Relevant bibliographies by topics / Stock price prediction

Academic literature on the topic 'Stock price prediction'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Stock price prediction"

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BIKSHAM, V., B. VISHAL KUMAR, C. RAHUL, G. VENU, and M. BHARGAV SAI. "STOCK PRICE PREDICTION." YMER Digital 21, no. 05 (May 2, 2022): 1–6. http://dx.doi.org/10.37896/ymer21.05/01.

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Machine learning has many important applications in the stock price prediction. Here, we will discuss about predicting the returns on stocks. This has uncertainties and it is a very complex task. This project will be developed into two parts: First, we will learn how to predict stock price using the Long Short-Term Memory neural networks. Predicting stock market prices involves human-computer interaction. For stock market analysis, conventional batch processing methods cannot be utilized efficiently due to the correlated nature of stock prices. We suggest an algorithm that utilizes a kind of recurrent neural network (RNN) called Long Short-Term Memory (LSTM), where using stochastic gradient descent the weights are adjusted for individual data points
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Cheruvu, Sai Manoj. "Stock Price Prediction Using Time Series." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 375–81. http://dx.doi.org/10.22214/ijraset.2021.39296.

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Abstract: Predicting Stock price of a company has been a challenge for analysts due to the fluctuations and its changing nature with respect to time. This paper attempts to predict the stock prices using Time series technique that proposes to observe various changes in a given variable with respect to time and is appropriate for making predictions in financial sector [1] as the stock prices are time variant. Keywords: Stock prices, Analysis, Fluctuations, Prediction, Time series, Time variant
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Al-Hasnawi, Salim Sallal, and Laith Haleem Al-Hchemi*. "CLOSING PRICE PREDICTION OF STOCK LISTED ON THE IRAQ STOCK EXCHANGE USING ANN-LSTM." JURISMA : Jurnal Riset Bisnis & Manajemen 12, no. 2 (October 30, 2022): 173–85. http://dx.doi.org/10.34010/jurisma.v12i2.8103.

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Financial markets are highly reactive to events and situations, as seen by the very volatile movement of stock values. As a result, investors are having difficulties guessing prices and making investment decisions, especially when statistical techniques have failed to model historical prices. This paper aims to propose an RNNs-based predictive model using the LSTM model for predicting the closing price of four stocks listed on the Iraq Stock Exchange (ISX). The data used are historical closing prices provided by ISX for the period from 2/1/2019 to 24/12/2020. Several attempts were conducted to improve model training and minimize the prediction error, as models were evaluated using MSE, RMSE, and R2. The models performed with high accuracy in predicting closing price movement, despite the Intense volatility of time series. The empirical study concluded the possibility of relying on the RNN-LSTM model in predicting close prices at the ISX as well as decisions making upon. Keywords: Stock, LSTM, Prediction, ANN, RNN, ISX
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Peng, Luna. "Stock Price Prediction of “Google” based on Machine Learning." BCP Business & Management 34 (December 14, 2022): 912–18. http://dx.doi.org/10.54691/bcpbm.v34i.3111.

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By 2022, many countries have declared the epidemic's end, both an opportunity and a challenge for many investors. More and more investors are manipulating prices to influence the stock market. So investors want to predict the price of stocks to make suitable investments. The author wants to start with the platform YouTube to study the price trend of this stock and make predictions to analyze whether there are traces of the factors affecting the stock price based on linear regression and random forest regression models. The author first backtested the price of this stock and analyzed the data according to the highest and lowest day. Then, the author used the method of Linear Regression and Random Forest Regression to predict the price. The error of the Linear Regression prediction results was within 5%, within the normal range, but the Random Forest Regression 5 days prediction's accuracy is much lower (65%). It shows that the stock price prediction model--Linear Regression is more credible and is worthy of reference for investors.
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Rammurthy, Shruthi Komarla, and Sagar B. Patil. "An LSTM-Based Approach to Predict Stock Price Movement for IT Sector Companies." International Journal of Cognitive Informatics and Natural Intelligence 15, no. 4 (October 2021): 1–12. http://dx.doi.org/10.4018/ijcini.20211001.oa3.

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A stock market is an aggregation of buyers and sellers where issuance, buying, and selling of stocks happen. Predicting stock price is a significant concern due to volatility. Historical stock price and historical price data reveal the effect of such factors. Since stock data is time series and prediction can be made accurately with time series forecasting model. LSTM (Long Short Term Memory) model, a particular kind of RNN (Recurrent Neural Network), based on time series forecasting used to predict stock price. LSTM doesn’t have long term dependencies because of its distinctive structure. The study focuses on major IT firms considering the company’s low and high prices. But, mid-price, which is a mean of the low and close price, is considered for the prediction. LSTM based methodology employing mid-price is effective in predicting values compared to other attributes and accuracy of prediction using the LSTM model. We conclude with the present model is more efficient in stock price prediction with a decrease in mean square error.
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Bhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M, and Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (August 12, 2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.

