Future Stock Price Research Articles

Stock Price Prediction for Technology Company

Individuals aim to develop accurate models for stock prices to make informed decisions as investors, so they can determine opportune moments to purchase and sell stocks for maximizing profits. This paper select Apple stock from yahoo finance range from Aug 1st 2013 to Aug 1st 2023, and then forecasting its future 30 days stock price. This study contain four models, which are XGboost, linear regression, K-Nearest Neighbors (KNN) and Long Short-Term Memory (LSTM). Those models are all fit the train and test data and then draw a visualization plot. For selecting the best model, this paper use root mean squared error(RMSE) metrics and mean absolute percentage error(MAPE) and got the best model are Linear Regression and LSTM. Depending on the mechanism of four models, LSTM can treated as the best one to predict future stock price. In future study, researchers can use LSTM model more to predict stock price for other companies in order to get the best result.

A Comparative Study of Future Stock Price Prediction Through Artificial Neural Network and ARIMA Modelling

Stock price estimation and prediction have been the popular topics for ages for various market participants, including investors, traders, financial analysts and researchers. Understanding the expected direction of stock prices can provide insights into broader market trends and sentiments. Different methods have been tried to find out future predictions with the least degree of error. Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are two popular methods for time series forecasting, including stock price prediction. This paper attempts to forecast stock prices with more accuracy by using ARIMA and ANN tool modelling with secondary data NIFTY 50 of the National Stock Exchange from December 2005 to July 2019. Stock price prediction through ARIMA and ANN was calculated and compared. Results obtained revealed that the ANN method of forecasting stock prices has more accuracy as compared to ARIMA modelling. Future studies can be based on different sector-based data across different sectors and stock markets.

Portfolio Management Strategy Based on LSTM

Mean-variance theory emphasizes the consideration of returns and risks simultaneously. However, traditional optimization of the risk-return trade-off may not always align with the preferences of individual investors. Given that outcomes about daily LSTM predictions have been inconclusive, this study uses weekly price dataset for prediction aiming to improve the signal-to-noise ratio. The objective is to develop a portfolio management strategy that prioritizes achieving reasonable returns while effectively managing risk. To achieve this, a selection process identifies 12 stocks from different sectors. LSTM models are trained using historical stock price data, and these models predict future stock prices. The predictions guide the weekly rebalancing of the portfolio based on a designed principle and the performance of the strategy-based portfolio is evaluated by comparing it to two benchmarks: the NASDAQ index and a 1/N investment portfolio. The results demonstrate that the strategy portfolio outperforms both benchmarks, achieving significant improvements in returns. The strategy portfolio exhibits a remarkable return of 116.21%, a Sharpe ratio of 2.944 and a maximum drawdown of 13.1%. Although there is a slight increase in volatility, the strategy effectively manages risk while delivering superior returns.

PENERAPAN MODEL GEOMETRIC BROWNIAN MOTION DAN PERHITUNGAN NILAI VALUE AT RISK PADA SAHAM BANK CENTRAL ASIA TBK

Stock price fluctuations are difficult to predict, resulting in uncertain profits. Therefore, a mathematical model is needed to predict future stock prices, namely the Geometric Brownian Motion (GBM) model based on a stochastic process. Stocks are also accompanied by risks that have potential for loss. The risk can be measured using Value at Risk (VaR) which can estimate the maximum loss that may happen from an investment at a certain level of confidence and period of time. The purpose of this research is to implement the GBM model in predicting stock prices and estimating the maximum loss of stock investment using VaR. This research analyzes the daily closing stock price of PT Bank Central Asia (BBCA) for the period November 1, 2021, to December 31, 2022. The stock price predictions with the GBM model are used to estimate the VaR value. Based on the analysis results, GBM is highly accurate model with an average MAPE value of 5.77% and the smallest MAPE value of 1.45%. The VaR values obtained at the 80%, 90%, 95% and 99% confidence level are 1,17%, 1,74%, 2,19% and 2,86% of the total fund investment for the next one-day period, respectively.

Stock Market Analysis and Prediction Using LSTM: A Case Study on Technology Stocks

This research explores the application of Long Short-Term Memory (LSTM) networks for stock market analysis and prediction, focusing on four major technology stocks: Apple Inc. (AAPL), Google LLC (GOOG), Microsoft Corporation (MSFT), and Amazon.com Inc. (AMZN). Historical stock price data from Yahoo Finance spanning from January 1, 2012, to the present is utilized. The study aims to develop and evaluate an LSTM-based prediction model for forecasting future stock prices. The LSTM model consists of two LSTM layers with 128 and 64 units, respectively, followed by two dense layers. The model is trained using the Adam optimizer and mean squared error (MSE) loss function. Evaluation of the model is done using the root mean squared error (RMSE) metric. The results demonstrate the potential of LSTM models in capturing complex patterns in stock price movements and making reasonably accurate predictions.

