Technical Trading Rules Research Articles

Optimizing Genetic Algorithm With Momentum Strategy for Technical Trading Rules: Evidence From Futures Markets

ABSTRACTThis paper introduces an innovative genetically optimized dynamic composite strategy for achieving profitability in futures markets. Utilizing daily data from 35 actively traded futures contracts (1984–2022), we highlight the potential advantages of integrating the momentum effect into dynamic moving average strategies. This enhancement can boost the strategy's capability to capture and capitalize on market trends, ensuring consistent and stable returns. The developed dynamically composite technical trading strategy aspires to be a valuable reference for investors and the finance academic community, contributing to advancements in the field.

Investigating profitability of Technical Trading Rules around COVID pandemic: Evidence from PSX

This paper investigates the role of technical analysis in predicting the stock prices of listed companies on the Pakistan Stock Exchange (PSX). Technical indicators such as Moving Average Convergence/Divergence (MACD), Relative Strength Index (RSI), Bollinger Bands (BB), and Money Flow Index (MFI) are evaluated to explore their significance for stock gains in the Pakistan Stock Exchange. For this purpose, daily price data from 29 companies listed on the PSX was collected during the period from 2018 to 2022. This sample period provides a unique opportunity to analyze the impact of Covid-19 on the accuracy of technical trading signals. MACD, RSI, BB, and MFI are calculated, and the accuracy of the results is then assessed. Evidence from the PSX highlights that these indicators perform differently across various sectors. The t-test outcomes indicate significant differences in results before and after Covid-19, emphasizing the impact of the pandemic on stock movements. Based on these results an index has been developed and proposed to aid future decision-making processes.

Are simple technical trading rules profitable in bitcoin markets?

This paper examines the profitability of simple technical trading rules in bitcoin markets comprehensively, by taking into account realistic investor behaviour and transaction costs, and data mining problems. Realistic investor behaviour is replicated by first employing 75,360 simple technical trading rules, divided over 6 commonly used trading rule classes and daily and intraday frequencies. Next, we select the best performing rules after transaction costs using a multiple hypothesis procedure. Finally, we form portfolios combining the selected rules and analyse their out-of-sample performance. We find that, especially risk–return wise, simple technical trading rules can outperform a buy-and-hold strategy in the bitcoin market out-of-sample.

Profitability of technical trading rules in the Chinese stock market

This article examines the profitability of technical analysis on the Shanghai Stock Exchange Composite Index (SSEC) and the Growth Enterprise Market (GEM) Index, employing stepwise generalized error rate control procedures to address the data-snooping bias. Our comprehensive study encompasses 38,456 trading rules, extending beyond prior research by incorporating simple rules alongside their contrarian and complex counterparts. Upon rectifying data-snooping bias, we show that only a few complex rules outperform SSEC in different testing sub-periods, and no trading rules outperform GEM. As the outperforming rules are based on relatively high frequent trades, most lucrative rules yield negative returns in the out-of-sample periods once transaction costs were considered. Our new findings shed light on the efficiency of the Chinese stock market from 2010 to 2021.

Do futures improve genetically trained high-frequency technical trading rules for the Chinese index ETF market?

Using the evolutionary genetic program (GP) to search for optimal technical trading rules (TTRs) in high-frequency Chinese index exchange traded funds (ETFs), we investigate whether profit-taking opportunities can improve through index arbitrage. Our results show that with information spillover from index futures, consistent improvements in both the market timing and out-of-sample profitability of ETF TTRs are obtained, which are particularly pronounced for small-cap markets and TTRs trained using lower transaction costs. The additional information externality that futures provide, however, appears to have been eroded by lower futures liquidity levels and regulatory trading restrictions in effect since 2015.

Do technical trading rules outperform the simple buy-and-hold strategy in the cryptocurrency market?

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.

Beating the market with a simple proposed portfolio and technical trading rules

This study presents evidence that well-known technical trading rules (TTRs) applied to a proposed simple exchange-traded fund (ETF) portfolio provide better risk-adjusted performance than the ‘market’ and top-ranked balanced funds (BFs). We form a portfolio of two well-known ETFs and apply some well-known TTRs like Moving Average (MA), Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD) techniques to this simple proposed portfolio from January 31, 2007 to August 31, 2021. We design two strategies: (1) Long/Money and (2) Leverage/Money. We compare the risk-adjusted performance of some well-known trading rules applied to our proposed portfolio with strategy (1), Long/Money, which is a more conservative investment strategy, to the risk-adjusted performance of 14 top-ranked BFs. The best trading rules with strategy (1) applied to our portfolio outperform all the top-ranked BFs for the entire period and over two equal sub-periods. Similarly, the best trading rules applied to our portfolio with strategy (2) outperform the ‘market’, a Buy and Hold (B&H) strategy for the entire period and over two equal sub-periods. This paper contributes to the literature examining the common assumptions of efficient markets and could have important practical implications for many fund managers. In addition, the social impact of our paper could affect thousands of retail traders who are trying to ‘beat’ the market.

