machine learning  stock price
221299294
Stock Price Prediction Based on Machine Learning Approaches
Research of quantitate investment on stock price prediction is effective to help investors increase profits. Recently, technologies of machine learning have been well applied to explore the issue of stock trading. In this paper, Logistic Regression and Support Vector Machines (SVM) were adopted to solve the problem of predicting the trend of stock movements. The experiment showed that these two models could be effectively used in the stock market of China. Returns based on strategies we constructed were significantly better than the HS300 index. In different models, we analyzed the relationship between stock returns and different models. It found that the SVM model results are optimal. The annual return of the strategy based on SVM reached 17.13% and the maximum Drawdown was 0.32. In the future, we will not only focus on the stock market, but also plan to apply this strategy to other investment fields, such as trading of digital currency. We will also use other algorithms for research and comparison, such as andom forests, XGBoost.
DSIT
Hongming Wang
230119602
Company's Stock Price Predictor using Machine Learning
1Assistant Professor, Dept. of Computer Science Engineering, Datta Meghe Institute of Engineering Technology Research, Wardha, Maharashtra, India 2,3,4,5,6 Student, Dept. of Computer Science Engineering, Datta Meghe Institute of Engineering Technology Research, Wardha, Maharashtra, India Abstract Due to the unsettled, composite and time to time changing of prices, stock market prediction has attracted a lot of interest from investors and researchers for quite a time. It is very difficult to make dependable predictions. The stock market prices are predicted using the machine learning algorithms like k Nearest Neighbors, in this system. The model that has been highly triumphant for classification and regression is the k Nearest Neighbors model. KNN algorithm is a very popular classification algorithm demonstrating good performance characteristics and also a short period of training time. We must specify such sets of neighborhoods that might approve the close neighbors, as stated by K nearest neighbor's distribution. We use Support vector machine for the purpose of classification and it is a machine learning model. This model is also used for classification for most of the times. Based on historical data, this system provides the information whether the price of stock will go up or down using these techniques. This also provides deep knowledge of the models that are used in this process.
Vikas R Palekar Piyush Zade Bhumika Vaidya Rachana Mehra N Pradhan Ashwini Lambat
225655103
The two stage machine learning ensemble models for stock price prediction by combining mode decomposition, extreme learning machine and improved harmony search algorithm
As stock data is characterized by highly noisy and non stationary, stock price prediction is regarded as a knotty problem. In this paper, we propose new two stage ensemble models by combining empirical mode decomposition (EMD) (or variational mode decomposition (VMD) extreme learning machine (ELM) and improved harmony search (IHS) algorithm for stock price prediction, which are respectively named EMD ELM IHS and VMD ELM IHS. Furthermore, to demonstrate the efficiency and performance of the proposed models, the results were compared with those obtained by other methods, including EMD based ELM (EMD ELM) VMD based ELM (VMD ELM) autoregressive integrated moving average (ARIMA) ELM, multi layer perception (MLP) support vector regression (SVR) and long short term memory (LSTM) models. The results show that the proposed models have superior performance in terms of its accuracy and stability as compared to the other models. Also, we find that the sizes of sliding window and training set have a significant impact on the predictive performance.
Manrui Jiang Lifen Jia Zhensong Chen W Chen
219094950
Time series data analysis of stock price movement using machine learning techniques
Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange's NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35% perceptron is 75.88% and logistic regression is 86.98% Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93% perceptron of 76.68% and logistic regression of 89.93% respectively.
Soft Comput.
Irfan Ramzan Parray Surinder Singh Khurana Munish Kumar Ali A Altalbe
226485510
Stock Price Prediction using Adaptive Time Series Forecasting and Machine Learning Algorithms
Author(s) Chen, Lumeng Advisor(s) Wu, Yingnian Abstract: In this thesis, ARIMA model, Long Short Term Memory (LSTM) model and Extreme Gradient Boosting (XGBoost) models were developed to predict daily adjusted close price of selected stocks from January 3, 2017 to April 24, 2020. Daily stock price data includes columns of open, close, adjusted close, high, low and volume. In ARIMA and LSTM models, the only features we used as model inputs were previous N days' stock prices. Prediction on day N+1 was calculated based on previous N values. RMSE and MAPE were calculated from this rolling forecast and the actual price in the test dataset. Optimal parameters were selected to be the setting that yielded the lowest RMSE score. Residuals diagnostic was performed to check model assumption for the final ARIMA model. In XGBoost model, feature engineering was used to create two additional features from open, close, high and low price. Same with LSTM model, previous N days features were used as features in day N+1 for prediction. In both LSTM and XGBoost models, training dataset was scaled for model fitting. Features and output from cross validation and test dataset were scaled too based on previous N days' values. The prediction results were then reverted back to original scale before calculation of RMSE and MAPE scores. In conclusion, looking at the prediction versus actual stock price plot for each stock and their RMSE and MAPE scores, all three models produced good forecast of next day's stock price. However, during the time with great volatility, the lag between forecast value and actual value is more noticeable. In our models, historical N days stock price on its own could provide a relatively accurate prediction on N+1 day's stock price. In XGBoost model particularly, we found out that N=2 provided better RMSE and MAPE( results than other larger values of N (previous N days) As N gets larger, prediction accuracy got lower in XGBoost. In XGBoost feature importance analysis, the most important factor to today's stock price is its price yesterday. Although the final ARIMA model achieved the lowest RMSE score, grid search for one step ARIMA forecast model parameters took the longest computing time, while XGBoost model with the second lowest RMSE score required the least time for parameter tuning and forecast calculation.