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In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a lot. So, our model predicts the price of stocks and cryptocurrencies, which helps the investors to invest in appropriate stocks and cryptocoins. The dataset used is taken from yahoo finance and twelve data using web scraping. The dataset retrieved is in raw format. It consists of collection of values of stock market data of various companies, and also data of various cryptocurrencies. First, raw data is converted into processed data, which is done using feature extraction. Then the dataset is splitted into training and test sets. We use the training dataset to train the model, and use test dataset to predict the future prices of stocks and cryptocurrencies. Now user can gain best knowledge about stock price trends of various companies and also cryptocurrency price trends, and can decide on for best investments in respective fields and gain best benefits.
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TOKMAK, Mahmut. "Stock Price Prediction Using Long-Short-Term Memory Network." Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi 6, no. 2 (September 29, 2022): 309–22. http://dx.doi.org/10.31200/makuubd.1164099.

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One of the most important transactions of the financial system is stock trading. Stock price data is handle as a financial time series. Stock price predictions using time series analysis are the activity of determining the future value of stocks listed on the stock market. Predicting the price of the stock correctly reduces the risk factor in the decisions to be taken by the investors. Therefore, it is an important issue for the investor. However, because there are many variables that affect the stock price, it is a very complex process to predict. Machine learning methods, especially deep learning algorithms, are frequently used in prediction in the field of finance, as in many other fields. In this study, stock price prediction was made using Long-Short-Term Memory networks, which is one of the deep learning methods. Four stocks within the scope of Borsa İstanbul Technology Index were determined and a 2578-day data set was created between 2012 and 2022, and training and testing was carried out with the established model. As a result of the test process, consistent and realistic predictions were obtained.
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Zhong, Shan, and David Hitchcock. "S&P 500 Stock Price Prediction Using Technical, Fundamental and Text Data." Statistics, Optimization & Information Computing 9, no. 4 (November 18, 2021): 769–88. http://dx.doi.org/10.19139/soic-2310-5070-1362.

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We summarized both common and novel predictive models used for stock price prediction and combined them with technical indices, fundamental characteristics and text-based sentiment data to predict S&P stock prices. A 66.18% accuracy in S&P 500 index directional prediction and 62.09% accuracy in individual stock directional prediction was achieved by combining different machine learning models such as Random Forest and LSTM together into state-of-the-art ensemble models. The data we use contains weekly historical prices, finance reports, and text information from news items associated with 518 different common stocks issued by current and former S&P 500 large-cap companies, from January 1, 2000 to December 31, 2019. Our study's innovation includes utilizing deep language models to categorize and infer financial news item sentiment; fusing different models containing different combinations of variables and stocks to jointly make predictions; and overcoming the insufficient data problem for machine learning models in time series by using data across different stocks.
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Zhao, Cheng, Xiaohui Liu, Jie Zhou, Yuefeng Cen, and Xiaomin Yao. "GCN-based stock relations analysis for stock market prediction." PeerJ Computer Science 8 (August 11, 2022): e1057. http://dx.doi.org/10.7717/peerj-cs.1057.

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Most stock price predictive models merely rely on the target stock’s historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.
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L, Dushyanth. "A SURVEY ON STOCK PRICE PREDICTION USING DEEP LEARNING." International Research Journal of Computer Science 9, no. 2 (February 28, 2022): 5–8. http://dx.doi.org/10.26562/irjcs.2022.v0902.002.

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Stock is a curve with a lot of unknowns. Stock market forecasting is fraught with complications and unpredictability. One of the most challenging and sophisticated methods of doing business is investing in the stock market. Stock forecasting is a difficult and time-consuming activity since the stock market is extremely volatile with stock prices fluctuating due to a variety of variables. Investors nowadays want quick and precise information to make informed decisions, thanks to the rapid growth of technology in stock price prediction. Understanding a company's stock price pattern and estimating its future development and financial growth will be quite advantageous. As the stock is made up of dynamic data, data is the critical source of efficiency. In the current trend of predicting stocks, deep learning is the most popular among the prediction of datasets. To forecast and automate operations, deep learning employs several prediction models and algorithms. The paper briefs about different algorithms and methods used for stock market prediction.
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Dissertations / Theses on the topic "Stock price prediction"

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Ghodsi, Boushehri Ali. "Applying fuzzy logic to stock price prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ54332.pdf.

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Eadie, Edward Norman. "Small resource stock share price behaviour and prediction." Title page, contents and abstract only, 2002. http://web4.library.adelaide.edu.au/theses/09CM/09cme11.pdf.

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Wang, Nancy. "Spectral Portfolio Optimisation with LSTM Stock Price Prediction." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273611.

