Project Report from the year 2018 in the subject Computer Science - Technical Computer Science, , course: Computer Science, language: English, abstract: Modeling and Forecasting of the financial market have been an attractive topic to scholars and researchers from various academic fields. The financial market is an abstract concept where financial commodities such as stocks, bonds, and precious metals transactions happen between buyers and sellers. In the present scenario of the financial market world, especially in the stock market, forecasting the trend or the price of stocks using machine learning techniques and artificial neural networks are the most attractive issue to be investigated. As Giles explained, financial forecasting is an instance of signal processing problem which is difficult because of high noise, small sample size, non-stationary, and non-linearity. The noisy characteristics mean the incomplete information gap between past stock trading price and volume with a future price. The stock market is sensitive with the political and macroeconomic environment. However, these two kinds of information are too complex and unstable to gather. The above information that cannot be included in features are considered as noise. The sample size of financial data is determined by real-world transaction records. On one hand, a larger sample size refers a longer period of transaction records; on the other hand, large sample size increases the uncertainty of financial environment during the 2 sample period. In this project, we use stock data instead of daily data in order to reduce the probability of uncertain noise, and relatively increase the sample size within a certain period of time. By non-stationarity, one means that the distribution of stock data is various during time changing. Non-linearity implies that feature correlation of different individual stocks is various. Efficient Market Hypothesis was developed by Burton G. Malkiel in 1991.
Given the volatility of the stock market and the multitude of financial variables at play, forecasting the value of stocks can be a challenging task. Nonetheless, such prediction task presents a fascinating problem to solve using machine learning. The stock market can be affected by news events, social media posts, political changes, investor emotions, and the general economy among other factors. Predicting the stock value of a company by simply using financial stock data of its price may be insufficient to give an accurate prediction. Investors often openly express their attitudes towards various stocks on social medial platforms. Hence, combining sentiment analysis from social media and the financial stock value of a company may yield more accurate predictions. This thesis proposes a method to predict the stock market using sentiment analysis and financial stock data. To estimate the sentiment in social media posts, we use an ensemble-based model that leverages Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN) models. We use an LSTM model for the financial stock prediction. The models are trained on the AAPL, CSCO, IBM, and MSFT stocks, utilizing a combination of the financial stock data and sentiment extracted from social media posts on Twitter between the years 2015-2019. Our experimental results show that the combination of the financial and sentiment information can improve the stock market prediction performance. The proposed solution has achieved a prediction performance of 74.3%.
Stock market is a market that enables seamless exchange of buying and selling of company stocks. Every Stock Exchange has their own Stock Index value. Index is the average value that is calculated by combining several stocks. This helps in representing the entire stock market and predicting the market's movement over time. The Equity market can have a profound impact on people and the country's economy as a whole. Therefore, predicting the stock trends in an effective manner can minimize the risk of investing and maximize profit. In our paper, we are using the Time Series Forecasting methodology for predicting and visualizing the predictions. Our focus for prediction will be based on the technical analysis using historic data and ARIMA Model. Autoregressive Integrated Moving Average (ARIMA) model has been used extensively in the field of finance and economics as it is known to be robust, efficient and has a strong potential for short-term share market prediction.
Stock market trading has gained popularity in today's world with the advancement in technology and social media. With the help of today's technology we can aim to predict the stock market for the future value of stocks. To make informed predictions, time series analysis is used by most stock brokers around the world. This paper explains and analyzes the prediction of a stock by using machine learning. In this paper, I propose a machine learning approach that will be trained from the available stock data by using acquired knowledge for a prediction with accuracy. In this context, the study will use a machine learning technique called Support Vector Machine (SVM) and Long Short term memory (LSTM) to predict stock prices.
The major objective of this study is to develop a proposal for building an expert system model that can be used to help the traders and investors take trading decisions before an event of price change takes place in future. The proposed model uses an amalgamated approach of combining fuzzy logic, time series data, data mining and document analysis. The novelty of the proposed system is the use of a unique technique developed during the research process named as fuzzy document based information retrieval scheme (FDIRS). For performing the experiments, four global stock market indices and thirteen Indian stock market stocks that cover all the major sectors that drive the economy of India, were used so that the performance of the proposed model could be validated properly. RMSE used as the performance parameter helps to test the forecasting ability of the proposed model. In order to assess the performance of the proposed model the RMSE values of the already established methods and the proposed method is compared. The data set used while testing the proposed model was the same data set given to the already established methodologies, this approach enabled to compare the outputs and performance factors more critically..
