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Can technical oscillators outperform the buy and hold strategy?
Applied Economics
Abstract and Figures
This study compares returns from the traditional buy and hold (B&H) strategy to well-known technical oscillators applied to diverse indices leading the global market (DJI, FTSE, NK225 and TA100) during the period 2007-2012. Our aim was to establish whether technical tools can consistently achieve returns exceeding those of the B&H strategy across various financial markets. We found the relative strength index (RSI) to be the best oscillator, outperforming the DJIA, the FTSE100 and the NK225 for five of the six years examined. The only index that did better than the RSI was TA100, which outperformed all the examined oscillators. In second place was the moving average convergence/divergence (MACD) oscillator, which outperformed the NK225 B&H strategy and came in second for TA100. The results show that during bear markets the RSI and MACD generally produce better gains than the indices, while the opposite occurs during bull markets.
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... A escolha deste indicador deve-se ao facto de ser um dos indicadores mais utilizados pelos investidores no mercado financeiro O principal objetivo desta investigação é analisar as estratégias, RSI e MACD e perceber se estas conseguem superar o mercado com diferentes ativos e em momentos recentes. A escolha destes indicadores deve-se ao facto de serem dos indicadores mais utilizados pelos investidores no mercado financeiro e pelo facto de pertencerem a categorias de análises distintas, o que poderá originar uma análise diferente do mercado (Chong & Ng, 2008;Cohen & Cabiri, 2015;Eiamkanitchat, Moontuy, & Ramingwong, 2017;Kamble, 2017 ...
... Quando o período se encontra em bear market a estratégia RSI & MACD configurou-se como a melhor estratégia a investir. Quando o mercado se encontra em recessão, os IT geralmente produzem melhores resultados do que a estratégia passiva e por outro lado, quando o mercado está em alta, a estratégia Buy and Hold supera os indicadores, estes resultados estão a favor dos resultados recentemente obtidos porCohen e Cabiri (2015). As estratégias com IT demonstraram ser mais rentáveis para o benchmark. ...
The main goal of this paper is to analyze the performance of four investment strategies within a recent period of international political uncertainties. RSI and MACD supported three competing investment strategies, which were compared to the conservative Buy and Hold strategy. Five companies of the Euro Stoxx 50 Index were selected through the Markowitz Theory, and the DAX index was established as a benchmark. The time period considered was between the start of Donald Trump’s official campaign to the US elections and the first date set for Brexit.
Two subsequent additional studies were performed to evaluate their profitability. In the first, the entry and exit points were determined by international economic reports. In the second, alternative time lengths for RSI window were considered.
Our results suggest that, when the market is bear or undefined, the investor should have a strategy supported on technical analysis and he should consider more than one indicator, in order to increase the information that is taken from the market. The passive Buy and Hold strategy should be considered when the market is considered as a bull market.
As far as the authors are aware, this is the first time that a study has been carried out in a surprising period that marked political and economic actors and a large part of public opinion. The investment strategies performance depends on external factors of companies but also the management indicators. Thereby, this article may be interesting for managers, investors and civil society. This article adds value as it is unique in addressing this topic on this new dimension, with respect to the sample and indicators presented.
... Technical analysis gained interest in the 1970s due to advancements in information technology (Walkshäusl 2019;Wang et al. 2019). Cohen and Cabiri (2015) studied the RSI and MACD indicators across various indices. They discovered that RSI outperformed the Buy and Hold strategy for the Dow Jones index, while MACD yielded negative returns. ...
This article aims to contribute to the academic knowledge in the field of scientific production regarding decision support tools for investments in the capital market, specifically focusing on fundamental analysis, technical analysis, and Ichimoku dynamics. Bibliometric analysis, following the three main laws (Bradford’s Law, Lotka’s Law, and Zipf’s Law), was employed to evaluate scientific production, identify publication patterns, and uncover gaps and collaboration networks over the last thirty years. To achieve these objectives, 1710 relevant academic publications on the topic were analyzed and retrieved from the Web of Science (WOS) database, pertaining to the last 30 years, between 1990 and 22 May 2023. The significance of this article lies in the contributions of the findings, which advance scientific knowledge by identifying gaps in the knowledge and research, particularly in the limited literature on Ichimoku; our review reveals a growing trend of research in this area. Another notable conclusion is the emergence of new research topics and areas of interest, as well as the identification of collaboration networks among authors, institutions, and countries. Moreover, the article provides valuable insights for financial professionals and investors who are interested in applying these methodologies as methods for price forecasting. The highlighted results support investment decision making, trading strategies, and portfolio management.
