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The profitability of technical trading rules in the Bitcoin market
Abstract
We apply seven trend-following indicators to assess the profitability of technical trading rules in the Bitcoin market. Using daily price data from July 2010 to January 2019, our main results show that specific technical analysis trading rules, mainly trading range breakout, contain significant forecasting power for Bitcoin prices, allowing the outperformance of the buy-and-hold strategy through the Sharpe ratio computed via the bootstrapping method. Results from various sub-periods, representing normal and boom markets, generally confirm our main finding and show that the added value of the trading range breakout rule delivers outperformance in strongly trending markets.
... The goal of this strategy is to capture and profit from the momentum of the price movement after the breakout occurs. Relevant studies have revealed that momentum strategies, including the TRB trading rule, can profit in various markets (Gerritsen, 2016;Gerritsen, Bouri, Ramezanifar, & Roubaud, 2020;Kouaissah & Hocine, 2021;Yap, Lau, & Ismail, 2022;Yu, Nartea, Gan, & Yao, 2013), suggesting that TRB rule can be an effective trading rule for grasping momentum movement in various markets. ...
... Research has shown that momentum strategies, including TRB, can be profitable in various markets and asset classes. In addition, TRB can be an effective trading strategy for capturing momentum in various markets and asset classes (Gerritsen, 2016;Gerritsen et al., 2020;Kouaissah & Hocine, 2021;Yap et al., 2022;Yu et al., 2013). As such, market participants can use TRB as a tool to achieve their goal of greater profits in the stock market. ...
... Previous research has already highlighted the effectiveness of momentum strategies for various asset classes, including stocks and cryptocurrencies (Berggrun, Cardona, & Lizarzaburu, 2020;Gerritsen et al., 2020;Lai & Lau, 2010). However, in contrast to previous research shown above, the impressive findings revealed in Panel B of Table 2 (particularly these two cases of breaking below the 150-day low and breaking below the 200-day low) are derived from using contrarian strategies instead of momentum strategies, as shown that the results displayed in Panel B of Table 2 (using contrarian strategies) outperform those display in Panel A of Table 2 (using momentum strategies). ...
... So, I test Parabolic SAR, Directional Movement, Moving Averages, RSI, Stochastic, MACD, Williams together, effectively evaluating which of the them is better suited for predicting bitcoin prices. In addition, I extend and complement previous studies on technical trading analysis in cryptocurrency markets [36][37][38] by applying two more parameterized trading rules (Parabolic SAR and Directional Movement). The parabolic SAR attempts to give traders an edge by highlighting the direction an asset (bitcoin) is moving. ...
... Their finding indicates that using big data and technical analysis can help predict Bitcoin returns that are hardly driven by fundamentals. [37] study the profitability of technical trading rules in the Bitcoin market. They use seven trend-following indicators on daily data from July 2010 to January 2019. ...
... My results (after deduction of transactions costs) are in line with the existing literature on the performance of technical trading rules [39,37,40,41,43,46,66,36,48,50,51]. However, the findings can be seen as new evidence against the market efficiency of Bitcoin extending the aforementioned studies that consider the predictability of Bitcoin prices based on attention, trading volume and uncertainty. ...
This study examines the profitability of nine novel technical trading rules in the Bitcoin market over the period 2010 to 2021. The technical rules that will be explored are variations of moving averages , Parabolic SAR, Directional Movement, RSI, Stochastic MACD and Williams. I compare technical trading strategies employing traditional standard tests and bootstrap methodology under GARCH (1,1) model. The results indicate that the examined rules have indeed a predictive power in the Bitcoin market. Overall, trading strategies based on technical indicators significantly outperform the buy-and-hold benchmark. My findings contradict the Efficient Market Hypothesis as traders and investors can gain abnormal returns using various trading strategies on the cryptocurrency ecosystem.
... Technical analysis (TA), which focuses on the recognition of repeating mathematicallydefinable price patterns, is one method of forecasting. Recently, a new strand of cryptocurrency literature has studied the profitability of TA returns beyond buy-and-hold in cryptocurrency markets (Corbet et al., 2019;Grobys et al., 2020;Gerritsen et al., 2020;Jaquart et al., 2021;Svogun and Bazán-Palomino, 2022). Despite this evidence, scholars have paid relatively little attention to the drivers of TA profits. ...
... Svogun and Bazán-Palomino (2022) examine sixty-nine Breakout (BO) and M oving Average (MA) rules in daily and 1-minute time frequencies, pooled from Corbet et al. (2018), Grobys et al. (2020), andGerritsen et al. (2020), including and not including transaction costs in five cryptocurrencies. Their results showed that, adjusted by transaction costs, daily TA rules produce adjusted return above buy-and-hold more often than that of intra-day trade rules. ...
... The M A rule is defined by short period M A (M A S ), a long period M A (M A L ), and a band parameter (band param ), either 0 or 0.01. Following the notation of Gerritsen et al. (2020), an MA rule is defined as follows: ...
The cryptocurrency literature on technical analysis has largely ignored drivers of technical analysis return adjusted by transaction costs (i.e., adjusted returns). To that end, we propose a Heterogeneous Autoregressive Distributed Lag Model of Returns (HARDL-R) to examine the impact from EPU, VIX, and SP500 returns to adjusted returns. We provide evidence that these three drivers matter during bubble periods compared to non-bubble periods. When not differentiating bubble periods, we find that VIX is the only driver influencing the dynamics of adjusted returns from 2016 to 2021. These findings remain relatively stable after controlling for the volume of transactions. JEL Classification : G14, G20, G30, G32
... To make money, investors aim to create trend trading rules or strategies based on technical analysis to respond quickly to price changes, either between days or within a day. A new strand of the cryptocurrency literature has explored the profitability of technical trading rules, finding positive returns with Bitcoin (Miller et al., 2019;Corbet et al., 2019;Gerritsen et al., 2020) and other major cryptocurrencies Ahmed et al., 2020). Their results might be evidence of inefficiency in crytocurrency markets, and challenge the weak form of the efficient market hypothesis (EMH). ...
... This paper studies the impact of transaction costs and bubble periods on the returns of technical trading rules in cryptocurrency markets. To that end, we first extend and complement the most recent literature on technical trading analysis in cryptocurrency markets by applying 69 parameterized trading rules from Corbet et al. (2019), Gerritsen et al. (2020), and Grobys et al. (2020) to Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), and Bitcoin Cash (BCH), from 2016 to 2021. In particular, we calculate 69 trading returns, with and without transaction costs, in the form of moving average and breakout strategies in the 1-minute and 1-day frequency. ...
... This paper contributes to the growing literature on trading rules in cryptocurrency markets in several ways. First, previous literature has used either daily Gerritsen et al., 2020;Grobys et al., 2020) or intraday (Miller et al., 2019;Corbet et al., 2019) data without transaction costs. We close this gap in the literature by calculating trading returns with transaction costs in both the 1-minute and 1-day price frequency. ...
The study of technical analysis in cryptocurrencies has largely ignored the implications of often high transaction costs and bubble periods on trade rule performance. We study the daily and 1-minute returns of 69 technical trade rules in the form of moving average and breakout strategies, with and without transaction costs, during price bubbles in the 2016-2021 period. For the most profitable trade rules, we find that bubble periods increase the likelihood that Ethereum, Ripple and Litecoin beat buy-and-hold, but not Bitcoin and Bitcoin Cash. Transaction costs decrease this likelihood for Ripple and Litecoin, but increase it for Bitcoin and Ethereum.
... More meaningfully, the study revealed that simple Moving Averages techniques are superior when dealing with daily data. 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), andDetzel et al. (2021). ...
... 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), andDetzel et al. (2021). ...
... Finally, the momentum effect in TA was investigated as a possible secondary mechanism to corroborate the results. The study's findings are similar to those of Gerritsen et al. (2020) and Grobys et al. (2020), who found that TTRs are more cost-effective than the buy-hold strategy when dealing with Bitcoin daily data. Their study investigates TA using trend-following tactics like the simple moving average. ...
Purpose
The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns.
Design/methodology/approach
The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020.