Advances in telecommunication and software technologies have changed the way that securities are traded on the stock market. Algorithmic trading, which is also referred to as automated or black box trading, accounts for a large percentage of orders placed in the market, especially after the year 2000. It provides investors with many benefits such as reduced transaction costs, higher accuracy and speed, anonymity, transparency, and also access to different markets. Yet, it has a few limitations including lack of intelligence and lack of adaptability to the market conditions. Algorithms execute blindly what they are trained without having the capability to distinguish different conditions in the market. Such weaknesses make it vulnerable to unforseen events like market crises, which may result in a large amount of loss. For example, on May 6th, 2010, the Dow Jones Industrial Average fell 600 points in about five minutes that led to a loss of $600 billion in the market value of US corporate stocks. A large number of researchers attribute that crash to algorithmic trading orders, which were not intelligent enough to find out the financial crisis. Algorithms should be able to determine when to place different orders such as buy and sell, and more importantly algorithms through supervised learning processes is of great importance to algorithmic trading. In this thesis, a new algorithmic based on value trading is proposed to identify when to place, buy, sell, or stop orders. After classifying the order into those three categories, an Artificial Neural Network (ANN) with three layers, input, hidden, and the output is used to learn from previous trades. The ANN has five neurons in the input layer, ten neurons in the hidden layer, and three neurons in the output layer and is used to learn from the past patterns and make predictions for the future. In the last phase, the learning performance measures including accuracy, precision, recall, and F-score are measured.
Bachelor Thesis from the year 2021 in the subject Business economics - Review of Business Studies, grade: 1,0, Technical University of Munich, language: English, abstract: The focus of this bachelor thesis is the equity market of the Netherlands. The Amsterdam Stock Exchange is one of the oldest or even the oldest stock exchange of the world. Several interesting companies like Adyen (fintech company) and ASML (semiconductor company) are listed at the Netherlands market. However, this thesis is not about predicting individual stock returns, but about predicting the Netherlands stock market in general, and therefore, a broad stock index (the Netherlands-Datastream Market) is investigated, that contains (nearly) every stock of the Netherlands. Equity Market Prediction is an quite interesting topic for investment bankers and the academia. It plays an important role in topics like asset allocation, asset pricing, risk management and capital budgeting. Being able to predict the capital markets would result in a huge gain for investors. Even companies may benefit from equity market prediction, because they could time the market by deciding for example the optimal time of an initial public offering (IPO) or pricing this IPO correctly without leaving money on the table. Therefore, this bachelor thesis examines different predictor variables, that are grouped into market valuation, trend, sentiment, and macroeconomic (macro) variables. Predictor variables are variables that are said to be able to predict the equity market. To test the predictability of these predictors this thesis runs several in-sample and out-of-sample prediction trials with a defined regression framework. In-sample, both univariate as well as multivariate regressions are carried out. Out-of-sample, the predictive power of each predictor is tested stand-alone and compared to a simple benchmark model. In the end a trading strategy resulting from these return predictions may be evaluated.
Included in this volume are Louise McWhirter's theories and numerous, fully-explained and detailed examples for: Forecasting business cycles and stock market trends, forecasting trends of individual stocks, and forecasting monthly and daily trends on the New York stock exchange.
About this book This book provides you the powerful and brand new knowledge on predicting financial market that we have discovered in several years of our own research and development work. This book will help you to turn your intuition into the scientific prediction method. In the course of recognizing the price patterns in the chart of Forex and Stock market, you should be realized that it was your intuition working at the background for you. The geometric prediction devised in this book will show you the scientific way to predict the financial market using your intuition. Many of us made a mistake of viewing the financial market with deterministic cycle. Even though we knew that market would not show us such a simple prediction pattern, we never stop using the concept of deterministic cycle to predict the financial market, for example, using Fourier transform, and other similar techniques. Why is that so? The reason is simple. It is because no one presented an effective way of predicting stochastic cycle. Stochastic cycle is the true face of the financial market because many variables in the market are suppressing the predictable cycle with fixed time interval. So how we predict the stochastic cycle present in the financial market? The key to answer is the Fractal Pattern and Fractal Wave. The geometric prediction on Fractal Wave solves the puzzles of the stochastic cycle modelling problem together. In another words, your intuition, more precisely your capability to recognize geometric shape, is more powerful than any other technical indicators available in the market. Hence, the geometric prediction, which comes from your intuition, would maximize your ability to trade in the financial market. In this book, Geometric prediction is described as the combined ability to recognize the geometric regularity and statistical regularity from the chart. We provide the examples of geometric regularity and statistical regularity. In addition, we will show you how these regularities are related to your intuition. The chart patterns covered in this book include support, resistance, Fibonacci Price pattern, Harmonic Pattern, Falling Wedge pattern, Rising Wedge pattern, and Gann Angles with probability. We use these chart patterns to detect geometric regularity. Then, we use the turning point probability as the mean of detecting statistical regularity. In our trading, we combine both to improve the trading performance.