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In stock and flight price time series diffusion and jumps govern price evolution over time. A jump-diffusion dyadic particle filter is proposed for price prediction. In stock price prediction, the dyad comprises a latent vector mo...
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In stock and flight price time series diffusion and jumps govern price evolution over time. A jump-diffusion dyadic particle filter is proposed for price prediction. In stock price prediction, the dyad comprises a latent vector modeling each stock and a latent vector modeling the group of companies in the same category. In flight price prediction, the dyad consists of a departure latent vector and an arrival latent vector, respectively. A particle coefficient is introduced to encode both diffusion and jumps. The diffusion process is assumed to be a geometric Brownian motion whose dynamics are modeled by a Kalman filter. The negative log-likelihood of the posterior distribution is approximated by a Taylor expansion around the previously observed drift parameter. Efficient approximations of the first and second-order derivatives of the negative log-likelihood with respect to the previously observed drift parameter are derived. To infer sudden price jumps, a reversible jump Markov chain Monte-Carlo framework is used. Experiments have demonstrated that price jump and diffusion inference mechanisms lead to more accurate predictions compared to state-of-the-art techniques. Performance gains are attested to be statistically significant.
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This paper studies the problem of the locked quantity of stocks by means of the stock price plasticity model and obtains the theoretical formula of the locked quantity of stocks and the basic stock price plasticity equation. Based...
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This paper studies the problem of the locked quantity of stocks by means of the stock price plasticity model and obtains the theoretical formula of the locked quantity of stocks and the basic stock price plasticity equation. Based on the characteristic of plasticity and elasticity of stock price, the relation between the stock price plasticity coefficient and the stock price is carefully examined and several statistical conclusions of the qualitative prediction of the stock prices are proposed. This paper also uses the stock price plasticity model to compare the international stock markets and gets some instructive results.
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Recurrent Neural Networks (RNNs) have gained popularity in the field of prediction of stock market prices due to their extraordinary performance in time-sequential tasks. Today, LSTMs are one of the most popular sequential deep le...
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Recurrent Neural Networks (RNNs) have gained popularity in the field of prediction of stock market prices due to their extraordinary performance in time-sequential tasks. Today, LSTMs are one of the most popular sequential deep learning neural network architectures. The main idea behind LSTM neural networks is to allow the network to “forget” or ignore certain past observations so that it can give weight to important information in the current prediction. In this article, we propose to use LSTMs for predicting the stock values of Facebook, Tesla, Apple and Microsoft, quotes that are widely followed by investors around the world.
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State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long-Short-Term Memory ...
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State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mechanism in Long-Short-Term Memory (LSTM) network based stock price movement prediction. Our proposed model significantly enhances the LSTM prediction performance in the Hong Kong stock market. The attention LSTM (AttLSTM) model is compared with the LSTM model in Hong Kong stock movement prediction. Further parameter tuning results also demonstrate the effectiveness of the attention mechanism in LSTM-based prediction method.
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Under the background of big data and Internet finance, quantitative investment is becoming more and more critical, and the prediction of the stock price has become the focus of investors' concern and research. The purpose of this ...
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Under the background of big data and Internet finance, quantitative investment is becoming more and more critical, and the prediction of the stock price has become the focus of investors' concern and research. The purpose of this work is to apply neural network and BP algorithm onto the classification and prediction of stock price patterns. The method is to use the BP algorithm neural network for the transaction data of 5 consecutive days as input samples, so there are 20 input layer nodes. The final value of the next day is used as the output sample, and the number of nodes in the output layer is 1. The purpose of network training is to find 20 spline functions. After the training of the BP algorithm neural network, the test data (stock price data for 5 consecutive days) independent of the training data is leveraged as the input of the neural network, and the closing price of the next day is used as the target output of the network. Through the error between the actual output and the target output, the stock price prediction performance of the network model is analyzed. The results have shown that the prediction accuracy of the stock price is 62.12% under the prediction of deep learning fuzzy algorithm and 73.29% under the prediction of the BP algorithm neural network. When the prediction range is between 15 days, the error of 30 prediction values relative to the real value is within ± 10%, accounting for 90% of the total days, and the prediction effect is the best. By analyzing the prediction of the number of hidden layers on the stock price and different ranges, it can be concluded that the prediction of the stock price trend prediction model of BP algorithm neural network is better than that of the deep learning fuzzy algorithm prediction model. This algorithm provides investors with a certain value for stock forecasting, which makes government gain a more active position in macroeconomic regulation and control.
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One of the main problems of predicting stock price with regression approach is overfitting a model. An overfit model becomes tailored to fit the random noise in the dataset rather than reflecting the overall population. For this i...
