Technical Trading Rules Research Articles

Portfolio Size and Profitability of Moving Average Timing Strategy

Using popular technical trading rule of moving averages, this study documents the profitability of technical analysis for equal weighted random portfolios from Australian Securities Exchange (ASX) and New Zealand Exchange (NZX) equity markets. The findings confirm prior evidence on the profitability of technical analysis for portfolio trading in relatively illiquid financial markets that are part of small open economies. This study contributes to the technical analysis literature regarding the number of stocks in a portfolio (hereafter “portfolio size”) that a trader must hold to outperform a passive benchmark strategy. Precisely, the findings suggest that an investor can consistently outperform buy and hold strategy return by applying MA timing strategy to a portfolio that contains 80 or more random stocks. In addition, the study identifies market volatility and short-term market trend as major determinants of moving average strategy’s success.

Size, trading volume, and the profitability of technical trading

PurposeThe purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume.Design/methodology/approachThis paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests.FindingsThe result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection.Originality/valueUnlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

Popularity versus Profitability: Evidence from Bollinger Bands

Many return predictability anomalies disappear over time. One explanation is that investors arbitrage profits away. But investors have always used technical analysis, so this argument may not apply to technical analysis strategies. In this article, the authors study what happens when a new technical trading rule appears and becomes popular over time. Bollinger Bands, introduced in 1983, provide a natural experiment. Before their introduction, trading on Bollinger Bands was very profitable. However, since their introduction, their predictive power seems to have gradually decreased, and it has largely disappeared in most international markets since 2001, the year that Bollinger published his book on Bollinger Bands.

Popularity versus Profitability: Evidence from Bollinger Bands

Many so-called return predictability anomalies disappear over time. One theoretical explanation is that investors arbitrage profits away through their trading. But investors have used technical analysis strategies for ages, so this argument may not necessarily hold for seemingly profitable technical analysis trading strategies. To verify whether such an argument has merit, we investigate what would happen if a completely new technical trading rule appeared that investors had never used before but which became more popular over time. Bollinger Bands, introduced in 1983, provide a natural experiment. Before their introduction, trading on Bollinger Bands would have been an extremely profitable trading strategy in international stock markets. However, ever since their introduction, their predictive power seems to have gradually decreased and has largely disappeared since the influential publication on Bollinger Bands in 2001. Moreover, their predictability disappeared in the US market first, where Bollinger Bands originated, and then in other international markets.

Robust Technical Trading with Fuzzy Knowledge-Based Systems

This paper proposes a framework of robust technical trading with fuzzy knowledge-based systems (KBSs). Particularly, our framework consists of two modules, i.e.: (i) a module for preparing candidate investment proposals and, (ii) a module for their evaluation to construct a well-performed portfolio. Moreover, our framework effectively utilizes fuzzy KBSs for representation of human expert knowledge: Precisely, in the 1st module, three sets of fuzzy IF-THEN rules implement linguistic technical trading rules, which are designed specifically for getting well performance in different market phases. On the other hand, the 2nd module exploits fuzzy logic to evaluate the prepared investment candidates in terms of multilateral performance measures frequently used in practice. In an out-of-sample numerical experiment, our framework successfully generates a series of portfolios, which show long-term satisfactory records in the prolonged slumping Japanese stock market.

Does intraday technical trading have predictive power in precious metal markets?

Previous research has identified that investors place more emphasis on technical analysis than fundamental analysis, however the research has largely been confined to daily data and stock market indices. This paper studies whether intraday technical trading rules have any significant predictive power in the precious metals market through three popular moving average rules. We find that using the standard parameters previously used in the literature, technical trading rules offer no predictive power whatsoever. However after utilising a universe of parameters, we find a number of parameter combinations offer significant predictability in the gold market, but there remains no significant predictability in the silver market. Our results show that the longer parameters of the technical trading rules are more successful than the traditional parameters chosen in the literature. Therefore intraday technical trading rules have some predictive power in the gold market but offer no significant predictability in the silver market.

Do trend following strategies work in Chinese futures markets?

We examine the performance of trend following strategies in Chinese commodity futures markets. We provide evidence that trend following‐based technical trading rules yield better performance than the buy and hold strategy on both individual contracts and sorted portfolios. The outperformance is robust to transaction costs, data frequency, sub‐prime crisis, shorting constraint, delayed execution, liquidity and parameters. Finally, the profitability of the trend following strategy may be subject to data snooping bias.

Profitability of Trading Strategies Before and During the Greek Crisis: An Empirical Study.

