Fuzzy logic,trading uncertainty and technical trading

From the market microstructure perspective, technical analysis can be profitable when informed traders make systematic mistakes or when uninformed traders have predictable impacts on price. However, chartists face a considerable degree of trading uncertainty because technical indicators such as moving averages are essentially imperfect filters with a nonzero phase shift. Consequently, technical trading may result in erroneous trading recommendations and substantial losses. This paper presents an uncertainty reduction approach based on fuzzy logic that addresses two problems related to the uncertainty embedded in technical trading strategies: market timing and order size. The results of our high-frequency exercises show that ‘fuzzy technical indicators’ dominate standard moving average technical indicators and filter rules for the Euro-US dollar (EUR-USD) exchange rates, especially on high-volatility days.     

The Forward Exchange Rate Bias Puzzle Is Persistent: Evidence from Stochastic and Nonparametric Cointegration Tests

An important puzzle in international finance is the failure of the forward exchange rate to be a rational forecast of the future spot rate. We document that even after accounting for nonstationarity, nonnormality, and heteroskedasticity using parametric and nonparametric tests on data for over a quarter century, U.S. dollar forward rates for the major currencies (the British pound, Japanese yen, Swiss franc, and the German mark) are generally not rational forecasts of future spot rates. These findings deepen the forward exchange rate bias puzzle, especially as these markets are the most liquid foreign exchange markets with very low trading costs.     

On the short-term predictability of exchange rates: A BVAR time-varying parameters approach

In this paper we propose a Bayesian vector autoregressive model with time-varying parameters (BVAR-TVP) to examine the short-term predictability of exchange rates. An important contribution of the paper is the application of the BVAR-TVP model, for the first time, to daily data using information from financial markets. Another contribution is the production of forecasts in real time at the very short horizon of one-trading day-ahead typically used by traders and investors in financial markets. We employ financial criteria and recently developed statistical tests to assess the exchange rate predictability. We find that the BVAR-TVP model outperforms the random walk for all exchange rates. These forecast gains are due primarily to the time-variation of coefficients, and secondly to information from other financial markets. It is shown that international investors could have made statistically significant excess profits if they had followed an inter-day trading strategy based on the buy/sell signals generated by the model’s one-day-ahead exchange rate forecasts, even after allowing for transaction costs and risk factors.     

Higher Moments and Exchange Rate Behavior

This paper uses 15‐minute exchange rate returns data for the six most liquid currencies (i.e., the Australian dollar, British pound, Canadian dollar, Euro, Japanese yen, and Swiss franc) vis‐à‐vis the United States dollar to examine whether a GARCH model augmented with higher moments (HM‐GARCH) performs better than a traditional GARCH (TG) model. Two findings are unraveled. First, the inclusion of odd/even moments in modeling the return/variance improves the statistical performance of the HM‐GARCH model. Second, trading strategies that extract buy and sell trading signals based on exchange rate forecasts from HM‐GARCH models are more profitable than those that depend on TG models.     

Real effective exchange rate volatility and growth: A framework to measure advantages of flexibility vs. costs of volatility

By devising a real effective exchange rate (REER) index where bilateral exchange rates are weighted for relative trade shares, we find that the REER volatility (differently from the bilateral exchange rate volatility with the dollar) has significant impact on growth of per capita income after controlling for other variables traditionally considered in conditional convergence estimates. We also find that this (cost of volatility) effect can be reconciled with the concurring negative and significant effect on growth of the adoption of a fixed exchange rate regime (advantage of flexibility effect), where the latter may be also interpreted as the cost of choosing pegged regimes without harmonization of rules and macroeconomic policies with main trading partners. The adoption of an REER volatility measure, instead of a bilateral exchange rate with the dollar, has the advantage of making it possible a joint test for these two effects. This is because, while fixed exchange rate regimes are strongly negatively correlated, and almost collinear, with bilateral exchange rate volatility with the dollar, the correlation is much weaker when considering our REER volatility measure.     

The Impact of Order Flow on the Foreign Exchange Market: A Copula Approach

Using the copula function, I propose a new econometric method to measure the state-dependent impact of order flows on returns in foreign exchange markets and examine whether this impact is affected by the number of informed traders. My results indicate that the impact of the order flow decreases as trading becomes more informed. This finding suggests an especially important theoretical implication: that the effect of competition among informed traders tends to dominate that of the adverse selection problem faced by uninformed traders in the euro/dollar and yen/dollar markets.     

INFORMATION AND NOISE IN FINANCIAL MARKETS: EVIDENCE FROM THE E‐MINI INDEX FUTURES

I examine the informational contributions and effects on transitory volatility of trades initiated by different types of traders in three actively traded index futures markets. The results show that trades initiated by exchange member firms account for more than 60% of price discovery during the trading day. These institutional trades appear to be more informative than trades of individual exchange members or off‐exchange traders. I also find that off‐exchange traders introduce more noise into the prices than do exchange members. My findings provide new evidence on the role of different types of traders in the price formation process.     

