Predicting stock prices using permutation decision trees and strategic trailing

As an indispensable part in financial industry, stock investment has gradually become more and more important in people's life.The major fluctuation in stock market directly affects the stability of financial markets and the healthy development of the economy.The successful stock price and trend prediction will help investors to yield profit and provide timely and reasonable guidance to market regulators.Therefore, how to forecast stock market is a crucial and worthy topic in financial area.Neural network simulated how brain functions, can automatically extract features from the historical data and related information to predicate future trend, with the primary characteristics of self-organization and adaption.Therefore, neural network is suitable for processing prediction in the field of nonlinear time series.This paper presents a novel data mining algorithm based on the BP neural network and its applications on stock price prediction.Taking improving the efficiency of the traditional back propagation network, we revise the perspective error, and modify incentive function and the adaptive learning rate in order to avoid the local minimum and decrease training time.Moreover, to eliminate the influences caused by noisy data, we combine the fuzzy rough set theory to pre-process the original data.The experimental result verifies feasibility of our model, without the local minimum and slow convergence speed, compared to the other state-of-the-art approaches.Therefore, our algorithm on the predications of the exact stock price value and future trend are applicable and satisfactory.

In the era of artificial intelligence, machine learning methods are successfully used in various fields. Machine learning has attracted extensive attention from investors in the financial market, especially in stock price prediction. However, one argument for the machine learning methods used in stock price prediction is that they are black-box models which are difficult to interpret. In this paper, we focus on the future stock price prediction with the historical stock price by machine learning and deep learning methods, such as support vector machine (SVM), random forest (RF), Bayesian classifier (BC), decision tree (DT), multilayer perceptron (MLP), convolutional neural network (CNN), bi-directional long-short term memory (BiLSTM), the embedded CNN, and the embedded BiLSTM. Firstly, we manually design several financial time series where the future price correlates with the historical stock prices in pre-designed modes, namely the curve-shape-feature (CSF) and the non-curve-shape-feature (NCSF) modes. In the CSF mode, the future prices can be extracted from the curve shapes of the historical stock prices. Conversely, in the NCSF mode, they can’t. Secondly, we apply various algorithms to those pre-designed and real financial time series. We find that the existing machine learning and deep learning algorithms fail in stock price prediction because in the real financial time series, less information of future prices is contained in the CSF mode, and perhaps more information is contained in the NCSF. Various machine learning and deep learning algorithms are good at handling the CSF in historical data, which are successfully applied in image recognition and natural language processing. However, they are inappropriate for stock price prediction on account of the NCSF. Therefore, accurate stock price prediction is the key to successful investment, and new machine learning algorithms handling the NCSF series are needed.

Prediction of stock prices has been the primary objective of an investor. Any future decision taken by the investor directly depends on the stock prices associated with a company. This work presents a hybrid approach for the prediction of intra-day stock prices by considering both time-series and sentiment analysis. Furthermore, it focuses on long short-term memory (LSTM) architecture for the time-series analysis of stock prices and Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment analysis. LSTM is a modified recurrent neural network (RNN) architecture. It is efficient at extracting patterns over sequential time-series data, where the data spans over long sequences and also overcomes the gradient vanishing problem of RNN. VADER is a lexicon and rule-based sentiment analysis tool attuned to sentiments expressed in social media and news articles. The results of both techniques are combined to forecast the intra-day stock movement and hence the model named as LSTM-VDR. The model is first of its kind, a combination of LSTM and VADER to predict stock prices. The dataset contains closing prices of the stock and recent news articles combined from various online sources. This approach, when applied on the stock prices of Bombay Stock Exchange (BSE) listed companies, has shown improvements in comparison to prior studies.

