Marco contextual Institucional

Based on the ability of order flow to both explain and forecast exchange rate movements, one would be tempted to think that a simple technical trading strategy deriving buy and sell signals from order flow may outperform other traditional price-based strategies. The intuition is that order flow creates the patterns that price-based trading strategies try to exploit. In its most simplest from, order flows could be used to generate a buy or sell recommendation based on whether the daily order flow is positive or negative.

However, there may be several potential problems associated with using order flows directly as a trading indicator. First and foremost, it is enormously difficult to find the

appropriate trigger value. The trigger value essentially specifies a critical value that leads to a buy, a hold or a sell recommendation. Clearly, to set a trigger value, one needs some sort of benchmark, such as the maximum or minimum price jump, for the entire sample period. If the trigger value is set too high, the indicator might miss profitable investment opportunities; if it is set too low, the signal might be noisy, thereby resulting in losses. Finding an appropriate trigger value is a challenge in choosing an order-flow-based indicator, as there are no clear benchmarks. The reason for this is that the distribution of order flows shows massive peaks (because of its non-normal distribution). Second, achieving significant trading gains requires information on both the directional accuracy and difference in magnitude, which poses a problem for any non-price-based indicator.70

The above considerations lead us to hypothesise that any technical trading strategy using the sign of order flow (i.e., the sign of the net of net buyer- and seller-initiated trades), as a trading indicator will not outperform traditional price-based strategies:

H1: Technical trading indicators deriving a buy or sell recommendation from

order flows will not outperform price-based technical trading indicators.

Though we do not expect that order flows can be used directly as an indicator, recent literature on nonlinear modelling has shown that order flows can be used to obtain an accurate prediction of spot rate movements. An example of this approach was provided by Gradojevic (2007), who used a neuro-fuzzy trading strategy, as mentioned in the literature review. We also use this approach; however, we hypothesise that the performance of this approach can be substantially boosted from incorporating the underlying volatility in the

70_{By comparing a simple filter rule with an order-flow-based indicator, we can further illuminate this argument.} In a filter rule, a trading signal is derived if the current price is above or below a certain percentage of the previous high or low. Therefore, this indicator will give a direction (positive or negative) and an indication of how big the future movement is going to be (measured by the percentage difference). On the other hand, an order-flow-based indicator will show the buying or selling pressure, but it is ambiguous as to whether a larger positive or negative order flow will indeed trigger a larger appreciation or depreciation.

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fuzzy logic setting, a motivation arising from Bekiros (2011) and Christoffersen and Diebold (2006).

The “sign-dependence” theory of Christoffersen & Diebold (2006) suggests that volatility dependence produces sign dependence, and therefore directional predictability, as long as expected returns are non-zero. The intuition behind this theory is that volatility changes will alter the probability of observing negative or positive returns. Specifically, the ‘‘higher’’ the volatility, the ‘‘higher’’ is the probability of a negative return arising, given that the expected returns are positive71.

Following this theory, we hypothesise about the probability of the exchange rate change72prediction having the correct sign based on current increases or decreases in the underlying volatility. As all our exchange rates are defined as the USD price of one unit of foreign currency, we hypothesise that an increase in the underlying market volatility73results in a lower future return from holding the foreign currency74.

In other words, a positive (negative) predicted return, i.e. a depreciation (appreciation) of the USD relative to the foreign currency, is more likely to be correct (or to have the correct sign) if the underlying market volatility decreases (increases). The probability of the return prediction having the correct sign will be largest for very high positive (negative) predicted returns accompanied by large decreases (increases) in the underlying volatility and will be smallest for small values of positive (negative) predicted returns accompanied by large

Note that this “sign-dependence” theory applies to all financial markets, whereas other theories on the asymmetric return-volatility relationship typically apply to equity markets only.

72

The exchange rate change prediction is equivalent to the predicted return (in USD) for holding one unit of foreign (non-USD) currency.

73

The underlying market refers to the underlying market for a particular exchange rate.

74_{This hypothesis further links to the flight to quality or flight to liquidity phenomena associated with increases} in risk or volatility.

increases (decreases) in the underlying volatility. Accordingly, the trading recommendations should be adjusted for very low probabilities of the return forecast having the “correct” sign.

Note that we do not aim to derive trading signals directly from volatility changes75, but instead take both predicted returns and the underlying volatility into account. We therefore hypothesise:

H2: We expect the highest returns from order flow information in a neuro-fuzzy

logic setting controlling for the underlying market volatility

The reason why we conjecture that this setting will provide the largest returns is as follows: The exact relationship and causality between volatility changes and future returns is not generally agreed upon. The fuzzy interaction allows us to empirically test different relationships by setting the rule base in advance and allowing us to specify the relationship in linguistic terms. Furthermore, the return prediction is possibly imprecise, incomplete or unreliable. The same applies to market volatility, which is hard to quantify in real terms. Fuzzy logic, by its very nature, tolerates uncertainty by defining variables as imprecise terms. .Other benefits include a smoother decision output, which is simply smoother trading recommendations, thereby reducing transaction costs.

A final key question is whether our proposed methodology can be used to boost the performance of simple price based trading strategies. Though we firmly believe that order- flow-based return forecasts are preferable over any other technical trading indicator, we are interested in whether even simple trading indicators could be improved by linking them to changes in the underlying market volatility. An interesting finding from Gradojevic and Gençay (2013) gives an initial indication of this claim, showing that a MA fuzzy logic control setting provides the higher intraday returns when the conditional volatility is higher. This

75_{As outlined in Gradojevic, Lento, and Wright (2007), the economic value of volatility based trading} indicators, such as Bollinger bands, is questionable.

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finding is in line with any risk premium considerations (Kho, 1996) and contradicts the findings in Reitz (2006), suggesting that increases in volatility deteriorate the performance of technical trading indicators. Based on the findings in Gradojevic and Gençay (2013) we hypothesise:

H3: Even the performance of a simple MA indictor can be boosted significantly if

we link the signal of the former to changes in the underlying market volatility.

To test this hypothesis, we will use the same fuzzy logic methodology that was used to evaluate the second hypothesis. This hypothesis is an add-on, and if proven correctly will show the increased benefits of linking changes in a currency pair’s volatility to simple price- based technical trading signals.

In the next section, we describe the trading rules. The dataset used is the same as that in Chapter 4, with the addition of the oil price, CBOE VIX and EPU data which we obtain from Thomson Reuters DataStream. The results are reported and discussed in Sections 4.5, 4.6 and 4.7. Section 4.8 concludes the chapter.