CAN INEFFICIENT TRADERS CREATE VALUE?

I examine the aggregate expected profit generated by informed traders of diverse ability in a competitive market. I assume that efficient traders get perfect information on asset values whereas inefficient traders get noisy information. In the presence of order size restrictions, I show that the aggregate expected profit generated by efficient and inefficient traders together can be higher than that generated by efficient traders alone. Thus, inefficient traders can create value in a constrained trading environment.     

Random walk or a run. Market microstructure analysis of foreign exchange rate movements based on conditional probability

Using tick-by-tick data for the dollar–yen and euro–dollar exchange rates recorded on the actual transaction platform, a ‘run’—continuous increases or decreases in deal prices for the past several ticks—does have some predictable information on the direction of the next price movement. Deal price movements, that are consistent with order flows, tend to continue a run once it is started. Indeed, conditional probabilities of a run continuing in the same direction after several consecutive observations exceed 0.5. However, quote prices do not show such a run tendency. Hence, a random walk hypothesis is refuted in a simple test of a run using tick-by-tick data. In addition, a longer continuous increase of the price tends to be followed by a larger reversal. The findings suggest that those market participants who have access to real-time, tick-by-tick transaction data may have an advantage in predicting exchange rate movements. The findings reported here also lend support to the momentum trading strategy.     

Trading around macroeconomic announcements: Are all traders created equal?

This paper examines the effects of macroeconomic announcements on equity index markets using high frequency transactions data for the regular and E-mini S&P 500 index futures contracts. For ten types of announcements that significantly affect prices, we analyze the price adjustment process and the trading patterns of exchange locals and off-exchange customers around the announcements. We find a large increase in trading activity immediately after the announcement. The results also show that during this initial surge in trading activity, locals are able to time their trades better than off-exchange traders even when locals do not have the advantage of access to the order flow. The trading strategy followed by exchange locals in the first 20 seconds after the announcement tends to be profitable, while off-exchange traders tend to make losing trades over the same time period. These results lend evidence that local traders tend to react to the macroeconomic information faster than off-exchange traders.     

The information content of retail investors’ order flow

In this paper, we provide evidence that the trading activity of small retail investors carries significant genuine information that can be exploited for the short-term out-of-sample forecasting of foreign exchange rates. Our findings are based on a unique dataset of around 2000 retail investors from the OANDA FXTrade electronic trading platform. Our results are consistent with the view that in the foreign exchange market private information is highly dispersed, but can be extracted by observing customer order flow. Previous studies, however, focused on the information content of costumer order flow of dealers in the interbank market, whose clients are themselves large institutional and professional investors. Our study is the first that analyzes a crowd of small retail investors and shows that even the trading activity of these investors contains, on aggregate, important non-public information that can be exploited for short-term exchange rate forecasting. Our findings lead us to conjecture that retail investors (on aggregate) are not pure noise traders but process dispersed information at least partially in a similar way as large institutional investors and hence place their orders accordingly.     

Noise traders,exchange rate disconnect puzzle,and the Tobin tax

This paper proposes a framework to explain the “exchange rate disconnect puzzle”. Two types of foreign exchange traders, rational traders and noise traders are introduced into a sticky-price general-equilibrium model. The presence of noise traders creates deviations from the uncovered interest parity. Combined with local currency pricing and consumption-smoothing behavior, our model can help to explain the “disconnect puzzle”. The excess exchange rate volatility caused by noise traders can be reduced by the ‘Tobin tax’. However, the effect of the ‘Tobin tax’ depends on the market structure and the interaction between the Tobin tax and other trading costs.     

Information, speed vs. cost trade-offs, and order routing decisions in U.S. equity markets

Prior research indicates that both execution speed and cost are important to traders, but that these two dimensions of execution quality are negatively related across U.S. equity markets. In our paper, we examine how U.S. equity traders, who are (un)informed about future price changes, trade-off between speed and cost in their order-routing decisions. We find that informed traders are more likely to choose trading systems that allow them to trade-off lower cost for faster speed; whereas, uninformed traders are more likely to choose trading systems that allow them to sacrifice speed for lower costs. Our results indicate that traders have varying preferences for the different dimensions of execution quality based on their information levels. These differences subsequently influence order-routing decisions.     

Exchange rate expectations and the pricing of Chinese cross-listed stocks

I show that the price discounts of Chinese cross-listed stocks (American Depositary Receipts (ADRs) and H-shares) to their underlying A-shares indicate the expected yuan/US dollar exchange rate. The forecasting models reveal that ADR and H-share discounts predict exchange rate changes more accurately than the random walk and forward exchange rates, particularly at long forecast horizons. Using panel estimations, I find that ADR and H-share investors form their exchange rate expectations according to standard exchange rate theories such as the Harrod-Balassa-Samuelson effect, the risk of competitive devaluations, relative purchasing power parity, uncovered interest rate parity, and the risk of currency crisis.     

