Performance of technical trading rules: evidence from Southeast Asian stock markets Full Text

On the other hand, the sell signal to enter a short position (or to close a prior long position) is generated when the MACD crosses below its own Signal Line (Bearish signal). The RSI measures the current and historical strength or weakness of stock or market price movements based on closing prices of a recent trading period. Stocks which have had stronger positive changes have a higher RSI than stocks which have had stronger negative changes. Later on, behavioral models are developed to explain how profitable trading opportunities based on past trading data can still exist. Basically, these types of models show that price adjusts slowly to new information due to noise trading, feedback trading or herding behavior. That is, an evaluation of the study hypotheses, H1, H2 and H3, relating to the mean-difference test (Eqn (7)), spread test (Eqn (8)) and momentum test (Eqn (9)), respectively.

This conjectures that, inter alia, investment strategies will “wax and wane, performing well in certain environments and performing poorly in other environments”. It is possible to draw on existing theoretical and empirical evidence to shed light on the environments which are likely to give rise to investment strategy success (and failure). Specifically, within a linearity-generating processes framework, Gabaix (2012) provides compelling theoretical motivation for the time-varying relationship between predictability and the probability of rare large disasters.

They find that Taiwan, Thailand and Mexico emerge as markets where technical trading strategies may be profitable. The modelling objective of unconditional and conditional CAPM is to compare the equilibrium returns from both models based on the outcomes of buy and sell signals (TTRs). “If markets are efficient and the assumed model of asset pricing [CAPM] is correct, the technical rule returns in excess of the time-varying expected returns should have a zero conditional mean” (Kho, 1996, p. 279). This means that there should be no difference between expected returns under the two models.

Time-varying short-horizon predictability

Therefore, we use the pooled estimator “S”, the standard error of daily returns estimated from the entire sample, to estimate both σbuy and σsell. It is noteworthy that this number can be negative and yet the trading system generates a positive net profit. The reason is that the number is the average over the number of trades and thus it is possible that the profits from a fraction of trades can more than compensate the losses from unprofitable ones. A positive index number, say 20, reveals that overall a trading strategy generates a positive net profit. The return is 20 % of the amount of risk as measured by a possible change, both positive and negative, in the equity value from an initial investment. The index with a value of “100” mean that a trading strategy generates a positive net profit and there is never a principal loss during a simulation.

This number is the ratio of an average profit from profitable trades over an average loss from unprofitable ones. A good trading system would let the profit run while cutting losses quickly, resulting in a high ratio. Wilder (1978) also introduces Average Directional Movement Index (ADX) as a measure of trend strength. The buy or sell signals are generated from the DMI only if the ADX indicates that there is a strong trend. The buy signal to enter a long position (or to cover a prior short position) is generated when the MACD crosses above its own Signal Line (Bullish signal).

This study supports Gerritsen et al. (2020), who find TTRs to be more cost-effective than the buy–hold strategy when dealing with daily Bitcoin data. Other studies that reach favourable conclusions regarding the benefit of TA in cryptocurrency exchange rates include Tiwari et al. (2018), Miller et al. (2019), Gerritsen et al. (2020), and Detzel et al. (2021). Recent research, on the other hand, has shown that simple trading strategies can be useful in predicting stock market returns. These results were extended and confirmed by Bessembinder and Chan (1998) using US data, and Bessembinder and Chan (1995) using data from Asia–Pacific stock markets. The studies made an important observation that TA has less explanatory power in the more developed markets, but is more successful in emerging markets (Ito, 1999; Ratner and Leal, 1999; Miller et al., 2019; Grobys et al., 2020). Therefore, TA profitability (or its possibility) may be seen as one of the EMH anomalies arising from weak form efficiency.

Market efficiency and the returns to technical analysis

Professional analysts often use technical analysis in conjunction with other forms of research. Retail traders may make decisions based solely on the price charts of a security and similar statistics, but practicing equity analysts rarely limit their research to fundamental or technical analysis alone. One of the most popular methods of technical analysis is based on the notion that history repeats itself. What this means is that charts tend to form shapes that have occurred historically, and the analysis of past patterns helps technical analysts in predicting future market movements. This principle  focuses on the technical analyst’s belief that trading is highly connected with probability, and the analysis of historical shapes provides the analyst with an edge before opening a trade. Trading swaps and over-the-counter derivatives, exchange-traded derivatives and options and securities involves substantial risk and is not suitable for all investors.