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Nobel Prize-winning modern portfolio theory (MPT) has been considered to be one of the most important and influential economic theories within finance and investment management. MPT assumes investors to be riskaverse and uses the variance of asset returns as a proxy of risk to maximise the performance of a portfolio. Successful portfolio management reply, thus on accurate risk estimate and asset return prediction. Risk estimates are commonly obtained through traditional asset pricing factor models, which allow the systematic risk to vary over time domain but not in the frequency space. This approach can impose limitations in, for instance, risk estimation. To tackle this shortcoming, interest in applications of spectral analysis to financial time series has increased lately. Among others, the novel spectral portfolio theory and the spectral factor model which demonstrate enhancement in portfolio performance through spectral risk estimation [1][11]. Moreover, stock price prediction has always been a challenging task due to its non-linearity and non-stationarity. Meanwhile, Machine learning has been successfully implemented in a wide range of applications where it is infeasible to accomplish the needed tasks traditionally. Recent research has demonstrated significant results in single stock price prediction by artificial LSTM neural network [6][34]. This study aims to evaluate the combined effect of these two advancements in a portfolio optimisation problem and optimise a spectral portfolio with stock prices predicted by LSTM neural networks. To do so, we began with mathematical derivation and theoretical presentation and then evaluated the portfolio performance generated by the spectral risk estimates and the LSTM stock price predictions, as well as the combination of the two. The result demonstrates that the LSTM predictions alone performed better than the combination, which in term performed better than the spectral risk alone.
Den nobelprisvinnande moderna portföjlteorin (MPT) är utan tvekan en av de mest framgångsrika investeringsmodellerna inom finansvärlden och investeringsstrategier. MPT antar att investerarna är mindre benägna till risktagande och approximerar riskexponering med variansen av tillgångarnasränteavkastningar. Nyckeln till en lyckad portföljförvaltning är därmed goda riskestimat och goda förutsägelser av tillgångspris. Riskestimering görs vanligtvis genom traditionella prissättningsmodellerna som tillåter risken att variera i tiden, dock inte i frekvensrummet. Denna begränsning utgör bland annat ett större fel i riskestimering. För att tackla med detta har intresset för tillämpningar av spektraanalys på finansiella tidsserier ökat de senast åren. Bland annat är ett nytt tillvägagångssätt för att behandla detta den nyintroducerade spektralportföljteorin och spektralfak- tormodellen som påvisade ökad portföljenprestanda genom spektralriskskattning [1][11]. Samtidigt har prediktering av aktierpriser länge varit en stor utmaning på grund av dess icke-linjära och icke-stationära egenskaper medan maskininlärning har kunnat använts för att lösa annars omöjliga uppgifter. Färska studier har påvisat signifikant resultat i aktieprisprediktering med hjälp av artificiella LSTM neurala nätverk [6][34]. Detta arbete undersöker kombinerade effekten av dessa två framsteg i ett portföljoptimeringsproblem genom att optimera en spektral portfölj med framtida avkastningar predikterade av ett LSTM neuralt nätverk. Arbetet börjar med matematisk härledningar och teoretisk introduktion och sedan studera portföljprestation som genereras av spektra risk, LSTM aktieprispredikteringen samt en kombination av dessa två. Resultaten visar på att LSTM-predikteringen ensam presterade bättre än kombinationen, vilket i sin tur presterade bättre än enbart spektralriskskattningen.
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Karlsson, Nils. "Comparison of linear regression and neural networks for stock price prediction." Thesis, Uppsala universitet, Signaler och system, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445237.

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Stock market prediction has been a hot topic lately due to advances in computer technology and economics. One economic theory, called Efficient Market Hypothesis (EMH), states that all known information is already factored into the prices which makes it impossible to predict the stock market. Despite the EMH, many researchers have been successful in predicting the stock market using neural networks on historical data. This thesis investigates stock prediction using both linear regression and neural networks (NN), with a twist. The inputs to the proposed methods are a number of profit predictions calculated with stochastic methods such as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA). By contrast the traditional approach was instead to use raw data as inputs. The proposed methods show superior result in yielding profit: at best 1.1% in the Swedish market and 4.6% in the American market. The neural network yielded more profit than the linear regression model, which is reasonable given its ability to find nonlinear patterns. The historical data was used with different window sizes. This gives a good understanding of the window size impact on the prediction performance.
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Burgard, Andrew. "Can Business News Provide Insight into a Stock’s Future Price Performance?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1673.

