Stop Loss: 50,      Profit Target: 40,           Trailing Stop: 40,40
	Long + Short		Long + Short
Net Profit	-$1,290.46	Net Profit	$4,879.53
Profit per Bar	-$14.83	Profit per Bar	$2.64
Number of Trades	50	Number of Trades	50
Avg Profit/Loss	-$25.81	Avg Profit/Loss	$97.59
Avg Profit/Loss %	-0.02%	Avg Profit/Loss %	0.06%
Avg Bars Held	1.74	Avg Bars Held	37.02
Winning Trades	14	Winning Trades	32
Winning %	28.00%	Winning %	64.00%
Gross Profit	$3,049.91	Gross Profit	$13,969.71
Avg Profit	$217.85	Avg Profit	$436.55
Avg Profit %	0.14%	Avg Profit %	0.28%
Avg Bars Held	1.5	Avg Bars Held	40.44
Max Consecutive	2	Max Consecutive	10
Losing Trades	36	Losing Trades	18
Losing %	72.00%	Losing %	36.00%
Gross Loss	-$4,340.37	Gross Loss	-$9,090.17
Avg Loss	-$120.57	Avg Loss	-$505.01
Avg Loss %	-0.08%	Avg Loss %	-0.32%
Avg Bars Held	1.83	Avg Bars Held	30.94
Max Consecutive	11	Max Consecutive	6
Max Drawdown	-$2,100.31	Max Drawdown	-$4,310.14
Max Drawdown Date	7/09/2001	Max Drawdown Date	1/09/2000

Fixed Stop Loss: 70,      Profit Target: 50,           Trailing Stop: 50,50		Fixed Stop Loss: 90,      Profit Target: 60,           Trailing Stop: 60,60
	Long + Short		Long + Short
Net Profit	$12,999.64	Net Profit	$16,369.63
Profit per Bar	$3.86	Profit per Bar	$3.76
Number of Trades	50	Number of Trades	50
Avg Profit/Loss	$259.99	Avg Profit/Loss	$327.39
Avg Profit/Loss %	0.17%	Avg Profit/Loss %	0.21%
Avg Bars Held	67.34	Avg Bars Held	87.02
Winning Trades	39	Winning Trades	40
Winning %	78.00%	Winning %	80.00%
Gross Profit	$20,739.77	Gross Profit	$25,399.73
Avg Profit	$531.79	Avg Profit	$634.99
Avg Profit %	0.34%	Avg Profit %	0.41%
Avg Bars Held	62.1	Avg Bars Held	80.5
Max Consecutive	10	Max Consecutive	18
Losing Trades	11	Losing Trades	10
Losing %	22.00%	Losing %	20.00%
Gross Loss	-$7,740.13	Gross Loss	-$9,030.10
Avg Loss	-$703.65	Avg Loss	-$903.01
Avg Loss %	-0.45%	Avg Loss %	-0.57%
Avg Bars Held	85.91	Avg Bars Held	113.1
Max Consecutive	2	Max Consecutive	2
Max Drawdown	-$1,790.02	Max Drawdown	-$2,750.02
Max Drawdown Date	3/10/1997	Max Drawdown Date	4/12/1998