Can Machine Learning Outperform the Market? Testing the Weak-form Efficiency Hypothesis of the Indian Stock Market Using Support Vector Machines

This article examines the validity of the weak form of the efficient market hypothesis (EMH) in the context of the Nifty stock market index by applying a support vector machine (SVM) model. The aim is to forecast future stock prices using historical data and to evaluate the performance of the SVM model based on accuracy, precision, recall and the area under the receiver operating characteristic (ROC) curve (AUC). The findings offer important implications for the efficiency of the Nifty market and its consequences for investors. The EMH posits that stock prices incorporate all available information, making it impossible to consistently beat the market using historical data. This article tests this proposition by using an SVM model to forecast future stock prices using historical data. The methodology consists of applying the SVM algorithm on historical data of the Nifty stock market index. Performance measures, such as accuracy, precision, recall and AUC, are used to assess the effectiveness of the SVM model. The results show an accuracy of 63.25% in forecasting stock prices, indicating a substantial agreement between predicted and actual labels. The precision score of the model is 97.97%, indicating a high proportion of correctly predicted positive instances. However, the recall score is relatively low at 34.36%, suggesting that some actual positive instances were overlooked. The ROC curve visually illustrates the trade-off between true positive rate and false positive rate for different classification thresholds. This article contributes to the literature on market efficiency by applying a novel SVM model to forecast future stock prices of the Nifty index and finding that the model outperforms random chance, thus challenging the weak form of the EMH.

THE MODERATING ROLE OF ESG DISCLOSURE ON FIRM STRATEGY AND STOCK PRICE CRASH RISK

This study aims to examine the effect of the company's business strategy on the stock price crash risk and examine the role of ESG disclosure as moderation in the relationship between business strategy and the stock price crash risk. The study population includes all companies listed on the Indonesia Stock Exchange (IDX) for the period 2017–2022. The sampling technique used was saturation sampling, with a total of 609 observations. Data collection techniques use content analysis. The data analysis technique uses ordinary least squares. This study found that firm strategy with the prospector typology affect the stock price crash risk, while the defenders and analyzers typologies have no effect on the stock price crash risk. ESG disclosure can weaken the effect of the business strategies of the prospector, defender, and analyzer typologies on stock price crash risk. Additional analysis found that ESG can reduce the risk of future stock price crashes. The contribution to signal theory is that there is consistently a strategic motivation for management to control good and bad information for the public. Bad information can cause the risk of price crashes to be higher than good information. Through ESG disclosure, it can provide positive signals to the public so that the risk of stock price crashes is low. The practical contribution for investors is as an investment strategy by paying more attention to the tendency of stock price crashes. For the government as a basis for making economic policies to create a well-maintained business climate.

COMPARATIVE ANALYSIS OF GEOMETRIC BROWNIAN MOTION, ARTIFICIAL NEURAL NETWORK AND NAIVE BAYESIAN TECHNIQUES USING NIGERIA STOCK EXCHANGE DATA

This paper presents a comparative analysis of three market price forecast models, namely Geometric Brownian Motion, Artificial Neural Network, and Naive Bayesian techniques, using data from the Nigeria Stock Exchange. The exploratory data analysis results indicate slight variations in the mean and median of the log stock price over a 5-year period. The data shows a relatively small spread from the mean and approximately symmetric distribution, as indicated by a skewness value close to zero. The normality test confirms that the log stock price data follows a normal distribution. The forecast using Artificial Neural Network (ANN) shows a minimal change in future stock price, suggesting low returns and moderate risk in the Nigerian stock market. The graphical representation of the ANN model demonstrates a constant path with little variation. Similarly, the Naive Bayesian technique provides a similar forecast to the ANN model, indicating limited profit potential. The Geometric Brownian Motion model also forecasts little variation in future stock prices, with 2023 showing slightly higher values. The accuracy of the forecast models is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the ANN model has an error of 4.60%, the Naive Bayesian model has an error of 9.29%, and the Geometric Brownian Motion model has an error of 12.67%. These findings suggest that the ANN model performs better in terms of accuracy compared to the other two models.