Technical trading rules, loss avoidance, and the business cycle

We show that simple technical trading rule (TTR) strategies substantially reduce investment left tail risk. An investor following a TTR strategy can also avoid a high percentage of extremely negative returns. This percentage increases substantially during recessions. Interestingly, tail risk reduction does not come at a cost of lower performance – risk adjusted returns of TTR strategies are in fact higher than those of a buy-and-hold strategy. Our findings are robust to changes in trading strategy specifications. They hold in 38 international equity markets, as well as in a large sample of individual US stocks, and survive a reality check bootstrap.

Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

Technical trading relies heavily on analysis, most of which is statistical in nature. When the data to be modeled is nonlinear, imprecise, or complicated, fuzzy inference systems (FISs) are used in conjunction with computational, mathematical, and statistical modeling methodologies to simulate technical trading. Fuzzy logic may be modeled using linear, nonlinear, geometric, dynamic, and integer programming. These techniques, when combined with fuzzy logic, help the decision-maker arrive at a better solution while still facing some degree of ambiguity or uncertainty. The moving average method is a useful metric that may give trade recommendations to aid investors further. While trading signals inform investors of when to purchase and sell, a simple moving average provides no such information. In this research, we suggest a fuzzy moving average approach in which the intensity of trading signals, measured in terms of trading volume, is determined by using the fuzzy logic rule. In this research, we propose using fuzzy logic technical trading rules, which are more resistant to decision-making mistakes, to mitigate the trading uncertainty inherent in the conventional technical indicators method.

The predictive ability of technical trading rules: an empirical analysis of developed and emerging equity markets

We investigate the predictability of leading equity indices of 23 developed and 18 emerging markets with a set of 6406 technical trading rules over up to 66 years. Using a state-of-the-art test for superior predictive ability to control for data snooping bias, we find in-sample evidence for technical heuristics with significant outperformance over a simple buy-and-hold strategy in the majority of markets. The proportion of heuristics with superior performance is much higher among emerging market indices, and the predictability diminishes drastically over time in all markets. In particular, markets turn unpredictable in the last years of our sample. Moreover, the results are very sensitive to the introduction of moderate transaction costs. An out-of-sample analysis shows that the performance of technical rules is not persistent in the sense that recently best-performing rules perform significantly worse than simple buy-and-hold strategies in the future. Overall, our results cast serious doubt on whether investors could have earned any excess profits using the broad range of considered technical trading rules.

Further evidence on the returns to technical trading rules: Insights from fourteen currencies

This work studies the returns to technical trading rules utilising a sample of 14 currency pairs by applying five trading rules: Filter rules, Trading Range Breakout, Moving Average, and Bollinger bands over varying market conditions. The mixed results indicate that technical analysis based on the applied trading rules is appropriate for the trading strategies examined. The one per cent filter, 150-day MA rule and the Bollinger bands trading rule surpassed the other applied techniques in general performance. What is also clear is that these trading rules are rewarding over varying market conditions. Results from various subperiods, representing normal and stressful market conditions, generally confirm our main findings and show the added value of the trading range breakout rule, which delivers performance in strongly trending markets. Evidence from bootstrap analysis demonstrates the predictive ability of the Moving Average and Bollinger band trading rules.

Performance Evaluation of Technical Analysis in the Nepalese Stock Market: Implications for Investment Strategies

Purpose: This study aims to examine the performance of five widely used technical indicators on Nepal Stock Exchange (NEPSE) indexes. Specifically, the study evaluates indicators such as the simple moving average, moving average convergence and divergence, relative strength index, stochastic oscillator, and Bollinger Band. Design/methodology/approach: The study utilizes a descriptive and quantitative research design. Daily closing index data of NEPSE and its six sub-indices are analyzed for a substantial period, ranging from 1st September 2012 to 31st August 2022. The performance of the technical indicators is examined through modeling, backtesting, and statistical analysis. The technical trading rules of each indicator are incorporated into the modeling process, and the results are analyzed using different performance metrics. Furthermore, the predictability of the indicators is tested through standard statistical analysis and bootstrap techniques utilizing a random walk model. Findings: The results of the study reveal that the technical strategies, as represented by the analyzed indicators, generally support the effectiveness of technical analysis in the NEPSE. However, the relative strength index stands out as an exception, generating negative returns. The second stage of the analysis indicates that the simple moving average, relative strength, and Bollinger band fail to generate significant returns in certain indices. The findings from the bootstrap techniques further contradict the forecasting ability of technical strategies in the NEPSE, raising questions about their real performance in the Nepalese stock market. Practical Implications: This research provides valuable insights for investors, traders, and market participants in understanding the nuances and limitations of technical analysis in the context of the NEPSE. The findings suggest that technical indicators, notably the relative strength index, should be interpreted cautiously and that investing decisions should take into account additional factors. The study contributes to the ongoing discussion on the effectiveness of technical analysis in the Nepalese stock market, assisting practitioners to make informed decisions and better understand market dynamics.

