Trading Rules Research Articles

The profitability of seasonal trading timing: Insights from energy-related markets

Evaluating subsequent performance is critical in investment decision-making since investors make investment decisions now, but the profits generated will only be known later. However, the research on the significance of subsequent performance as trading signs issued by technical trading regulations in different seasons (hereafter referred to as seasonal effects) is limited and understudied, despite the widespread exploration of January and weekly effects in relevant studies. This study aimed to address this research gap and examine the impact of seasonal effects on clean energy and energy markets, revealing several critical findings. First, contrarian (momentum) trading rules generated better succeeding performance for the energy (clean energy) index than the clean energy (energy) index. Second, momentum (contrarian) trading rules generated better succeeding performance for the clean energy (energy) index than the energy (clean energy) index. Third, momentum (contrarian) trading rules exhibited stronger seasonal effects in Season 1 (Season 2) than in other seasons. Fourth, the analysis of RSI trading signals in Season 1 showed that the better succeeding index performance for clean energy was shown over 15% average holding period returns, compared to the near-zero mean daily returns for both clean energy and energy index returns over the data period. This study also indicates that considerable seasonal effects exist in both clean energy and energy markets.

Double auction for trading perfect complements

AbstractFor a trading problem where a buyer is interested in an aggregate resource with fragmented ownership, the individually owned resources are perfect complements in trade. A double auction, chosen in accordance with a value alignment principle which we formulate, is shown to be strategy proof for owners. Since it also values the aggregate resource correctly, it mitigates the holdout problem by changing the source of inefficiency from complementarity on owners' side to lack of competition on buyer side. The value alignment principle implies that this double auction has a majority trading rule. With multiple buyers, a suitable modification makes the double auction strategy proof even for the buyers, thus mitigating the holdout problem by achieving approximate ex post efficiency when the number of owners is large.

Visualizing profitability: A heatmap approach to evaluate Bitcoin futures trading using VMA trading rules

Given that technical trading charts are publicly available on popular financial websites such as Bloomberg and MarketWatch, it stands to reason that the same technical trading approaches may be applied to cryptocurrency markets. One of these trading strategies is the variable length moving average (VMA), whose flexibility benefit has not been fully explored in prior research. To fill this gap, we evaluate Bitcoin futures using VMA trading rules and provide the results in a heatmap diagram. This approach allows investors to choose the most effective VMA rules, potentially leading to profits. Furthermore, our approach may shed new light on previously unexplored investment thinking and practices that have the potential to improve investment outcomes.

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

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

On the role of interest rate differentials in the dynamic asymmetry of exchange rates

Motivated by the lack of empirical evidence in favour of the uncovered interest rate parity rule, we revisit the informational content of interest rate differentials (IRD) to explain daily exchange rates variations. Proposing a novel version of a GARCH model, we allow for the IRD to impact on the time-varying conditional asymmetry of the depreciation rate. We find IRD to be a significant factor for the Euro (EUR), the Swiss franc (CHF), the Swedish Krona, the Japanese Yen and the British Pound. These findings empirically support currency crash theories, suggesting that the larger the difference between interest rates, the more likely the high-yield currency appreciates on average but also exhibits greater risk of a large depreciation. Compared to random walk and buy-and-hold benchmarks, we document superior out-of-sample mean returns of a trading rule exploiting IRD information for EUR and CHF.

Technical trading rules, loss avoidance, and the business cycle

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

Does price limit improve price discovery? Evidence from IPO market in a quasi-natural experiment

The two major Chinese stock exchanges imposed a new trading rule with a price-move limit of 44% on the first trading day for IPOs after 2013. Following this quasi-natural experiment, market sentiment on IPOs drastically rises, resulting in extreme price run-up in initial trading days. Newly listed stocks under the trading rule experience greater illiquidity, higher turnover and volatility than earlier ones. Account-level data from the Shanghai Stock Exchange shows that following the new rule, net buyers of IPO change from institutional to retail investors. The evidence suggests that this price limit delays price discovery by arousing sentiment and crowding out informed investors.

European Soccer Transfer Market Analysis: The Determinant Factors in Transfer Fee

This paper examines the European soccer transfer market, which, as the worlds most popular sport, has its unique rules for player trading. The transfers are the most exciting events in the soccer world each year. While most studies focus on a players market value, this paper intends to study the actual transfer amount, which represents the transaction that occurred, rather than an estimated result. In the preparation phase, this study gathered data on the top 25 transfers over the past 14 years (350 transactions in total) in an Excel spreadsheet. The study aims to determine the important substances that contribute to a players transfer fee by constructing several linear multiple regression models using statistical software R Studio. It is concluded that age and in-field performance (goal and assist) are the most related factors for the whole dataset. However, the determining factors will vary depending on a players position. Many of the factors are somewhat counterintuitive and further investigation is required.

