The profitability of technical trading

The Profitability Of Technical Trading Rules In Emerging Markets

Introduction

Technical analysis is a form of security analysis to predict future price movements based on historical price and volume trends in securities. In particular, technical trading rules (i.e. filter rules, moving average, channels, and momentum oscillators) as the quantitative techniques of technical analysis have been widely tested in academic research (Park and Irwin, 2007). Since Alexander (1961)'s results confirmed profitability of technical trading rules on individual US stocks, there is a strong interest in testing the predictability and profitability of technical trading rules. Most previous studies concentrate on developed countries, such as US, UK, and Japan (see, for example, Fama and Blume, 1966; Brock et al., 1992; Curcio et al., 1997). In addition, Park and Irwin (2007) has clearly reviewed the empirical literature on technical analysis and group it into “early” and “modern” studies from developed markets.

With regard to emerging markets, they are supposed to exhibit higher volatility than developed markets and higher persistence in excess returns even after adjusting for risk factors (Hatgioannides and Mesoments, 2007). What is more, relatively lower level of liquidity makes investors in emerging markets react to new information with a gradual pattern (ibid.). Van Der Hart et al. (2003) point out that such characteristics of a market would induce technical trading rules profitable. If so, academic research might provide some references for practitioners. Although Biley et al. (1990) and Pan et al. (1991) investigated that prices in several Asian security markets exhibit enormous deviations from random-walk behavior, these derivations had not been judged to reflect profit opportunities or profit realized. Until recent decades, the controversy about the usefulness of technical analysis has led to a number of studies started to examine the issue for emerging markets, especially in stock and foreign exchange markets.

The purpose of this project is trying to produce a thorough review of literature for emerging markets in this field, as well as discuss persistence and reliability of these studies over time and across different countries. In section 2, the literature will be classified into two groups, evidence in stock markets and evidence in foreign exchange markets. Besides temporary market inefficiency as the reason for excess returns, other possible explanations for this issue will also be generalized and analyzed in section 3. Section 4 will summarize and conclude. More importantly, testing approaches and procedures used in empirical studies, such as treatment of data-snooping problems and structural changes, and controlling for exchange rate regime and other government policies will be paid attention to further study.

The Empirical Evidence

Stock Markets

Many studies for emerging markets use a model-based bootstrap approach which is firstly adopted by Brock et al (1992) to test statistical significance of excess return of technical trading rules. Therefore, it is necessary to review this seminal paper first.

Brock et al. (1992) combined technical trading rules and bootstrap techniques together, and then develop an approach to test the trading strategies under different null models for the process of stock returns. That is because they recognize the danger of data-snooping or data-mining problem, and mitigate it by selecting a simple line of rules over a long time period and emphasizing the robustness of results through various non-overlapping sub-periods. They test two technical trading systems, a moving average oscillator (buy signals are emitted when the short-term average exceeds the long-term average by at least a percentage band) and trading range break (buy signals are emitted when the current price exceeds the recent maximum by at least a prespecified band) by utilizing Dow Jones Index 1897-1986, and report that returns generated by buy signals are much higher than that generated by sell signals. Many of later studies (i.e. Bessembinder and Chan, 1995; Ratner and Leal, 1999) apply the same rules.

Bessembinder and Chan (1995) extend the study of Brock et al. (1992) to several Asian emerging markets and find significant prediction power of technical trading rules for these stock markets. They applied same rules of Brock et al., variable length moving average (VMA) rules, fixed length moving average (FMA) rules and trading range break (TRB) rules in Malaysia and Thailand stock market for the period of 1975-1991. Results support the profitability of technical analysis that rules emit buy signals more than sell signals by 0.095% per day. They estimate break-even round-trip costs which are larger than that in study of Rhee et al. (1990) for the same emerging markets. Even though one pitfall of this research is that Bessembinder and Chan (1995) do not control for relative riskiness of technical strategies which is different from riskiness of buy and hold strategies. To remedy the defect, Ito (1999) demonstrates the risk factors and use bootstrap tests to reflect risk-return relationship in the time-varying equilibrium asset pricing models. It is argued that although Bessembinder and Chan (1995) indirectly prove the technical rules capture the time-variation of returns in global capital market, they don't test any trading strategies against asset pricing models (Ito, 1999).