Overconfident individual day traders: Evidence from the Taiwan futures market

A specific day-trading policy in Taiwan futures market allows an investigation of the performance of day traders. Since October 2007, investors who characterize themselves as “day traders” by closing their day-trade positions on the same day enjoy a 50% reduction in the initial margin. Because we can identify day traders ex ante, we have a laboratory to explore trading behavior without the contamination of potential behavioral biases. Our results show that the 3470 individual day traders in the sample incur on average a significant loss of 61,500 (26,700) New Taiwan dollars after (before) transaction costs over October 2007–September 2008. This implies that day traders are not only overconfident about the accuracy of their information but also biased in their interpretations of information. We also find that excessive trading is hazardous only to the overconfident losers, but not to the winners. Last, we provide evidence that more experienced individual investors exhibit more aggressive day trading behavior, although they do not learn their types or gain superior trading skills that could mitigate their losses.     

LINEAR RELATIONSHIP BETWEEN THE AUD/USD EXCHANGE RATE AND THE RESPECTIVE STOCK MARKET INDICES: A COMPUTATIONAL FINANCE PERSPECTIVE

In the recent era, computational intelligence techniques have found an increased popularity in addressing varied financial issues, including foreign exchange rate prediction. This article, through an intelligent system research framework, relates the Australian dollar (AUD)/US dollar (USD) exchange rate to the Australian and the US stock market indices. Information for exchange rate, All Ordinaries Index (AOI) and Dow Jones Industrial Average (DJI) for the trading days over the period January 1991–May 2011 is considered in this research. Utilizing a set of statistical and computational intelligence techniques, the research establishes that the AUD/USD exchange rate is best estimated by a linear forecast model compared with the nonlinear and ensemble‐based intelligent system models. This research further highlights that, among the competing linear models, the model with both the stock market indices and historical exchange rate values as the predictors is the best forecaster. Parameters of the linear model are deduced through a Monte Carlo stochastic approach. Relative importance of the predictors is also studied, and the influence of historical exchange rates, the immediate impact of AOI and the lagged effect of DJI are noted. Copyright © 2012 John Wiley & Sons, Ltd.     

Trading rule profits in european currency spot cross-rates

The moving average (MA) trading rule is applied to six European spot cross-rates — JY/BP, DM/BP, JY/DM, SF/DM, SF/BP, and JY/SF — to see if opportunities for profitable trading exist. The results suggest that MA trading rules are marginally profitable only for the JY/DM and the JY/SF cross-rates, while trading rules are not profitable for the other four cross-rates. Bootstrapping and out-of-sample tests provide similar results. Examination of subsamples characterized by central bank intervention do not produce different results. Computation of Box-Pierce statistics adjusted for heteroscedasticity show that daily returns for all six cross-rates are serially uncorrelated. Overall, the results suggests that cross-rates are sufficiently transparent to eliminate MA trading rule profit.     

Can Affine Term Structure Models Help Us Predict Exchange Rates?

This paper proposes an arbitrage-free model to extract the information that the term structure of forward premia contains for forecasting future spot exchange rates. Using monthly data on four U.S. dollar bilateral exchange rates, we find evidence that this model provides statistically better forecasts than those produced by a random walk for the British pound and Canadian dollar exchange rates. Negative results for the German mark/Euro and Swiss franc are explained by a rejection of the restrictions imposed by the term structure model.     

FX Trading and Exchange Rate Dynamics

I examine the sources of exchange rate dynamics by focusing on the information structure of FX trading. This structure permits the existence of an equilibrium distribution of transaction prices at a point in time. I develop and estimate a model of the price distribution using data from the Deutsche mark/dollar market that prroduces two striking results:(1) Much of the short-term volatility in exchange rates comes from sampling the heterogeneous trading decisions of dealers in a distribution that, under normal market conditions, changes comparatively slowly; (2) public news is rarely the predominant source of exchange rate movements over any horizon.     

Countercyclical currency risk premia

We describe a novel currency investment strategy, the ‘dollar carry trade,’ which delivers large excess returns, uncorrelated with the returns on well-known carry trade strategies. Using a no-arbitrage model of exchange rates we show that these excess returns compensate U.S. investors for taking on aggregate risk by shorting the dollar in bad times, when the U.S. price of risk is high. The countercyclical variation in risk premia leads to strong return predictability: the average forward discount and U.S. industrial production growth rates forecast up to 25% of the dollar return variation at the one-year horizon. The estimated model implies that the variation in the exposure of U.S. investors to worldwide risk is the key driver of predictability.