... In the shipping industry, using indices to predict future market conditions is confided to BDI analysis; however, in the finance-market literature, there are various methods to analyse the future of the markets. Relative Strength Index is one of the methods to predict the future of the markets and widely applied by scholars (Cohen & Cabiri 2015;Hong, Lim & Stein 2000;Levy 1967). Price to Net Asset Value (P/NAV) is also one of the methods applied in various financial studies to predict the market value of a publicly-traded company, which was applied by Liow (2003), Gentry, Jones and Mayer (2004) and Lee, Sing and Tran (2013). ...
(Extended Abstract) The cyclical nature of the bulk market underlines the importance of the timing of investments as asset prices reflect the market conditions. Despite the cyclical nature of the market and the importance of market entry or exit timing, investment decisions are still based on intuition (Scarsi 2007) or following investment behaviour of peers (Papapostolou, Pouliasis & Kyriakou 2017) due to uncertain future incomes and the ownership structure in the maritime industry (Harlaftis 2007). Consequently, this leads to irrational investment behaviour and asset bubbles in the shipping market as the ones clearly observed from 2003 to 2008 (Duru 2013). To address this concern, this paper investigates the long-term annualised return performance of shipping Q, which is an adaptation of Tobin Q investment model to shipping asset management metric (Celik Girgin, Karlis & Duru 2019), in the asset play strategy comparing to conventional benchmarks such as price to net asset value (NAV) and income-based value to market price. In contrast to momentum oscillators (e.g. RSI), shipping Q is an asset value-based approach reflecting price-value disparity between short-run pricing (mark-to-market pricing) and the long-term valuation (income approach with underlying mean/trend reversion). Momentum oscillators have shorter horizons without an underlying valuation theory. Therefore, shipping Q is expected to fill this gap and represent long-short interactions in the ship valuation spectrum. To test the predictability of shipping Q along with other two indexes, Price to Net Asset Value (P/NAV) was calculated by utilising a publicly-traded company data, Eagle Bulk, and Relative Strength Index (RSI) was calculated from time-charter rate of Handymax. The index calculation results reflected volatile market conditions during the time of historical peak, 2005-2008. To illustrate the profitability of each index, their buy/sell signals applied to an investment scenario from 2005 to 2017 considering data availability. The highest investment return was obtained from RSI signals, it was followed by SQ-Handymax and P/NAV ratio. Noted that SQ index signals could be majorly considered by risk-averse investors, since the index never signalled "buy" during the TC-rate peak times, and yielded stable profit over the specified period, while the profitability of P/NAV investment operations were considerably lower, even negative from 2011 to 2017. RSI signal based investment plan showed the highest profitability during 2006-2007, and the following investment operation's profitability was slightly lower.
... According to Sanel (2016), the calculation of the MACD considered two moving averages (slow (θ 1 ) and fast (θ 2 )) for its calculation. The moving averages were obtained according to the following criteria: θ 1 made up with 26 data (market standard), θ 2 with 12 data (usual for the financial market), θ 1 -θ 2 (required for the construction of the MACD line) and finally, the moving average was calculated with the data from the MACD line (standardized with 9 data points, according to the expression SMA 9 ...
The implementation of tools such as Genetic Algorithms has not been exploited for asset price prediction despite their power, robustness, and potential application in the stock market. This paper aims to fill the gap existing in the literature on the use of Genetic Algorithms for predicting asset pricing of investment strategies into stock markets and investigate its advantages over its peers Buy & Hold and traditional technical analysis. The Genetic Algorithms strategy applied to the MACD was carried out in two different validation periods and sought to optimize the parameters that generate the buy-sell signals. The performance between the machine learning-based approach, technical analysis with the MACD and B&H was compared. The results suggest that it is possible to find optimal values of the technical indicator parameters that result in a higher return on investment through Genetic Algorithms, beating the traditional technical analysis and B&H by around 4%. This study offers a new insight for practitioners, traders, and finance researchers to take advantage of Genetic Algorithms for trading rules application in forecasting financial asset returns under a more efficient and robust methodology based on historical data analysis.