Following the bootstrap technique, Ratner and Leal (1999) use a trading band of zero and one standard deviation of actual inflation adjusted return series to account for differences in volatility more accurately. This approach was not employed in Brock et al. (1992) and Bessembinder and Chan (1995)'s work although later it is criticized as more suitable for developed markets since it could not account for large difference in volatility across emerging markets. In addition, Ratner and Leal (1999) extend sample to both Latin America and Asia emerging markets from 1982 to 1995. It is advocated that trading rule combinations predict movements of stock returns in ten emerging markets whatever they are significant or not. After imposing transaction costs, there is strong evidence that technical trading rules in Thailand and Mexico deliver consistent and significant profits. However, only one rule is significant in Malaysia and the Philippines; all rules are insignificant in India, Brazil and Chile. This contrasts with the result of a study focusing on Chilean stock market from 1987 to 1998. In that study, Parisi and Vasquez (2000) provide strong support for technical strategies' effectiveness.

With regard to Indian stock market, result is consistent in several academic researches. Gunasekarage and Power (2001) state that nine moving average rules in Indian market has underperformed a buy-and-hold strategy and Chang et al. (2004) even say there is no forecast power of 1559 trading rules in India. That may because India is the relatively large market among emerging markets, with large number of foreign investors and thus would be relatively effective market (Gunasekarage and Power, 2001).

Recently many authors do researches from Chinese equity market. Profitability in early time is very prominent while it slowly diminishes over time. Bessembinder and Chan (1998) detect that simple technical rules in Chinese market successfully predicted stock price direction and generated excess returns in 1990s. Tian et al. (2002) also advocate this using 412 sample rules. Extending Tian et al. (2002)'work, Cai et al. (2005) analyze sub periods across the 1990s, but they find return has lessened as the decade progressed.

In general, the effectiveness of technical trading rules dominate in Asian emerging markets rather than Latin American markets. Hatgioannides and Mesomeris (2007) use Morgan Stanley Capital International (MSCI) daily stock price series (1988-2002) for eight emerging capital markets which comprise Latin American countries (Argentina, Brazil, Chile and Mexico) and Asian countries (Indonesia, Philippines, Taiwan and Thailand) to measure the risk-adjusted performance of trading rules. Results show that these rules beat the buy-and-hold benchmark strategy in all markets before trading costs and, predominately, in Asian markets and Chile after transaction costs. That might because during this sample period, Latin American market is more open than Asian market and thus more efficient (Hatgioannides and Mesomeris, 2007).

Some studies imply that possibly “best” trading rule could be investigated in emerging markets. Bessembinder and Chan (1995) find TRB rules forecast subsequent index price changes to an greater degree than MA-based rules. That is to say, different technical rules possibly generate different profits. Refer to the length of each moving average trading rule (i.e. 1, 2, 5, and 50, 150, 200 days), it does not affect significance of models (Bessembinder and Chan, 1995; Ratner and Leal, 1999). However, given more recent data (1988-2003), Lai and Lau (2006) find that length of 20 days and 60 days appear to be the most profitable for VMA and FMA rules in sample of China, Thailand, Malaysian, and Indonesian stock markets. This dovetails with Fified et al. (2008)'s study which states a trading strategy employed short moving average lengths tend to earn higher profits.

Foreign Exchange Markets

With regard to currency markets, the performance of technical trading rules is very district with that in stock markets. Excess return in Latin American markets is generally obvious than that in Asian markets.