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Mutual funds and money managers have recently come under fire for their inability to beat market level returns since the Great Recession. With the recent trend towards passive money management through ETFs and other market-based securities, many investors have come to doubt whether above market returns are realizable in today’s economic climate. This paper examines whether business news has any predictable impact on stock price. Specifically, the paper explores the impact of analyst reports, mergers & acquisition news, legal affairs, insider buying and selling and changes to executive leadership on a stock’s excess returns. The results show that optimistic analyst ratings are correlated with positive excess returns before, during, and after the ratings are released. Furthermore, pessimistic analyst ratings are correlated with negative excess returns over the same time frame. These results provide support for a short term trading strategy that mirrors analyst opinions.
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Troeman, Reamflar Elvio Estebano, and Lisa Fischer. "Politics, Artificial Intelligence, Twitter and Stock Return : An Interdisciplinary Test for Stock Price Prediction Based on Political Tweets." Thesis, Internationella Handelshögskolan, Jönköping University, IHH, Företagsekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48436.

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As the world is gravitating toward an information economy, it has become more and more critical for an investor to understand the impact of data and information. One of the sources of data that can be converted into information are texts from microblogging platforms, such as Twitter. The user of such a microblogging account can filtrate opinion and information to millions of people. Depending on the account holder, the opinion or information originated from the designated account may lead to different societal impact. The microblogging scope of this investigation are politicians holding a Twitter account. This investigation will look into the relationship between political tweets' sentiment and market movement and the subsequent longevity of such an effect. The classified sentiments are positive or negative. The presence of artificial intelligence is vital for a data-driven investigation; in the context of this investigation, artificial intelligence will be used to classify the sentiment of the political tweet. The methods chose to assess the impact of a political tweet and market movement is event-study. The impact is expressed in either a positive or a negative cumulative abnormal return subsequent to the political tweet. The findings of the investigation indicate that on average, there is no statistical evidence that a political tweets' sentiment leads to an abnormal return. However, in specific cases, political tweet leads to abnormal return. Moreover, it has been determined that the longevity of the effect is rather short. This is an interdisciplinary approach that can be applied by individual and institutional investors and financial institutions.
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Van, Niekerk J. P. de T. "The application of neural networks to the prediction of share price indices on the JSE." Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/53086.

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Thesis (MBA)--Stellenbosch University, 2002.
ENGLISH ABSTRACT: The dream of finding the ultimate tool for forecasting market instruments like share prices has long eluded investors throughout the world. Various forecasting techniques have been examined with a view to helping the investor or analyst to gain a better understanding of price behaviour in the open market. These techniques have been based mainly on traditional statistical analysis of data to forecast price behaviour. Though used by almost all serious investors, these techniques have yielded limited success as investment instruments. The reason for this is that most of these methods explored linear relationships between variables in the forecasting model, while in fact, most relationships found between variables in the share market are non-linear. Neural networks present a unique opportunity for the investor to overcome this problem. Neural networks are mathematical models of the human brain and have the ability to map complex nonlinear relationships between data sets. This study focuses on developing a neural network model to predict the price changes of the ALSI index on the JSE one and five days into the future. The results of the neural network model were then compared to forecasting results obtained by using a traditional statistical forecasting technique namely ARIMA modelling. The study found that the neural network models did not significantly perform better than the ARIMA models. A further test was done to determine the performance of the five-day forecasting model when analysing different time windows within the given data set. The test indicated that the model did perform better when using the inputs of certain time frames. This indicates that the neural network model needs to be updated regularly to ensure optimum model performance. The results of the neural network models were also used in a trading simulation to determine whether these results could be applied successfully to trading the ALSI index on the JSE. Unfortunately, the results of the trading simulation showed that using the neural network results as trading strategy yielded poorer results than using a buy/hold investment strategy. It can therefore be concluded that, although the neural network models performed relatively well relative to traditional forecasting techniques in forecasting the ALSI index, the forecasts were still not accurate enough to be useful as inputs in a trading strategy.
AFRIKAANSE OPSOMMING: Die droom om die perfekte vooruitskattingsinstrument te vind om die prysgedrag van verskillende markinstrumente vooruit te skat, ontwyk al generasies lank die meeste beleggers. Verskillende tegnieke is al ondersoek om die belegger te help om ’n beter gevoel van prysveranderinge in die vrye mark te verkry. Die meeste van hierdie tegnieke het gefokus op tradisionele statistiese vooruitskattingstegnieke. Alhoewel hierdie tegnieke wêreldwyd deur investeerders gebruik word, was hierdie metodes se sukses as investeringsinstrument beperk. Die rede vir hierdie beperkte sukses lê in die feit dat hierdie tegnieke slegs die lineêre verwantskappe tussen veranderlikes gebruik het om voorspellings te maak, terwyl die meeste verwantskappe wat tussen veranderlikes in die vrye mark bestaan, nie-lineêr is. Neurale netwerke bied ’n unieke geleentheid vir beleggers om bogenoemde probleme te oorkom. Neurale netwerke is wiskundige modelle wat op die werking van die menslike brein gebaseer is en besit die vermoë om komplekse nie-lineêre verwantskappe tussen datastelle te herken. Hierdie studie fokus op die ontwikkeling van ’n neurale netwerk(e) om die prysverandering van die ALSI indeks op die JEB een en vyf dae in die toekoms vooruit te skat. Die resultate van die neurale netwerk model is verder vergelyk met die resultate van tradisionele statistiese vooruitskattingstegnieke soos byvoorbeeld ARIMA tegnieke. Die studie het gevind dat die neurale netwerk modelle nie beduidend beter gevaar het as die ARIMA modelle in die vooruitskatting van die ALSI indeks in beide die een- en vyfdag vooruitskattings nie. ’n Verdere toets is gedoen om die toepaslikheid van die gekose vyfdagmodel op verskillende tydvensters van die tydreeks te bepaal. Die toets het aangetoon dat die model baie meer akkuraat is vir sekere tydvensters as vir ander tydvensters. Dit dui dus daarop dat die neurale netwerk model gereeld heropgelei behoort te word om seker te maak dat die model optimaal presteer gegewe die spesifieke insetdata. Die resultate van die neurale netwerk model is ook gebruik in ’n simulasie om te bepaal of die resultate die belegger kan help om beter investeringsbesluite rakende die ALSI indeks op die JEB te maak. Ongelukkig het die simulasie resultate gewys dat ’n beleggingstrategie gebaseer op die neurale netwerk resultate swakker opbrengste gerealiseer het as ’n koop/hou beleggingstrategie. Ten slotte het die studie getoon dat alhoewel die neurale netwerk modelle relatief goed in vergelyking met tradisionele statistiese modelle gevaar het in die vooruitskatting van die ALSI indeks, hierdie vooruitskattings nie akkuraat genoeg is om as inset tot ’n investeringstrategie gebruik te word nie.
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Karlemstrand, Roderick, and Ebba Leckström. "Using Twitter Attribute Information to Predict Stock Prices." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299835.

