The thesis describes the stock exchange environment, the system and its basic operating principles. The thesis further focuses on the stock exchange data and its analysis. The author describes the development of the technical analysis; he mentions the classical theory and the classical graphical methods, the modern graphical methods, the technical indicators and finally the latest analytical methods, the so-called Artificial Intelligence. The research focuses on the real stock market prediction using the artificial intelligence methods and knowledge of the modern technical analysis.

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.

<p>The purpose of this Bachelor thesis is to analyze if there is a correlation between stock prices and the amount of searches of the companies names on Google. The theories used in the study were Capital Asset Pricing Model (CAPM) and Efficient Market Hypothesis (EMH). Regressions analysis is used as the statistical method to see if there is a significant correlation between the stock prices and the amout of searches of the company name on Google. The data used were the rate of return of three companies (ABB, Oriflame and Sandvik) on the Nasdaq OMX Nordic stock market, the rate of return of the Nasdaq OMX Nordic stock market index (OMX Stockholm_PI) and the Google search frequency from Google Trends on each company. The result showed no significance and the conclusion of the thesis is that there is no significant correlation between the three studied companies and their search frequency on the search engine Google.</p> / <p><strong>Syfte</strong>: Syftet med uppsatsen är att undersöka ifall det finns ett samband mellan företags aktiekurser och sökfrekvens på företagets namn på söktjänsten Google.</p><p><strong>Data: </strong>Daglig avkastning på ABB:s, Oriflames och Sandviks aktier, Aktieindex samt Googels sökfrekvens.</p><p><strong>Teorier: </strong>Capital Asset Pricing Model (CAPM), Effektiva marknadshypotesen (EMH)</p><p><strong>Slutsats: </strong>Det råder inget signifikant samband mellan de undersökta företagens aktiekurser och deras företagsnamns sökfrekvens på söktjänsten Google.</p>

The purpose of this Bachelor thesis is to analyze if there is a correlation between stock prices and the amount of searches of the companies names on Google. The theories used in the study were Capital Asset Pricing Model (CAPM) and Efficient Market Hypothesis (EMH). Regressions analysis is used as the statistical method to see if there is a significant correlation between the stock prices and the amout of searches of the company name on Google. The data used were the rate of return of three companies (ABB, Oriflame and Sandvik) on the Nasdaq OMX Nordic stock market, the rate of return of the Nasdaq OMX Nordic stock market index (OMX Stockholm_PI) and the Google search frequency from Google Trends on each company. The result showed no significance and the conclusion of the thesis is that there is no significant correlation between the three studied companies and their search frequency on the search engine Google. / Syfte: Syftet med uppsatsen är att undersöka ifall det finns ett samband mellan företags aktiekurser och sökfrekvens på företagets namn på söktjänsten Google. Data: Daglig avkastning på ABB:s, Oriflames och Sandviks aktier, Aktieindex samt Googels sökfrekvens. Teorier: Capital Asset Pricing Model (CAPM), Effektiva marknadshypotesen (EMH) Slutsats: Det råder inget signifikant samband mellan de undersökta företagens aktiekurser och deras företagsnamns sökfrekvens på söktjänsten Google.

Stock market plays an important role in the modern capital market. As a result, the prediction of financial assets attracts people in different areas. Moreover, it is commonly accepted that stock price movement generally follows a major trend. As a result, forecasting the market trend becomes an important mission for a prediction method. Accordingly, we will predict the long term trend rather than the movement of near future or change in a trading day as the target of our predicting approach. Although there are various kinds of analyses for trend prediction, most of them use clear cuts or certain thresholds to classify the trends. Users (or investors) are not informed with the degrees of confidence associated with the recommendation or the trading signal. Therefore, in this research, we would like to study an approach that could offer the confidence of the trend analysis by providing the probabilities of each possible state given its historical data through Dynamic Bayesian Network. We will incorporate the well-known principles of Dow¡¦s Theory to better model the trend of stock movements. Through the results of our experiment, we may say that the financial performance of the proposed model is able to defeat the buy and hold trading strategy when the time scope covers the entire cycle of a trend. It also means that for the long term investors, our approach has high potential to win the excess return. At the same time, the trading frequency and correspondently trading costs can be reduced significantly.

The rapid growth of the Internet has shaped up the global economy. The stock market information is thus more and more transparent. Although the investors can get more helpful information to judge future trend of the stock market, they may get wrong judgments because the stock market data are too huge to be completely analyzed. Therefore, the purpose of this study is to develop an artificial stock market analyst by employing the information technology with high speed and performance, as well as integrating the artificial intelligence techniques. We exploit case-based reasoning to simulate the analysts in using history stock market data, employ the artificial neural network to imitate the analysts in analyzing the macrofactors of stock market, and apply the fuzzy logic to humanize the artificial stock market analyst in making judgments close to the real stock market analysts. The artificial stock market analyst would use the modified case-based reasoning system combined with the artificial neural network, and incorporate the designed membership functions for macrofactors of stock market. We expect the system to improve the accuracy of Taiwan electric stock price prediction by applying macrofactors from the technical analysis indicators and financial crisis factors, and make better stock trading strategies.

