Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.
피드포워드 계산 신경망 모델(CNN)을 구축하려면 네트워크 토폴로지 결정과 가중치 추정이라는 두 가지 별개의 작업이 필요합니다. 문제의 적절한 네트워크 토폴로지를 지정하는 것이 핵심 문제이며 이 기여의 주요 초점입니다. 지금까지 이 문제는 공간 응용 분야에서 완전히 무시되었거나 검색 발견적 방법으로 해결되었습니다(Fischer and Gopal 1994 참조). 지리적 공간에 대한 모델링 상호 작용의 관점에서 이 논문은 이 문제를 전역 최적화 문제로 간주하고 역전파 학습을 유전 알고리즘의 진화 패러다임에 포함시키는 새로운 접근 방식을 제안합니다. 이것은 최적의 CNN 토폴로지를 찾기 위한 유전자 검색을 네트워크 매개변수를 결정하기 위한 기울기 기반 역전파 학습과 결합하여 수행됩니다. 따라서 모델 빌더는 기울기 하강 오류의 역전파와 같은 간단하지만 강력한 학습 메커니즘으로 문제를 해결할 수 있는 적절한 CNN 토폴로지를 식별하는 부담에서 벗어날 것입니다. 이 접근 방식은 성능을 설명하고 견고성을 평가하기 위해 오스트리아의 지역간 통신 트래픽 데이터를 사용하는 단일 은닉 레이어, 단일 출력 피드포워드 CNN 모델의 3가지 입력 제품군에 적용되었습니다.
신경망(NN)은 재무 분석 및 예측, 특히 단기 예측에 사용할 수 있는 컴퓨터 과학의 인공 지능 분야의 비선형 도구입니다. 이는 판별 분석 및 회귀와 같은 기존 방법에 대한 유용한 대안을 제공하며, 특히 대규모의 때로는 불완전한 데이터 세트에서 비선형 또는 알려지지 않은 패턴을 탐색할 때 유용합니다. 강력한 NN에는 주요 제한 사항이 있습니다. 결과는 때때로 강력하지 않지만 훈련에 따라 다르며 복제하기 어렵습니다. 채권 등급과 같은 인접 범주를 정렬할 때 종종 높은 오류율이 발생합니다. 신경망을 처리하는 데 사용되는 소프트웨어를 사용할 수 있지만 데이터 세트의 크기와 신경망의 복잡성에 따라 응용 프로그램에 시간과 비용이 많이 소요될 수 있습니다.
신뢰성은 오늘날의 HPC 환경에서 잘 알려진 문제이며 차세대 페타 규모 시스템에서는 더욱 어려워질 것으로 예상됩니다. 현재의 내결함성 접근 방식(예: 체크포인트/재시작 메커니즘)은 성능 및 확장성 문제로 인해 비효율적인 것으로 간주되기 때문에 사전 예방적 결함 방지(PFA)와 같은 개선된 내결함성 접근 방식이 현재 조사 중입니다. PFA 접근 방식은 장애 예측 및 마이그레이션을 기반으로 애플리케이션에 대한 장애의 영향과 복구 시간을 모두 줄입니다. 이 문서에서는 PFA 컨텍스트에서 결함 예측 개선을 위한 인공 신경망(ANN) 기술의 사용을 탐구합니다. 처음에 지도 역전파 학습 알고리즘으로 피드포워드 네트워크를 훈련함으로써 이 네트워크는 클러스터에서 수집한 과거 IPMI 센서 데이터를 제공받습니다. 결과는 이전 "임계값 트리거" 접근 방식에 비해 예측 성능이 향상되었음을 보여줍니다.
Stock market decision making is a very challenging and difficult task of �financial data prediction. Prediction about stock market with high accuracy movement yield pro�fit for investors of the stocks. Because of the complexity of stock market �financial data, development of efficient models for prediction decision is very difficult and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Arti�cial Neural Network(ANN) and Random Forest has been used for developing of prediction model. Technical indicators are calculated from the stock prices based on time-line data and it is used as inputs of the proposed prediction models. Ten years of stock market data has been used for signal prediction of stock. Based on the data set, these models are capable to generate buy/hold signal for stock market as a output. The main goal of this project is to generate output signal(buy/hold) as per users requirement like amount to be invested, time duration for investment, minimum profit, maximum loss using data mining and machine learning techniques.
In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.
The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.