This paper investigates the profitability of technical trading rules in the Athens Stock Exchange (ASE), utilizing the FTSE Large Capitalization index over the seven-year period 2005-2012, which was before and during the Greek crisis. The technical rules that will be explored are the simple moving average, the envelope (parallel bands) and the slope (regression). We compare technical trading strategies in the spirit of Brock, Lakonishok, and LeBaron (1992), employing traditional t-test and Bootstrap methodology under the Random Walk with drift, AR(1) and GARCH(1,1) models. We enrich our analysis via Fourier analysis technique (FFT) and more statistical tests. The results provide strong evidence on the profitability of the examined technical trading rules, even during recession period (2009-2012), and contradict the Efficient Market Hypothesis.

Large Price Changes and Pervasive Non-Linearity in Financial Markets: Implications and Interpretations

We show that expected returns on US stocks and all major stock world market indices are non-linearly dependent on previous returns. The expected sign of returns tends to reverse after large price movements and trends tend to continue after small movements. This property can be captured by a simple polynomial model. Incorrectly fitting a simple linear model to the data leads to substantial bias in coefficient estimates and the polynomial model can be used to eliminate trends in the data. In addition, well known technical trading rules may be substantially driven by the non-linear behavior observed.

An intelligent hybrid trading system for discovering trading rules for the futures market using rough sets and genetic algorithms

Discovering intelligent technical trading rules from nonlinear and complex stock market data, and then developing decision support trading systems, is an important challenge. The objective of this study is to develop an intelligent hybrid trading system for discovering technical trading rules using rough set analysis and a genetic algorithm (GA). In order to obtain better trading decisions, a novel rule discovery mechanism using a GA approach is proposed for solving optimization problems (i.e., data discretization and reducts) of rough set analysis when discovering technical trading rules for the futures market. Experiments are designed to test the proposed model against comparable approaches (i.e., random, correlation, and GA approaches). In addition, these comprehensive experiments cover most of the current trading system topics, including the use of a sliding window method (with or without validation dataset), the number of trading rules, and the size of training period. To evaluate an intelligent hybrid trading system, experiments were carried out on the historical data of the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. In particular, trading performance is analyzed according to the number of sets of decision rules and the size of the training period for discovering trading rules for the testing period. The results show that the proposed model significantly outperforms the benchmark model in terms of the average return and as a risk-adjusted measure.

Sampling frequency and the performance of different types of technical trading rules

The predictive ability of technical trading rules has been studied in great detail however many papers group all technical trading rules together into one basket. We argue that there are two main types of technical trading rules, namely rules based on trend-following and mean reversion. Utilising high-frequency commodity ETF data, we show that mean-reversion based rules perform increasingly better as sampling frequencies increase and that conversely the performance of trend-following rules deteriorate at higher-frequencies. These findings are possibly related to noise created by high-frequency traders.

FX technical trading rules can be profitable sometimes!

This paper investigates the profitability of technical trading rules in the foreign exchange market taking into account data snooping bias and transaction costs. A universe of 7650 trading rules is applied to six currencies quoted in U.S. dollars over the 1994:3–2014:12 period. The Barras, Scaillet, and Wermers (2010) false discovery rate method is employed to deal with data snooping and it detects almost all outperforming trading rules while keeping the proportion of false discoveries to a pre-specified level. The out-of-sample results reveal a large number of outperforming rules that are profitable over short periods based on the Sharpe ratio. However, they are not consistently profitable and so the overall results are more consistent with the adaptive markets hypothesis.

Moving Average Trading Rules for NASDAQ Composite Index

This paper tests a few moving average technical trading rules for the NASDAQ Composite and Goldman Sack commodity indexes from 1972 to 2015. Our results indicate that moving average rules do exhibit strong predictive power for NADSAQ composite index but much weaker predictive power for GSCI. Can a trader use this predictive to beat the B&H strategy? We show that MA-100 days could most of the time make an abnormal profit in the case of NASDAQ composite index by considering both transaction costs and risk.

TECHNICAL ANALYSIS: CONCEPT OR REALITY?

The paper investigates the validity of technical analysis tools for sample period of 1997 to 2014 on Karachi Stock Exchange for the following three aspects; first KSE-100 index do not follow random walk model by applying the Wright‘s rank based variance ratio test; secondly a variety of extremely popular technical trading rules based on simple moving averages, exponential moving averages, relative strength index (RSI) and stochastic RSI (RSISt) to find the predictive ability of these indicators. These trading rules have predictive power over future price behavior. It is also evidenced that the inclusion of oscillators like RSI and RSISt increase the performance in generating abnormal return. Based on these trading rules, the study proposed two trading strategic to know that whether investor beat buy-andhold strategy. The results indicate that strategy based on these rules have the ability to outperform the buy-and-hold strategy, even after considering the transactional cost.