Algorithmic trading using combinational rule vector and deep reinforcement learning

Algorithmic trading rules are widely used in financial markets as technical analysis tools for security trading. However, traditional trading rules are not sufficient to make a trading decision. In this paper, we propose a new algorithmic trading method called CR-DQN, which incorporates deep Q-learning with two popular trading rules: moving average (MA) and trading range break-out (TRB). The input of deep Q-learning is combinational rule vectors, whose component is a linear combination of 140 rules produced by MA and TRB with different parameters. Due to non-stationary characteristics, we devise a reward driven combination weight updating scheme to generate combinational rule vectors, which can capture intrinsic features of financial data. Since the sparse reward exists in CR-DQN, we design a piecewise reward function which shows great potential in the experiments. Taking combinational rule vectors as input, the LSTM based Deep Q-learning network is used to learn an optimal algorithmic trading strategy. We apply our model to both Chinese and non-Chinese stock markets, and CR-DQN exhibits the best performance on a variety of evaluation criteria compared to many other approaches, demonstrating the effectiveness of the proposed method.

Matching problems in biodiversity offset markets: a case study of the New South Wales biodiversity offsets scheme

Biodiversity offset credits in New South Wales are transacted within a regulatory environment defined by detailed trading rules and many different types of biodiversity credits that can lead to thin markets and high transaction costs. This paper describes a market designed to facilitate efficient and effective transactions. The market includes a search algorithm to identify who can exchange with whom, according to the regulatory constraints, and an online exchange tool to facilitate efficient price discovery and allocation of offset contracts.

QUANTITATIVE INVESTMENT DECISIONS BASED ON MACHINE LEARNING AND INVESTOR ATTENTION ANALYSIS

According to the trading rules and financial data structure of the stock index futures market, and considering the impact of major emergencies, we intend to build a quantitative investment decision-making model based on machine learning. We first adopt the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) signal decomposition technology to separate the short-term noise, cycle transformation and long-term trend from the original series, and use the CSI 500 Baidu index series to reflect the investors’ attention, which provides data support for establishing a more effective forecasting model. Then, the CEEMDANBP neural network model is designed based on the obtained effective information of low-frequency trend series, investor attention index and CSI 500 stock index futures market transaction data. Finally, an Attention-based Dual Thrust quantitative trading strategy is proposed and optimized. The optimized Attention-based Dual Thrust strategy solves the core problem of breakout interval determination, effectively avoids the risk of subjective selection, and can meet investors’ different risk preferences. The quantitative investment decision-making model based on CEEMDAN-BP neural network utilizes the advantages of different algorithms, avoids some defects of a single algorithm, and can make corresponding adjustments according to changes in investors’ attention and the occurrence of emergencies. The results show that considering investor attention can not only improve the predictive ability of the model, but also reduce the cognitive bias of the market, effectively control risks and obtain higher returns.

Trading rule discovery using technical analysis and a template matching technique for pattern recognition: Evidence from the Chinese stock market

AbstractThis paper examines the potential profit of bull flag trading rules in the Shangai Stock Exchange Composite Index (SSE) using a template matching technique based on price pattern recognition. This paper fills a gap in the literature by applying a template matching technique for the recognition of bull flag patterns in the Shangai Stock Exchange Composite Index (SSE) during the period of 1991–2021. To the best of our knowledge, no previous study has applied bull flag trading rules to the Chinese stock market. Our results indicate that bull flag trading rules can correctly predict the price movement direction of the index most of the time, achieving significantly positive excess profits. Moreover, shorter fitting windows and better quality of price fit values for lower holding periods are associated with better performance. This research may have relevant practical implications for investors who opt for this indicator in their asset allocation decisions.