Findings
The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research.
Practical implications
The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market.
Originality/value
The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.
... Analisis teknikal adalah analisis pergerakan harga melalui data historis, yaitu harga pada saat open, harga close, harga tertinggi, harga terendah, dan volume yang diperdagangkan setiap waktunya (Baining & Fadhillah, 2017). Setiap indikator analisis teknikal yang digunakan memiliki perbedaan baik dalam keakuratan maupun nilai return yang dihasilkan (Pinakin & Manubhai, 2015), (Detzel et al., 2018), (Gerritsen et al., 2020), (Baining & Fadhillah, 2017), (Göncü et al., 2018), (Pramudya & Ichsani, 2020), (Abdul-Rahim et al., 2016), (Sešek, 2018), (Resta et al., 2020), (Natannael, 2016), (Teguh Imano et al., 2019), (Corbet et al., 2019), (Nandini & Samal, 2020), (Lee et al., 2020). Hal ini dikarenakan parameter yang digunakan setiap analisis teknikal berbeda serta adanya peristiwa tidak terduga seperti virus covid-19 yang membuat para investor panik akan terjadinya hal itu. ...
... Hasil penelitian ini sejalan dengan penelitian yang dilakukan oleh (Pinakin & Manubhai, 2015), (Detzel et al., 2018), (Gerritsen et al., 2020), (Baining & Fadhillah, 2017), (Göncü et al., 2018), (Pramudya & Ichsani, 2020), (Abdul-Rahim et al., 2016), (Sešek, 2018), (Resta et al., 2020), (Natannael, 2016), (Teguh Imano et al., 2019), (Corbet et al., 2019), (Nandini & Samal, 2020), (Lee et al., 2020) yang mengemukakan bahwa setiap analisis teknikal memiliki perbedaan baik terhadap keakuratan maupun terhadap nilai return yang diahsilkan. ...
Sebelum mengambil keputusan dalam melakukan investasi, seorang investor harus mengetahui analisis teknikal. Penelitian ini bertujuan untuk mengetahui perbedaan keakuratan dan nilai return yang dihasilkan pada cryptocurrency periode 2019 – 2020 dengan menggunakan analisis teknikal. Jenis penelitian ini menggunakan pendekatan kuantitatif dengan metode komparatif. Sampel penelitian ini menggunakan purposive sampling, sehingga diperoleh 2 jenis cryptocurrency yang diperdagangkan pada platform Indodax. Teknik analisis data penelitian ini menggunakan platform Indodax untuk menganalisa harga dan dibantu dengan Microsoft Excel. Hipotesis dalam penelitian ini menggunakan uji beda Kruskal-Wallis dengan bantuan software SPSS 25. Hasil dari penelitian ini menunjukkan bahwa analisis teknikal moving average, bollinger band, dan rekative strength index baik dalam keakuratan ataupun nilai return yang hasilkan memiliki perbedaan secara signifikan pada cryptocurrency periode 2019 – 2020 dari segi statistik. Hal ini terjadi karena parameter yang digunakan setiap indikator analisis teknikal berbeda serta terjadinya covid-19 yang telah diumumkan sebagai global poandemic.
... In general, the results indicate that employing strategies based on technical indicators is justified. Gerritsen et al. [2020] investigated whether seven selected technical trading rules may outperform a "buy-and-hold" strategy in the BTC market using daily data. Their results indicate that specific trading rules, mainly trading range breakout, outperform the "buy-and-hold" strategy. ...
... In general, the research findings indicate inefficiencies in the BTC and ETH markets as it is possible to consistently generate higher rates of returns and Sharpe ratios on active strategies based on fundamental factors than the passive strategy. Consequently, the results corroborate these obtained by other researchers [Huang et al., 2019;Gerritsen et al., 2020;Resta et al., 2020;Detzel et al., 2021;Hudson and Urquhart, 2021]. ...
This article sheds new light on the informational efficiency of the cryptocurrency market by analyzing investment strategies based on structural factors related to on-chain data. The study aims to verify whether investors in the cryptocurrency market can outperform passive investment strategies by applying active strategies based on selected fundamental factors. The research uses daily data from 2015 to 2022 for the two major cryptocurrencies: Bitcoin (BTC) and Ethereum (ETH). The study applies statistical tests for differences. The findings indicate informational inefficiency of the BTC and ETH markets. They seem consistent over time and are confirmed during the COVID-19 pandemic. The research shows that the net unrealized profit/loss and percent of addresses in profit indicators are useful in designing active investment strategies in the cryptocurrency market. The factor-based strategies perform consistently better in terms of mean/median returns and Sharpe ratio than the passive "buy-and-hold" strategy. Moreover, the rate of success is close to 100%.
... However, these returns are not statistically significant. Similarly, Gerritsen et al. (2020) use daily Bitcoin data and seven trading rules, including the so-called trading range breakout and Bollinger Bands, to analyse the profitability of technical trading rules in the Bitcoin market. They show that the trading range breakout rule outperforms the others in strongly trending markets. ...
... Additionally, Brock et al. (1992) show that this rule has significant predictive power for US equity index returns. Their results were confirmed by Yu et al. (2013) for the Malaysian market and Gerritsen et al. (2020) for Bitcoin prices. ...
Since the formulation of the Efficient Market Hypothesis, countless studies have been developed that try to either prove or refute it. Event studies, analysing the impact of different events on asset prices, are one of the most important research fields but there is a lack of evidence on cryptocurrencies. For that reason, we analyse the existence of over- and under- reaction effects on Bitcoin after hourly price shocks defined by filter sizes. We also do this using three alternative approaches. Our results show clear evidence of overreaction after negative shocks. We also observe that these overreactions tend to be greater as more hours pass after the event, with those that occur between 6 and 24 hours after the event being especially important. These results have important economic implications because they show that investors would be able to develop a profitable trading strategy simply by focusing on investing after negative shocks.
... However, it is unclear if this is relevant for forecasting Bitcoin price volatility. Recent studies such as Gerritsen et al. (2020) and Grobys et al. (2020) have applied several technical trading rules in the cryptocurrency market and showed that trading range breakout contains significant forecasting power for Bitcoin prices; still, their main focus has been on prices and not volatility. Similarly, Hudson and Urquhart (2021) show that technical trading rules have a predictive ability and power to generate better risk-adjusted returns in the cryptocurrency market, but their results are insignificant in the case of Bitcoin when the out-of-sample F I G U R E 1 The Bitcoin's trading volume and price ($) period is considered. ...
... Various studies of forecasting argue that technical indicators exhibit superior out-of-sample performance in the financial markets, such as equity premium (Neely et al., 2014) and commodity price . Inspired by their works and recent work considering the cryptocurrency market (Gerritsen et al., 2020;Grobys et al., 2020;Hudson & Urquhart, 2021), we first explored the forecasting ability of technical indicators for predicting Bitcoin volatility. ...
Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state.
... • Directional Index (DX) measures the trend strength by quantifying the amount of price movement. • The rate of change (ROC) is the speed at which variable changes over a period [20]. • Ultimate Oscillator (ULTSOC) measures the price momentum of an asset across multiple timeframes [21]. ...
... • Williams %R (WILLR) measures overbought and oversold levels [43]. • On Balance Volume (OBV) measures buying and selling pressure as a cumulative indicator that adds volume on up-days and subtracts volume on down-days [20]. • The Hilbert Transform Dominant (HT) is used to generate inphase and quadrature components of a detrended real-valued signal to analyze variations of the instantaneous phase and amplitude [41]. ...
Designing profitable and reliable trading strategies is challenging in the highly volatile cryptocurrency market. Existing works applied deep reinforcement learning methods and optimistically reported increased profits in backtesting, which may suffer from the false positive issue due to overfitting. In this paper, we propose a practical approach to address backtest overfitting for cryptocurrency trading using deep reinforcement learning. First, we formulate the detection of backtest overfitting as a hypothesis test. Then, we train the DRL agents, estimate the probability of overfitting, and reject the overfitted agents, increasing the chance of good trading performance. Finally, on 10 cryptocurrencies over a testing period from 05/01/2022 to 06/27/2022 (during which the crypto market crashed two times), we show that the less overfitted deep reinforcement learning agents have a higher Sharpe ratio than that of more over-fitted agents, an equal weight strategy, and the S&P DBM Index (market benchmark), offering confidence in possible deployment to a real market.