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Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media. With the help of Machine Learning, complex patterns in data can be identified beyond the human intellect. In this thesis, a Machine Learning model for time series forecasting is created and tested to predict stock prices. The model is based on a neural network with several layers of Long Short-Term Memory (LSTM) and fully connected layers. It is trained with historical stock values, technical indicators and Twitter attribute information retrieved, extracted and calculated from posts on the social media platform Twitter. These attributes are sentiment score, favourites, followers, retweets and if an account is verified. To collect data from Twitter, Twitter’s API is used. Sentiment analysis is conducted with Valence Aware Dictionary and sEntiment Reasoner (VADER). The results show that by adding more Twitter attributes, the Mean Squared Error (MSE) between the predicted prices and the actual prices improved by 3%. With technical analysis taken into account, MSE decreases from 0.1617 to 0.1437, which is an improvement of around 11%. The restrictions of this study include that the selected stock has to be publicly listed on the stock market and popular on Twitter and among individual investors. Besides, the stock markets’ opening hours differ from Twitter, which constantly available. It may therefore introduce noises in the model.
Att kunna förutspå aktiekurser kan sägas vara aktiespararnas outtalade önskan. Även om aktievärden är komplicerade att förutspå finns det många teorier om vad som påverkar dess rörelser, bland annat räntor, nyheter och sociala medier. Med hjälp av maskininlärning kan mönster i data identifieras bortom människans intellekt. I detta examensarbete skapas och testas en modell inom maskininlärning i syfte att beräkna framtida aktiepriser. Modellen baseras på ett neuralt nätverk med flera lager av LSTM och fullt kopplade lager. Den tränas med historiska aktievärden, tekniska indikatorer och Twitter-attributinformation. De är hämtad, extraherad och beräknad från inlägg på den sociala plattformen Twitter. Dessa attribut är sentiment-värde, antal favorit-markeringar, följare, retweets och om kontot är verifierat. För att samla in data från Twitter används Twitters API och sentimentanalys genomförs genom VADER. Resultatet visar att genom att lägga till fler Twitter attribut förbättrade MSE mellan de förutspådda värdena och de faktiska värdena med 3%. Genom att ta teknisk analys i beaktande minskar MSE från 0,1617 till 0,1437, vilket är en förbättring på 11%. Begränsningar i denna studie innefattar bland annat att den utvalda aktien ska vara publikt listad på börsen och populär på Twitter och bland småspararna. Dessutom skiljer sig aktiemarknadens öppettider från Twitter då den är ständigt tillgänglig. Detta kan då introducera brus i modellen.
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Kennedy, Pauline. "Three essays on the prediction and identification of currency crises /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3102540.

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Yin, Pei. "Volatility estimation and price prediction using a hidden Markov model with empirical study." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4795.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on December 18, 2007) Vita. Includes bibliographical references.
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Books on the topic "Stock price prediction"

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J, Millard Brian. Channel analysis: The key to share price prediction. 2nd ed. Chichester, England: J. Wiley, 1997.