Most of the new investors, who are dominated by millennial investors, do not understand the basics of the capital market, so they have to suffer losses. Therefore, a strategy for investing in mutual funds is needed. This study aims to compare the performance of return-chasing investments with the buy-and-hold strategy in providing the best return to stock mutual fund investors. Beers (2020) states that “buy-and-hold” is a strategy in which investors buy stocks (or other types of securities, such as exchange-traded funds) and hold them for a long time regardless of market fluctuations. The data used is the net asset value of mutual funds which is then processed to obtain rank one based on annual returns. Simulations will be carried out to see the investment results of the two strategies and then the Wilcoxon signed-rank statistical test will be carried out on the profit/loss percentage to see the significance of the difference. The results of statistical tests show that there is no significant difference in investment returns between the chase return and buy and hold strategies. This result indicates the chasing return strategy provides much better investment returns than the buy-and-hold strategy for five periods on mutual fund instruments. The implication of this research for investors in using a chasing return strategy is that investors must use technical analysis, namely analyzing and finding out which mutual funds have the best prospects in that year.
Realizar operações no mercado de ações é uma tarefa desafiadora, por isso, os investidores estão muito interessados em buscar maneiras de melhorar suas operações na bolsa. Prever o valor das ações desempenha um papel importante na definição de uma estratégia de negociação. Nessa perspectiva, este artigo propõe uma abordagem que visa apresentar uma estratégia de compra e venda de ações com base em análises comparativas e padrões de mercado. Foram realizadas análises, trabalhando com duas estratégias aplicadas em padrões na bolsa de valores, buscando melhorar a assertividade e eficiência das operações da estratégia proposta. Os resultados mostram que a estratégia desenvolvida superou a estratégia buy and hold com um ganho médio de 11.139,638%, indicando seu potencial para auxiliar na tomada de decisões no mercado de ações.
Objetivo: Este artículo propone para la literatura sobre inversión de activos de renta variable una metodología aplicada a través de Algoritmos Genéticos (AG) y su implementación en el sector empresarial de inversión. Método: El estudio se desarrolló mediante simulación de las estrategias de inversión B&H, AT y AG sobre el índice accionario. El proceso de maximización del rendimiento de inversión para los activos financieros se realizó mediante Algoritmos Genéticos, los cuales se basaron en ecuaciones definidas en la tipificación cromosómica con operaciones inmersas en los genes. Se inició con una población aleatoria de individuos con cromosomas representando una solución para lograr el mayor rendimiento posible. Resultados: La aplicación de algoritmos con el RSI generó rendimientos superiores al 9 y 16% respecto a B&H y el análisis técnico, lo que quiere decir, mayor riesgo de inversión para B&H con volatilidad 17,6% pero comparable a las exhibidas por algoritmos genéticos y análisis técnico. Conclusiones: Mejor relación rendimiento-riesgo y eficiencia en los parámetros fundamentales de la Teoría de Portafolio es posible a través de estrategias de inversión basadas en algoritmos genéticos incluyendo el oscilador RSI. Este estudio sugiere que un mejoramiento del rendimiento de inversión puede ser anticipado mediante los parámetros stop loss y take profit y un rango de movimiento del precio del activo previo la toma de posición.
The massive reliance on the consumption of non-renewable energy resources for bitcoin mining has environmental implications. This paper addresses a critical research gap related to the performance of carbon-intensive equity funds in China – the country with maximum hashrate − during the crypto boom and bust periods between 2013 and 2021. We employ event study methodology and assess funds based on their investment exposure. The findings demonstrate significant abnormal returns for high-emission funds during the price booms. The abnormal returns reduce as we move across categories and are non-existent for the pro-ecological funds. We also report a positive relationship between investment flows and increasing bitcoin prices, and the impact becomes less profound for low-carbon funds. While demonstrating the effects of variation in crypto prices on investment performance and flows, the results indicate possible impediments to a sustainable financial system. Our findings highlight the relevance of using renewable resources to mitigate climatic concerns.
Recently, the deep learning architecture has been used with an increasing rate for forecasting in financial markets. In this paper, the LSTM model is used to forecast the daily closing price direction of the BTC/USD. Both model accuracy and the profit or loss of the trades made based on the proposed model are analyzed. In addition, the effects of the MACD indicator and the input matrix dimension on forecasting accuracy are evaluated. The potential risks and actual risks encountered by the trader who trades based on the proposed model were also analyzed. The obtained results indicate that the optimization of the LSTM parameters using the Bayesian optimization model has enhanced the model’s accuracy. The results obtained from analyzing the drawdown and reward/risk resulting from the trades made based on the model show that the model enables the trader to trade with peace of mind due to the low level of actual risks and potential risks.
In this paper, we develop a trading strategy based on the estimated results of an interactive agent-based model. We examine our trading strategy in nine markets proxied by market indices. Our strategy is able to ride the rising trend and avoid declining periods. In addition, our strategy could outperform market index in some financial markets.