... The problem of efficient management of the investment strategies portfolio arises at this stage. Considerably high requirements concerning the possibilities of constant improvement, riskiness, and profitability to the formation of such portfolios designed for cryptocurrency trading are imposed [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. Taking into account that high-frequency trading (HFT) [58] trading systems become the most common variant of algorithmic trading in the cryptocurrency market, there are specific risks which should be taken into account when forming portfolios of trading strategies. ...
... It provides multiple solutions of nonlinear optimization problems, using various initial conditions, to find the global extreme, which is used for the operative re-forming of the portfolio strategies, to select the best one for a current situation and to follow the market signals that are generated by the technical analysis indicators. The development supposes the forecasting of currency prices that are contained in the portfolio using a multi-model approach, as well as the methods of structural and parametric adaptation presented in the studies [52][53][54][55][56][57][58]63]. ...
The article describes the original information technology of the algorithmic trading, designed to solve the problem of forming the optimal portfolio of trade strategies. The methodology of robust optimization, using the Ledoit–Wolf shrinkage method for obtaining stable estimates of the covariance matrix of algorithmic strategies, was used for the formation of a portfolio of trade strategies. The corresponding software was implemented by SAS OPTMODEL Procedure. The paper deals with a portfolio of trade strategies built for highly-profitable, but also highly risky financial tools—cryptocurrencies. Available bitcoin assets were divided into a corresponding proportion for each of the recommended portfolio strategies, and during the selected period (one calendar month) were used for this research. The portfolio of trade strategies is rebuilt at the end of the period (every month) based on the results of trade during the period, in accordance with the conditions of risk minimizing or income maximizing. Trading strategies work in parallel, being in a state of waiting for a relevant trading signal. Strategies can be changed by moving the parameters in accordance with the current state of the financial market, removed if ineffective, and replaced where necessary. The efficiency of using a robust decision-making method in the context of uncertainty regarding cryptocurrency trading was confirmed by the results of real trading for the Bitcoin/Dollar pair. Implementation of the offered information technology in electronic trading systems will allow risk reduction as a result of making incorrect decisions or delays in making decisions in a systemic trading.
... Thirdly, we propose profitable trading opportunities with intraday trading strategies based on the functional forecasting methods, and then evaluate their performance. The results show the positive performance of such strategies, which is somewhat comparable to recent evidence on the profitability of technical trading rules in the Bitcoin market (e.g., Corbet, Eraslan, Lucey, & Sensoy, 2019;Gerritsen, Bouri, Ramezanifar, & Roubaud, 2019;Nakano et al., 2018). Fourthly, we provide overall results that point to the inefficiency of Bitcoin by showing how it is possible to develop a profitable trading strategy based on historical intraday prices. ...
... Additional analysis might be needed to optimize the accuracy of the price prediction and increase the risk-adjusted performance of the trading strategy. Therefore, future studies can consider including information on technical indicators such as trading range breakout (Gerritsen et al., 2019), and machine learning techniques (Nakano et al., 2018) in order to potentially increase the risk-adjusted return of the trading strategy. ...
Motivated by the potential inferences from intraday price data in the controversial Bitcoin market, we apply functional data analysis techniques to study cumulative intraday return (CIDR) curves. First, we indicate that Bitcoin CIDR curves are stationary, non-normal, uncorrelated, but exhibit conditional heteroscedastic, although we find that the projection scores of CIDR curves could be serially correlated during some certain periods. Second, we show the possibility of predicting the CIDR curves of Bitcoins based on the projection scores and then assess the forecasting performance. Finally, we utilize the functional forecasting methods to explore the intraday trading opportunities of Bitcoins and the results provide evidence of profitable trading opportunities based on intraday trading strategies, which confronts the efficient market hypothesis.
... Yu (2019) demonstrated that the leverage effect significantly effects future Bitcoin volatility. Other studies focused on the crypto-market factors such as cryptocurrency's technical indicators (e.g., Gerritsen et al., 2020) and the impact of media attention (e.g. Philippas et al., 2019). ...
This paper analyzes the response of cryptocurrency returns to the movement of economic policy uncertainty (EPU) and stock market volatility (VIX), as well as a few macroeconomic variables: gold price, interest rate, inflation rate, and oil price. Vector error correction model and regression model are applied to examine the linkage between these variables using data from 2015 to 2022. The analysis reveals that the selected variables have a positive and significant impact on cryptocurrency returns. This suggests that cryptocurrency can be considered a safe haven for investment. The paper also suggests a number of policies to ensure the protection of investment, control money supply and stock market instability, stabilize economic uncertainty, and systematize economic variables. This paper advocates a well-connected network and active participation of stakeholders such as government, central bank, security exchange, and financial institutions will help to streamline irrational movements and enhance the acceptability of cryptocurrencies through the framing and implementation of necessary regulations.
... For the Odean indicator, this is the average of the opening and closing price of the asset in the respective time window, for this study an hourly window was used, in contrast to a daily window that is typically the basis for calculating technical indicators. For the technical indicators established buy and sell rules for ̄ are applied, e.g., MACD buy (GR) for values greater zero, sell (LR) for below Table 6 for the complete ruleset similar to a previous study by Gerritsen et al. (2019). ...
Investors commonly exhibit the disposition effect—the irrational tendency to sell their winning investments and hold onto their losing ones. While this phenomenon has been observed in many traditional markets, it remains unclear whether it also applies to atypical markets like cryptoassets. This paper investigates the prevalence of the disposition effect in Bitcoin using transactions targeting cryptoasset exchanges as proxies for selling transactions. Our findings suggest that investors in Bitcoin were indeed subject to the disposition effect, with varying intensity. They also show that the disposition effect was not consistently present throughout the observation period. Its prevalence was more evident from the boom and bust year 2017 onwards, as confirmed by various technical indicators. Our study suggests irrational investor behavior is also present in atypical markets like Bitcoin.
... Cryptocurrency returns have been the subject of recent research, as investors see cryptocurrencies as a viable alternative asset in financial markets (Yen and Cheng, 2021). Market and macro factors are core predictors of cryptocurrency development (Gerritsen et al., 2020). The EPU index was developed by Baker et al. (2016) because investors may lose confidence in their monetary system or become worried about the economy as a whole if government policies are uncertain (Demir et al., 2018). ...
This study analyzes the impact of economic policy uncertainty (EPU) on cryptocurrency returns for a sample of 100 highly capitalized cryptocurrencies from January 2016 to May 2021. The results of the panel data analysis and quantile regression show that increases in global EPU have a positive impact on cryptocurrency returns for lower cryptocurrency returns quantiles and an adverse impact for upper quantiles. In line with the existing literature, the Covid-19 pandemic resulted in higher returns for cryptocurrencies. Inclusion of a Covid-19 dummy in the models strengthened the impact of EPU on cryptocurrency returns. Furthermore, the relationship between the change in EPU and cryptocurrency returns was direct in the pre-Covid-19 period but inverse in the post-Covid-19 period. These results imply that cryptocurrencies act more like traditional financial assets in the post-Covid-19 era.
... Gerritsen et al. analyzed daily price data of the Bitcoin market from July 2010 to January 2019 using different trend-following indicators. The authors found the profitability of specific trading rules is stable across the sample and in better trading markets than just buying and holding strategies, which means when investors use a specific trading combination to trade, they often earn far more than the average trader [8]. Sahoo gives a critical analysis of Bitcoin's continued growth as a cryptocurrency in the future. ...
