A multiple regression model for trend change prediction

Park, N. J.; George, K. E.; Park, N.

doi:10.1109/icfte.2010.5499430

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Even though numerous propositions exist in the literature on prediction of stock prices, most of them fall into one of three broad categories. The propositions in the first category are all based on simple linear regression on multivariate crosssectional data [8][9][10]. However, in most of the real-world scenarios, these models fail to achieve a high level of accuracy in forecasting since the underlying assumptions for the best performance of the models are not met by the financial time series data.…”

Section: Related Workmentioning

confidence: 99%

Stock Price Prediction Using Deep Learning Models

Sen¹,

Mehtab²,

Nath³

2021

Preprint

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Prediction of future movement of stock prices has been a subject matter of many research work. On one hand, we have proponents of the Efficient Market Hypothesis who claim that stock prices cannot be predicted, on the other hand, there are propositions illustrating that, if appropriately modeled, stock prices can be predicted with a high level of accuracy. There is also a gamut of literature on technical analysis of stock prices where the objective is to identify patterns in stock price movements and profit from it. In this work, we propose a hybrid approach for stock price prediction using five deep learning-based regression models. We select the NIFTY 50 index values of the National Stock Exchange (NSE) of India, over a period of December 29, 2014 to July 31, 2020. Based on the NIFTY data during December 29, 2014 to December 28, 2018, we build two regression models using convolutional neural networks (CNNs), and three regression models using long-and-short-term memory (LSTM) networks for predicting the open values of the NIFTY 50 index records for the period December 31, 2018 to July 31, 2020. We adopted a multi-step prediction technique with walk-forward validation. The parameters of the five deep learning models are optimized using the grid-search technique so that the validation losses of the models stabilize with an increasing number of epochs in the model training, and the training and validation accuracies converge. Extensive results are presented on various metrics for all the proposed regression models. The results indicate that while both CNN and LSTM-based regression models are very accurate in forecasting the NIFTY 50 open values, the CNN model that previous one week’s data as the input is the fastest in its execution. On the other hand, the encoder-decoder convolutional LSTM model uses the previous two weeks’ data as the input is found to be the most accurate in its forecasting results.

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Section: Related Workmentioning

confidence: 99%

Stock Price Prediction Using Deep Learning Models

Sen¹,

Mehtab²,

Nath³

2021

Preprint

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“…While extensive work has been done on these areas, most of the existing propositions in the literature can be categorized into three broad types. The frameworks belonging to the first category are essentially built one multivariate ordinary least square regression [8][9][10]. However, these models fail to perform well on real-world data as the stringent requirements that these models impose on the data are usually not satisfied.…”

Section: Related Workmentioning

confidence: 99%

Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models

Mehtab¹,

Sen²

2020

2020 International Conference on Decision Aid Sciences and Application (DASA)

100

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Designing robust and accurate predictive models for stock price prediction has been an active area of research over a long time. While on one side, the supporters of the efficient market hypothesis claim that it is impossible to forecast stock prices accurately, many researchers believe otherwise. There exist propositions in the literature that have demonstrated that if properly designed and optimized, predictive models can very accurately and reliably predict future values of stock prices. This paper presents a suite of deep learning-based models for stock price prediction. We use the historical records of the NIFTY 50 index listed in the National Stock Exchange (NSE) of India, during the period from December 29, 2008 to July 31, 2020 for training and testing the models. Our proposition includes two regression models built on convolutional neural networks (CNNs), and three long-and-short-term memory (LSTM) network-based predictive models. For the purpose of forecasting the open values of the NIFTY 50 index records, we adopted a multi-step prediction technique with walk-forward validation. In this approach, the open values of the NIFTY 50 index are predicted on a time horizon of one week, and once a week is over, the actual index values are included in the training set before the model is trained again, and the forecasts for the next week are made. We present detailed results on the forecasting accuracies for all our proposed models. The results show that while all the models are very accurate in forecasting the NIFTY 50 open values, the univariate encoder-decoder convolutional LSTM with previous two weeks' data as the input is the most accurate model. On the other hand, a univariate CNN model with previous one week's data as the input is found to be the fastest model in terms of its execution speed.

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“…Pairs Trading is a common market neutral strategy that opens long and short position for two securities simultaneously to capture the price differential [9]. Moving Average usually applies to univariate time series for studying the relationship between a variable and its lagged terms [22], while Multiple Linear Regression Model is used for modelling multivariate time series [25].…”

Section: Related Workmentioning

confidence: 99%

“…Extensive research has been conducted on algorithmic trading and various trading models have been proposed, for instance, Pairs Trading [9,18], Moving Average [17,22], Linear Regression [10,25], Neural Networks [14,21] and Sentiment Analysis [6,20]. Trading algorithms built on these trading models might involve complicated trading rules in an attempt to achieve a higher prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

TradAO: A Visual Analytics System for Trading Algorithm Optimization

Tsang¹,

Li²,

Lam³

et al. 2020

Preprint

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Figure 1: TradAO assists users in exploring the whole optimization process of a trading algorithm and evaluating its detailed performances. A) The algorithm evolution view explores how a trading algorithm evolves along with the configuration of different parameters. B) The parameter correlation view examines if a trading algorithm instance is overfitting. C) The trading residual view helps traders confirm the performance consistency of a trading algorithm instance more efficiently. D) The cash usage view facilitates monitoring the cash usage along different trading periods. E) The trading history view allows users to explore trading order information along the whole trading period.

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A multiple regression model for trend change prediction

Cited by 10 publications

References 5 publications

Stock Price Prediction Using Deep Learning Models

Stock Price Prediction Using Deep Learning Models

Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models

TradAO: A Visual Analytics System for Trading Algorithm Optimization

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