scholarly journals Stock Trading System Based on Machine Learning and Kelly Criterion in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lili Chen ◽  
Lingyun Sun ◽  
Chien-Ming Chen ◽  
Mu-En Wu ◽  
Jimmy Ming-Tai Wu
Keyword(s):  
Genetic Algorithm ◽  
Internet Of Things ◽  
Financial Services ◽  
Stock Price ◽  
Prediction Models ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Financial Industry ◽  
Stock Trading ◽  
Kelly Criterion

The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.

Design of 3D-Route Planning Based on the Improvement of Simple Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1690-1694
Author(s):  
Jian Xia Zhang ◽  
Tao Yu ◽  
Ji Ping Chen ◽  
Ying Hao Lin ◽  
Yu Meng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Scientific Research ◽  
Route Planning ◽  
Optimal Solution ◽  
Global Search ◽  
Global Search Algorithm ◽  
Simple Genetic Algorithm ◽  
Local Optimal Solution ◽  
Simulation Results

With the wide application of UAV in the scientific research,its route planning is becoming more and more important. In order to design the best route planning when UAV operates in the field, this paper mainly puts to use the simple genetic algorithm to design 3D-route planning. It primarily introduces the advantages of genetic algorithm compared to others on the designing of route planning. The improvement of simple genetic algorithm is because of the safety of UAV when it flights higher, and the 3D-route planning should include all the corresponding areas. The simulation results show that: the improvement of simple genetic algorithm gets rid of the dependence of parameters, at the same time it is a global search algorithm to avoid falling into the local optimal solution. Whats more, it can meet the requirements of the 3D-route planning design, to the purpose of regional scope and high safety.

An Improved Genetic Algorithm of Web Services Composition with QOS

2012 ◽  
Vol 532-533 ◽  
pp. 1836-1840 ◽  
Author(s):  
Yan Yan Zhang ◽  
Hai Ling Xiong ◽  
Yong Chun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Web Services ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Experimental Result ◽  
Web Services Composition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Services Composition ◽  
Fitness Value

A web service composition method based on the adaptive genetic operator was proposed to deal with the issues of the lack of adaptability and the easy-premature phenomena in web services composition genetic algorithm. Adaptive crossover and mutation operator were designed according to the individual adaptability and evolution stage for enlarging local search range and increasing convergent speed. Moreover, use for reference the idea of taboo table in taboo search algorithm, we can inhibit the algorithm from converging to false optimal solution untimely; meanwhile, an evolution strategy was adopted to prevent the loss of composite service with high fitness value. The experimental result shows that better composite services can be gotten through the improved algorithm; moreover the convergence speed has also been improved.

scholarly journals Valuation of FinTech Innovation Based on Patent Applications

2020 ◽  
Vol 12 (23) ◽  
pp. 10158
Author(s):  
Jelena Kabulova ◽  
Jelena Stankevičienė
Keyword(s):  
Financial Services ◽  
Stock Price ◽  
Patent Application ◽  
Disruptive Technology ◽  
Financial Industry ◽  
Negative Effects ◽  
Services Sector ◽  
Machine Learning Applications ◽  
Research Findings ◽  
Patent Applications

The financial services sector, perhaps more than any other, is being disrupted by advances in technology. The purpose of this study is to provide comprehensive data and evidence on value of the FinTech innovation event. First, a text-based filtering method for identifying FinTech patent applications is provided. Using machine learning applications, innovations are classified into major technology groups. The methodology for valuation of FinTech innovation is based on data of stock price changes. To assess the value impact, Poisson flow rates and stock price movements were combined. Further, to evaluate the effect of FinTech patents on the company’s value, a combination of CAR of patent application and Poisson intensities were used. Research findings provide evidence that FinTech innovations bring significant value for innovators and Blockchain being especially valuable. Such innovations as blockchain, robo-advising and mobile transactions are the most valuable for the financial sector. On one side of the spectrum, the financial industry can be affected more negatively by the innovation of nonfinancial startups that carry disruptive technology at their core. However, on the other side of the spectrum, market leaders who make significant investments in their innovations can evade most of these negative effects. This helped to form an overall view of FinTech innovations.

scholarly journals An enhanced hybrid genetic algorithm for solving traveling salesman problem

2020 ◽  
Vol 18 (2) ◽  
pp. 1035
Author(s):  
Zeravan Arif Ali ◽  
Subhi Ahmed Rasheed ◽  
Nabeel No’man Ali
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Traveling Salesman ◽  
Mutation Operators ◽  
Np Hard Problem ◽  
The Traveling Salesman Problem

<span>Robust known the exceedingly famed NP-hard problem in combinatorial optimization is the Traveling Salesman Problem (TSP), promoting the skillful algorithms to get the solution of TSP have been the burden for several scholars. For inquiring global optimal solution, the presented algorithm hybridizes genetic and local search algorithm to take out the uplifted quality results. The genetic algorithm gives the best individual of population by enhancing both cross over and mutation operators while local search gives the best local solutions by testing all neighbor solution. By comparing with the conventional genetic algorithm, the numerical outcomes acts that the presented algorithm is more adequate to attain optimal or very near to it. Problems arrested from the TSP library strongly trial the algorithm and shows that the proposed algorithm can reap outcomes within reach optimal. For more details, please download TEMPLATE HELP FILE from the website.</span>

scholarly journals The Impact of the Covid-19 Pandemic on Stock Investment in the Indonesian Capital Market

2021 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Maulana Majied Sumatrani Saragih ◽  
Sarman Sinaga ◽  
Faisal Faisal ◽  
Rico Nur Ilham ◽  
T Nurhaida
Keyword(s):  
Financial Services ◽  
Stock Price ◽  
Trading Volume ◽  
Price Index ◽  
Stock Exchange ◽  
Volume Data ◽  
Stock Trading ◽  
Stock Investment ◽  
Stock Price Index ◽  
The Impact