카오스 시계열과 시뮬레이션에 의한 기술적 분석 전략의 유효성 분석

카오스 시계열과 시뮬레이션에 의한 기술적 분석 전략의 유효성 분석

Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems

Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency.

Can Investors Profit from Utilizing Technical Trading Rules During the COVID-19 Pandemic?

In the past, it was believed that investors may generate abnormal returns (AR) for trading stocks by employing technical trading rules. However, since the COVID-19 pandemic broke out, stock markets around the world seem to suffer a serious impact. Therefore, whether investors can beat the markets by applying technical trading rules during the period of COVID-19 pandemic becomes an important issue for market participants. The purpose of this study is to examine the profitability of trading stocks with the use of technical trading rules under the COVID-19 pandemic. By trading the constituent stocks of DJ 30 and NASDAQ 100, we find that almost all of the trading rules employed in this study fail to beat the market during the COVID-19 pandemic period, which is different from the results in 2019. The revealed findings of this study may shed light on that investors should adopt technical trading with care when stock markets are seriously affected by black swan events like COVID-19.

Forecasting the equity premium with frequency-decomposed technical indicators

Practitioners widely use technical trading rules to forecast the U.S. equity premium. I decompose technical indicators into components with frequency-specific information, showing that the predictive power comes from medium-frequency variation in buy and sell signals without much evidence of predictability outside this frequency band. This pattern can be observed for common strategies based on volume, momentum, and moving average rules. In line with previous work, I find that the statistical predictability of technical indicators is centered in recessions and vanishes after sharp market rebounds. However, the out-of-sample R2 increases from less than 4% for the unadjusted indicators to more than 8% for the filtered counterparts in these periods. The medium-frequency components have a better market timing ability, and the economic gains are statistically significant. A mean–variance investor would be willing to pay a fee of 100 to 150 basis points annually to access the filtered indicators. I show that the best-performing frequencies can be identified in real time. The predictive power of medium-frequency components stems from their ability to anticipate discount rate news and changes in investor sentiment. Results are robust to different country indices and transaction costs.

Machine Learning and the Stock Market

AbstractPractitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.

Investment and trading strategies in the maritime sector: an application to the secondhand containership market

As shipping market players operate in a competitive and volatile business environment, their highly capital-intensive investment decisions to target value-generating projects are exposed to critical risks. Therefore, the paper investigates the efficiency of investment strategies based on the combination of technical trading rules and fundamental analysis in selling and purchasing ships in the container shipping market. Using historical datasets of second-hand vessel prices and time-charter rates from October 1996 to June 2021, long-run cointegrating implications and short-run causality spillover effects are examined for three groups of containerships, distinguished by their transportation capacity, viz. 725 TEUs, 1700 TEUs, and 3500 TEUs. In addition, the moving average trading rules are used to indicate the timing of investment or divestment decisions through the analysis period. The results for vessel prices and earnings for 3500 TEU containerships appeared to be more volatile compared to smaller ships (725 TEUs and 1700 TEUs), while time-charter earnings are seen to exert an impact on second-hand prices across all vessel types. Moreover, due to higher volatility, the trading strategies based on price-earning ratios significantly outperform the buy-and-hold strategies for the 3500 TEU and the 1700 TEU containerships. On the contrary, the decision to buy-and-hold smaller container ships (725 TEU) yields higher profits than the active sale and purchase strategy. The insights provided in this paper can be used by multiple stakeholders, such as liner operators, investors, lessors, and researchers.

Technical trading rule profitability in currencies: It’s all about momentum

Recent academic and practitioner attention has focused on currency momentum. In this paper we replicate technical trading rules to assess their relationship with momentum. We find the effectiveness of technical trading rules falls significantly over time, with the mean Sharpe Ratio of our sample of portfolios falling from 0.66 in our in-sample period to 0.06 out-of-sample. Further, the returns do not survive modest transaction costs out-of-sample. We identify time series momentum as the single common factor driving returns across the range of strategies. Any abnormal return generated by technical trading rules is fully explained by time series momentum.

Can exchange-traded funds be profitably traded with the trading range breakout technical trading rule?

This study investigates whether the trading range breakout technical trading rule can be applied in exchange-traded funds. Using the samples of crude oil exchange-traded funds, namely USO, USL, UCO, and DBO, that track West Texas intermediate (WTI), the results indicate that the trading range breakout technical trading rule outperforms the benchmark buy-and-hold strategy. In addition, a volatility-based trading range breakout technical trading rule is proposed to enhance trading performance. The results suggest that the proposed TGARCH-MA can be used as a technical trading rule to assist investors and fund managers interested in using the trading range breakout in exchange-traded funds.