Despite the extent of a theoretical framework in financial market studies, a vast majority of the traders, investors and computer scientists have relied only on technical and timeseries data for predicting future prices. So far, the forecasting models have rarely incorporated macro-economic and market fundamentals successfully, especially with short-term predictions ranging less than a month. In this investigation on the predictability of certain financial markets, an attempt has been made to incorporate a un-exampled and encompassing set of parameters into an Artificial Neural Network prediction system. Experiments were carried out on three market instruments ??? namely currency exchange rates, share prices and oil prices. The choice of parameters for inclusion or exclusion, and the time frame adopted for the experimental sets were derived from the market literature. Good directional prediction accuracies were achieved for currency exchange rates and share prices with certain parameters as inputs, which consisted of predicting short-term movements based on past movements. These predictions were better than the results produced by a traditional least square prediction method. The trading strategy developed based on the predictions also achieved a higher percentage of winning trades. No significant predictions were observed for oil prices. These results open up questions in the microstructure of the markets and provide an insight into the inputs required for market forecasting in the corresponding time frame, for future investigation. The study concludes by advocating the use of trend based input parameters and suggests ways to improve neural network forecasting models.

Predicting stock market trends is a complex task due to the inherent volatility and unpredictability of financial markets. Nevertheless, accurate forecasts are of critical importance to investors, financial analysts, and stakeholders, as they directly inform decision-making processes and risk management strategies associated with financial investments. Inaccurate forecasts can lead to notable financial consequences, emphasizing the crucial and demanding task of developing models that provide accurate and trustworthy predictions. This article addresses this challenging problem by utilizing a long-short term memory (LSTM) model to predict stock market developments. The study undertakes a thorough analysis of the LSTM model's performance across multiple datasets, critically examining the impact of different timespans and epochs on the accuracy of its predictions. Additionally, a comparison is made with a support vector regression (SVR) model using the same datasets and timespans, which allows for a comprehensive evaluation of the relative strengths of the two techniques. The findings offer insights into the capabilities and limitations of both models, thus paving the way for future research in stock market prediction methodologies. Crucially, the study reveals that larger datasets and an increased number of epochs can significantly enhance the LSTM model's performance. Conversely, the SVR model exhibits significant challenges with overfitting. Overall, this research contributes to ongoing efforts to improve financial prediction models and provides potential solutions for individuals and organizations seeking to make accurate and reliable forecasts of stock market trends.

A wide variety of machine learning algorithms havebeen used to predict stock prices. The aim of this project hasbeen to implement a machine learning algorithm using supportvector regression to predict the stock price of two well knowncompanies—Apple and Microsoft—one day into the future usingthe current day’s stock price, macroeconomic data and microeconomicdata and to compare the prediction error with the differentdata inputs. The results show that the addition of macroeconomicand microeconomic data did not improve the prediction error.This suggests that the macroeconomic and microeconomic dataused in this project does not contain additional information aboutfuture stock prices. The results also show that support vectorregression performs worse than linear regression, however inthis case no definite conclusion can be drawn since only onekernel and a handful of parameter values were considered whentraining and testing the algorithm. However, these results mightalso suggest that using the current day’s data is not sufficient tobe able to predict the non-linear relationships. / Ett flertal maskininlärnings-algoritmer har använts för att förutspå aktiepriser. Målet med det här projektet har varit att implementera en maskininlärnings-algoritm som använder sig av support vector regression för att förutspå aktiepriset av två välkända företag—Apple och Microsoft—en dag in i framtiden genom att använda dagens aktiepris, makroekonomisk data och mikroekonomisk data samt att jämföra prediktionsfelet med dem olika indata. Resultaten indikerar att additionen av makroekonomisk och mikroekonomisk data inte förbättrade prediktionsfelet. Detta antyder att den makroekonomiska och mikroekonomiska data som användes i projektet inte innehåller någon ytterliggare information om framtida aktiepriser. Resultaten indikerade också att linjär regression presterar bättre än support vector regression, men i detta fallet kan ingen definitiv slutsats dras eftersom endast en kernel och ett par parameter-värden användes för att träna och testa algoritmen. Däremot kan dessa resultat också antyda att a inte är tillräcklig för att kunna förutspå dem icke-linjära förhållandena. / Kandidatexjobb i elektroteknik 2021, KTH, Stockholm

This work deals with stock market prediction via technical and psychological analysis. We introduce theoretical resources of technical and psychological analysis. We also introduce some methods of artificial intelligence, specially neural networks and genetic algorithms. We design a system for stock market prediction. We implement and test a part of system. In conclusion we discuss results.