Search Results (11)

In the context of heightened oil price volatility, mastering technical trading strategies is essential for informed investment and sound decision making. This study explores the effectiveness of contrarian technical trading strategies in the Brent crude oil market, aiming to enhance returns in the face of persistent market fluctuations. Utilizing historical price data, this research formulates trading rules based on overbought and oversold signals derived from the Relative Strength Index (RSI) and the Stochastic Oscillator Indicator (SOI). It assesses their performance through a range of Average Holding Period Return (AHPR) metrics, emphasizing the 250-day AHPR as a proxy for one-year returns. The findings show that RSI-based strategies, especially those using a threshold of 25, are most effective in oversold conditions, achieving peak profitability of over 40% in Quarter 2. The conclusions highlight the importance of parameter flexibility, strategic timing, and responsiveness to market dynamics in optimizing the contrarian strategy performance. The implications suggest investors and managers can refine strategies by accounting for behavioral biases, market timing, and flexible parameters, while enhancing big data analytics in technical trading. Full article

In the early stage of the COVID-19 outbreak, a vast majority of research predicted its potential economic impacts based on various possible scenarios, believing that looking forward earlier and recovering better. In contrast, through contrarian thinking from an economic recovery perspective, this study empirically investigates the direct impact of COVID-19 on China’s economy. This reveals that China’s economy experiences a V-shaped recovery; it is in the recovery process and will achieve the pre-pandemic level in the coming years. Consumption, international trade, and investment indicators are synchronously recovering, which may be attributed to the fact that the pandemic has had little impact on China’s economy, although it remarkably hits national consumption, international trade, and investment that are less interrupted. Empirical evidence shows that the pandemic is unlikely to alter China’s industrial structure, as it has primarily affected the service and manufacturing sectors while leaving agriculture relatively unaffected. In light of these findings, China’s economy is facing challenges beyond the pandemic. By adopting a recovery-oriented contrarian approach, we can better identify the dynamic consequences and optimize economic strategies to mitigate potential long-term impacts on sustainable economic growth. These insights may also help guide economic recovery efforts in other developing countries. Full article

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration. Full article

In this paper we offer a comprehensive review of Sociophysics, focusing on relevant models as well as selected applications in social trading, behavioral finance and business. We discuss three key aspects of social diffusion dynamics, namely Opinion Dynamics (OD), Group Decision-Making (GDM) and Knowledge Dynamics (KD). In the OD case, we highlight special classes of social agents, such as informed agents, contrarians and extremists. As regards GDM, we present state-of-the-art models on various kinds of decision-making processes. In the KD case, we discuss processes of knowledge diffusion and creation via the presence of self-innovating agents. The primary question we wish to address is: to what extent does Sociophysics correspond to social reality? For that purpose, for each social diffusion model category, we present notable Sociophysics applications for real-world socioeconomic phenomena and, additionally, we provide a much-needed critique of the existing Sociophysics literature, so as to raise awareness of certain issues that currently undermine the effective application of Sociophysics, mainly in terms of modelling assumptions and mathematical formulation, on the investigation of key social processes. Full article

In contrast to finding better monthly performance shown in a specific month, such as the January effect (i.e., better stock price performance in January as opposed to other months), which has been extensively studied, the goal of this study is to determine whether investors would obtain better subsequent performance as technical trading signals emitted in a specific month because, from the investment perspective, investors purchasing stocks now would not know their performance until later. We contend that our analysis emphasizes its critical role in steering investment decisions and enhancing profitability; nonetheless, this issue appears to be overlooked in the relevant literature. As such, utilizing big data to analyze the constituent stocks of the DJI 30 and NDX 100 indices from 2003 to 2022 (i.e., two-decade data), this study investigates whether trading these stocks as trading signals emitted via contrarian regulation of stochastic oscillator indicators (SOIs) and the relative strength index (RSI) in specific months would result in superior subsequent performance (hereafter referred to as “monthly effects”). This study discovers that the oversold signals generated by these two contrarian regulations in March were associated with higher subsequent performance for holding 100 to 250 trading days (roughly one year) than other months. These findings highlight the importance of the trading time and the superiority of the RSI over SOIs in generating profits. This study sheds light on the significance of oversold trading signals and suggests that the “monthly effect” is crucial for achieving higher returns. Full article

