STOCK MARKET FORECASTING: AN APPLICATION OF LONG SHORT TERM MEMORY (LSTM) RECURRENT NEURAL NETWORK

Predicting stock market prices is regarded as a challenging task of financial time series, due to its chaotic, non-linear, non-stationary and dynamic nature. In this project we address the problem of stock market forecasting by making a comparison between different machine learning prediction models mainly Support Vector Machine (SVM), Artificial Neural Networks (ANN), Random Forest (RS), and Long Short Term Memory (LSTM) Recurrent Neural Network. For this goal, different models are built for predicting stock prices for 10 days in advance, and a number of experiments were executed based on ten years of historical data for stock prices from different sectors of the industry of the Qatari and the American markets. The results were analyzed using Mean Squared Error (MSE) and Mean Absolute Error (MAE) measuring metrics. Furthermore, we developed an application for predicting stock prices and trend movement with a motivation that trading strategies and investment decisions are more reliable and efficient when guided by forecasts which could lead to more profit

Forex Trading Signal Extraction with Deep Learning Models

The rise of AI technology has popularized deep learning models for financial trading prediction, promising substantial profits with minimal risk. Institutions like Westpac, Commonwealth Bank of Australia, Macquarie Bank, and Bloomberg invest heavily in this transformative technology. Researchers have also explored AI's potential in the exchange rate market. This thesis focuses on developing advanced deep learning models for accurate forex market prediction and AI-powered trading strategies. Three deep learning models are introduced: an event-driven LSTM model, an Attention-based VGG16 model named MHATTN-VGG16, and a pre-trained model called TradingBERT. These models aim to enhance signal extraction and price forecasting in forex trading, offering valuable insights for decision-making. The first model, an LSTM, predicts retracement points crucial for identifying trend reversals. It outperforms baseline models like GRU and RNN, thanks to noise reduction in the training data. Experiments determine the optimal number of timesteps for trend identification, showing promise for building a robotic trading platform. The second model, MHATTN-VGG16, predicts maximum and minimum price movements in forex chart images. It combines VGG16 with multi-head attention and positional encoding to effectively classify financial chart images. The third model utilizes a pre-trained BERT architecture to transform trading price data into normalized embeddings, enabling meaningful signal extraction from financial data. This study pioneers the use of pre-trained models in financial trading and introduces a method for converting continuous price data into categorized elements, leveraging the success of BERT. This thesis contributes innovative approaches to deep learning in algorithmic trading, offering traders and investors precision and confidence in navigating financial markets

Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

Financial news contains useful information on public companies and the market. In this paper we apply the popular word embedding methods and deep neural networks to leverage financial news to predict stock price movements in the market. Experimental results have shown that our proposed methods are simple but very effective, which can significantly improve the stock prediction accuracy on a standard financial database over the baseline system using only the historical price information

PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin

Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.Comment: 21 pages, submitted preprint to Elsevier Expert Systems with Application

Application of Machine Learning: An Analysis of Asian Options Pricing Using Neural Netwoprk

Pricing Asian Option is imperative to researchers, analysts, traders and any other related experts involved in the option trading markets and the academic field. Not only trading highly affected by the accuracy of the price of Asian options but also portfolios that involve hedging of commodity. Several attempts have been made to model the Asian option prices with closed-form over the past twenty years such as the Kemna-Vorst Model and Levy Approximation. Although today the two closed-form models are still widely used, their accuracy and reliability are called into question. The reason is simple; the Kemna-Vorst model is derived with an assumption of geometric mean of the stocks. In practice, Average Priced Options are mostly arithmetic and thus always have a volatility high than the volatility of a geometric mean making the Asian options always underpriced. On the other hand, the Levy Approximation using Monte Carlo Simulation as a benchmark, do not perform well when the product of the sigma (volatility) and square root maturity of the underlying is larger than 0.2. When the maturity of the option enlarges, the performance of the Levy Approximation largely deteriorates. If the closed-form models could be improved, higher frequency trading of Asian option will become possible. Moreover, building neural networks for different contracts of Asian Options allows reuse of computed prices and large-scale portfolio management that involves many contracts. In this thesis, we use Neural Network to fill the gap between the price of a closed-form model and that of an Asian option. The significance of this method answers two interesting questions. First, could an Asian option trader with a systematic behavior in pricing learned from previous quotes improve his pricing or trading performance in the future? Second, will a training set of previous data help to improve the performance of a financial model? We perform two simulation experiments and show that the performance of the closed-form model is significantly improved. Moreover, we extend the learning process to real data quote. The use of Neural Network highly improves the accuracy of the traditional closed-form model. The model’s original price is not so much accurate as what we estimate using Neural network and could not capture the high volatility effectively; still, it provides a relative reasonable fit to the problem(Especially the Levy Model). The analysis shows that the Neural Network Algorithms we used affect the results significantly.Computer Scienc

Analysis of S&P500 using News Headlines Applying Machine Learning Algorithms

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceFinancial risk is in everyone’s life now, directly or indirectly impacting people´s daily life, empowering people on their decisions and the consequences of the same. This financial system comprises all the companies that produce and sell, making them an essential factor. This study addresses the impact people can have, by the news headlines written, on companies’ stock prices. S&P 500 is the index that will be studied in this research, compiling the biggest 500 companies in the USA and how the index can be affected by the News Articles written by humans from distinct and powerful Newspapers. Many people worldwide “play the game” of investing in stock prices, winning or losing much money. This study also tries to understand how strongly this news and the Index, previously mentioned, can be correlated. With the increased data available, it is necessary to have some computational power to help process all of this data. There it is when the machine learning methods can have a crucial involvement. For this is necessary to understand how these methods can be applied and influence the final decision of the human that always has the same question: Can stock prices be predicted? For that is necessary to understand first the correlation between news articles, one of the elements able to impact the stock prices, and the stock prices themselves. This study will focus on the correlation between News and S&P 500

Deep Learning-based Information Fusion Frameworks for Stock Price Movement Prediction

The challenges of modeling behaviour of financial markets, such as its high volatility, poor predictive behaviour, and its non-stationary nature, have continuously attracted attention of the researchers to employ advanced engineering methods. Within the context of financial econometrics, stock market movement prediction is a key and challenging problem. The research works reported in this thesis are motivated by the potentials of Artificial Intelligence (AI) and Machine Learning (ML)-based models, especially Deep Neural Network (DNN) architectures, for stock movement prediction. Considering recent progress in design and implementation of advanced DNN-based models, there has been a surge of interest in their application for predicting stock trends. In particular, the focus of the thesis is on utilization of information fusion to combine Twitter data with extended horizon market historical data for the task of price movement prediction. In this regard, the thesis made a number of contributions, first, the Noisy Deep Stock Movement Prediction Fusion (ND-SMPF) framework is proposed to extract news level temporal information; identify relevant words with highest correlation and effects on the stock trends, and; perform information fusion with historical price data. A real dataset is incorporated to evaluate performance of the proposed ND-SMPF framework. In addition, given that recent COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe, a unique COVID-19 related PRIce MOvement prediction (\CDATA) dataset is constructed. The objective is to incorporate effects of social media trends related to COVID-19 on stock market price movements. A novel hybrid and parallel DNN-based framework is then designed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (\SMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical market data and perform accurate price movement prediction during a pandemic crisis