Cheung and Wong (1997) test filter rules (hold the asset when its price is going up and maintain a short or a neutral position when the price is weakening) on Malaysian ringgit against US dollar and find maximized 10% profit annually obtained by trading rules. Nevertheless, there is no trend pattern appears in the exchange rate can be exploited by filter rules when considering risk premium and transactions. In addition, Lee et al. (2001a) also find no statistically significant profit out-of-sample of technical analysis on nine Asian countries (i.e. Malaysian ringgit, Philippine peso and Thai baht) during 1988 and 1995.

In sharp contrast to profitability in Asian countries, prominent returns followed in Latin American markets. Lee et al. (2001b) apply moving average (MA) and channel (CH) trading rules to 13 Latin American currencies during 1992 and 1999, and find both MA and CH rules are profitable for Brazilian real and Mexican peso. They additionally mention that best trading rule could be identified for particular Latin American currency when using genetic programming method introduced by Neely et al. (1997). However, Martin (2001) give a different result from Lee et al. (2001b) although they find Latin American markets (22.27%) exhibit much larger break-even transaction costs than that in Asian countries (0.36%). The risk-adjusted performance measures indicate that trading rules don't outperform a simple risk-free strategy and regarding Brazil the trading rules are found to significantly underperform a buy-and hold strategy (Martin, 2001).

Different-sample-time-periods research contributes to in-depth analysis. Using a different sample period (1999-2006) when Brazil has applied floating exchange rate system, Tabak and Lima (2009) could not calculate profit even before transaction costs concerned. This is compared with the sample period (1992-1999) when Brazilian exchange rate was fixed and followed a crawling peg regime (Lee et al., 2001b). Therefore, it might imply different exchange rate regime would influence profitability of technical analysis.

Excluding Brazilian real, Ahmed et al. (2005) investigate the daily spot exchange rates of Chile, Mexico, Indonesia, the Philippines and Thailand and find four of moving average rules are profitable. In Chong and Ip (2009)'s study same currencies of emerging markets were identified and sample period is extended from 1985 to 2004. They also detect strongly significant annual return of over 30% higher than the zero-return and equal-weighted benchmarks, especially Latin American countries. More importantly, abnormal returns of these return-based relative strength rules are relatively steady in subsample periods and don't decline over time.

Explanation For Technical Trading Profits

Various explanation or sources of excess returns have been identified for technical trading rules and some are special for emerging markets.

Temporary Market Inefficiency

Market efficiency claims that prices only reflect all known information rather than future information (Fama, 1970). That implies if market is efficient, any investment strategy based on an informational signal could not earn any abnormal return. In other words, market inefficiency could cause technical rules profits. There are many empirical studies (i.e. Bessembinder and Chan, 1995; Ito, 1999; Urrutia, 1995) for emerging markets demonstrate this point. However, when emerging markets liberalize to the foreign investors gradually, profitability seems to diminish over time, indicated as temporary market inefficiency. Compared with Asian countries, Latin American countries opened to foreign investors much earlier and broadly. Hatgioannides and Mesomeris (2007) report prominent returns in Asian countries as foreign ownership restrictions were quite high during the sample period of 1988-2002; while Latin American markets were accessible to foreign investment prior to 1988 (Bekaert and Harvey, 2000).

Chinese stock market is a special case which could reflect the difference above. Li and Wang (2007) first published a research to investigate the effectiveness of technical strategies between A- and B-share markets. Before 2001, A share is traded among domestic investors and B share is only traded among foreign investors. According to Hong and Stein (1999)'s gradual-information-diffusion model, information will be more gradually distributed across heterogeneous investors (foreign investors). Therefore, it is reasonably to explain no evidence support the predictability and profitability of technical analysis in A-share market and notable excess profits in B-share market. When Chinese government removed the restriction on B-share market, that is, allowed both domestic investors (much dominating) and foreign investors to trade, information unlikeness disappeared and profitability of technical analysis declined over time.