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization. Full article

The purpose of the study is to investigate the overreaction hypothesis in relation to the Ho Chi Minh Stock Exchange (HOSE). The data used in this study consist of a monthly price series of 392 stocks traded on the HOSE, covering the period starting on 5 January 2004 through to 30 June 2021. The findings derived from the tests examining the differences in excess returns across the winner and loser portfolios confirm that the overreaction phenomenon exists in the HOSE. More specifically, following the creations of the portfolios, the loser portfolio outperformed the winner portfolio by 1.80% and 2.17% in the second and third month, respectively. In addition, the differences in cumulative abnormal returns between the loser and winner portfolios were significantly positive for almost all tracking periods. These findings support the hypothesis that the Vietnam stock market is inefficient in its weak form. Based on these results, we suggest that investors can earn abnormal returns by using contrarian trading strategies in the Vietnam stock market. Full article

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. Full article

In contrarian trading, investors buy and sell loser stocks (lowest average historical prices) and winner stocks (highest average historical prices), respectively. This study examines whether (a) Thailand Sustainability Investment-listed companies outperform Stock Exchange of Thailand (SET)-listed companies (from 1 January 2016 to 31 December 2019) in contrarian profits, (b) the five-factor model outperforms their 1993 three-factor model in explaining contrarian profits, and (c) risk drives the earnings of contrarians. Companies were divided into portfolios of winners and losers based on the average of the daily historical prices held in various eras. The SET-listed companies perform better in generating profits. The root mean squared error and mean absolute error—measurements of model accuracy—report that the error from the three-factor model is smaller than the one from the five-factor model. Thus, the three-factor model is applied to estimate the risk-adjusted return. Zero contrarian profits after risk adjustment confirms that they are risk-driven. Full article

Based on technological innovation and climate change, clean energy has been paid increasing attention to by worldwide investors, thereby increasing their interest in investing in firms that specialize in clean energy. However, traditional energy still plays an important role nowadays, because extreme weather has often occurred in the winters of recent years. We thus explore whether investing the strategies adopted by diverse technical trading rules would matter for investing in energy-related ETFs. By employing two representative global ETFs with more than 10 years of data, iShares Global Clean Energy ETF as the proxy of clean energy performance and iShares Global Energy ETF as that of traditional energy performance, we then revealed that momentum strategies would be proper for buying the green energy ETF, but contrarian strategies would be appropriate for buying the energy ETF. Furthermore, based on investment strategies adopted by diverse technical trading rules, we showed that the performance of clean energy outperforms that of energy, indicating that green energy does matter for the economy. Moreover, while observing the price trend of these two ETFs, we found that such two ETFs may have opposite share price performances, implying that, while the green energy ETF reached a relatively high price, investors following the contrarian strategies suggested in this study may reap profits by investing the energy ETF. Full article

This research deals with stock market reactions of Central Eastern and South Eastern European (CESEE) markets to the COVID-19 pandemic, via the event study methodology approach. Since the stock markets react quickly to certain announcements, the used methodology is appropriate to evaluate how the aforementioned markets reacted to certain events. The purpose of this research was to evaluate possibilities of obtaining profits on the stock markets during great turbulences, when a majority of the participants panic. More specifically, the contrarian trading strategies are observed if they can obtain gains, although a majority of the markets suffer great losses during pandemic shocks. The contributions to the existing literature of this research are as follows. Firstly, empirical research on CESEE stock markets regarding other relevant topics is still scarce and should be explored more. Secondly, the event study approach of COVID-19 effects utilized in this study has (to the knowledge of the author) not yet been explored on the aforementioned markets. Thirdly, based on the results of CESEE market reactions to specific announcements regarding COVID-19, a simulation of simple trading strategies will be made in order to estimate whether some investors could have profited in certain periods. The results of the study indicate promising results in terms of exploiting other investors’ panicking during the greatest decline of stock market indices. Namely, the initial results, as expected, indicate strong negative effects of specific COVID-19 announcements on the selected stock markets. Secondly, the obtained information was shown to be useful for contrarian strategy in order to exploit great dips in the stock market indices values. Full article