Rising volume confirms that new money is supporting the prevailing trend. Declining volume is often a warning that the trend is near completion. A solid price uptrend should always be accompanied by rising volume.

A simple way to estimate bid-ask spreads from daily high and low prices

It is, therefore, no surprise that the performance of TTRs has been extensively investigated (see, e.g., Park and Irwin, 2007, for a review). The bulk of the evidence suggests that profits can be obtained when such rules are employed. These findings are demonstrated to be consistent with theoretical models that predict a relationship between TTR performance and market conditions. Firstly, in terms of market timing, we find that using technical indicators does not help much. The trade efficiency measures, our indicators of market timing ability, are normally low with few exceptions.

The study offers evidence that the MA trading rule can predict Bitcoin returns even after accounting for time-varying risk premiums. Similar to known literature (Ülkü and Prodan, 2013), the profits found in Bitcoin exchange rates are found to diminish after accounting for time-varying risk premiums. In the evaluation of the momentum effect, the study finds similar evidence to that of Borgards (2021), in that the Bitcoin market possesses a momentum effect. Emerging markets are known to be less liquid than industrialised markets and have more concentrated trade.

When he gets a sell signal on any particular day, he will liquidate all stocks holding into cash on the following trading day at the opening price. For short-only strategies, the rules are similar but with opposite transactions. All long and short positions are closed at the end of the simulation. Transaction costs are ignored at this stage as their impact would be investigated https://trading-market.org/ with the round-trip breakeven costs later. Brown and Jennings (1989) develop a noisy rational expectations model, in which the current price does not fully reveal private information. In a feedback model (DeLong et al. 1990), there are noise traders who irrationally trade on noise and follow a positive feedback strategy by buying when prices rise and selling when prices fall.

Start with the weekly price chart to establish the long term trend, and then work down through the daily and hourly charts to trade in the direction of that trend. The odds are better if you are trading in the direction of the long term trend. Our goal as traders is to capture price moves inside our time frame, while limiting our drawdowns in capital. The longer I have traded, the more I have become an advocate of price action. The highest (lowest) price is the maximum (minimum) close price when the trading position is still open.

Over the years, academics and practitioners have demonstrated an overwhelming interest in the profitability of technical analysis (TA) on practically all financial systems and assets. More recent empirical studies suggest that technical trading rules (TTRs) may generate positive profits in certain speculative markets, most notably in foreign exchange and futures markets (Nazário et al., 2017). Various theoretical and empirical explanations have been proposed for TA-based profits. Regardless of the origin or description of profits, the current study only asks the question of whether TA trading is profitable, particularly in the Bitcoin case. Among analysts, technical trading rules are widely used for forecasting security returns. Recent literature provides evidence that these rules may provide positive profits after accounting for transaction costs.

Results of trading rules with optimized parameters

Technical trading rules help to counter this bias by allowing profits to run in profitable trades while cutting losses in unprofitable ones. That is how profitable strategies like MACD and STOCH-D beat a Buy-and-Hold (BH) strategy. The implication is that even if the market is weak-form efficient, the use of technical trading rules may still be beneficial to individual investors as it counters the above bias.

The first is that, similar to the efficient market hypothesis, the market discounts everything. Second, they expect that prices, even in random market movements, will exhibit trends regardless of the time frame being observed. The repetitive nature of price movements is often attributed to market psychology, which tends to be very predictable based on emotions like fear or excitement. By using both formal statistical tests and technical trading performance measures, this paper finds three new insights not mentioned in previous studies.

This can all be done through books, online courses, online material, and classes. Once the basics are understood, from there you can use the same types of materials but those that focus specifically on technical analysis. Investopedia’s course on technical analysis is one specific option. References to over-the-counter (“OTC”) products or swaps are made on behalf of StoneX Markets LLC (“SXM”), a member of the National Futures Association (“NFA”) and provisionally registered with the U.S. Commodity Futures Trading Commission (“CFTC”) as a swap dealer. SXM’s products are designed only for individuals or firms who qualify under CFTC rules as an ‘Eligible Contract Participant’ (“ECP”) and who have been accepted as customers of SXM.