Based on the global economic downturn, the price of Bitcoin has recovered, and new investors are constantly pouring into the Bitcoin market. To ensure that new investors have a basic understanding of Bitcoin and avoid unnecessary losses, this article will analyze Bitcoin's Trading Mechanisms, Price Influencers, and Trading Recommendations The main research finds that when Bitcoin is used as a currency, commodity, risk asset, and digital gold, the price factors are quite different. For example, when it is used as a risk asset, its price is affected by capital flows, market sentiment, and policy regulation. The multiple nature of Bitcoin will have a greater impact on investors' judgments. Through research, it is found that Bitcoin is still the virtual currency with the largest volume, the largest transaction volume, and the brightest future development prospects. The diversity of its nature brings risks but also brings a variety advantages of to the single currency or other financial products.
... Although much research cover both the challenge of predicting stock market price movements and the creation of effective trading techniques based on those recommendations, which made it critical to validate the relevance of such studies in new and emerging markets, particularly the Cryptocurrency market. Much research has been conducted solely to investigate the behaviour of the famous decentralized digital currency, i.e., Bitcoin (e.g., Gerritsen et al.;2020, Atsalakis et al.;2019, Valencia et al.;2019, Huang et al.;2019, Adcock and Gradojevic;. Intending to forecast cryptocurrency market movements for Bitcoin, Ethereum, Ripple, and Litecoin, the researchers' Valencia, Gómez-Espinosa and Valdés-Aguirre (2019) suggested using conventional ML algorithms on publicly available social media data (Twitter). ...
In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.
... Brière et al. (2015), Dyhrberg (2016), and Bouri et al. (2017) examine the hedging and diversification capabilities of bitcoin. The potential profitability of applying technical trading rules versus a buy-and-hold strategy in the bitcoin market is assessed in Gerritsen et al. (2020). Chen et al. (2020) study the predictive accuracy of machine learning models considering the sample's granularity and feature dimensions for bitcoin prices. ...
In this paper we provide measurements of the underlying complex dynamic structure in bitcoin market activity. More specifically, we present empirical estimates of extremity (large fluctuations) in bitcoin market action variables such as price returns, trading volume, and number of trades using a block maxima estimation for the tail exponent. We juxtapose the estimated scale exponents in bitcoin market dynamics to those for traditional financial assets as well as to the theoretical predictions for stock market activity variables as modelled by Gabaix et al. (2006). Based on a dynamic stability analysis, the tail exponent for bitcoin price returns appears to have undergone a substantial temporal shift in the sample period.
... Several compelling rationales for technical trading rules and heuristics have been proposed, including imperfect information (Brown and Jennings, 1989;Blume et al., 1994), private information propagation (Treynor and Ferguson, 1985), selffulfilling prophecies (Taylor and Allen, 1992), non-linear dynamics in the spirit of chaos theory (Clyde and Osler, 1997), limits to arbitrage and trading costs (Bessembinder and Chan, 1998), price clustering (Osler, 2003), arbitrage-like properties of technical trading (Jackson and Ladley, 2016), heterogeneous agent beliefs (Zheng et al., 2018), and informational uncertainty premia (Han et al., 2013). However, early empirical research on technical analysis performance has been subsequently criticised for use of anecdotal evidence, data mining, and unsatisfactory quantitative rigour (Park and Irvin, 2007), and some behavioural studies align technical trading rules with common cognitive biases (Zielonka, 2004 (Brock et al., 1992;Hudson et al., 1996;Lo et al., 2000;Wong et al., 2003;Wang, 2004;Hu and Kuan, 2005;Ulku and Prodan, 2015;Bley and Saad, 2020), foreign exchange (Osler, 2003;Schulmeister, 2006;Coakley et al., 2016;Frommel and Lampaert, 2017;Zarrabi et al., 2017), and commodity (Holmberg et al., 2013;Hudson et al., 2017) markets, as well as novel cryptocurrency markets (Corbet et al., 2019;Gerritsen et al., 2020;Grobys et al., 2020;Anghel, 2021;Hudson and Urquhart, 2021 Osler (2000) x x x MacLean (2005) x x x x Bhattacharya and Kumar (2006) x x x x x x Erdogan and Doguc (2006) x x x x Brown (2008) x Soeini et al. (2012) x x x x x x Glover et al. (2013) x x x x x Otake and Fallou (2013) x x x Kumar (2014) x x x x x Kempen (2016) x Davies et al. (2019) x x x Ramli et al. (2020) x x x Sethi et al. (2020) x x x x x Lusindah and Sumirat (2021) x Gurrib et al. (2022) x x x x x Tsinaslanidis et al. (2022) x x x x Table 1. Fibonacci retracement levels in the literature. ...
... Positive shocks that increase the demand and price of cryptocurrency, cause more volatility than negative shocks (Baur and Dimpfl, 2018). In the absence of a transparent price discovery mechanism (Gerritsen et al., 2020;Kumar, 2020), investors resort to "pump and dump" culture and are subjected to herding owing to "FOMO" (fear of missing out) from the crowd. Additionally, fractional trading in cryptocurrencies has resulted in small ticket size investments by investors who lack adequate information and jump on the bandwagon during bull periods to make quick profits. ...
This paper examines the evidence of herding in the revolutionary cryptocurrency market for the period from January 2017 to December 2020. The study employs quantile regression technique for investigating herd behaviour during market asymmetries of rising and falling returns, extreme market returns, high volatility, and the exogenous event of the COVID-19 pandemic. The results provide evidence of pronounced herding during the bull phase, extreme down-markets, and high volatility. These results indicate that herd hunch is prevalent in the cryptocurrency market as investors exhibit imitation while ignoring their own knowledge and beliefs. Also, the phenomenon is more vividly observed during the panic period of COVID-19.
... 5 Stambaugh, Yu, and Yuan (2015) show that the combined effects of arbitrage risk and arbitrage asymmetry (i.e., relatively less arbitrage activity directed towards overpriced relative to underpriced stocks) can result in a negative relationship between aggregate idiosyncratic volatility and expected market return. 6 While Fama and Blume (1966) and Jensen and Benington (1970) concluded that technical analysis is not useful in generating profits, Brock, et al. (1992), Lo, et al. (2000), Shynkevich (2012), Smith et al. (2016), Marshall et al. (2017), Nazário et al. (2017), Kang, et al. (2019), Gerritsen, et al. (2020), and many other studies have documented strong evidence of technical trading profits in the equity markets. Bessembinder and Chan (1995), Ito (1999), and Ratner and Leal (1999) show the profitability of trading rules in emerging equity markets. ...
Using a testable Slutsky equation derived from a formal utility maximization model of portfolio choice under uncertainty, we examine whether the momentum component in daily returns is induced by the interaction of the intertemporal risk-return tradeoff and investor tendency to correct prior mispricing. We find that a substantial portion of short-horizon momentum is generated by the asymmetric intertemporal risk-return tradeoff that the positive risk-return relation is strengthened conditional on a prior negative market return but is attenuated conditional on a prior positive market return. With the observation of a highly positive correlation between the trading signals and price change dummies, we further explore the link between technical trading profits and the two pricing factors. Our empirical results provide strong evidence that the profits associated with technical strategies come from exploiting the same momentum component induced by the interaction of the risk-return relation and investor adjustment behavior. We conclude that technical trading profits are the result of rational pricing factors and therefore not evidence of market inefficiency.
... Chen et al (2021) [4] also implemented an MV portfolio model with their optimized XGBoost as preselection. Ma et al (2021) [5] constructed MV and Omega portfolios based on the prediction results 75 generating from multiple deep learning models. These studies detailed investigate the superiority of artificial intelligence techniques on forecasting but ignoring to overcome the conservatism of robust portfolio models through machine learning algorithms and deep learning algorithms. ...