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One of the main problems of predicting stock price with regression approach is overfitting a model. An overfit model becomes tailored to fit the random noise in the dataset rather than reflecting the overall population. For this it is necessary to construct an integrated regression-classification model to approximate the true model for the entire population in the dataset. The proposed model integrates Multiple Linear Regression algorithm and One Rule (OneR) classification algorithm. Initially the prediction was treated with regression approach where the outputs were in numerical values. After that a classification model was used to interpret the regression outputs and then classified the outcomes into Profit and Loss class labels. The test results were compared to those obtained with standard classification algorithms which included OneR, Zero Rule (ZeroR), Decision Tree and REP Tree. The results showed that the regression-classification model were significantly more successful than the standard classification algorithms.
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Stock price prediction has drawn huge attention due to its impact on economic stability. Accurate stock price prediction is highly essential to reduce the risk associated with it so as to decide good investment strategies. There a...
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Stock price prediction has drawn huge attention due to its impact on economic stability. Accurate stock price prediction is highly essential to reduce the risk associated with it so as to decide good investment strategies. There are various factors influencing the prediction of stock indices namely gross margin, exchange rate, inflation rate, relative index and so on. Feature selection plays a vital role in effective and accurate prediction of stock indices. This paper aims to provide a clear review of widely used features affecting the stock price fluctuations, feature selection techniques and prediction models from the recent literature. The study also highlights the future directions in this domain focusing the enhancement of the prediction performance.
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Abstract Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools ar...
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Abstract Stock markets are a popular kind of financial markets because of the possibility of bringing high revenues to their investors. To reduce risk factors for investors, intelligent and automated stock market forecast tools are developed by using computational intelligence techniques. This study presents a hyperparameter optimal genetic programming-based forecast model generation algorithm for a-day-ahead prediction of stock market index trends. To obtain an optimal forecast model from the modeling dataset, a differential evolution (DE) algorithm is employed to optimize hyperparameters of the genetic programming orthogonal least square (GpOls) algorithm. Thus, evolution of GpOls agents within the hyperparameter search space enables adaptation of the GpOls algorithm for the modeling dataset. This evolutionary hyperparameter optimization technique can enhance the data-driven modeling performance of the GpOls algorithm and allow the optimal autotuning of user-defined parameters. In the current study, the proposed DE-based hyper-GpOls (DEHypGpOls) algorithm is used to generate forecaster models for prediction of a-day-ahead trend prediction for the Istanbul Stock Exchange 100 (ISE100) and the Borsa Istanbul 100 (BIST100) indexes. In this experimental study, daily trend data from ISE100 and BIST100 and seven other international stock markets are used to generate a-day-ahead trend forecaster models. Experimental studies on 4 different time slots of stock market index datasets demonstrated that the forecast models of the DEHypGpOls algorithm could provide 57.87% average accuracy in buy–sell recommendations. The market investment simulations with these datasets showed that daily investments to the ISE100 and BIST100 indexes according to buy or sell signals of the forecast model of DEHypGpOls could provide 4.8% more average income compared to the average income of a long-term investment strategy.
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In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU),...
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In order to obtain better prediction results, this paper combines improved complete ensemble EMD (ICEEMDAN) and the whale algorithm of multi-objective optimization (MOWOA) to improve the bidirectional gated recurrent unit (BIGRU), which makes full use of original complex stock price time series data and improves the hyperparameters of the BIGRU network. To address the problem that BIGRU cannot make full use of the stationary data, the original sequence data are processed using the ICEEMDAN decomposition algorithm to derive the non-stationary and stationary parts of the data and modeled with the BIGRU and the autoregressive integrated moving average model (ARIMA), respectively. The modeling process introduces a whale algorithm for multi-objective optimization, which improves the probability of finding the best combination of parameter vectors. The R2, MAPE, MSE, MAE, and RMSE values of the BIGRU algorithm, ICEEMDAN-BIGRU algorithm, MOWOA-BIGRU algorithm, and the improved algorithm were compared. An average improvement of 14.4% over the original algorithm's goodness-of-fit value will greatly improve the accuracy of stock price predictions.
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This study analyses the sentiment towards the Japanese economy that might appear in daily news articles between 1 January 2007 and 30 September 2012. To quantify such a sentiment, we created an index that accounts for the frequenc...
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This study analyses the sentiment towards the Japanese economy that might appear in daily news articles between 1 January 2007 and 30 September 2012. To quantify such a sentiment, we created an index that accounts for the frequency of occurrence of words that affirmatively or negatively describe the current economic situation. Articles were taken from the Nikkei, a popular newspaper in Japan comparable with the Wall Street Journal in the USA. Using a cutting-edge text mining technique, we counted the numbers of 'positive' as well as 'negative' words in the newspaper articles. Constructing a daily summary index, we then performed statistical analysis to examine correlations between the sentiment index and Tokyo Stock Exchange prices. One interesting finding is that the index significantly predicts stock prices of three days in advance.
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