According to Bessembinder and Chan (1998), the additional return (π) generated by technical trading rules relative to a buy-and-hold strategy is given as follows. On the other hand, the Thai market is the opposite extreme case. The STOCH-D, MACD, DMI and OBV trading strategies all generate significant average daily returns. Only the STOCH-D fails to have a breakeven trading cost higher than the actual one.

• First, determine which one you’re going to trade and use the appropriate chart.
• The fundamentals of John’s approach to technical analysis illustrate that it is more important to determine where a market is going (up or down) rather than the reason behind its direction.
• Their study investigates TA using trend-following tactics like the simple moving average.
• A positive change in equity value is measured by a positive net profit from a trading system.

The OBV is a volume-based indicator that relates volume to price change. If a closing price today is higher (lower) than a closing price yesterday, then the entire today’s volume will be added (deducted) to (from) the previous day OBV to get today OBV. In sum, if enough people use the same signals, they could cause the movement foretold by the signal, but over the long run, this sole group of technical trading rules traders cannot drive the price. Charles Dow released a series of editorials discussing technical analysis theory. His writings included two basic assumptions that have continued to form the framework for technical analysis trading. Technical analysis most commonly applies to price changes, but some analysts track numbers other than just price, such as trading volume or open interest figures.

Rather, technical analysts focus on the chart itself and the shapes, patterns and formations occurring on the chart. Fibonacci retracements may be used to define market entry/exit as well as to align profit targets and stop losses. The levels most commonly scrutinized by active traders are 38.2 percent and 61.8 percent. Bad things happen below the 200 day; downtrends, distribution, bear markets, crashes, and bankruptcies. Moving averages quantify trends and create signals for entries, exits, and trailing stops.

Trend lines are one of the simplest and most effective charting tools. Prices will often pull back to trend lines before resuming their trend. The breaking of trend lines usually signals a change in trend.

Bull Markets have no long term resistance, and Bear Markets have no long term support. Relying on fact, rather than being tossed around by your own subjective feelings, will insure your long term profitability. We would like to thank Mahidol University and the College of Management for providing a financial support to get this paper published.

The Singaporean market is an extreme case as there is no technical trading strategies studied that generate a significant average daily return. In addition, none have breakeven trading costs higher than the actual one. This implies that seemingly profitable strategies like MACD, DMI and OBV are in fact not profitable at all after transaction costs. Interestingly, papers that study emerging markets in Asian markets tend to find profitability of technical trading rules. For instance, Lento (2006), which studied performance of nine technical trading rules in eight Asian-Pacific equity markets from 1987 to 2005, find that technical trading rules seem to be profitable in six Asian markets.

However, if the unconditional model outperforms the conditional model (or equal performance) it can be concluded that the abnormal profits derived from TTR are only reflecting time-varying risk premium. That is, investors who find TA profitable are merely compensated for their risk-bearing abilities. The current paper contributes to the literature in a number of ways. Second, it employs an out-of-sample procedure in which investors update their portfolio of stock positions each month based on the performance (net of transaction costs) of a set of trading rules observed in previous periods. The methods used here are novel in that we go beyond the cumulative wealth approach (equivalently, an expanding window of past performance) and consider past performance that is robust to structural breaks.

This would be contrary to the theory of the efficient market hypothesis which states that security prices cannot be forecasted from their past values or other past variables. Evidence of nonlinear predictability is found in the stock market returns by using the past returns and the buy and sell signals of the moving average rules. The paper examines the profitability of 24 TTRs in each of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR). The results show that the chosen FMA TTRs are all successful in generating profitable signals for Bitcoin returns. The buy signals generate positive returns and sell signals generate negative returns, which are, on average, significantly different from the returns earned by the buy–hold strategy. The study results are consistent with Grobys et al. (2020) and Borgards and Czudaj (2021) who found that the trading rules are successful in predicting Bitcoin price movements.

According to the efficient market hypothesis (EMH), which states that asset prices incorporate all available and relevant information, it is impossible to make risk-adjusted profits by trading on the past trading data. Therefore, any attempt to make profits by technical analysis is ultimately futile. However, even from a theoretical perspective, the EMH has been increasingly challenged.