Stock price exhibits distinct features during different time scales due to the effects of complex factors. Analyzing these features can help delineate the mechanisms that determine the stock price and enhance the prediction accuracy of the stock price. By using singular spectrum analysis (SSA), this paper first decomposes the original price series into a trend component, a market fluctuation component and a noise component to analyze the stock price. The economic meanings of the three components are identified as a long-term trend, effects of significant events and short-term fluctuations caused by noise in the market. Then, to take into account the features of the above three components to the stock price prediction, a novel combined model that integrates SSA and support vector machine (SVM) (e.g., SSA–SVM) is proposed. Compared with SVM, adaptive network-based fuzzy inference system (ANFIS), ensemble empirical mode decomposition-ANFIS (EEMD–ANFIS), EEMD–SVM and SSA–ANFIS, SSA–SVM demonstrates the best prediction performance based on four criteria, indicating that the proposed model is a promising approach for stock price prediction.

As the stock price decoupling between domestic and foreign stock markets is prolonged, the so-called ‘Seohak ants’ are emerging to expand investing targets to the global stock market. This study is to predict the stock prices and volatility of global automotive companies and analyze whether economic value appears through performance analysis of investment strategy. The investment target was selected as automakers in Germany, the U.S., Japan, and Korea, which are advanced countries in the global automotive industry and fiercely competing as the paradigm of electric and self-driving cars emerged. Machine learning models were used to predict the stock prices, which show good results in predictive studies of time series data such as irregular and nonlinear moving stock prices. The empirical results from January 2011 to September 2022 data are as follows. First, daily low prices showed the highest importance score in feature variables for stock price prediction. Second, the random forest model in stock price prediction and the support vector regression model in volatility prediction showed excellent predictive power. Third, the investment strategy using the predicted stock price showed higher profitability than the benchmark, finding the economic value of the predicted stock price. This study is of academic and practical significance in that it predicted the stock prices volatility of the global automotive industry by using the machine learning models in the face of expanding global stock investment. In future studies, it is necessary to diversify the investment target and add sophisticated prediction techniques to study to improve the predictive performance.

From past to present, the prediction of stock price in stock market has been a knotty problem. Many researchers have made various attempts and studies to predict stock prices. The prediction of stock price in stock market has been of concern to researchers in many disciplines, including economics, mathematics, physics, and computer science. This study intends to learn fluctuation of stock prices in stock market by using recently spotlighted techniques of deep learning to predict future stock price. In previous studies, we have used price-based input-features to measure performance changes in deep learning models. Results of this studies have revealed that the performance of stock price models would change according to varied input-features configured based on stock price. Therefore, we have concluded that more novel input-feature in deep learning model is needed to predict patterns of stock price fluctuation more precisely. In this paper, for predicting stock price fluctuation, we design deep learning model using 715 novel input-features configured on the basis of technical analyses. The performance of the prediction model was then compared to another model that employed simple price-based input-features. Also, rather than taking randomly collected set of stocks, stocks of a similar pattern of price fluctuation were filtered to identify the influence of filtering technique on the deep learning model. Finally, we compared and analyzed the performances of several models using different configuration of input-features and target-vectors.

Over the past two years, global stock markets have gradually recovered and new investors have entered the market. While there are many factors affecting stock prices and the stock market is changing rapidly, the way to accurately predict stock prices has become the focus of investors. This paper will use the concept of machine learning to predict the stock prices of three listed companies based on three different regression models (i.e., OLS, Ridge and XGBoost). According to the analysis, the OLS model and the Ridge model are very accurate in predicting stock prices, especially in the low and middle price ranges. In contrast to these typical linear regression models, the XGBoost model is not as accurate in predicting stock prices and even has a significant prediction bias in the high price range. These results will enable subsequent research to make better choices when selecting models for forecasting, especially for data sets with different characteristics.

