scholarly journals An intelligent trading mechanism based on the group trading strategy portfolio to reduce massive loss by the grouping genetic algorithm

2021 ◽  
Author(s):  
Chun-Hao Chen ◽  
Yu-Hsuan Chen ◽  
Vicente Garcia Diaz ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Genetic Algorithm ◽  
Financial Markets ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Genetic Operators ◽  
Grouping Genetic Algorithm ◽  
Balance Weight ◽  
Trading Mechanism ◽  
Stop Loss

AbstractIt is always difficult and challenge to obtain suitable trading signals for the desired securities in financial markets. The popular way to deal with it is through the use of trading strategies (TSs) made up of technical or fundamental indicators. Due to the different properties of TSs, an algorithm was proposed to find trading signals by obtaining the group trading strategy portfolio (GTSP), which is composed of strategy groups that can be employed to generate various TS portfolios (TSP) instead of a single TS. The stop-loss and take-profit points (SLTP) are widely utilized by shareholders to avoid massive losses. However, the appropriate SLTP is hard to set by users. Therefore, in this paper, the algorithm, namely GTSP-SLTP algorithm, is proposed to not only obtain a reliable GTSP but also find appropriate SLTP using the grouping genetic algorithm. A chromosome is encoded by the generated SLTP and GTSP along with the weights for strategy groups that are the SLTP, grouping, weight, and strategy parts. To assess the goodness of a chromosome, the evaluation function that consists of the group balance, weight balance, risk factor, and profit factor, is employed. Genetic operators are then performed to produce new solutions for next population. The genetic process is performed iteratively until the stop conditions have achieved. Last but not the least, empirical experiments were conducted on three financial datasets with different trends and a case study is also given to reveal the effectiveness and robustness of the designed GTSP-SLTP algorithm.

scholarly journals Risks of Traders on the World Stock Market on the Example of Ukraine

2019 ◽  
Vol 67 ◽  
pp. 06001 ◽  
Author(s):  
George Abuselidze ◽  
Olga Mohylevska ◽  
Nina Merezhko ◽  
Nadiia Reznik ◽  
Anna Slobodianyk
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
World Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Practical Significance ◽  
External Shocks ◽  
Domino Effect ◽  
The World ◽  
The Impact

The article reveals the essence and features of the development of the stock market in Ukraine. It was established that the vigorous activity of countries in the world financial markets means that they also face a risk of global financial turmoil (the so-called “domino effect”). It is determined that the impact of global financial instability on the country depends on the openness of its economy that will lead to significant external “shocks”. The possibility of providing effective influence on domestic stock market activity with taking into account the changing world situation, development of perfect trading strategies for each participant is substantiated. The conducted analysis of the world market conditions of stock markets in recent years has made it possible to assess the real risks for new participants in the stock market and become the basis for the development of an appropriate effective trading strategy. The practical significance of the results is that they allow for a measurable approach to assessing the existing risk when choosing one or another trading strategy to move to the world stock market.

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

scholarly journals Futures Quantitative Trading Strategies Based on Market Capital Flows

2018 ◽  
Vol 5 (2) ◽  
pp. 175
Author(s):  
QiaoXu Qin ◽  
GengJian Zhou ◽  
WeiZhou Lin
Keyword(s):  
Capital Flows ◽  
Turnover Rate ◽  
Futures Market ◽  
Trading Strategy ◽  
Trading Strategies ◽  
High Turnover ◽  
Back Testing ◽  
Quantitative Trading Strategies ◽  
Stop Loss ◽  
Futures Funds

The purpose of this paper is to establish a futures quantitative trading strategy based on the characteristics of capital flows in the futures market and the factors that influence the Futures rate of return. Firstly, PCA and logistic regression are used as the theoretical basis to analyze the characteristics of future futures with high turnover rate and futures yield in the future, and summarize the characteristics of rotation, continuity and similarity of the capital flow in the futures market. Then combining with the characteristics of the flow of futures funds and the idea of taking profit and stop loss, we establish the quantitative trading strategy of futures. Using the partial futures data from 2014-2015 for back testing, the strategy returns better and provides a new investment perspective for the futures market investors.