Conservatism is the notorious problem of the worst-case robust portfolio optimization, and this issue has raised broad discussion in academia. To this end, we propose the hybrid robust portfolio models under ellipsoidal uncertainty sets in this paper, where both the best-case and the worst-case counterparts are involved. In the suggested models, we introduce a trade-off parameter to adjust the portfolio optimism level. Machine learning algorithms including Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) are used to evaluate the potential market movements and provide forecasting information to generate the hyperparameter for modeling. Additionally, we develop a clustering-based algorithm for properly constructing joint ellipsoidal uncertainty sets to reduce conservatism further. In the modeling phase, we design the hybrid portfolios based on variance (HRMV) and value at risk (VaR) and prove the equivalent relationship between the hybrid robust mean-VaR model (HRMVaR) and the hybrid robust mean-CVaR (conditional value at risk) according to the existing research. The US 12 industry portfolio data set retrieved from Kenneth R. French is employed for the in-sample and out-of-sample numerical experiments. The experimental results demonstrate the effectiveness and robustness of the proposed portfolios, where HRMV models have better Sharpe ratios and Calmar ratios than the corresponding nominal mean–variance model, and HRMVaR models outperform the baseline VaR-based portfolios in terms of returns. Sensitivity analysis supports the superiority of the joint ellipsoidal uncertainty set Uδ2, where the proposed portfolios constrained with Uδ2 show stable risk characteristics.
... For example, Zhang et al. (2021) discussed the properties of Bitcoin and its interplay with other conventional assets for the sake of asset allocation and risk management. Gerritsen et al. (2020) applied a number of trend indicators to assess the profitability of technical trading rules in the bitcoin market. Naeem et al. (2021) examined the asymmetric efficiency of a number of cryptocurrencies, including bitcoin. ...
The study investigates and develops the ability of the extreme value theory (EVT) to predict bitcoin return. EVT is used to deal with rare but extreme events, such as severe losses or excessive damages. It is being used as a powerful statistical tool in various disciplines, including finance, engineering, environmental science, and actuarial science. As the largest among all cryptocurrencies in existence, bitcoin's behavior is primarily characterized by great volatility. Predicting bitcoin return is complex and important, primarily because of the extreme nature of its return. There is not enough substantial research involving EVT in bitcoin analysis. This study has three objectives. First, confirming the extreme nature of bitcoin return by various statistical tests; second, modeling the bitcoin return using two different EVT approaches (block maxima approach and peak over threshold approach); and third, assessing uncertainties by predicting bitcoin return levels for 5-, 10-, 20-, 50-, and 100-years with a 95% confidence interval using both of these methods. These results could certainly serve policymakers and investors, as these return levels can be useful in characterizing bearish and bullish trends and predicting the same. Moreover, these can serve as starting points for future studies regarding the stationary and non-stationary properties of bitcoin return.
... Their market participants are mostly young individuals, with a low level of education, an "animal" spirit, large cultural differences, and their information is irregular. Furthermore, the cryptocurrency markets have weak regulatory frameworks and weak information disclosure, and there is a lack of fundamental models to evaluate the price of a cryptocurrency (Gerritsen et al., 2020). These malfunctions can push crypto-traders to ignore their own opinions and herd toward the market consensus, leading to abnormal volatility. ...
We provide an empirical analysis of herding behavior in cryptocurrency markets during COVID-19 and periods of cyber-attacks, differentiating between fundamental and nonfundamental herding. The results show that herding behavior is driven by fundamental information during the full sample period and the cyber-attack days. However, herding is not prevalent during the COVID-19 outbreak, either when reacting to fundamental or nonfundamental information. This finding suggests heterogeneity in the behaviors of participants in the cryptocurrency markets during the COVID-19 period.
... Empirical approaches emerged under the umbrella of "Chartism" (e.g., Berardi 2011). Chartists-or empirically minded technical analysts-have used extrapolative rules to discover statistical regularities in the time series for prices (e.g., Frankel and Froot 1990;Lo 2004;Gerritsen et al. 2020). Additionally, a burgeoning literature on agent-based financial market models emerged, allowing various interactions between chartists and fundamentalists (e.g., Day and Huang 1990). ...
Machine learning in finance has been on the rise in the past decade. The applications of machine learning have become a promising methodological advancement. The paper’s central goal is to use a metadata-based systematic literature review to map the current state of neural networks and machine learning in the finance field. After collecting a large dataset comprised of 5053 documents, we conducted a computational systematic review of the academic finance literature intersected with neural network methodologies, with a limited focus on the documents’ metadata. The output is a meta-analysis of the two-decade evolution and the current state of academic inquiries into financial concepts. Researchers will benefit from a mapping resulting from computational-based methods such as graph theory and natural language processing.
... In related literature, Hudson and Urquhart (2019) and Gerritsen et al. (2020) show that technical analysis can be used to predict Bitcoin prices. Moreover, Bouri and Gupta (2019), Kraaijeveld and De Smedt (2020), and Trimborn and Li (2021) find that Twitter and other crowd sentiment have predictive power for returns of Bitcoin and other cryptocurrencies. ...
Using a hand-collected dataset containing bullish, neutral, and bearish predictions for Bitcoin published by crypto experts, we show that neutral and bearish predictions are followed by negative abnormal returns whereas bullish predictions are not associated with nonzero abnormal returns. Based on all outstanding predictions, we compute prediction changes relative to (i) the latest issued prediction and (ii) the outstanding consensus prediction. Downward revisions are followed by negative abnormal returns. We conclude that crypto experts are skilled information intermediaries on the Bitcoin market.
The higher discharge of carbon emissions in environment is endangering human life as well as eco system. With the emergence of digital technology, crypto trading and blockchain technologies are also causing the paradigm shift in technological needs, however, in parallel, creating unresolved environmental problems especially in the top 10 crypto-friendly Asian economies due to the higher percentage of poisonous emissions. Thereby, the study attempts to scrutinize the nexus among crypto-trading, environmental degradation, economic growth and energy usage. CO2 emissions are taken as a proxy of environmental degradation in the context of crypto friendly Asian economies. The data extracted from WDI and coin market cap covering the period from 2012-2020. The article applied CS-ARDL technique in order to find the association between variables in short and long run. Granger Causality test is also applied to find out the causality among constructs. Findings expose that bi-directional causality exists between crypto trading and carbon emission and crypto trading and energy usage, hence, disturbing environmental sustainability due to high emissions. The paper suggests policy makers and other institutional officers to de-socialize those block chain technologies which consumes high energy and use green technology as their substitute. This helps in the reduction of environmental damages.
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.
Purpose
This study reviews existing cryptocurrency research to provide answers to three puzzles in the literature. First, is cryptocurrency more like gold (i.e., a commodity) or should it be classified as a new financial asset? Second, can we apply our knowledge of the traditional capital market to the emerging cryptocurrency market? Third, what might be the future of cryptocurrency?
Design/methodology/approach
Bibliometric analysis is used to assess 2,098 finance-related cryptocurrency publications from the Web of Science (WoS) Core Collection database from January 2009 to April 2022. Three key research streams are identified, namely, (1) cryptocurrency features, (2) behaviour of the cryptocurrency market and (3) blockchain implications.
Findings
First, cryptocurrency should be viewed and regulated as a new asset class rather than a currency or a new commodity. While it can provide diversification benefits to the portfolio, cryptocurrency cannot work as a safe haven asset. Second, crypto markets are typically inefficient. Asset bubbles exist and are exacerbated by behavioural finance factors. Third, cryptocurrency demonstrates increasing potential as a medium of exchange and store of value.
Originality/value
Extant review papers primarily study one or two particular research topics, overlooking the interaction between topics. The few existing systematic literature reviews in this area typically have a narrow focus on trend identification. This study is the first study to provide a comprehensive review of all financial-related studies on cryptocurrency, synthesising the research findings from 2,098 publications to answer three cryptocurrency puzzles.
Recently, the algorithmic trading of financial assets is rapidly developing with the rise of deep learning. In particular, deep reinforcement learning, as a combination of deep learning and reinforcement learning, stands out among many approaches in the field of decision-making because of its high performance, strong generalization, and high fitting ability. In this paper, we attempt to propose a hybrid method of recurrent reinforcement learning (RRL) and deep learning to figure out the algorithmic trading problem of determining the optimal trading position in the daily trading activities of the stock market. We adopt deep neural network (DNN), long short-term memory neural network (LSTM), and bidirectional long short-term memory neural network (BiLSTM) to automatically extract higher-level abstract feature information from sequential trading data, respectively, and then generate optimal trading strategies by interacting with the environment in a reinforcement learning framework. In particular, the BiLSTM consisting of two LSTM models with opposite directions is able to make full use of the information from both directions in attempting to capture more effective information. In experiments, the daily data of Dow Jones, S&P500, and NASDAQ (from Jan-01, 2005 to Dec-31, 2020) are applied to verify the performance of the newly proposed DNN-RL, LSTM-RL, and BiLSTM-RL trading systems. Experimental results show that the proposed methods significantly outperform the benchmark methods, such as RRL and Buy and Hold, with higher scalability and better robustness. Especially, BiLSTM-RL performs better than other methods.