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Channel analysis: The key to share price prediction. Bramhall: Qudos, 1990.

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Goval, Amit. A comprehensive look at the empirical performance of equity premium prediction. Cambridge, MA: National Bureau of Economic Research, 2004.

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Lin, Jie-Shin. The top management changes of construction firms: Stock price reaction and possibility of prediction. Manchester: UMIST, 1996.

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Stevens, Leigh. Essential technical analysis: Tools and techniques to spot market trends. New York, NY: Wiley, 2002.

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Essential technical analysis: Tools and techniques to spot market trends. New York: Wiley, 2002.

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The spiral calendar: And its effects on financial markets and human events. Gainesville, Ga: New Classics Library, 1992.

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Goyal, Amit. Predicting the equity premium with dividend ratios. Cambridge, MA: National Bureau of Economic Research, 2002.

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I, Ellison George, ed. Stock returns cyclicity, prediction and economic consequences. Hauppauge, NY: Nova Science Publishers, 2009.

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Baker, Malcolm. Pseudo market timing and predictive regressions. Cambridge, Mass: National Bureau of Economic Research, 2004.

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Book chapters on the topic "Stock price prediction"

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Joyce, Philip. "Stock Price Prediction." In Practical Numerical C Programming, 73–92. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6128-6_4.

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D’Mello, Lynette, Aditya Jeswani, and Janice Johnson. "Stock Price Prediction Using Grammatical Evolution." In Algorithms for Intelligent Systems, 379–89. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3242-9_36.

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Rani, Poonam, Jyoti Shokeen, Anshul Singh, Anmol Singh, Sharlin Kumar, and Naman Raghuvanshi. "Stock Price Prediction Using Reinforcement Learning." In Advances in Intelligent Systems and Computing, 69–76. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2597-8_6.

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Liu, Ping. "Stock Price Prediction Using Deep Learning." In Applied Economics and Policy Studies, 196–200. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0564-3_20.

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Devi, Sanjana, and Virrat Devaser. "Stock Market Price Prediction Using SAP Predictive Service." In Communications in Computer and Information Science, 135–48. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_13.

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R., Abirami, and Vijaya M.S. "Stock Price Prediction Using Support Vector Regression." In Communications in Computer and Information Science, 588–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29219-4_67.

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Chanduka, Bhabesh, Swati S. Bhat, Neha Rajput, and Biju R. Mohan. "A TFD Approach to Stock Price Prediction." In Intelligent Computing and Communication, 635–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_61.

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Pawar, Kriti, Raj Srujan Jalem, and Vivek Tiwari. "Stock Market Price Prediction Using LSTM RNN." In Advances in Intelligent Systems and Computing, 493–503. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2285-3_58.

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Barik, Rhada, Amine Baina, and Mostafa Bellafkih. "The Prediction Stock Market Price Using LSTM." In Lecture Notes on Data Engineering and Communications Technologies, 444–53. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15191-0_42.

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Das, Suman Kumar, Soumyabrata Saha, and Suparna DasGupta. "Prediction of Stock Price Using Machine Learning." In Studies in Autonomic, Data-driven and Industrial Computing, 141–55. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7305-4_15.

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Conference papers on the topic "Stock price prediction"

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Agarwal, Shiva, and Naresh Babu Muppalaneni. "Stock Market Price and Cryptocurrency Price Prediction." In 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2022. http://dx.doi.org/10.1109/icdcece53908.2022.9793088.

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Vora, Chintan, Dhairya Sheth, Bhavya Shah, and Nasim Banu Shah. "Stock Price Analysis and Prediction." In 2021 International Conference on Communication information and Computing Technology (ICCICT). IEEE, 2021. http://dx.doi.org/10.1109/iccict50803.2021.9510159.

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Bai, Muqing, and Yu Sun. "An Intelligent and Social-Oriented Sentiment Analytical Model for Stock Market Prediction using Machine Learning and Big Data Analysis." In 8th International Conference on Artificial Intelligence and Applications (AI 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.121819.

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In an era of machine learning, many fields outside of computer science have implemented machine learning as a tool [5]. In the financial world, a variety of machine learning models are used to predict the future prices of a stock in order to optimize profit. This paper preposes a stock prediction algorithm that focuses on the correlation between the price of a stock and its public sentiments shown on social media [6].We trained different machine learning algorithms to find the best model at predicting stock prices given its sentiment. And for the public to access this model, a web-based server and a mobile application is created. We used Thunkable, a powerful no code platform, to produce our mobile application [7]. It allows anyone to check the predictions of stocks, helping people with their investment decisions.
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Li, Wei, Ruihan Bao, Keiko Harimoto, Deli Chen, Jingjing Xu, and Qi Su. "Modeling the Stock Relation with Graph Network for Overnight Stock Movement Prediction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/626.