The application of technical trading rules developed from spot market prices on futures market prices using CAPM

Futures markets have seen a phenomenal success since their inception both in developed and developing countries during the last four decades. This success is attributable to the tremendous leverage the futures provide to market participants. This study contributes to the literature by analyzing a trading strategy which benefits from this leverage by using the Capital Asset Pricing Model (CAPM) and cost-of-carry relationship. We apply the technical trading rules developed from spot market prices, on futures market prices using a CAPM based hedge ratio. Historical daily prices of twenty stocks from each of the ten markets (five developed markets and five emerging markets) are used for the analysis. Popular technical indicators, along with artificial intelligence techniques like Neural Networks and Genetic Algorithms, are used to generate buy and sell signals for each stock and for portfolios of stocks. The performance of the trading strategies is then calculated and compared. The results show that, although equal amounts invested in both spot and futures markets, the profit from the strategies applied on futures is considerably higher than that from the spot market in both developed and emerging markets. Moreover, the overall performance of the artificial intelligence strategies is far better than the traditional ones.

Performance of Technical Trading Rules: Evidence from the Crude Oil Market

This study investigates the debatable success of technical trading rules, through the years, on the trending energy market of Crude Oil. In particular, the large universe of 7846 trading rules proposed by Sullivan et al. (1999) divided in 5 families (filter rules, moving averages, support and resistance rules, channel breakouts, and on-balance volume averages) is applied to the daily prices of West Texas Intermediate (WTI) light sweet crude oil futures as well as United States Oil (USO) fund since 2006. We employ the k-familywise error rate (k-FWER) and false discovery rate (FDR) techniques proposed by Romano and Wolf (2007) and Bajgrowicz and Scaillet (2012) respectively, accounting for data snooping in order to identify significantly profitable trading strategies. Our findings explain that there is no persistent nature on rules performance contrary to the in-sample outstanding results, although tiny profits can be achieved in some periods. Overall, our results seem to be in favor of the adaptive markets hypothesis.

Return predictability in emerging equity market sectors

ABSTRACTThis article investigates the predictive power of technical trading rules in the emerging equity market sector portfolios and finds that trading strategies based on technical indicators significantly outperform the buy-and-hold benchmark. Combination of data snooping bias, data measurement errors in the form of non-synchronicity bias and fluctuations in currency exchange rates is unable to explain the observed outperformance. The introduction of transaction costs tempers the results but technical analysis still possesses significant predictive power for a number of sectors. The performance of technical analysis in the emerging equity market sectors does not conform to historical trends observed in the developed equity markets as well as in the emerging equity markets when broadly diversified portfolios are considered, where predictive power of technical trading rules has been shown to decline over time.

Are chartists artists? The determinants and profitability of recommendations based on technical analysis

The value of technical analysis (TA) has been debated for decades; however, limited evidence exists on the profitability of investment recommendations issued by technical analysts. These ‘chartists’ sometimes claim that TA is an art rather than a science. We evaluated >5000 TA-based buy and sell recommendations for stocks and a market index in the Netherlands issued during the period 2004–2010. The sign of a recommendation was generally in line with trading signals resulting from technical trading rules. While recommendation levels were positively associated with price trends prior to the recommendation, we did not find evidence of (abnormal) stock returns after the publication of these recommendations. In addition, stop-loss levels did not contain informational value as no meaningful returns were detected after these trigger levels were met. Given that technical recommendations follow well-known trading rules and that these recommendations are not associated with future abnormal returns, we conclude that technical analysts do not exhibit ‘artistic’ skills.

Developing a rule change trading system for the futures market using rough set analysis

Many technical indicators have been selected as input variables in order to develop an automated trading system that determines buying and selling trading decision using optimal trading rules within the futures market. However, optimal technical trading rules alone may not be sufficient for real-world application given the endlessly changing futures market. In this study, a rule change trading system (RCTS) that consists of numerous trading rules generated using rough set analysis is developed in order to cover diverse market conditions. To change the trading rules, a rule change mechanism based on previous trading results is proposed. Simultaneously, a genetic algorithm is employed with the objective function of maximizing the payoff ratio to determine the thresholds of market timing for both buying and selling in the futures market. An empirical study of the proposed system was conducted in the Korea Composite Stock Price Index 200 (KOSPI 200) futures market. The proposed trading system yields profitable results as compared to both the buy-and-hold strategy, and a system not utilizing a genetic algorithm for maximizing the payoff ratio.

Technical trading: Is it still beating the foreign exchange market?

We carry out a large-scale investigation of technical trading rules in the foreign exchange market, using daily data over 45years for 30 developed and emerging market currencies. Employing a stepwise test to counter data-snooping bias and examining over 21,000 technical rules, we find evidence of substantial predictability and excess profitability in both developed and emerging currencies, measured against a variety of performance metrics. We cross-validate our results using out-of-sample analysis. We find time series and cross-sectional variation in subperiods and cultural and/or geographic groups, respectively, suggesting that temporarily not-fully-rational behavior and market immaturity generate technical predictability and potential excess profitability.