Lumeng Chen
226674952
Stock Price Prediction Using DCF and Machine Learning
Understanding the long term outlook on a particular stock is very important for investors to see the growth of their investment. This paper will give the idea about how to predict the fair price of the shares using DCF method and understanding short term outlook of that investment. The various technologies used in the paper are free cash flow, the terminal value, net present value, ARIMA, Linear Regression. Traditional techniques lack in covering long term stock price movements and so new approaches have been developed for analysis of stock price variations.
Anish Santosh Dinesh N Iyer Mahalaxmi Aher Dharti Mistry Bhagyashree Dhakulkar
221091643
An Integrated Machine Learning Framework for Stock Price Prediction
Predicting the future price of financial assets has always been an important research topic in the field of quantitative finance. This paper attempts to use the latest artificial intelligence technologies to design and implement a framework for financial asset price prediction. The framework we use is divided into three modules: Feature Engineering, Regressor, and Hyper Optimizer. The Feature Engineering module extract multiple features using technical indicators, FinBERT, FFT, ARIMA, stacked auto encoder, PCA and XGBoost. The Regressor module consists of a generative adversarial network, where the generator is Seq2Seq and the discriminator is GRU. The HyperOptimizer module will tune the parameter in GAN using the Bayesian optimization algorithm. Finally, we conducted numerical experiments on our framework, which shows that the framework implemented in this paper performs better than the benchmark method.
CCIR
Quanzhi Bi Hongfei Yan C Chen Qi Su
219625002
Market Stock Price Prediction Using Machine Learning
Successful predictions of future stock will maximize the profit of the investors. The Prediction of the stock market is the task to determine the upcoming value of instrument traded on a company stock or financial exchange. The Report proposes a Simple AI model to foresee the stock value esteem. The proposed calculation incorporates molecule swarm advancement (PSO) and least help vector (LS SVM) PSO calculation has been utilized to streamline LS SVM to foresee costs of every day stock. The proposed model depends on the investigation of verifiable information and specialized pointers. The PSO calculations have been utilized to streamline LS SVM to foresee of every day costs of day by day stock. The proposed model depends on the investigation of verifiable information and specialized markers. The PSO calculation chooses a blend of sans best parameters for LS SVM so that over fittings and nearby minima issues can be kept away from and improve forecast exactness. The proposed model was assessed utilizing thirteen benchmark money related datasets and it was contrasted and the Artificial Neural Network with Leavenberg Marquard (LM) calculation. The outcomes got uncovered that the proposed model can have better expectation exactness and PSO calculation in improving LS SVM. Several financial institutions use the powerful ML for predicting time series data with unmatched accuracy level. The present day research is focused on improving this model day by day. In a simple words one can say that Machine learning is a process in which computer algorithms are used to make the machine learn from the available data and information to improve the result. This study with deals with several machine learning methods that can be applied to predict the market stock price with the help of previous data set provided by organization or company.
Vishal Dwivedi Sheenu Rizvi
225226044
Prediction Analysis of Stock Price using Machine Learning Approach
Ms Neha
229233648
Forward Forecast of Stock Price Using LSTM Machine Learning Algorithm
Stock market prediction is the demonstration of attempting to decide the future estimation of an organization stock or other monetary instrument exchanged on a trade. This paper will exhibit how to perform stock expectations utilizing Machine Learning calculations. Foreseeing securities exchange costs is an intricate assignment that generally includes broad human PC communication. Because of the connected idea of stock costs, customary bunch preparing techniques can't be used productively for securities exchange examination. In the current framework, the Sliding window calculation is used. This calculation investigates the information, with a window pushing ahead, in the wake of examining the information. It is very tedious for expectation of stocks. While, in the proposed framework, the utilization of LSTM (Long Short Term Memory) calculation, gives compelling outcomes. While analyzing, the superfluous information is overlooked. The current framework is additionally not viable, in taking care of non straight information. What's more, it is less proficient contrasted with LSTM algorithm. So, to help defeat these, LSTM helps in dealing with the information in a productive way. Indeed, speculators are exceptionally intrigued by the exploration zone of stock value expectations. For decent and fruitful speculation, numerous financial specialists are sharp in knowing the future circumstance of the share trading system. Great and viable expectation frameworks for securities exchange encourage brokers, financial specialists, and investigators by giving steady data like the future course of the share trading system. In this work, an intermittent neural system (RNN) and Long Short Term Memory (LSTM) are presented, a way to deal with anticipate securities exchange lists. The proposed model is a promising prescient procedure for a very non direct time arrangement, whose designs are hard to catch by customary models.
K Rajakumari M Srinivasa Kalyan Meenu Bhaskar