Systemic Risk and Trading Strategy Based on Correlation-Based Networks in Stock Markets

2020 ◽  
Vol 19 (03) ◽  
pp. 2050028
Author(s):  
Jia-Wei Yu ◽  
Qin-Qin Huang ◽  
Yong-Han Guo ◽  
Zhi-Qiang Jiang ◽  
Wen-Jie Xie
Keyword(s):  
Financial Markets ◽  
Systemic Risk ◽  
Debt Crisis ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Risk Indicators ◽  
Future Price ◽  
Trading Costs ◽  
Excess Returns ◽  
Systemic Risks

In this paper, we construct five systemic risk indicators and test their performances based on four different datasets. It is observed that the five indicators can accurately indicate the increment of systemic risks during the periods of sub-prime crisis and European debt crisis. Trading strategies based on the risk indicators are further designed to test the warning ability of future price drops. The backtests reveal that trading based on the five indicators provides satisfied excess returns when the trading costs are included. Our results provide insights to find new network-based risk indicators to early warn the systemic risks in financial markets.

scholarly journals A Sustainable Application Based on Grouping Genetic Algorithm for Modularized Redesign Model in Apparel Reverse Supply Chain

2018 ◽  
Vol 10 (9) ◽  
pp. 3013 ◽  
Author(s):  
Manoj Paras ◽  
Lichuan Wang ◽  
Yan Chen ◽  
Antonela Curteza ◽  
Rudrajeet Pal ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Natural Resources ◽  
Study Finding ◽  
Practical Relevance ◽  
Reverse Supply Chain ◽  
Specific Knowledge ◽  
Grouping Genetic Algorithm ◽  
Fitness Calculation

The scarcity of natural resources and the problem of pollution have initiated the need for extending the life and use of existing products. The concept of the reverse supply chain provides an opportunity to recover value from discarded products. The potential for recovery and the improvement of value in the reverse supply chain of apparel has been barely studied. In this research, a novel modularized redesign model is developed and applied to the garment redesign process. The concept of modularization is used to extract parts from the end-of-use or end-of-life of products. The extracted parts are reassembled or reconstructed with the help of a proposed group genetic algorithm by using domain and industry-specific knowledge. Design fitness is calculated to achieve the optimal redesign. Subsequently, the practical relevance of the model is investigated with the help of an industrial case in Sweden. The case study finding reveals that the proposed method and model to calculate the design fitness could simplify the redesign process. The design fitness calculation is illustrated with the example of a polo t-shirt. The redesigned system-based modularization is in accordance with the practical situations because of its flexibility and viability to formulate redesign decisions. The grouping genetic algorithm could enable fast redesign decisions for designers.

scholarly journals An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using Grouping Genetic Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 7313-7325 ◽  
Author(s):  
Chun-Hao Chen ◽  
Yu-Hsuan Chen ◽  
Jerry Chun-Wei Lin ◽  
Mu-En Wu
Keyword(s):  
Genetic Algorithm ◽  
Trading Strategy ◽  
Grouping Genetic Algorithm

2D Cutting Stock Problem Using Hybrid 3-D Overlapped Grouping Genetic Algorithm

2014 ◽  
Author(s):  
Maged R. Rostom ◽  
Ashraf O. Nassef ◽  
Sayed M. Metwalli
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Algorithms ◽  
Sheet Material ◽  
Real Life ◽  
Setup Time ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Grouping Genetic Algorithm ◽  
Material Utilization

The cutting stock problem (CSP) is a business problem that arises in many areas, particularly in manufacturing industries where a given stock material must be cut into a smaller set of shapes. It has gained a lot of attention for increasing efficiency in industrial engineering, logistics and manufacturing. This paper presents a hybrid new 3-D overlapped grouping Genetic Algorithm (GA) that solves two-dimensional cutting stock problems for nesting the rectangular shapes. The objective is the minimization of the wastage of the sheet material which leads to maximizing material utilization and the minimization of the setup time. The model and its results are compared with real life case study from a steel workshop in a bus manufacturing factory. The effectiveness of the proposed approach is shown by comparing and shop testing of the optimized cutting schedules. The results reveal its superiority in terms of waste minimization comparing to the current cutting schedules and show that our approach outperforms existing heuristic algorithms. The whole procedure can be completed in a reasonable amount of time by the developed optimization program.

Banking on Failure

2020 ◽  
Author(s):  
Richard S Collier
Keyword(s):  
Financial Markets ◽  
Financial Institutions ◽  
Current Position ◽  
Tax System ◽  
Number Of Factors ◽  
Tax Systems

This book seeks to explain why and how banks ‘game the system’. More specifically, its objective is to account for why banks are so often involved in cases of misconduct and why those cases often involve the exploitation of tax systems. To do this, a case study is presented in Part I of the book. This case study concerns a highly complex transaction (often referred to as ‘cum-ex’) designed to exploit a flaw at the intersection of the tax system and the financial markets settlements system. It was entered into by a very large number of banks and other financial institutions. A number of factors make the cum-ex transaction remarkable, including the sheer scale of the financial amounts involved, the large number of banks and financial institutions involved, the comprehensive failure of the controls infrastructure in this highly regulated sector, and the fact that authorities across Europe have found it so difficult to deal with the transaction. Part II of the book draws out the wider significance of cum-ex and what it tells us about modern banks and their interactions with tax systems. The account demonstrates why the exploitation of tax systems by banks is practically inevitable due to a variety of systemic features of the financial markets and of tax systems themselves. A number of possible responses to the current position are suggested in the final chapter.

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