A Tensorized Hierarchical Graph Attention Network for Stock Market Forecasting

Abstract: Stock market forecasting is a tough mission because of its complex and dynamic nature. Deep learning models have recently been proven to be successful at stock market predictions. Traditional deep learning models, however, frequently disregard the hierarchical structure and temporal relationships of the stock market. Here, we introduce the StockTensor, a brand-new tensorized hierarchical graph attention network for stock market forecasting. StockTensor models the hierarchical structure of the stock market data by constructing a hierarchical graph of stocks. At each level of the hierarchy, StockTensor uses a graph attention network to learn the relationships between stocks and aggregate the information from neighboring stocks. StockTensor also models the temporal dependencies of the stock market data by using a recurrent neural network. The recurrent neural network learns to predict future stock prices based on the current stock prices and the historical stock prices. We evaluate StockTensor on two real-world stock market datasets. The results show that StockTensor outperforms several stateof-the-art stock market forecasting models.

Tone management and stock price crash risk

Managers have substantial discretion over qualitative disclosures. At the same time, disclosure tone is an important source of information to investors. In this study, I examine the relation between tone management and future stock price crash risk. Consistent with the view that managers use their discretion over disclosure tone for intentional information management, I find that tone management in the Management Discussion and Analysis of 10-K files is, on average, positively associated with future stock price crash risk, even after controlling for quantitative crash determinants and financial disclosure readability. Cross-sectional analyses reveal that the effect is more pronounced when managers have more incentives and are less constrained in manipulating disclosure tone. Collectively, my results suggest that tone management can have greater and broader capital market consequences than previously documented.

Stock price prediction based on the long short-term memory network

Stock analysis is a challenging task that involves modelling complex and nonlinear dynamics of stock prices and volumes. Long Short-Term Memory (LSTM) is a type of recurrent neural network that can capture long-term dependencies and temporal patterns in time series data. In this paper, a stock analysis method based on LSTM is proposed that can predict future stock prices and transactions using historical data. Yfinance is used to obtain stock data of four technology companies (i.e. Apple, Google, Microsoft, and Amazon) and apply LSTM to extract features and forecast trends. Various techniques are also used such as moving average, correlation analysis, and risk assessment to evaluate the performance and risk of different stocks. When compare the method in this paper with other neural network models such as RNN and GRU, the result show that LSTM achieves better accuracy and stability in stock prediction. This paper demonstrates the effectiveness and applicability of LSTM method through experiments on real-world data sets.

Using transformer in stock trend prediction

Large transformer model had achieved good results in many tasks, such as computer vision (CV) and natural language processing (NLP). However, in financial domains, the application of large deep learning models is rarely observed. Stock Trend Prediction (STP) is a task that using Limit Order Books (LOBs) to predict the future stock price trend by the sequence of historical limit order information, the trend can be Current works are mostly based on the structure of Convolutional Neural Network (CNN) + Recurrent Neural Networks (RNN). This structure is hard to parallel and cannot make full use of GPU resources. It is also difficult to increase the dimension to fit more complex data and performs poor when time sequence is long. Recently, some works proposed that CNN + Transformer model can also work is solving this task. This paper verifies that Transformer can be directly used into STP task and gain a good result, and proposes a novel Transformer-based model, Transformer-LOB, to enhance the basic transformer model performance. This model uses attention mechanisms to extract temporal information rather than using RNN, which utilizes the GPU effectively. Since all the feature extractions are based on transformer modules, the model is scalable and easy to parallel. Transformer-LOB is tested on FI-2010 LOB dataset and SZ-2015 LOB dataset, and outputs ideal results on both datasets.

Price prediction of the Borsa Istanbul banks index with traditional methods and artificial neural networks

In terms of asset size, the banking system constitutes 83% of the financial markets in Turkiye. Given the importance of the banking system in the Turkish capital market, this study offers a price forecasting analysis of the Borsa Istanbul Banks Index, which represents the domestic banking system, between December 27, 1996, to August 31, 2023, using the traditional Autoregressive Integrated Moving Average (ARIMA) Model and two artificial intelligence-based deep learning models, namely, the Facebook Prophet Model (FPM) and Convolutional Neural Networks Model (CNNM). The findings indicate that the CNNM perform better than the other models. The results are useful for researchers working with time series data at the stage of method selection and investment firms and managers that are forecasting future stock price movements. Policy implications of the findings are discussed.

Research on securities price prediction and insurance strategy optimization based on deep learning algorithm

The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers’ overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R 2, and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research.

