Pattern Prediction in Stock Market
17 Nov 2009-Vol. 5866, pp 81-90
TL;DR: A new approach to predict pattern of the financial time series in stock market for next 10 days is presented and compared with the existing method of exact value prediction and it has been shown that the proposed pattern prediction technique performs better than value prediction.
Abstract: In this paper, we have presented a new approach to predict pattern of the financial time series in stock market for next 10 days and compared it with the existing method of exact value prediction [2, 3, and 4]. The proposed pattern prediction technique performs better than value prediction. It has been shown that the average for pattern prediction is 58.7% while that for value prediction is 51.3%. Similarly, maximum for pattern and value prediction are 100% and 88.9% respectively. It is of more practical significance if one can predict an approximate pattern that can be expected in the financial time series in the near future rather than the exact value. This way one can know the periods when the stock will be at a high or at a low and use the information to buy or sell accordingly. We have used Support Vector Machine based prediction system as a basis for predicting pattern. MATLAB has been used for implementation.
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01 Jan 2005
TL;DR: In this article, an agent-based scheme for Efficient multicast application in mobile networks is presented, where an enhanced One Way Function Tree Rekey Protocol based on Chinese Remainder Theorem is used for admission control for Multicast Routing with Quality of Service in ad hoc networks.
Abstract: Invited Speakers.- Keeping Viruses Under Control.- Online Auctions: Notes on Theory, Practice, and the Role of Agents.- Computer Networks.- A Unified Approach to Survivability of Connection-Oriented Networks.- SCTP Based Framework for Mobile Web Agent.- An Agent-Based Scheme for Efficient Multicast Application in Mobile Networks.- An Enhanced One Way Function Tree Rekey Protocol Based on Chinese Remainder Theorem.- Admission Control for Multicast Routing with Quality of Service in Ad Hoc Networks.- An Efficient On-line Job Admission Control Scheme to Guarantee Deadlines for QoS-Demanding Applications.- A Methodology of Resilient MPLS/VPN Path Management Under Multiple Link Failures.- Sensor and Satellite Networks.- Comparison of Hyper-DAG Based Task Mapping and Scheduling Heuristics for Wireless Sensor Networks.- A Markov-Based Model to Analyze the Temporal Evolution and Lifetime of a Sensor Network.- Power-Efficient Seamless Publishing and Subscribing in Wireless Sensor Networks.- Group-Oriented Channel Protection for Mobile Devices in Digital Multimedia Broadcasting.- IP Traffic Load Distribution in NGEO Broadband Satellite Networks - (Invited Paper).- Cross-Layer Management of Radio Resources in an Interactive DVB-RCS-Based Satellite Network-(Invited Paper).- Aggressive Back off Strategy in Congestion Management Algorithm for DBS-RCS - (Invited Paper).- TCP-Peach++: Enhancement of TCP-Peach+ for Satellite IP Networks with Asymmetrical Bandwidth and Persistent Fades-(Invited Paper).- Security and Cryptography.- Automatic Translation of Serial to Distributed Code Using CORBA Event Channels.- Fault Tolerant and Robust Mutual Exclusion Protocol for Synchronous Distributed Systems.- Exact Best-Case End-to-End Response Time Analysis for Hard Real-Time Distributed Systems.- A Formal Policy Specification Language for an 802.11 WLAN with Enhanced Security Network.- A Generic Policy-Conflict Handling Model.- A Truly Random Number Generator Based on a Continuous-Time Chaotic Oscillator for Applications in Cryptography.- A New Cryptanalytic Time-Memory Trade-Off for Stream Ciphers.- SVM Approach with a Genetic Algorithm for Network Intrusion Detection.- Performance Evaluation.- Modeling Access Control Lists with Discrete-Time Quasi Birth-Death Processes.- Stochastic Bounds on Partial Ordering: Application to Memory Overflows Due to Bursty Arrivals.- QoS Evaluation Method in Multimedia Applications Using a Fuzzy Genetic Rule-Based System.- Impact of Setup Message Processing and Optical Switch Configuration Times on the Performance of IP over Optical Burst Switching Networks.- Characterizing Gnutella Network Properties for Peer-to-Peer Network Simulation.- Computing Communities in Large Networks Using Random Walks.- Fame as an Effect of the Memory Size.- Keeping Viruses Under Control.- Distributed Evaluation Using Multi-agents.- Classification of Volatile Organic Compounds with Incremental SVMs and RBF Networks.- E-Commerce and Web Services.- Agent Based Dynamic Execution of BPEL Documents.- A Fair Multimedia Exchange Protocol.- A Pervasive Environment for Location-Aware and Semantic Matching Based Information Gathering.- A Web Service Platform for Web-Accessible Archaeological Databases.- A WSDL Extension for Performance-Enabled Description of Web Services.- A Novel Authorization Mechanism for Service-Oriented Virtual Organization.- Metrics, Methodology, and Tool for Performance-Considered Web Service Composition.- Brazilian Software Process Reference Model and Assessment Method.- Multiagent Systems.- A Secure Communication Framework for Mobile Agents.- A Novel Algorithm for the Coordination of Multiple Mobile Robots.- Multiagent Elite Search Strategy for Combinatorial Optimization Problems.- Managing Theories of Trust in Agent Based Systems.- Applying Semantic Capability Matching into Directory Service Structures of Multi Agent Systems.- Self-organizing Distribution of Agents over Hosts.- Machine Learning.- Evolutionary Design of Group Communication Schedules for Interconnection Networks.- Memetic Algorithms for Nurse Rostering.- Discretizing Continuous Attributes Using Information Theory.- System Identification Using Genetic Programming and Gene Expression Programming.- ARKAQ-Learning: Autonomous State Space Segmentation and Policy Generation.- Signature Verification Using Conic Section Function Neural Network.- Fusion of Rule-Based and Sample-Based Classifiers - 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84 citations
01 Jan 2014
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01 Jan 2007
TL;DR: In this paper, the authors present an algorithm for finding Nash Equilibria in Bimatrix games and two-sided markets, and an optimization approach for approximating Nash equilibria.