Central Bank Intervention

Academicians have explained the rationale of relationship between central bank intervention and technical analysis in foreign exchange markets. According to Saacke (2002), if central banks try to prevent the exchange rate from jumping by learning against wind with the objective of dampening foreign exchange volatility, exchange rate will form a trend during its adjustment. Martin (2001) has test the validity of relationship between central bank intervention and technical trading profits in developing countries. Although he could not find obvious relation between profitability and stated exchange rate regime, he detect that greater foreign reserves may be more indicative of central banks' ability to defend their currencies.

Data-Snooping Or Data-Mining Problem

Data-snooping problem occurs when a given set of data is used more than once for inference or model selection (Qi and Wu, 2005). Only the best technical trading rules will be reported as statistical inference and other inferior ones will be ignored (ibid.). Thus research would overestimate the profits if this problem exists. Brock et al. (1992) employ a bootstrap approach to mitigate it by deliberate choice of sample rules which have been used for a long period. Nevertheless, Brock et al. (1992) are not able to compute a comprehensive test across all technical trading rules. They only capture simple rules of moving average and trading range break-out for a long time which is likely to subject to survivorship bias. Many empirical studies in emerging markets inherit Brock et al.'s simple rules and thus are also likely to overestimate excess returns. To avoid these arbitrarily selected 26 trading rules, Tian et al. (2002) expand to 412 rules. However, this also does not solve the problem in essence. White (2000) develops a bootstrap simulation technique “Reality Check” which allows data mining to be conducted with a confidence level so that avoiding pure-chance selection. Although this methodology has been applied in testing profitability in developed countries (see: for example Sullivan et al., 2003; Qi and Wu, 2002), it has not yet been spread into emerging markets literatures.

Financial Crisis

It is presumably that financial crisis would influence the performance of technical trading rules in emerging markets. The relationship between technical trading rules in emerging markets and the 1997 Asian financial crisis is discussed by Mackenzie (2007). He investigates that before crisis mean return generated by a buy signal is positive and 10% of total trading rules could provide abnormal return; there is a declining performance trend in the post-crisis period with only 7% of trading rules generating excess returns (Mackenzie, 2007). Therefore, it is important to point out effects of changed nature of the market on trading rules performance for such volatile emerging markets. (还有其他人的资料么?)

Conclusion

As substantial growth of developing countries potentially provides more profitability opportunities, effectiveness of technical trading rules has been questioned in these markets. This topic is of enormous interest from practitioners and academicians. This review covers a substantial bulk of literatures, which could represent main streams in this field. Remarkable empirical studies have been conducted in stock and foreign exchange markets. However, very few work conducted in futures markets. This is one dimension required in further research.

Given the complex and mixed results in emerging stock markets, Asian countries seem to exhibit excess returns by technical trading rules rather than Latin American countries which liberalized early. As increasing restrictions are released and markets become more efficient, the profitability tends to mitigate recently. The situation in currency markets is also very vague, partially due to different exchange rate regime among sample countries. Therefore classifying the sample countries into more homogeneous groups is required in further studies. For example, emerging markets could be grouped according to market size and exchange rate regime, and then be compared with each group. If so, results will be more comparable. The important implications of existing literature for the profitability of stock returns suggest it is worth pursuing further research using much longer data sets and different markets to establish the derived results on a more solid basis, for example including African countries.

Four possible explanations in accordance with empirical evidence are identified. Temporary market inefficiency demonstrates there is a trend of declining excess returns. This implies that technical trading strategies might not be able to generate profits only depends on past market inefficiency because nature of market varies over time. The central bank intervention also influences degree of profitability. More importantly, data-snooping problem has not been solved as many existing studies adopt Brock et al. (1992)'s bootstrap approach to test usefulness of technical trading rules in emerging markets. This approach with arbitrary rules is argued to also subject to data-snooping problem and cause returns overestimated. Testing approach in emerging markets is thus required to improve in further study (i.e. using reality check). Recently some authors associate technical trading rules in emerging markets and financial crisis together. Literature in this dimension is nevertheless very limited and worth for deeper research in future.

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