We examine the performance of various types of technical trading rules in the emerging Cyprus Stock Market (CSE). Furthermore, we examine the predictability of daily returns for the CSE with respect to the extent that the technical analysis method of moving averages can win the buy-and-hold policy. Also, we further our analysis via the bootstrap methodology under the model of EGARCH. This study becomes necessary given the important changes, which Cyprus is going through since joining the European Union on May 1 May 2004 and as it is preparing for the adoption of the Euro as its currency.
This study investigates the impact of market integration on the profitability of two simple and popular technical trading rules, the Simple Moving Average (SMA) and the Trading Range Break (TRB) in Hong Kong. Using data from 1972 to 2006, we find that the SMA (1, 50) consistently outperforms the market before the integration of stock exchanges in 1986. Under the (1, 50) rule, a variable length moving average performs better than the fixed length moving average rule by 2.5 to 5% (annual) before transaction costs because it includes the information of the first 9 days into investors' decision. The results are robust to the out of sample tests for the validity of the profitability of the trading rules. The returns of the trading range break rules are insignificant over the 35-year span. Our results support the conjecture that stock market integration may lead to better information efficiency. The findings of significant pre-1986 profits and insignificant post-1986 profits, contradict previous findings that returns are predictable in Hong Kong, suggesting that the Hong Kong stock market may be weak-form efficient after 1986. Overall, our results suggest that technical analysis matters for asset pricing.
This new stock market trading rule uses three-steps to remove random unsystemic risk from stock price data to smooth volatility. Proving that a predetermined technical analysis relative maxima and minima trading rule for an S&P 500 Index portfolio substantially beats a naïve buy-and-hold policy, at significantly lower risk. Calling key theories in economics and finance into question. The relative maxima and minima trading rule succeeds because of market participants’ emotions. Investor fear and panic selling plunges stock prices downward below equity intrinsic values at market bottoms. Investor greed brings stock prices above equity intrinsic values at market tops. Empirical evidence—consisting of 81 years of data—confirms this conclusion.
We find consistent value and momentum return premia across eight diverse markets and asset classes, and a strong common factor structure among their returns. Value and momentum returns correlate more strongly across asset classes than passive exposures to the asset classes, but value and momentum are negatively correlated with each other, both within and across asset classes. Our results indicate the presence of common global risks that we characterize with a three-factor model. Global funding liquidity risk is a partial source of these patterns, which are identifiable only when examining value and momentum jointly across markets. Our findings present a challenge to existing behavioral, institutional, and rational asset pricing theories that largely focus on U.S. equities.
This paper compares the Tel-Aviv Stock Exchange (TASE) 25 Index (TA25) to the S&P 500 Index with respect to the extent that the Technical Analysis method of moving average can beat the buy-and-hold policy. Previous research on the S&P 500 Index is inconclusive, while TA25 has never been tested in this respect. For 1,500 daily observations, our test results imply weak-form efficiency of the S&P 500 Index. For TA25, no market efficiency is found for relatively short moving averages. The results imply that market efficiency is higher in developed financial markets than in an emerging capital market, such as the TASE.
In the finance literature, statistical inferences for large-scale testing problems usually suffer from data snooping bias. In this paper we extend the “superior predictive ability” (SPA) test of Hansen (2005, JBES) to a stepwise SPA test that can identify predictive models without potential data snooping bias. It is shown analytically and by simulations that the stepwise SPA test is more powerful than the stepwise Reality Check test of Romano and Wolf (2005, Econometrica). We then apply the proposed test to examine the predictive ability of technical trading rules based on the data of growth and emerging market indices and their exchange traded funds (ETFs). It is found that technical trading rules have significant predictive power for these markets, yet such evidence weakens after the ETFs are introduced.
We consider whether popular moving average and trading range breakout technical trading rules are profitable on a subset of the US stocks with certain size, liquidity and industry characteristics. We find these rules are rarely profitable during the period 1990 to 2004, however there is some evidence that they are more profitable for smaller, less liquid stocks. There is no evidence to any industry bias in applying these rules and when a rule does produce statistically significant profits on a stock, these profits tend to be greater for longer decision period rules.
A slowly mean-reverting component of stock prices tends to induce negative autocorrelation in returns. The autocorrelation is weak for the daily and weekly holding periods common in market efficiency tests but stronger for long-horizon returns. In tests for the 1926-85 period, large negative autocorrelations for return horizons beyond a year suggest that predictable price variation due to mean reversion accounts for large fractions of 3 to 5-year return variances. Predictable variation is estimated to be about 40 percent of 3 to 5-year return variances for portfolios of small firms. The percentage falls to around 25 percent for portfolios of large firms. Copyright 1988 by University of Chicago Press.