Stock price prediction is a highly complex task due to the inherent volatility and non-linear patterns of financial markets. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have yielded promising results in time series forecasting. This paper provides an in-depth analysis of LSTM models applied to the stock price prediction of major technology companies, including Apple (AAPL), Tesla (TSLA), Microsoft (MSFT), and Nvidia (NVDA). The study utilizes historical stock data to train and evaluate LSTM models, comparing their performance against various other regression models such as Linear Regression, Support Vector Regression (SVR), Decision Tree Regression, and Gradient Boosting Decision Trees (GBDT).The results demonstrate that the LSTM model outperforms traditional models, particularly in its ability to capture long-term dependencies and manage complex, volatile stock data. Among the companies analyzed, the LSTM model achieved the most accurate predictions for Apple, as indicated by the lowest Mean Squared Error (MSE). Tesla's predictions, while less accurate due to high volatility, still showed improvement compared to other models. In conclusion, the LSTM network proved to be the most effective method for stock price prediction in this study, particularly excelling in forecasting long-term trends and reducing prediction errors for volatile stocks.

In a common parlance, analysing or evaluating a company‘s stock and stock prices is most erratic and unpredictable. The reason for difficulty in predicting stock prices are its correlation with many macroeconomic variables like economic growth, Inflation, exchange rate, political stability etc… It may be difficulty in terms of above said exogenous methods but endogenous method has certain relevancy in predicting stock prices. This study uses most relevant endogenous method called data mining technique to evaluate its effectiveness in predicting stock prices. Data mining is well founded on the theory that the historic data holds the essential memory for predicting the future direction. Data mining technology helps everyone in knowing the hidden patterns in the data of the stocks and thus helps them in their investment decisions. Thus the prediction of stock marketing has always been challenging. In this connection, this study uses Moving Average, Typical Price, Chaikin Money Flow Indicator, Bollinger Bands and Relative Strength Index to analyse the stock market.

Sentiment analysis has emerged as a powerful tool in financial markets, offering the ability to harness market participants' collective wisdom and emotions to make more informed investment decisions. This research paper presents a comprehensive literature review on sentiment analysis models in predicting stock prices, focusing on the Bank Nifty Index, a critical benchmark in the Indian financial sector. The review begins by elucidating the significance of sentiment analysis in stock price prediction, highlighting its pivotal role in capturing market sentiment, investor emotions, and their impact on asset valuations. A detailed overview of sentiment sources, encompassing financial news, social media, reports, and other relevant data streams, provides insight into the rich tapestry of information that sentiment analysis can leverage. The paper delves into various sentiment analysis approaches, from traditional lexicon-based and rule-based methods to cutting-edge machine learning and deep learning techniques. It elucidates the intricate process of integrating sentiment analysis with stock price prediction models, exploring feature concatenation, time series integration, and weighted data to incorporate sentiment insights effectively. Evaluation metrics crucial for assessing the performance of both sentiment analysis models and stock price prediction models are thoroughly discussed. It concludes by highlighting the substantial potential of sentiment analysis in augmenting stock price prediction, offering investors and traders valuable tools to navigate the complexities of financial markets.

Stock price prediction means using the previous data to predict future trend. With the development of deep learning technologies in artificial intelligence area, many works try to use single CNN or single LSTM to make prediction. However, both these two models have their own weakness and strengths. In this paper, we try to make a CNN-LSTM hybrid model to achieve a better performance on stock price prediction. It proposes that the CNN-LSTM hybrid model predicts stock prices using RMSE to measure higher accuracy than a single CNN and a single LSTM model. The experiment shows a great ability on generalization and provide reliable results, which can apply to other stocks to make prediction and give suggestions on investing strategy. The CNN-LSTM model can play the respective advantages of CNN and LSTM model to improve the accuracy of stock price prediction and has better performance in both short-term prediction and long-term trend.