Abstract: WINE 2007.- Getting to Economic Equilibrium: A Problem and Its History.- My Favorite Simplicial Complex and Some of Its Applications.- Markets and the Primal-Dual Paradigm.- The Computation of Equilibria.- A Note on Equilibrium Pricing as Convex Optimization.- New Algorithms for Approximate Nash Equilibria in Bimatrix Games.- A Unified Approach to Congestion Games and Two-Sided Markets.- An Optimization Approach for Approximate Nash Equilibria.- Gradient-Based Algorithms for Finding Nash Equilibria in Extensive Form Games.- Bluffing and Strategic Reticence in Prediction Markets.- Pari-Mutuel Markets: Mechanisms and Performance.- Information Sharing Communities.- Competitive Safety Strategies in Position Auctions.- Maintaining Equilibria During Exploration in Sponsored Search Auctions.- Stochastic Models for Budget Optimization in Search-Based Advertising.- Auctions with Revenue Guarantees for Sponsored Search.- Equilibrium Analysis of Dynamic Bidding in Sponsored Search Auctions.- Cooperative or Vindictive: Bidding Strategies in Sponsored Search Auction.- Cost-Balancing Tolls for Atomic Network Congestion Games.- Network Formation: Bilateral Contracting and Myopic Dynamics.- Who Should Pay for Forwarding Packets?.- On the Performance of Congestion Games for Optimum Satisfiability Problems.- Incentive-Compatible Interdomain Routing with Linear Utilities.- Mechanism Design I.- False-Name-Proof Mechanisms for Hiring a Team.- Mechanism Design on Trust Networks.- Stochastic Mechanism Design.- A Note on Maximizing the Spread of Influence in Social Networks.- A Network Creation Game with Nonuniform Interests.- A Theory of Loss-Leaders: Making Money by Pricing Below Cost.- PageRank as a Weak Tournament Solution.- Competitive Influence Maximization in Social Networks.- Advertisement Pricing I.- Sponsored Search with Contexts.- Capacity Constraints and the Inevitability of Mediators in Adword Auctions.- Cost of Conciseness in Sponsored Search Auctions.- Adwords Auctions with Decreasing Valuation Bids.- An Adaptive Sponsored Search Mechanism ?-Gain Truthful in Valuation, Time, and Budget.- Extending Polynomial Time Computability to Markets with Demand Correspondences.- Market Equilibrium Using Auctions for a Class of Gross-Substitute Utilities.- Continuity Properties of Equilibrium Prices and Allocations in Linear Fisher Markets.- Computing Market Equilibrium: Beyond Weak Gross Substitutes.- On Competitiveness in Uniform Utility Allocation Markets.- Total Latency in Singleton Congestion Games.- The Importance of Network Topology in Local Contribution Games.- Secure Relative Performance Scheme.- Selfishness, Collusion and Power of Local Search for the ADMs Minimization Problem.- The Wi-Fi Roaming Game.- On the Complexity of Pure Nash Equilibria in Player-Specific Network Congestion Games.- The Stable Roommates Problem with Globally-Ranked Pairs.- A PSPACE-complete Sperner Triangle Game.- Group Dominant Strategies.- Weighted Boolean Formula Games.- Core Stability of Vertex Cover Games.- Mechanism Design II.- Maximizing Revenue in Sequential Auctions.- Approximate Mechanisms for the Graphical TSP and Other Graph Traversal Problems.- To Be or Not to Be (Served).- Advertisement Pricing II.- Ad Auction Design and User Experience.- Personalized Ad Delivery When Ads Fatigue: An Approximation Algorithm.- Empirical Price Modeling for Sponsored Search.- Pay-per-action Model for Online Advertising.- Public Advertisement Broker Markets.- Mechanism Design III.- K-NCC: Stability Against Group Deviations in Non-cooperative Computation.- Monotone Properties of Randomized Symmetric Incentive Compatible Auctions.- Computing Optimal Bundles for Sponsored Search.- On the Price of Truthfulness in Path Auctions.- Characterizing Truthful Market Design.