A better start: board diversity matters in assessing stock price crash risk

PurposeThe determinants that contribute to reducing stock price crash risk have garnered attention from scholars and practitioners. However, our understanding of the relationship between board diversity and stock crash risk, as well as the contextual factors that influence this relationship, remains limited. To address this gap, this study aims to investigate how different attributes of board diversity affect stock price crash risk, particularly under conditions of higher performance hazard and ownership concentration.Design/methodology/approachUsing a two-stage least squares fixed-effects estimator, the authors analyze a panel data set of 1,792 firm-year observations across 282 firms listed on the KOSPI200 from 2010 to 2019.FindingsRelation-oriented diversity reduces future stock price crash risk, particularly when firms experience performance shortfalls and have concentrated ownership structures, but task-oriented diversity has no significant effects. The results imply that only relation-oriented diversity strengthens governance mechanisms by curtailing managerial bad news withholding behaviors, and the role of relation-oriented diversity in reducing stock crash risk becomes more crucial when firms have higher performance hazard and concentrated ownership.Originality/valueThis study makes crucial contributions as follows: the authors contribute to the stock crash risk literature by shifting the focus from how to when board diversity matters in assessing stock crash risk; the authors extend the board diversity research and enhance scholarly understanding of the effects of board diversity on corporate governance by highlighting that not all aspects of board diversity improve firm governance mechanisms; and the authors widen the lens from a single attribute to multiple attributes of diversity to reveal the effects of diversity on boards in assessing future crash risk.

Forecasting stock indices with the COVID-19 infection rate as an exogenous variable.

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. In this study, the effect of covariates has been analyzed for three stock indices: S & P 500, Morgan Stanley Capital International (MSCI) world stock index, and the Chicago Board Options Exchange (CBOE) Volatility Index (VIX). Results show that parametric approaches can be good forecasting models for the S & P 500 index and the VIX index. On the other hand, a random walk model can be adopted to forecast the MSCI index. Moreover, among the three random walk forecasting methods for the MSCI index, the naïve method provides the best forecasting model.

Non-controlling shareholders' governance participation and corporate misconduct: Evidence from voting in general meetings

This study explores the governance role of non-controlling shareholders (NCSs) in Chinese firms by examining the impact of their general meeting participation on corporate misconduct. We find that higher NCS voting shares lead to less corporate misconduct, and the impact is more pronounced for firms with worse internal control or audit quality and less analyst or media coverage. The two-stage regression results based on the instrumental variable approach suggest that the impact is casual. Channel tests show that NCSs' general meeting participation reduces misconduct by mitigating controlling shareholders' tunneling and improving firms' information environment. Finally, we document that the negative impact of NCSs' governance participation on corporate misconduct increases firm value by reducing the firm's future stock price crash risk.

Stop-loss adjusted labels for machine learning-based trading of risky assets

Since the rise of ML/AI, many researchers and practitioners have been trying to predict future stock price movements. In actual implementations, however, stop-loss is widely adopted to manage risks, which sells an asset if its price goes below a predetermined level. Hence, some buy signals from prediction models could be wasted if stop-loss is triggered. In this study, we propose a stop-loss adjusted labeling scheme to reduce the discrepancy between prediction and decision making. It can be easily incorporated to any ML/AI prediction models. Experimental results on U.S. futures and cryptocurrencies show that this simple tweak significantly reduces risk.

Presentation of social responsibility reports and the stock price crash risk

This study explores the relationship between the presentation forms of CSR reports and the stock price crash risk. It is found that the higher the proportion of image area and richer the color of CSR reports, the higher the future stock price crash risk. CSR reports presentation form affects the stock price crash risk through stock liquidity and investors' expectations of the company's future performance. These results indicate that presentation format in social responsibility information disclosure is mainly one of the tools used to build a positive corporate image.

Does sharia-compliant debt financing reduce stock price crash risk?

PurposeThis paper examines the impact of sharia-compliant debt financing on stock price crash risk. Unlike those previous studies that took Sukuk or sharia-compliant firms, this study tests the impact of the proportion reported sharia-compliant debt financing in the balance sheet on the risk of price crash of a firm.Design/methodology/approachUsing the data from 2,752 firm-year observations of 344 Malaysian non-financial listed companies from 2012 to 2019, this article used a robust panel data estimation technique for statistical inferences. This study also employs panel GMM and quantile least squares as the robustness check.FindingsThis study established a negative relationship between sharia-compliant debt financing and stock price crash risk. The robustness checks with different estimation techniques confirm the results. It implies that firms with a more significant proportion of Sharia-compliant financing tend to have lower future stock price crash risk.Practical implicationsConsistent with the Islamic finance literature, the present study contributes to the existing literature on Islamic capital markets from the perspective of stock price crash risk because it is vital for risk management and investment decision-making as a measure of tail risk for stocks. The findings of this research will assist investors in developing portfolio strategies that incorporate firms with higher levels of sharia-compliant debt financing in their balance sheets. Additionally, the results of this study suggest that policymakers and regulatory bodies should consider revising their monitoring approaches for publicly listed firms.Originality/valueThis study is interesting and unique, as it is a pioneer in testing the impact of sharia-compliant debt financing on reducing stock price crash risk.