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Stock movement prediction is a hot topic in the Fintech area. Previous works usually predict the price movement in a daily basis, although the market impact of news can be absorbed much shorter, and the exact time is hard to estimate. In this work, we propose a more practical objective to predict the overnight stock movement between the previous close price and the open price. As no trading operation occurs after market close, the market impact of overnight news will be reflected by the overnight movement. One big obstacle for such task is the lacking of data, in this work we collect and publish the overnight stock price movement dataset of Reuters Financial News. Another challenge is that the stocks in the market are not independent, which is omitted by previous works. To make use of the connection among stocks, we propose a LSTM Relational Graph Convolutional Network (LSTM-RGCN) model, which models the connection among stocks with their correlation matrix. Extensive experiment results show that our model outperforms the baseline models. Further analysis shows that the introduction of the graph enables our model to predict the movement of stocks that are not directly associated with news as well as the whole market, which is not available in most previous methods.
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Gupta, Rubi, and Min Chen. "Sentiment Analysis for Stock Price Prediction." In 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2020. http://dx.doi.org/10.1109/mipr49039.2020.00051.

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Roy, Ranjan Kumar, Koyel Ghosh, and Apurbalal Senapati. "Stock Price Prediction: LSTM Based Model." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.19.

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Stock price prediction is a critical field used by most business people and common or retail people who tried to increase their money by value with respect to time. People will either gain money or loss their entire life savings in stock market activity. It is a chaos system. Building an accurate model is complex as variation in price depends on multiple factors such as news, social media data, and fundamentals, production of the company, government bonds, historical price and country's economics factor. Prediction model which considers only one factor might not be accurate. Hence incorporating multiple factors news, social media data and historical price might increase the model's accuracy. This paper tried to incorporate the issue when someone implements it as per the model outcome. It cannot give the proper result when someone implements it in real life since capital market data is very sensitive and news-driven. To avoid such a situation, we use the hedging concept when implemented.
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Ruan, Jinlong, Wei Wu, and Jiebo Luo. "Stock Price Prediction Under Anomalous Circumstances." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9378030.

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Tiwari, Shashank, Akshay Bharadwaj, and Sudha Gupta. "Stock price prediction using data analytics." In 2017 International Conference on Advances in Computing, Communication and Control (ICAC3). IEEE, 2017. http://dx.doi.org/10.1109/icac3.2017.8318783.

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Yoshida, Kenichi. "Interpreting Attention of Stock Price Prediction." In 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2022. http://dx.doi.org/10.1109/compsac54236.2022.00200.

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Sidogi, Thendo, Rendani Mbuvha, and Tshilidzi Marwala. "Stock Price Prediction Using Sentiment Analysis." In 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2021. http://dx.doi.org/10.1109/smc52423.2021.9659283.

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Reports on the topic "Stock price prediction"

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Dassanayake, Wajira, Chandimal Jayawardena, Iman Ardekani, and Hamid Sharifzadeh. Models Applied in Stock Market Prediction: A Literature Survey. Unitec ePress, March 2019. http://dx.doi.org/10.34074/ocds.12019.

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Stock market prices are intrinsically dynamic, volatile, highly sensitive, nonparametric, nonlinear, and chaotic in nature, as they are influenced by a myriad of interrelated factors. As such, stock market time series prediction is complex and challenging. Many researchers have been attempting to predict stock market price movements using various techniques and different methodological approaches. Recent literature confirms that hybrid models, integrating linear and non-linear functions or statistical and learning models, are better suited for training, prediction, and generalisation performance of stock market prices. The purpose of this review is to investigate different techniques applied in stock market price prediction with special emphasis on hybrid models.
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Derbentsev, V., A. Ganchuk, and Володимир Миколайович Соловйов. Cross correlations and multifractal properties of Ukraine stock market. Politecnico di Torino, 2006. http://dx.doi.org/10.31812/0564/1117.