The COVID-19 pandemic has hit various sectors, including the stock market where many people are hesitant to invest in stocks. Many industries have been affected by Covid-19, where since March 2020 the Indonesia Stock Exchange Composite Stock Price Index (IHSG) has decreased because many investors sold their shares, but since the third week of May 2020 to early June 2020 has shown an increase indicating stock trading has begun to show improvement. This study aims to analyze which sector stocks are still able to survive during the COVID-19 pandemic, by using stock trading volume data, Composite Stock Price Index (IHSG), weekly and monthly market capitalization values with a sample of 20 stocks - the highest stocks. based on sales volume and transaction value on the Indonesian stock exchange for the period March 2020 to June 2020 obtained from the Financial Services Authority (OJK) weekly report and the Indonesia Stock Exchange (IDX) Monthly Report. The results show that during the COVID-19 pandemic, investors can still get benefits in investing in stocks if every decision made by these investors is supported by careful calculations.

A New Genetic Algorithm Based on Optimal Solution Orientation

2010 ◽  
Vol 139-141 ◽  
pp. 1779-1784
Author(s):  
Quan Wang ◽  
Jin Chao Liu ◽  
Pan Wang ◽  
Juan Ying Qin
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Local Domain ◽  
Local Search Algorithm ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Optimum Solution ◽  
Standard Genetic Algorithm

Many researchers have indicated that standard genetic algorithm suffers from the dilemma---premature or non-convergence. Most researchers focused on finding better search strategies, and designing various new heuristic methods. It seemed effective. From another view, we can transform search space with a samestate-mapping. A special genetic algorithm applied to the new search space would achieve better performance. Thus, we present a new genetic algorithm based on optimal solution orientation. In this paper, a new genetic algorithm based on optimum solution orientation is presented. The algorithm is divided into "optimum solution orientation" phase and "highly accurately searching in local domain of global optimal solution" phase. Theoretical analysis and experiments indicate that OSOGA can find the "optimal" sub domain effectively. Cooperating with local search algorithm, OSOGA can achieve highly precision solution with limited computing resources.

scholarly journals Recommending System for Penny Stock Trading

2019 ◽  
Vol 9 (1S5) ◽  
pp. 85-91
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Reinforcement Learning ◽  
Test Data ◽  
Stock Price ◽  
Prediction Models ◽  
Poor Performance ◽  
Stock Trading ◽  
Financial Metrics ◽  
Penny Stocks

Penny stocks at times makes the investors wealthy by turning to be a multi-bagger stocks or erode the wealth of the investors with poor performance in volatile conditions. While there are many machine learning-based prediction models that are used for stock price evaluation, very few studies have focused on the dynamics to be considered in penny stock conditions. Though the pattern might remain the same for normal stocks and the penny stock classification, still some of the parameters to be evaluated in the process needs changes. The model discussed in this report is a comprehensive solution discussed as scope for evaluation of the penny stock pick, using trading and reporting financial metrics. Experimental study of the test data indicates that the model is potential and if can be used effectively with reinforcement learning pattern, it can turn to be sustainable solution.

scholarly journals A Metaheuristic Algorithm for VLSI Floorplanning Problem

2019 ◽  
Vol 8 (2S5) ◽  
pp. 249-254
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Search Algorithm ◽  
Design Method ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Chip Area ◽  
Optimum Solution ◽  
Vlsi Floorplanning

Floorplanning plays an important role within the physical design method of very large Scale Integrated (VLSI) chips. It’s a necessary design step to estimate the chip area before the optimized placement of digital blocks and their interconnections. Since VLSI floorplanning is an NP-hard problem, several improvement techniques were adopted to find optimal solution. In this paper, a hybrid algorithm which is genetic algorithm combined with music-inspired Harmony Search (HS) algorithm is employed for the fixed die outline constrained floorplanning, with the ultimate aim of reducing the full chip area. Initially, B*-tree is employed to come up with the first floorplan for the given rectangular hard modules and so Harmony Search algorithm is applied in any stages in genetic algorithm to get an optimum solution for the economical floorplan. The experimental results of the HGA algorithm are obtained for the MCNC benchmark circuits

Research on Intelligent Design Method of Ship Cabin Layout

2020 ◽  
Vol 54 (2) ◽  
pp. 79-92
Author(s):  
Yun-long Wang ◽  
Zhang-pan Wu ◽  
Guan Guan ◽  
Chao-guang Jin
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Intelligent Design ◽  
Search Algorithm ◽  
Design Method ◽  
Optimal Solution ◽  
Location Model ◽  
Improved Genetic Algorithm ◽  
Multi Objective ◽  
Constraint Model

AbstractThe design of the ship cabin layout is a multi-objective comprehensive optimization problem, which needs to consider the location of the passage, cabin layout, space utilization ratio, functional realization, comfort, safety, and so on. In this paper, an improved tabu-criterion genetic algorithm for the intelligent design of ship cabin layout is proposed. According to the characteristics of ship accommodation cabin layout design, a ship deck layout area model and a multi-objective optimization model including relative location model, absolute location model, and ergonomic constraint model are established. The neighborhood transformation criterion and tabu-criterion of tabu search algorithm are introduced into genetic algorithm to advance the local search ability of a genetic algorithm. At the same time, a new genetic algorithm coding method is proposed to avoid the generation of illegal solutions in cabin layout sequence crossover and mutation operation. The improved genetic algorithm can effectively improve the local search ability of a genetic algorithm and make the algorithm converge to the optimal solution as soon as possible at the later stage of calculation. Finally, the simulation results verify the feasibility and efficiency of the proposed intelligent design method for ship accommodation cabin layout.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

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