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04 Nov 2020
TL;DR: In this paper, the LSTM recurrent deep learning model over feed forward neural network and time series ARIMA model in terms of four prediction metrics i.e. mean square error, root mean square errors, mean average error and mean average percent error.
Abstract: Several research studies have been devoted for the last two decades to make estimates on or to forecast stock prices. Accurate stock prediction movement is still an open question for many companies and financial organizations. This article analyses the stock market prediction using deep learning model. The empirical results reveal the superiority of the LSTM recurrent deep learning model over Feed forward neural network and time series ARIMA model in terms four prediction metrics i.e. mean square error, root mean square error, mean average error and mean average percent error.
1 citations
References
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
"Pattern Prediction in Stock Market" refers methods in this paper
...In attempt to predict the stock markets behavior, study has been done on many prediction methods such as Support Vector Machines and Artificial Neural Networks etc [1,2,7,8]....
[...]
...Overall, techniques based on Support Vector Machines and Artificial Neural Networks have performed better than other statistical methods for prediction [1, 2]....
[...]
TL;DR: The experimental results show that SVM provides a promising alternative to stock market prediction and the feasibility of applying SVM in financial forecasting is examined by comparing it with back-propagation neural networks and case-based reasoning.
1,535 citations
"Pattern Prediction in Stock Market" refers methods in this paper
...In attempt to predict the stock markets behavior, study has been done on many prediction methods such as Support Vector Machines and Artificial Neural Networks etc [1,2,7,8]....
[...]
...In this section, we will discuss the feature set modeling for pattern prediction and value prediction [2] and subsequently, the performance of both the methods will be compared....
[...]
...Overall, techniques based on Support Vector Machines and Artificial Neural Networks have performed better than other statistical methods for prediction [1, 2]....
[...]
...The better prediction systems are based on Artificial Intelligence techniques such as Artificial Neural Networks [3] and Support Vector Machines (SVM) [2]....
[...]
Book•
01 Jan 2008
TL;DR: Getting to Economic Equilibrium: A problem and its history and my Favorite Simplicial Complex and Some of its Applications.
343 citations
TL;DR: This research applies Support-Vector Machines and Back Propagation neural networks for six Asian stock markets and the experimental results showed the superiority of both models, compared to the early researches.
Abstract: Recently, applying the novel data mining techniques for financial time-series forecasting has received much research attention. However, most researches are for the US and European markets, with only a few for Asian markets. This research applies Support-Vector Machines (SVMs) and Back Propagation (BP) neural networks for six Asian stock markets and our experimental results showed the superiority of both models, compared to the early researches.
136 citations
"Pattern Prediction in Stock Market" refers methods in this paper
...In attempt to predict the stock markets behavior, study has been done on many prediction methods such as Support Vector Machines and Artificial Neural Networks etc [1,2,7,8]....
[...]
...1 Value Prediction The basic concept of value prediction is to use previous N days to predict the value of next day [5, 6, 7]....
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TL;DR: The GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction and shows that the performance of the proposed model is better than two conventional methods for artificial neural networks.
Abstract: This paper compares a feature transformation method using a genetic algorithm (GA) with two conventional methods for artificial neural networks (ANNs) In this study, the GA is incorporated to improve the learning and generalizability of ANNs for stock market prediction Daily predictions are conducted and prediction accuracy is measured In this study, three feature transformation methods for ANNs are compared Comparison of the results achieved by a feature transformation method using the GA to the other two feature transformation methods shows that the performance of the proposed model is better Experimental results show that the proposed approach reduces the dimensionality of the feature space and decreases irrelevant factors for stock market prediction
106 citations
"Pattern Prediction in Stock Market" refers methods in this paper
...In attempt to predict the stock markets behavior, study has been done on many prediction methods such as Support Vector Machines and Artificial Neural Networks etc [1,2,7,8]....
[...]
...Overall, techniques based on Support Vector Machines and Artificial Neural Networks have performed better than other statistical methods for prediction [1, 2]....
[...]
...The better prediction systems are based on Artificial Intelligence techniques such as Artificial Neural Networks [3] and Support Vector Machines (SVM) [2]....
[...]