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This paper aims to investigate popular technical trading rules (TTRs) applied to the FTSE Turkish all-cap and small-cap indexes from September 23, 2003 to August 9, 2019 to determine rules that produce net excess returns over the Buy-and-Hold strategy (B&H).

Five TTRs, namely, simple moving average, relative strength index, moving average convergence divergence, momentum, and rate of change, are applied, singly (one indicator) and in combination (two indicators) for multiple time periods.

For the small-cap index, some TTRs – including the famous Golden Cross, when the 50-day moving average rises above 200-day moving average – produced net annual excess returns (NAERs) over the B&H strategy, for the entire period and each sub-period, after accounting for risk and transaction costs. Results were mixed for the large-cap index. The results support Cakici and Topyan (2013).

This study investigates several indicators, but future studies should examine others, especially based on volume and price.

Investors in the FTSE Turkish small-cap index may use some trading rules to earn NAERs over the B&H strategy.

This research is important because it addresses a gap in the research by examining numerous TTRs in the Turkish stock market. Studies of TTRs in Turkey are scarce.

The purpose of this paper is to investigate the profitability of momentum investment strategy and the predictive power of trading volume for equities listed in the Australian Stock Exchange.

Following the Lee and Swaminathan's approach, portfolios on past returns and past trading volume is constructed. In this approach, all stocks are ranked independently on the basis of past returns and past trading volume. The stocks are then assigned to one of five portfolios based on past returns and one of three portfolios based on trading volume over the same period.

A strong momentum effect for the Australian market during the period 1988 through 2002 is observed. Further, momentum plays an important role in providing information about stocks. Past trading volume appears to predict both the magnitude and persistence of price momentum.

Substantial momentum observed in monthly stock returns has investment implications. Abnormal returns vary from 0.3 to 7 per cent per month in the intermediate horizon.

This study provides an out of sample evidence by examining the relationship between “trading volume” (measured by the turnover ratio) and “momentum” strategies in an Australian setting.

We extend empirical evidence on the profitability of momentum trading to the realm of plain-equity ETFs.

We employ several ranking measures used in prior research, and for each we apply a traditional ranking based on total return, and a variation based only on the capital gain/loss portion of return.

While we find that past momentum is not a strong predictor of future performance in our overall sample period, 2007 to June 2018, we find that the percent off 52-week high price results in positive performance in the recovery years following the financial crisis of 2008–2009.

Our study is limited by the availability of ETF experience and data, and our test period covers just 2007 through June 2018. This period includes the financial crisis of 2008–2009, which previous research finding is associated with the momentum strategy's loss of profitability. When we exclude that period, we find evidence of a profitable momentum strategy based on the measure of percent off 52-week high price, enabling us to reject the null hypothesis that the momentum trading strategy is no longer profitable.

It is profitable based on both return measures used in the rankings. Our finding of a profitable momentum trading strategy suggests that the null hypothesis that the momentum strategy is no longer profitable can be rejected.

While perhaps not so strong as to reject the efficient markets hypothesis fully, our empirical findings are more consistent with a behavioral explanation and a market inefficiency. In view of the relative ease and low transactional costs of trading in ETFs, the markets have yet another opportunity to recognize an apparent mispricing and employ arbitrage based on it. To the extent that the relative ease of trading in ETFs makes momentum strategies easier to employ, the momentum anomaly might still be expected to disappear in an efficient market.

The purpose of this paper is to evaluate the profitability of investment strategies based on past price changes and trading volumes.

Data are employed from January 1998 to December 2011 for select emerging markets. Portfolios are formed on the basis of past information on prices and/or volumes. Unrestricted and risk adjusted returns for sample portfolios are analyzed. The risk models employed in study are Capital Asset Pricing Model (CAPM), Fama-French (F-F) Model and Fama-French augmented models.

Price momentum patterns are observed for Brazil, India, South Africa and South Korea, while there are reversals in Indonesia and China. Low-volume stocks outperform high-volume stocks for all sample countries except China. Further, volume and price based bivariate strategies do a better job than univariate strategies in case of India, South Africa and South Korea. The past price and volume patterns in stock returns are not fully explained by CAPM as well as the F-F Model. Price and volume momentum factors do play a role in explaining some of these return patterns. Finally, the unexplained returns seem to be an outcome of investor under or overreaction to past information. The sources of price and volume momentum seem to be partly risk based and partly behavioral.