This study explores the impact of artificial intelligence (AI) on stock price prediction in India, focusing on the application of Long Short-Term Memory (LSTM) networks and hybrid models. By analyzing data from the Bombay Stock Exchange (BSE) including stock prices, trading volumes, and sentiment data from social media and news, the study compares AI-based models with traditional methods. The research aims to determine whether AI models offer superior predictive accuracy using RMSE, MAE, and MAPE. The findings suggest that AI models, particularly LSTM and hybrid approaches, outperform traditional models in forecasting accuracy, offering a significant advantage for stock market prediction in India. The study concludes AI models provide a significant improvement in stock price prediction accuracy compared to traditional methods. This suggests the adoption of AI models in stock price prediction by Indian financial institutions and regulatory bodies. This study provides valuable insights for investors, financial institutions, and policymakers looking to integrate AI into market forecasting strategies.

Prediction of the stock price has always been a challenging task due to irregular patterns of the market. Uncertainty has made researchers think of some new and robust predictive methods. Many studies are available in the literature, with many models to predict the stock price accurately. Statistical, machine learning, deep learning, and other related approaches can create a predictive model. ARIMA model is the most commonly used statistical model for time series prediction. But ensemble learning techniques have not been explored much to predict future stock price. So, the present study stresses comparing statistical methods with ensemble learning methods. This paper compares the ARIMA, Random Forest, and Extreme Gradient Boosting models based on root mean squared error (RMSE) and mean absolute percentage error (MAPE). The subject chosen is Google’s stocks, and the data used is from NASDAQ stock exchange. The analysis results show that the ARIMA model performed fairly well for short-term predictions but relatively high MAPE value. The extreme gradient boosting model gave the best performance with the lowest RMSE and MAPE value. Hence, it is evident that after proper hyperparameter tuning, ensemble learning techniques can be used to create robust stock price-prediction models.

The purpose of this study was to determine whether investment decisions are affected by (1) alternative methods of accounting for income taxes and (2) the magnitude of deferred tax credits in the balance sheet. Financial analysts were asked to estimate net income and stock price, and to provide equivalence intervals as measures of uncertainty. The subjects based their predictions upon financial statements of a real company and selected financial information. Six different forms of the information cue set were used--three accounting methods (deferred method of comprehensive allocation, flow-through, and flow-through with footnote disclosure of deferred taxes as under the deferred method of comprehensive allocation) and two levels of deferred tax credits (high and low). The responses were analyzed using multivariate analysis of variance (MANOVA) in a 3 x 2 factorial experimental design. In addition, the mean square error (MSE) was used as a dependent variable to determine the effect of the accounting methods on the accuracy of the subjects' net income and stock price predictions. The research found evidence that the different methods of accounting for income taxes affect financial analysts' prediction of net income as well as their confidence in their net income predictions. However, the effect did not carry over to the analysts' prediction of stock price and their confidence in their stock price predictions. The magnitude of deferred tax credits in the balance sheet affects stock price prediction, but not net income prediction and the two confidence intervals. Also, financial analysts who received financial statements based on the deferred method of comprehensive allocation made more accurate net income predictions and had greater confidence in their net income predictions. There were no significant differences in the responses of financial analysts who received financial statements based on the flow-through method or flow-through method with footnote disclosure of deferred taxes as under the deferred method of comprehensive allocation.

Stock price prediction is useful for investors to see how the prospects of a company's stock investment in the future. Stock price prediction can be used to anticipate the deviation of stock prices. It can also helps investors in decision making. Artificial Neural Networks do not require mathematical models but data from problems to be solved. Information is conveyed through the data, and the Artificial Neural Network filters the information through training. Therefore, Artificial Neural Network is appropriate to solve the problem of stock price prediction. Learning method that will be used to predict stock price is Supervised Learning with Backpropagation algorithm. With this algorithm, networks can be trained using stock price data from the previous time, classify it and adjust network link weight as new input and forecast future stock prices. By using ANN, time series prediction is more accurate. After analyzing the problem of stock price movement system, the writer can know the pattern of what variables will be taken for further insert into the stock price forecasting system. This application can be used for stock price forecasting technique, so it will be useful for beginner investor as well as advanced investor as reference to invest in capital market. Implementing supervised learning backpropagation method will get accurate forecasting results more than 98%.Keyword - artificial neural network, stock, backpropagation.