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The 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.

This 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.

The 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.

Unlike 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.

This paper tests two moving average technical trading rules for four Asian markets. Our results indicate that moving average rules do indeed have predictive power and can discern recurring price patterns for profitable trading. Moreover, our results support the hypothesis that technical trading rules can outperform the buy‐and‐hold strategy. Break‐even one‐way trading costs are estimated to be high for all four markets. To confirm the test outcome, robust tests based on bootstrap and the related t‐tests among the markets are also carried out. We conclude from the statistical results that moving average rules are valid and indeed have predictive power. It is implied that the trading rules may be used to design a trading strategy that will beat the buy‐and‐hold strategy in the Hong Kong, Singapore, South Korea, and Taiwan markets. The contribution of the current study is that this is the first validation test of trading rules using four markets at a similar development stage and culture tradition; and in the tests, we use most current and longer periods than the periods used in previous literature. Our robust tests are unique and considered distribution‐free.

The authors test the weak-form efficiency in cryptocurrency markets using the most recent and comprehensive data as of 2021. The authors apply various technical indicators to take a long or short position on 99 cryptocurrencies and compare the 10-day returns based on the technical trading strategies to the simple buy-and-hold returns. The authors find that the trading strategies based on single indicators or the combination of two indicators do not generate higher returns than buy-and-hold returns among cryptos. These findings suggest that cryptocurrency markets are weak-form efficient in general.

Outlines previous research on stock market efficiency and technical trading rules in both developed and emerging markets. Uses variable moving average (VMA) models to develop five technical trading rules and applies them to markets in Taiwan, Thailand and The Phillippines 1994‐1999. Compares results with the US and Japan indices and a simple buy and hold strategy. Finds the VMA rules gave higher returns in Taiwan and very much higher returns in Thailand and The Phillippines, even after transaction costs, but not in Japan and the USA. Considers the reasons why and calls for further research.

This study aims to investigate Malaysian stock market efficiency from the view of Sharīʿah-compliant and conventional stocks based on the effectiveness of technical trading strategies.

This study uses unconventional trading strategies that mix buy recommendations of Bursa Malaysia analysts with sell signals generated from 10 selected technical trading strategies (simple moving average, moving average envelopes, Bollinger Bands, momentum, commodity channel index, relative strength index, stochastic, Williams percentage range, moving average convergence divergence oscillator and shooting star) that are detected using ChartNexus. The period from 1 January 2013 until 31 December 2015 produces a total sample consisting of 1,265 buy recommendations of 125 Sharīʿah-compliant stocks and 400 buy recommendations of conventional stocks. The study period is extended until 31 March 2016 to provide an ample time for detecting the sell signal especially for buy recommendations that are released towards the end of 2015.

The resulting Jensen’s alpha show 8 out of 10 strategies are effective in generating abnormal returns in Sharīʿah-compliant samples while only 3 out of 10 strategies are effective in conventional samples. Prominent effectiveness of technical trading strategies in Sharīʿah-compliant stocks implies clear inefficiency in that stock market segment as opposed to those of the conventional stocks.

The results based on unconventional trading strategies provide new insights of Malaysian stock market efficiency especially in Sharīʿah-compliant and conventional stocks. The paper provides more robust findings on market efficiency as firms’ equity level data were focussed together with analysts’ buy recommendations from Bursa Malaysia.

The purpose of this study is to evaluate the performance of the ensemble learning models, such as the Random Forest and Extreme Gradient Boosting models, in predicting the direction of the Japan real estate investment trusts (J-REITs) at different return horizons, based on input obtained from various technical indicators.

This study measures the predictability of J-REITs with technical indicators by using different horizons of REITs' return and machine learning models. The ensemble learning models includes Random Forest and Extreme Gradient Boosting models while the return horizons of REITs ranging from 1 to 300 days. The results were further split into individual years to check for the consistency of the performance across time.

The Extreme Gradient Boosting appears to be the best method in improving forecast accuracy but not the trading return. A wider return horizons platform seemed to deliver a relatively better performance in both forecast accuracy and trading return, when compared to the return horizon of one.

It is recommended that the Extreme Gradient Boosting and Random Forest model be considered by practitioners for back-testing trading model. In addition, selecting different return horizons so as to achieve a better performance in trading/investment should also be considered.

The predictability of J-REITs using technical indicators was compared among different returns horizons and the models (Extreme Gradient Boosting and Random Forest).

Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

This paper aims at developing a behavioral agent-based model for interacting financial markets. Additionally, the effect of imposing Tobin taxes on market dynamics is explored.

The agent-based approach is followed to capture the highly complex, dynamic nature of financial markets. The model represents the interaction between two different financial markets located in two countries. The artificial markets are populated with heterogeneous, boundedly rational agents. There are two types of agents populating the markets; market makers and traders. Each time step, traders decide on which market to participate in and which trading strategy to follow. Traders can follow technical trading strategy, fundamental trading strategy or abstain from trading. The time-varying weight of each trading strategy depends on the current and past performance of this strategy. However, technical traders are loss-averse, where losses are perceived twice the equivalent gains. Market makers settle asset prices according to the net submitted orders.

The proposed framework can replicate important stylized facts observed empirically such as bubbles and crashes, excess volatility, clustered volatility, power-law tails, persistent autocorrelation in absolute returns and fractal structure.

Artificial models linking micro to macro behavior facilitate exploring the effect of different fiscal and monetary policies. The results of imposing Tobin taxes indicate that a small levy may raise government revenues without causing market distortion or instability.

This paper proposes a novel approach to explore the effect of loss aversion on the decision-making process in interacting financial markets framework.

We model international short-term capital flow by identifying technical trading rules in short-term capital markets using Genetic Programming (GP). The simulation results suggest that the international short-term markets was quite efficient during the period of 1997–2002, with most GP generated trading strategies recommending buy-and-hold on one or two assets. The out-of-sample performance of GP trading strategies varies from year to year. However, many of the strategies are able to forecast Taiwan stock market down time and avoid making futile investment. Investigation of Automatically Defined Functions shows that they do not give advantages or disadvantages to the GP results.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.