An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

Sezer, Ömer Berat; Özbayoğlu, A. Murat; Doğdu, Erdoğan

doi:10.1145/3077286.3077294

Cited by 57 publications

(27 citation statements)

References 9 publications

(8 reference statements)

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“…Technical analysis is usually used to predict short-to medium-term time horizons. An artificial neural network-based stock trading system using technical analysis and big data framework has been proposed in the work of [22]. The results have shown that, by choosing the most appropriate technical indicators, the neural network model can obtain comparable results against the buy and hold strategy in most of the cases.…”

Section: Deep Learning In Stock Predictionmentioning

confidence: 99%

Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading

2020

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In quantitative trading, stock prediction plays an important role in developing an effective trading strategy to achieve a substantial return. Prediction outcomes also are the prerequisites for active portfolio construction and optimization. However, the stock prediction is a challenging task because of the diversified factors involved such as uncertainty and instability. Most of the previous research focuses on analyzing financial historical data based on statistical techniques, which is known as a type of time series analysis with limited achievements. Recently, deep learning techniques, specifically recurrent neural network (RNN), has been designed to work with sequence prediction. In this paper, a long short-term memory (LSTM) network, which is a special kind of RNN, is proposed to predict stock movement based on historical data. In order to construct an efficient portfolio, multiple portfolio optimization techniques, including equal-weighted modeling (EQ), simulation modeling Monte Carlo simulation (MCS), and optimization modeling mean variant optimization (MVO), are used to improve the portfolio performance. The results showed that our proposed LSTM prediction model works efficiently by obtaining high accuracy from stock prediction. The constructed portfolios based on the LSTM prediction model outperformed other constructed portfolios-based prediction models such as linear regression and support vector machine. In addition, optimization techniques showed a significant improvement in the return and Sharpe ratio of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Standard and Poor 500 (S&P 500) index in both active returns and Sharpe ratios.

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Section: Deep Learning In Stock Predictionmentioning

confidence: 99%

Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading

2020

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“…Sezer et al [25] proposed an artificial neural network that uses the technical analysis indicators such as MACD, RSI, Williams %R to predict Dow Jones 30 Stocks. Besides these approaches, evolutionary and genetic algorithms are used to forecast stock market prices and index in literature [1].…”

Section: Machine Learning and Evolutionary Solutionsmentioning

confidence: 99%

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Sezer¹,

Özbayoğlu²

2018

AUTOSOFT

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Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.

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“…The hidden layer and an output layer which gives a single value as an output makes this system a multilevel perceptron regression. So internally the network regresses different independent input variable onto the single dependent variable by using equation 4 to compute the loss function [21]: (4) It is also clear by the formula that it's a squared error function, and α>0 is a non-negative hyper parameter that controls the magnitude of the penalty. Now coming to optimization according to gradient decent which is an algorithm for finding a minimum of the function or let's say optimization algorithm the gradient ∇LossW of the loss with respect to the weights is computed by equation 5: 5Where, i is the iteration step, and ε is the learning rate with a value larger than 0.…”

Section: Sentiment Analysismentioning

confidence: 99%

Stock Price Prediction using Artificial Neural Model: An Application of Big Data

Shastri¹,

Roy²,

Mittal³

2018

ICST Transactions on Scalable Information Systems

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In recent time, stock price prediction is an area of profound interest in the realm of fiscal market. To predict the stock prices, authors have proposed a technique by first calculating the sentiment scores through Naïve Bayes classifier and after that neural network is applied on both sentiment scores and historical stock dataset. They have also addressed the issue of data cleaning using a Hive ecosystem. This ecosystem is being used for pre-processing part and a neural network model with inputs from sentiment analysis and historic data is used to predict the prices. It has been observed from the experiments that the accuracy level reaches above 90% in maximum cases, as well as it also provides the solid base that model will be more accurate if it trained with recent data. The intended combination of sentiment analysis and Neural networks is used to establish a statistical relationship between historic numerical data records of a particular stock and other sentimental factors which can affects the stock prices.

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An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

Cited by 57 publications

References 9 publications

Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading

Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading

Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks

Stock Price Prediction using Artificial Neural Model: An Application of Big Data

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