Design and Performance Study of Improved Fuzzy System with Genetic Algorithm

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

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

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

Bi-Level Emission Reduction Model of the Hybrid Power Market Based on Carbon Emission Flow Theory and Source–Load Coordination

Limited by the influence of network topology and other factors, the theory of carbon emission flow is unreasonable in the allocation of carbon responsibility on the user side, which leads to the low enthusiasm of users to respond to emission mitigation. The emergence of bilateral transactions provides users with the freedom to choose the type of power supply which is of great significance to exploring the potential of users to reduce emissions and promote the consumption of new energy. For this reason, this paper proposes a bi-level emission reduction model of the hybrid electricity market considering carbon emission flow and source–load coordination. The upper level aims to maximize the revenue of wind, photovoltaic, and thermal power generators and establishes a market-clearing model based on the trading rules of the hybrid electricity market to obtain the bid-winning power of each generator and the hybrid market electricity price. After the market is cleared, the carbon emission liability of the user side is calculated by using the carbon emission flow theory. The lower level takes the minimum cost of electricity consumption as the target and uses electricity price and carbon responsibility as incentives to establish a decision-making model for users to purchase electricity and guide users to actively choose green energy for consumption. The results of the example show that compared with the single pool market trading model the carbon emissions of the system are reduced by 11.9% while the income of the new energy power generations is increased by 9.84% and the electricity cost of the user is reduced by 21.2%, which underlines a mutually beneficial outcome for all stakeholders in the market.

Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes

This study explores the profitability of trading range breakout (TRB) trading rules and commonly used moving average (MA) trading rules in the NDX 100 and S&P 500 indices. Its objective is to offer practical insights for investors who own QQQ and SPY, two popular ETFs that track these two indices. Using big data analytics, the study reveals that investors who trade and hold these two ETFs can potentially profit from using TRB instead of MA trading rules. Unexpectedly as opposed to buying signals from TRB trading rules or momentum MA trading rules, investors may obtain much higher returns by purchasing these ETFs (i.e., QQQ and SPY) as selling signals emitted by TRB rules, particularly by buying these ETFs as stock prices break below 150-day low and 200-day low, a situation in which many investors may experience panic due to the continuously falling share price. As such, we argue that our findings may suggest that being greedy when others are fearful (i.e., the Warren Buffett quote) is of significance to this study.

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

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

Electricity price modeling from the perspective of start-up costs: incorporating renewable resources in non-convex markets

This paper constructs a comprehensive electricity market model in the context of China, highlighting the deviation caused by neglecting start-up costs from an engineering perspective. The model allows for the abandonment of excess wind and solar power generation, contributing to the achievement of research objectives in scenarios with a high proportion of renewable energy. Our method innovatively integrates fuel and carbon prices, clean energy expansion, and power system marginal prices according to the carbon trading rules of the Chinese power industry, providing a more accurate representation of market dynamics. Findings reveal that neglecting start-up costs can lead to significant biases in electricity prices. We demonstrate that the marginal price sometimes deviates from the fluctuation of the real value. While fuel and CO2 prices can be transmitted downstream, the value of new energy must be transmitted through its impact on the marginal unit. This insight is crucial for understanding the “missing money” problem in electricity markets. Based on these findings, we propose policy recommendations. We suggest considering fixed and average costs as pricing benchmarks and utilizing capacity utilization as a signal for demand response to adjust power pricing. Furthermore, we recommend trading different energy types separately in the spot market with different pricing benchmarks to ensure the homogeneity of marginal units.

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

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

Badanie ścieżki handlu uprawnieniami do emisji dwutlenku węgla w Chinach w kontekście tzw. podwójnego węgla

With the continuous development of the global economy, the rapid deterioration of the global ecological environment has caused a huge impact on the future development of the world. In order to solve the problem of global warming and enhance the self-development capacity of all countries, based on the concept of sustainable development, China has set the ambitious goal of dual carbon. To this end, China is actively promoting the establishment of a national carbon emissions trading system.In response to low price competitiveness, such as nonstandard trading system, the influence of the development of the carbon emissions trading system in the future, should not only attach importance to enrich and strengthen the basic function of the carbon market, also continue to carbon pricing system and in-depth reform of the fiscal and taxation system, clear up the thoughts to the carbon market trading rules, is on its relevant rights and obligations, firmly adhere to steadily promote carbon market links between countries. Currently, China’s carbon emission trading is still in its infancy, and its effect is still limited in specific practice. Meanwhile, carbon emission trading markets in developed countries such as the United States and the United Kingdom have begun to implement carbon tariffs and other means to maintain their carbon borders. Therefore, the construction of carbon emission trading is necessary for development, but also for the sustainable development of the country.The lag of China’s carbon emission market leads to the worsening of the problem of carbon excess emissions of industries in the regions not covered, and the increased economic burden caused by the carbon barriers of other countries in foreign trade. Of course, this requires China take the path of sustainable development to continue to strengthen the system construction of carbon emission rights and promote the further optimization of their functions.