이 다이어그램은 (Walter Freeman의 신경 역학 설명에서와 같이) 어트랙터 풍경의 복잡한 컬렉션의 논리적 구조를 나타내는 언어학에 대한 Sydney Lamb의 관계형 네트워크 개념의 사용을 탐구합니다. 척추동물 신경계와 같이 충분히 큰 시스템이 주어지면 어트랙터 net 자체가 뉴런 수준에서 실현되는 역학 시스템보다 높은 차수의 역학 시스템인 것으로 생각하고 싶을 수 있습니다. 구성에는 다양성('is-a' 상속), 단순한 움직임, 계산 및 장소 표기, 시간과 공간의 방향, 언어, 학습이 포함됩니다.
Effectiveness of the use of neural-net technology for the solving of shell theory problems is shown. Some results of neural-net interpolation and extrapolation for direct and inverse problems are discussed. Exact accuracy of neural-net solving opens wide latitude for shell constructions engineering design and optimization.
The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.
Predicting the direction and movement of stock index prices is difficult, often leading to excessive trading, transaction costs, and missed opportunities. Often traders need a systematic method to not only spot trading opportunities, but to also provide a consistent approach, thereby minimizing trading errors and costs. While mechanical trading systems exist, they are usually designed for a specific stock, stock index, or other financial asset, and are often highly dependent on preselected inputs and model parameters that are expected to continue providing trading information well after the initial training or back-tested model development period. The following research leads to a detailed trading model that provides a more effective and intelligent way for recognizing trading signals and assisting investors with trading decisions by utilizing a system that adapts both the inputs and the prediction model based on the desired output. To illustrate the adaptive approach, multiple inputs and modeling techniques are utilized, including neural networks, particle swarm optimization, and denoising. Simulations with stock indexes illustrate how traders can generate higher returns using the developed adaptive decision support system model. The benefits of adding adaptive and intelligent decision making to forecasts are also discussed.
Stock market decision making is a very challenging and difficult task of �financial data prediction. Prediction about stock market with high accuracy movement yield pro�fit for investors of the stocks. Because of the complexity of stock market �financial data, development of efficient models for prediction decision is very difficult and it must be accurate. This study attempted to develop models for prediction of the stock market and to decide whether to buy/hold the stock using data mining and machine learning techniques. The machine learning technique like Naive Bayes, k-Nearest Neighbor(k-NN), Support Vector Machine(SVM), Arti�cial Neural Network(ANN) and Random Forest has been used for developing of prediction model. Technical indicators are calculated from the stock prices based on time-line data and it is used as inputs of the proposed prediction models. Ten years of stock market data has been used for signal prediction of stock. Based on the data set, these models are capable to generate buy/hold signal for stock market as a output. The main goal of this project is to generate output signal(buy/hold) as per users requirement like amount to be invested, time duration for investment, minimum profit, maximum loss using data mining and machine learning techniques.
In this work we present an Artificial Neural Network (ANN) approach to predict stock market indices. In particular, we focus our attention on their trend movement up or down. We provide results of experiments exploiting different Neural Networks architectures, namely the Multi-layer Perceptron (MLP), the Convolutional Neural Networks (CNN), and the Long Short-Term Memory (LSTM) recurrent neural networks technique. We show importance of choosing correct input features and their preprocessing for learning algorithm. Finally we test our algorithm on the S&P500 and FOREX EUR/USD historical time series, predicting trend on the basis of data from the past n days, in the case of S&P500, or minutes, in the FOREX framework. We provide a novel approach based on combination of wavelets and CNN which outperforms basic neural networks approaches.
The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes.
Effectiveness of the use of neural-net technology for the solving of shell theory problems is shown. Some results of neural-net interpolation and extrapolation for direct and inverse problems are discussed. Exact accuracy of neural-net solving opens wide latitude for shell constructions engineering design and optimization.
The Foreign Exchange Market is the biggest and one of the most liquid markets in the world. This market has always been one of the most challenging markets as far as short term prediction is concerned. Due to the chaotic, noisy, and non-stationary nature of the data, the majority of the research has been focused on daily, weekly, or even monthly prediction. The literature review revealed that there is a gap for intra-day market prediction. Identifying this gap, this paper introduces a prediction and decision making model based on Artificial Neural Networks (ANN) and Genetic Algorithms. The dataset utilized for this research comprises of 70 weeks of past currency rates of the 3 most traded currency pairs: GBP\USD, EUR\GBP, and EUR\USD. The initial statistical tests confirmed with a significance of more than 95% that the daily FOREX currency rates time series are not randomly distributed. Another important result is that the proposed model achieved 72.5% prediction accuracy. Furthermore, implementing the optimal trading strategy, this model produced 23.3% Annualized Net Return.