The study analyzes combined role of price and volume in portfolio formation with post holding analysis. The work is useful for global portfolio managers, policy makers, market regulators and the academic community. The study contributes to asset pricing and behavioral finance literature for emerging markets.

The purpose of this paper is to assess the presence of the momentum effect on the Stock Exchange of Mauritius (SEM) and its implications for investors.

Data for stock trading activities of all listed companies on the SEM from 2001 through 2009 were subjected to the Jegadeesh and Titman methodology for the selection of stocks. The stock selection is based on their returns over the past three to 12 months, with holding periods that range from three to 12 months. In addition, the Capital Asset Pricing Model is used to estimate the risk adjusted returns for momentum portfolios and the impact of the strategies are evaluated for the “bullish” ‘(high growth) and “bearish” (moderate growth) periods.

The results show the existence/presence of the momentum effects on the SEM. However, the outcomes of the momentum strategies and particularly those of the winner and loser strategies are not consistently above (outperform) the effects of the Efficient Market Hypothesis. Accounting for heteroskedasticity did not alter the superiority of the Efficient Market Hypothesis.

The implication for investors with interest in trading on the SEM, or in Africa in general, is to focus on trading strategies that are in line with the Efficient Market Hypothesis.

The paper is a first formal attempt to fill the research gap on the momentum effect on the Mauritian equity market. The paper also contributes to the existing literature on momentum strategies in emerging markets.

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.

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.

The rapid change of technology has significantly affected the financial markets in Thailand. In order to enhance the market efficiency and liquidity, the Stock Exchange of Thailand (SET) has granted Thai stock brokers permission to develop and offer their customers algorithm and automatic stock trading. However, algorithm trading on SET was not widely adopted. This chapter intends to design and empirically estimate a model in explaining Thai investors’ acceptance of algorithm trading. The theoretical framework is based on the theory of reasoned action and technology acceptance model (TAM). A sample of 400 investors who have used online stock trading and 300 investors who have used algorithm stock trading were observed and analyzed using structural equations model (SEM) and generalized linear regression model (GLM) with a Logit specification. The results confirm that attitudes, subjective norm, perceived risks, and trust toward algorithm stock trading are factors determining investors’ behavior and acceptance of using algorithm stock trading. Investor’s perception and trust on algorithm stock trading as a trading strategy is a major factor in determining their perceived behavior and control, which affect their decision on whether to invest using algorithm trading. Accordingly, it can be concluded that Thai investors is willing to accept algorithm trading as a new financial technology, but still has concern about the reliability and profitable of this new stock trading strategy. Therefore, algorithm trading can be promoted by building investors’ trust on algorithm trading as a reliable and profitable trading strategy.

This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios.

The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics.

The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations.

The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.

Social trading platforms are considered to be amongst the major innovations in online trading. The purpose of this article is to analyze the trading activity of traders on social trading networks by taking a behavioral approach. Additionally, the authors investigate the factors that influence the irrational part of trading activity derived from the key characteristics of these platforms, i.e. those dealing with social interaction.

The investigation utilizes an extensive set of trading data from two major platforms in Germany to study the trading behavior. The authors apply a fixed effects two-stage least squares (2SLS) approach to quantify the relationship between trading activity and performance and define overconfidence as the part of trading activity that is irrationally motivated and results in negative returns.

The results provide evidence for the negative relationship between overconfidence and return on social trading platforms. The authors find that the number of followers and some platform-specific features significantly affect the trading behavior of the traders.

The authors contribute to the existing literature by exploring how the novel social interaction characteristics of online trading impact trading activity by giving rise to a new dimension of overconfidence. In addition, the authors evidence that the different frameworks of the platforms motivate heterogenous behavioral responses by the signalers. Finally, the authors refine existing studies by applying a distinct methodology for modeling overconfidence.