E-commerce is a disruptive technology that started as a replacement for information and has now evolved to include buying and selling goods. Family businesses are commercial enterprises in which decisions are impacted by older generations of a family who are related by blood, marriage, or adoption and who can influence the company's vision and willingness to pursue various goals. Family businesses have distinct priorities and outcomes because they were not established or managed like traditional commercial organizations. Family enterprises typically have a strong cultural orientation as contrasted to any other type of commercial firm's growth orientation. Decision-making is influenced by the expertise and knowledge of older generations. Therefore, we decided to identify the variable that influences family businesses' intentions to use and actual usage of e-commerce technologies, especially in India. A UTAUT model approach is used which included eight adoption-related theories. The variable perceived trust is added to the model because it has a direct effect on purchase intention.
Heaping praise on cryptocurrency network due to its transparency, portability, divisibility, and resistance to inflation, the crypto market has gained huge support from investors and traders worldwide. Given its rising popularity, most of the central banks today plan to launch their own cryptocurrencies. Such phenomenal growth trend has undoubtedly impacted academic research with hundreds of publications filling the academic space. This study provides a comprehensive overview of the academic trends and thematic dimensions of cryptocurrency research. Applying bibliometrics, we track ten thematic clusters identified as: cryptocurrency as a tool for risk management; decentralization of crypto transactions using blockchain and fintech; regulatory and digital framework of cryptocurrencies; market efficiency of cryptocurrencies; pricing efficiency of cryptocurrencies; price clustering and liquidity in crypto transactions; cryptocurrency as an investment asset; portfolio diversification using cryptocurrency; trading volume, return and volatility of cryptocurrencies; and role of information in the volatility of crypt prices. Along with an analysis of each of the thematic clusters, we provide directions for future research.
Blockchain and cryptocurrency are gradually going mainstream with new cryptocurrencies introduced every single day. The speculative nature of these digital assets expose their prices to large fluctuations. Trading these crypto-assets necessitate an adequate understanding of this emerging market as well as adequate tools to model the market risk and efficient allocation of funds. This may assist crypto investors in taking advantage of the highly volatile aspects of these assets. The portfolio consider in this study consists of six cryptocurrencies: four traditional cryptocurrencies (BTC, ETH, BNB and XRP) and two stablecoins (USDT and USDC). We examine the copula particle swarm optimization (CPSO) portfolio strategy against three other portfolio strategies, namely, the global minimum variance (GMV), the most diversified portfolio (MDP) and the minimum tail dependent (MTD). CPSO appears to be a promising strategy during extreme market conditions while GMV seem favorable during normal market conditions. Most importantly, hedge and safe-havens ability of the two stablecoins is clearly exhibited with CPSO, while their diversification property is inhibited.
This article explores the profitability of technical trading rules around the COVID-19 pandemic market meltdown for the S&P 500 index, Bitcoin, Comex gold spot, crude oil WTI, and the VIX. Trading rule profits are estimated from January to May 2020, including three sub-periods, on a high-frequency data set. The results reveal that the trading rules can beat the buy-and-hold trading strategy. However, only the Bollinger Bands and trading range break-out rules become profitable after transaction costs during the market crash. Moreover, it is found that composite trading signals effectively improve the profitability of technical analysis around the COVID-19 market crash.
We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investors’ demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
este artigo visa contribuir para o aumento do conhecimento do método de negociação
Ichimoku, através de evidências teórico-empíricas sobre a capacidade preditiva dessa dinâmica
de investimento. Apesar de essa dinâmica de investimento ter aparecido no Japão na década de
1930, só nos últimos anos começou a ganhar relevância para os investidores e académicos fora
do Japão, existindo ainda uma lacuna na existência de trabalhos de investigação académica. Na
persecução desse objetivo, estudaram-se cinco índices de mercados de capitais de diferentes zonas
geográficas, tendo sido analisadas 26.295 cotações diárias, testando-se diferentes estratégias de
negociação baseadas nas linhas Ichimoku. As estratégias de negociação produziram um conjunto
de 22.083 sinais de negociação, possibilitando avaliar a capacidade preditiva e performance do
sistema de negociação Ichimoku. O trabalho permitiu concluir que a dinâmica de negociação
Ichimoku fornece sinais de tendências de negociação, sendo que as estratégias implementadas
permitem criar valor para os investidores. Conclui-se também que a dinâmica Ichimoku apoia as
decisões de investimento e possibilita que os investidores reajam rapidamente no mercado bea�rish, sendo útil para sinalizar tendências e revertê-las. A estratégia baseada na linha chikou span
mostrou ser a mais rentável e a que propicia melhor remuneração por acréscimo de risco
este artigo visa contribuir para o aumento do conhecimento do método de negociação Ichimoku, através de evidências teórico-empíricas sobre a capacidade preditiva dessa dinâmica de investimento. Apesar de essa dinâmica de investimento ter aparecido no Japão na década de 1930, só nos últimos anos começou a ganhar relevância para os investidores e académicos fora do Japão, existindo ainda uma lacuna na existência de trabalhos de investigação académica. Na persecução desse objetivo, estudaram-se cinco índices de mercados de capitais de diferentes zonas geográficas, tendo sido analisadas 26.295 cotações diárias, testando-se diferentes estratégias de negociação baseadas nas linhas Ichimoku. As estratégias de negociação produziram um conjunto de 22.083 sinais de negociação, possibilitando avaliar a capacidade preditiva e performance do sistema de negociação Ichimoku. O trabalho permitiu concluir que a dinâmica de negociação Ichimoku fornece sinais de tendências de negociação, sendo que as estratégias implementadas permitem criar valor para os investidores. Conclui-se também que a dinâmica Ichimoku apoia as decisões de investimento e possibilita que os investidores reajam rapidamente no mercado bearish, sendo útil para sinalizar tendências e revertê-las. A estratégia baseada na linha chikou span mostrou ser a mais rentável e a que propicia melhor remuneração por acréscimo de risco.
In recent years, the tendency of the number of financial institutions to include cryptocurrencies in their portfolios has accelerated. Cryptocurrencies are the first pure digital assets to be included by asset managers. Although they have some commonalities with more traditional assets, they have their own separate nature and their behaviour as an asset is still in the process of being understood. It is therefore important to summarise existing research papers and results on cryptocurrency trading, including available trading platforms, trading signals, trading strategy research and risk management. This paper provides a comprehensive survey of cryptocurrency trading research, by covering 146 research papers on various aspects of cryptocurrency trading ( e . g ., cryptocurrency trading systems, bubble and extreme condition, prediction of volatility and return, crypto-assets portfolio construction and crypto-assets, technical trading and others). This paper also analyses datasets, research trends and distribution among research objects (contents/properties) and technologies, concluding with some promising opportunities that remain open in cryptocurrency trading.
This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.
Do behavioral factors mediate the relationship between industry returns and Bitcoin returns? We use four industry indices in technology, energy, clean energy, and banking, and the Sentiment index from Thomson Reuters Marketpsych Indices as a behavioral factor to investigate this question. We show that the sensitivities of technology and clean energy industry indices to Sentiment, positively and significantly, strengthen the relationship between sentiment and Bitcoin returns. By showing that behavioral factors mediate the association between the returns of industry indices and Bitcoin returns, we provide evidence that investors’ Sentiment captures the association between Bitcoin and sectors related to cryptocurrencies. Our results, however, do not support prior studies’ findings of a direct relationship between the industry indices and Bitcoin returns.