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Recently the statistical characterizations of financial markets based on physics concepts and methods attract considerable attentions. The correlation matrix formalism and concept of multifractality are used to study temporal aspects of the Ukraine Stock Market evolution. Random matrix theory (RMT) is carried out using daily returns of 431 stocks extracted from database time series of prices the First Stock Trade System index (www.kinto.com) for the ten-year period 1997-2006. We find that a majority of the eigenvalues of C fall within the RMT bounds for the eigenvalues of random correlation matrices. We test the eigenvalues of C within the RMT bound for universal properties of random matrices and find good agreement with the results for the Gaussian orthogonal ensemble of random matrices—implying a large degree of randomness in the measured cross-correlation coefficients. Further, we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the RMT bound display systematic deviations from the RMT prediction. We analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks. Our analysis of the remaining deviating eigenvectors shows distinct groups, whose identities correspond to conventionally identified business sectors. Comparison with the Mantegna minimum spanning trees method gives a satisfactory consent. The found out the pseudoeffects related to the artificial unchanging areas of price series come into question We used two possible procedures of analyzing multifractal properties of a time series. The first one uses the continuous wavelet transform and extracts scaling exponents from the wavelet transform amplitudes over all scales. The second method is the multifractal version of the detrended fluctuation analysis method (MF-DFA). The multifractality of a time series we analysed by means of the difference of values singularity stregth (or Holder exponent) ®max and ®min as a suitable way to characterise multifractality. Singularity spectrum calculated from daily returns using a sliding 250 day time window in discrete steps of 1. . . 10 days. We discovered that changes in the multifractal spectrum display distinctive pattern around significant “drawdowns”. Finally, we discuss applications to the construction of crushes precursors at the financial markets.
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Wideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche, and William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, December 2000. http://dx.doi.org/10.32747/2000.7575287.bard.

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Background PHS (pulmonary hypertension syndrome, ascites syndrome) is a serious cause of loss in the broiler industry, and is a prime example of an undesirable side effect of successful genetic development that may be deleteriously manifested by factors in the environment of growing broilers. Basically, continuous and pinpointed selection for rapid growth in broilers has led to higher oxygen demand and consequently to more frequent manifestation of an inherent potential cardiopulmonary incapability to sufficiently oxygenate the arterial blood. The multifaceted causes and modifiers of PHS make research into finding solutions to the syndrome a complex and multi threaded challenge. This research used several directions to better understand the development of PHS and to probe possible means of achieving a goal of monitoring and increasing resistance to the syndrome. Research Objectives (1) To evaluate the growth dynamics of individuals within breeding stocks and their correlation with individual susceptibility or resistance to PHS; (2) To compile data on diagnostic indices found in this work to be predictive for PHS, during exposure to experimental protocols known to trigger PHS; (3) To conduct detailed physiological evaluations of cardiopulmonary function in broilers; (4) To compile data on growth dynamics and other diagnostic indices in existing lines selected for susceptibility or resistance to PHS; (5) To integrate growth dynamics and other diagnostic data within appropriate statistical procedures to provide geneticists with predictive indices that characterize resistance or susceptibility to PHS. Revisions In the first year, the US team acquired the costly Peckode weigh platform / individual bird I.D. system that was to provide the continuous (several times each day), automated weighing of birds, for a comprehensive monitoring of growth dynamics. However, data generated were found to be inaccurate and irreproducible, so making its use implausible. Henceforth, weighing was manual, this highly labor intensive work precluding some of the original objectives of using such a strategy of growth dynamics in selection procedures involving thousands of birds. Major conclusions, solutions, achievements 1. Healthy broilers were found to have greater oscillations in growth velocity and acceleration than PHS susceptible birds. This proved the scientific validity of our original hypothesis that such differences occur. 2. Growth rate in the first week is higher in PHS-susceptible than in PHS-resistant chicks. Artificial neural network accurately distinguished differences between the two groups based on growth patterns in this period. 3. In the US, the unilateral pulmonary occlusion technique was used in collaboration with a major broiler breeding company to create a commercial broiler line that is highly resistant to PHS induced by fast growth and low ambient temperatures. 4. In Israel, lines were obtained by genetic selection on PHS mortality after cold exposure in a dam-line population comprising of 85 sire families. The wide range of PHS incidence per family (0-50%), high heritability (about 0.6), and the results in cold challenged progeny, suggested a highly effective and relatively easy means for selection for PHS resistance 5. The best minimally-invasive diagnostic indices for prediction of PHS resistance were found to be oximetry, hematocrit values, heart rate and electrocardiographic (ECG) lead II waves. Some differences in results were found between the US and Israeli teams, probably reflecting genetic differences in the broiler strains used in the two countries. For instance the US team found the S wave amplitude to predict PHS susceptibility well, whereas the Israeli team found the P wave amplitude to be a better valid predictor. 6. Comprehensive physiological studies further increased knowledge on the development of PHS cardiopulmonary characteristics of pre-ascitic birds, pulmonary arterial wedge pressures, hypotension/kidney response, pulmonary hemodynamic responses to vasoactive mediators were all examined in depth. Implications, scientific and agricultural Substantial progress has been made in understanding the genetic and environmental factors involved in PHS, and their interaction. The two teams each successfully developed different selection programs, by surgical means and by divergent selection under cold challenge. Monitoring of the progress and success of the programs was done be using the in-depth estimations that this research engendered on the reliability and value of non-invasive predictive parameters. These findings helped corroborate the validity of practical means to improve PHT resistance by research-based programs of selection.
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