Using a sample of Taiwanese listed firm stocks during 1992–2018, this paper tests the profitability of the moving average (MA) technical analysis over the firm life cycle (FLC). Building on the Dickinson (2011) FLC measure of, the results indicate a U-shape of MA profitability over the FLC—MA abnormal performance is higher in the introduction and shakeout/decline stages but lower in the growth and mature stages. Such a U-shape remains after controlling for various risk factors, such as the Fama–French five factors, the momentum factor, and the liquidity factor. The results of this study are robust to alternative lag length specifications for the MA, life cycle proxy, sub-period selection, equally/valued weighted sorted life-cycle portfolio, and various market stats, such as bullish/bearish market periods, high/low market volatility, and expansion/recession economic cycles. The overall results support the information uncertainty hypothesis and the dynamic resource-based life-cycle theory, postulating that a trend-chasing MA strategy is more profitable for firms in the introduction and shakeout/decline stages that are characterized by greater information uncertainty and, thus, longer underreaction-driven price continuation.
This paper explores whether asset market equilibria in cryptocurrency markets do exist. In doing so, it distinguishes between privacy and non-privacy coins. Most recently, privacy coins have attracted increasing attention in the public debate as non-privacy cryptocurrencies, such as Bitcoin, do not satisfy some users’ demands for anonymity. Analyzing ten cryptocurrencies with the highest market capitalization in each submarket in the 2016–2018 periods, we find that privacy coins exhibit a distinct market equilibrium. Contributing to the current debate on the market efficiency of cryptocurrency markets, our findings provide evidence of market inefficiency. Moreover, the asset market equilibrium of privacy coins appears to originate from non-privacy coins with highest market capitalizations. We argue that the reason for this finding could be that non-privacy coins may be the first choice for criminals who might prefer cryptocurrencies exhibiting both a high level of anonymity and liquidity.
Since Bitcoin has been the most popular digital currency in the global financial market, the prediction of its price has been an important area in finance. Recently, numerous researches on prediction of financial indices including Bitcoin based on machine learning techniques have been developed. However, little studies pay attention to the strategy of manipulating original distribution to obtain better performances. Because the return data of Bitcoin prices are highly concentrated near zero, small changes of values in the components can cause different results. Based on this characteristic, we propose flattening distribution strategy (FDS) based on the copula theory as a strategy of the manipulating distribution of components artificially to improve the prediction of Bitcoin price return. We consider multilayer perceptron (MLP), recurrent neural networks (RNN), and long short-term memory (LSTM) to assess the performances of FDS. Finally, we find that the proposed algorithms based on FDS improve significantly the prediction accuracy of the return of Bitcoin price for each of the three architectures.
This paper studies the MAX effect, the relationship between maximum daily returns and future returns in the cryptocurrency market. The cryptocurrency market is an ideal setting for the MAX effect due to its lottery-like features (i.e., large positive skewness). Contrary to findings in other markets, we demonstrate that cryptocurrencies with higher maximum daily returns tend to achieve higher returns in the future and call this the “MAX momentum” effect. We also find that the magnitude of the MAX momentum effect varies with market conditions, investor sentiment and the underpricing of cryptocurrencies. Additionally, this effect is robust to longer holding periods, different MAX measures and alternative sample selection criteria.
The value of technical analysis (TA) has been debated for decades; however, limited evidence exists on the profitability of investment recommendations issued by technical analysts. These ‘chartists’ sometimes claim that TA is an art rather than a science. We evaluated > 5000 TA-based buy and sell recommendations for stocks and a market index in the Netherlands issued during the period 2004–2010. The sign of a recommendation was generally in line with trading signals resulting from technical trading rules. While recommendation levels were positively associated with price trends prior to the recommendation, we did not find evidence of (abnormal) stock returns after the publication of these recommendations. In addition, stop-loss levels did not contain informational value as no meaningful returns were detected after these trigger levels were met. Given that technical recommendations follow well-known trading rules and that these recommendations are not associated with future abnormal returns, we conclude that technical analysts do not exhibit ‘artistic’ skills.
Bitcoin has emerged as a fascinating phenomenon of the financial markets.
Without any central authority issuing the currency, it has been associated with
controversy ever since its popularity and public interest reached high levels.
Here, we contribute to the discussion by examining potential drivers of Bitcoin
prices ranging from fundamental to speculative and technical sources as well as
a potential influence of the Chinese market. The evolution of the relationships
is examined in both time and frequency domains utilizing the continuous
wavelets framework so that we comment on development of the interconnections in
time but we can also distinguish between short-term and long-term connections.
Academic research relies extensively on macroeconomic variables to forecast the U.S. equity risk premium, with relatively little attention paid to the technical indicators widely employed by practitioners. Our paper fills this gap by comparing the predictive ability of technical indicators with that of macroeconomic variables. Technical indicators display statistically and economically significant in-sample and out-of-sample predictive power, matching or exceeding that of macroeconomic variables. Furthermore, technical indicators and macroeconomic variables provide complementary information over the business cycle: technical indicators better detect the typical decline in the equity risk premium near business-cycle peaks, whereas macroeconomic variables more readily pick up the typical rise in the equity risk premium near cyclical troughs. Consistent with this behavior, we show that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone. Overall, the substantial countercyclical fluctuations in the equity risk premium appear well captured by the combined information in technical indicators and macroeconomic variables.
Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1838 .
This paper was accepted by Wei Jiang, finance.
One of the most influential ideas in the past 30 years is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally and instantaneously. However, the emerging discipline of behavioral economics and finance has challenged this hypothesis, arguing that markets are not rational, but are driven by fear and greed instead. Recent research in the cognitive neurosciences suggests that these two perspectives are opposite sides of the same coin. In this article I propose a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution - competition, adaptation, and natural selection - to financial interactions. By extending Herbert Simon's notion of "satisficing" with evolutionary dynamics, I argue that much of what behavioralists cite as counterexamples to economic rationality - loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases - are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, the Adaptive Markets Hypothesis offers a number of surprisingly concrete implications for the practice of portfolio management.
This article tests the profitability of Bollinger Bands (BB) technical indicators. It is found that, after adjusting for transaction costs, the BB are consistently unable to earn profits in excess of the buy-and-hold trading strategy. However, the profitability is improved using a contrarian's approach.
This paper focuses on the role of technical analysis in signalling the timing of stock market entry and exit. Test statistics are introduced to test the performance of the most established of the trend followers, the Moving Average, and the most frequently used counter-trend indicator, the Relative Strength Index. Using Singapore data, the results indicate that the indicators can be used to generate significantly positive return. It is found that member firms of Singapore Stock Exchange (SES) tend to enjoy substantial profits by applying technical indicators. This could be the reason why most member firms do have their own trading teams that rely heavily on technical analysis.
This study applies a set of measures developed by Diebold and Yilmaz (2012, 2016) to examine connectedness via return and volatility spillovers across six large cryptocurrencies from August 7, 2015 to February 22, 2018. Regardless of the sign of returns, the results show that Litecoin and Bitcoin are at the centre of the connected network of returns. This finding implies that return shocks arising from these two cryptocurrencies have the most effect on other cryptocurrencies. Further analysis shows that connectedness via negative returns is largely stronger than via positive ones. Ripple and Ethereum are the top recipients of negative-return shocks, whereas Ethereum and Dash exhibit very weak connectedness via positive returns. Regarding volatility spillovers, Bitcoin is the most influential, followed by Litecoin; Dash exhibits a very weak connectedness, suggesting its utility for hedging and diversification opportunities in the cryptocurrency market. Taken together, results imply that the importance of each cryptocurrency in return and volatility connectedness is not necessarily related to its market size. Further analyses reveal that trading volume and global financial and uncertainty effects as well as the investment-substitution effect are determinants of net directional spillovers. Interestingly, higher gold prices and US uncertainty increase the net directional negative-return spillovers, whereas they do the opposite for net directional positive-return spillovers. Furthermore, gold prices exhibit a negative sign for net directional-volatility spillovers, whereas US uncertainty shows a positive sign. Economic actors interested in the cryptocurrency market can build on our findings when weighing their decisions.
This paper investigates the predictive power of global geopolitical risks (GPR) index on daily returns and price volatility of Bitcoin over the period July 18, 2010–May 31, 2018. Considering Bayesian Graphical Structural Vector Autoregressive (BSGVAR) technique, we find that GPR has a predictive power on both returns and volatility of Bitcoin. The results of the Ordinary Least Squares (OLS) estimations show that price volatility and returns of Bitcoin are positively and negatively related to the GPR, respectively. However, findings from the Quantile-on-Quantile (QQ) estimations state that the effects are positive at the higher quantiles of both the GPR as well as the price volatility and the returns of Bitcoin. Therefore, we conclude that Bitcoin can be considered as a hedging tool against global geopolitical risks.
Most of the limited evidence on the exponential price spikes (i.e. price explosivity) in the cryptocurrency market mainly considers the case of Bitcoin, although other cryptocurrencies have gradually eroded Bitcoin's dominance. Importantly, none has been documented as to whether explosivity periods in cryptocurrencies are contemporaneously related. Accordingly, we date-stamp price explosivity in leading cryptocurrencies and reveal that all cryptocurrencies investigated herein were characterised by multiple explosivity. Then, we determine whether explosivity in one cryptocurrency can lead to explosivity in other cryptocurrencies. Results show evidence of a multidirectional co-explosivity behaviour that is not necessarily from bigger to smaller and younger markets.
This paper examines the causal relationship between Bitcoin attention (measured by the Google Trends search queries) and Bitcoin returns for the period from January 1, 2013, to December 31, 2017. For this purpose, we employ the Copula-based Granger Causality in Distribution (CGCD) test. After implementing various robustness checks, we observe that there is a bi-directional causal relationship between Bitcoin attention and Bitcoin returns with the exception of the central distributions from 40% to 80%. To put it differently, the bidirectional causality mainly exists in the left tail (poor performance) and the right tail (superior performance) of the distribution.
Unlike prior studies, this study examines the nonlinear, asymmetric and quantile effects of aggregate commodity index and gold prices on the price of Bitcoin. Using daily data from July 17, 2010 to February 2, 2017, we employed several advanced autoregressive distributed lag (ARDL) models. The nonlinear ARDL approach was applied to uncover short- and long-run asymmetries, whereas the quantile ARDL was applied to account for a second type of asymmetry, known as the distributional asymmetry according to the position of a dependent variable within its own distribution. Moreover, we extended the nonlinear ARDL to a quantile framework, leading to a richer new model, which allows testing for distributional asymmetry while accounting for short- and long-run asymmetries. Overall, our results indicate the possibility to predict Bitcoin price movements based on price information from the aggregate commodity index and gold prices. Importantly, we report the nuanced result that most often the relations between bitcoin and aggregate commodity, on the one hand, and between bitcoin and gold, on the other, are asymmetric, nonlinear, and quantiles-dependent, suggesting the need to apply non-standard cointegration models to uncover the complexity and hidden relations between Bitcoin and asset classes.
This paper analyzes the prediction power of the economic policy uncertainty (EPU) index on the daily Bitcoin returns. Using the Bayesian Graphical Structural Vector Autoregressive model as well as the Ordinary Least Squares and the Quantile-on-Quantile Regression estimations, the paper finds that the EPU has a predictive power on Bitcoin returns. Fundamentally, Bitcoin returns are negatively associated with the EPU. However, the effect is positive and significant at both lower and higher quantiles of Bitcoin returns and the EPU. In the light of these findings, the paper concludes that Bitcoin can serve as a hedging tool against uncertainty.
We revisit the issue of informational efficiency of Bitcoin using a battery of computationally efficient long-range dependence estimators for a period spanning over July 18, 2010 to June 16, 2017. We report that the market is informational efficient as consistent to recent findings of Urquhart (2016), Nadarajah and Chu (2017) and Bariviera (2017).
Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the
conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-inquantiles
test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the
whole of their respective conditional distributions. The nonparametric characteristics of our test control for
misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December
2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in
Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and
accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading
volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the
conditional distribution.
We examine whether Bitcoin can hedge global uncertainty, measured by the first principal component of the VIXs of 14 developed and developing equity markets. After decomposing Bitcoin returns into various frequencies, i.e., investment horizons, and given evidence of heavy-tails, we employ quantile regression. We reveal that Bitcoin does act as a hedge against uncertainty: it reacts positively to uncertainty at both higher quantiles and shorter frequency movements of Bitcoin returns. Further, we use quantile-on-quantile regression and identify that hedging is observed at shorter investment horizons, and at both lower and upper ends of Bitcoin returns and global uncertainty.
In this paper, we provide a trend factor that captures simultaneously all three stock price trends: the short-, intermediate-, and long-term, by exploiting information in moving average prices of various time lengths whose predictive power is justified by a proposed general equilibrium model. It outperforms substantially the well-known short-term reversal, momentum, and long-term reversal factors, which are based on the three price trends separately, by more than doubling their Sharpe ratios. During the recent financial crisis, the trend factor earns 0.75% per month, while the market loses per month, the short-term reversal factor loses the momentum factor loses and the long-term reversal factor barely gains 0.03%. The performance of the trend factor is robust to alternative formations and to a variety of control variables. From an asset pricing perspective, it also performs well in explaining cross-section stock returns.
This paper aims to identify the likely determinants for cryptocurrency value formation, including for that of bitcoin. Due to Bitcoin’s growing popular appeal and merchant acceptance, it has become increasingly important to try to understand the factors that influence its value formation. Presently, the value of all Bitcoins in existence represent approximately $7 billion, and more than $60 million of notional value changes hands each day. Having grown rapidly over the past few years, there is now a developing but vibrant marketplace for bitcoin, and a recognition of digital currencies as an emerging asset class. Not only is there a listed and over-the-counter market for bitcoin and other digital currencies, but also an emergent derivatives market. As such, the ability to value bitcoin and related cryptocurrencies is becoming critical to its establishment as a legitimate financial asset.
This paper explores the financial asset capabilities of bitcoin using GARCH models. The initial model showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. The asymmetric GARCH showed that bitcoin may be useful in risk management and ideal for risk averse investors in anticipation of negative shocks to the market. Overall bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar on a scale from pure medium of exchange advantages to pure store of value advantages.
Brock et al. (1992) found technical trading rules to have predictive ability with regards to the Dow Jones Index. The current paper considers whether this result can be replicated on UK data. The paper also considers whether investors could earn excess returns from technical analysis in a costly trading environment. The paper concludes that although the technical trading rules examined do have predictive ability in terms of UK data, their use would not allow investors to make excess returns in the presence of costly trading.
We assess whether some simple forms of technical analysis can predict stock price movement in Asian markets. We find the rules to be quite successful in the emerging markets of Malaysia, Thailand and Taiwan. The rules have less explanatory power in more developed markets such as Hong Kong and Japan. On average for our sample, mean percentage changes in stock indices on days that the rules emit buy signals exceed means on days that the rules emit sell signals by 0.095% per day, or about 26.8% on an annualized basis. We estimate “break-even” round-trip transactions costs (which would just eliminate gains from technical trading) to be 1.57% on average. We also find that technical signals emitted by U.S. markets have substantial forecast power for Asian stock returns beyond that of own-market signals. This is consistent with the reasoning that the technical rules identify periods when global equilibrium expected returns deviate substantially from their unconditional mean.
Technica, or chartist, analysis of financial markets involves providing forecasts or trading advice on the basis of largely visual inspection of past prices, without regard to any underlying economic or 'fundamental' analysis. This paper reports the results of a questionnaire survey, conducted on behalf of the Bank of England, among chief foreign exchange dealers based in London in November 1988. Amongst other findings, it is revealed that at least 90 per cent of respondents place some weight on this form of non-fundamental analysis when forming views at one or more time horizons. There is also a skew towards reliance on technical, as opposed to fundamentalist, analysis at shorter horizons, which becomes steadily reversed as the length of horizon considered is increased. A very high proportion of chief dealers view technical and fundamental analysis as complementary forms of analysis and a substantial proportion suggest that technical advice may be self-fulfilling. (JEL F31).
This paper tests two of the simplest and most popular trading rules--moving average and trading range break--by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, their results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models. Copyright 1992 by American Finance Association.