404 research outputs found

    TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data

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    Stock market forecasting has been a challenging part for many analysts and researchers. Trend analysis, statistical techniques, and movement indicators have traditionally been used to predict stock price movements, but text extraction has emerged as a promising method in recent years. The use of neural networks, especially recurrent neural networks, is abundant in the literature. In most studies, the impact of different users was considered equal or ignored, whereas users can have other effects. In the current study, we will introduce TM-vector and then use this vector to train an IndRNN and ultimately model the market users' behaviour. In the proposed model, TM-vector is simultaneously trained with both the extracted Twitter features and market information. Various factors have been used for the effectiveness of the proposed forecasting approach, including the characteristics of each individual user, their impact on each other, and their impact on the market, to predict market direction more accurately. Dow Jones 30 index has been used in current work. The accuracy obtained for predicting daily stock changes of Apple is based on various models, closed to over 95\% and for the other stocks is significant. Our results indicate the effectiveness of TM-vector in predicting stock market direction.Comment: 24 pag

    Deep learning for trading and hedging in financial markets

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    Deep learning has achieved remarkable results in many areas, from image classification, language translation to question answering. Deep neural network models have proved to be good at processing large amounts of data and capturing complex relationships embedded in the data. In this thesis, we use deep learning methods to solve trading and hedging problems in the financial markets. We show that our solutions, which consist of various deep neural network models, could achieve better accuracies and efficiencies than many conventional mathematical-based methods. We use Technical Analysis Neural Network (TANN) to process high-frequency tick data from the foreign exchange market. Various technical indicators are calculated from the market data and fed into the neural network model. The model generates a classification label, which indicates the future movement direction of the FX rate in the short term. Our solution can surpass many well-known machine learning algorithms on classification accuracies. Deep Hedging models the relationship between the underlying asset and the prices of option contracts. We upgrade the pipeline by removing the restriction on trading frequency. With different levels of risk tolerances, the modified deep hedging model can propose various hedging solutions. These solutions form the Efficient Hedging Frontier (EHF), where their associated risk levels and returns are directly observable. We also show that combining a Deep Hedging model with a prediction algorithm ultimately increases the hedging performances. Implied volatility is the critical parameter for evaluating many financial derivatives. We propose a novel PCA Variational Auto-Enocder model to encode three independent features of implied volatility surfaces from the European stock markets. This novel encoding brings various benefits to generating and extrapolating implied volatility surfaces. It also enables the transformation of implied volatility surfaces from a stock index to a single stock, significantly improving the efficiency of derivatives pricing

    Online learning in financial time series

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    We wish to understand if additional learning forms can be combined with sequential optimisation to provide superior benefit over batch learning in various tasks operating in financial time series. In chapter 4, Online learning with radial basis function networks, we provide multi-horizon forecasts on the returns of financial time series. Our sequentially optimised radial basis function network (RBFNet) outperforms a random-walk baseline and several powerful supervised learners. Our RBFNets naturally measure the similarity between test samples and prototypes that capture the characteristics of the feature space. In chapter 5, Reinforcement learning for systematic FX trading, we perform feature representation transfer from an RBFNet to a direct, recurrent reinforcement learning (DRL) agent. Earlier academic work saw mixed results. We use better features, second-order optimisation methods and adapt our model parameters sequentially. As a result, our DRL agents cope better with statistical changes to the data distribution, achieving higher risk-adjusted returns than a funding and a momentum baseline. In chapter 6, The recurrent reinforcement learning crypto agent, we construct a digital assets trading agent that performs feature space representation transfer from an echo state network to a DRL agent. The agent learns to trade the XBTUSD perpetual swap contract on BitMEX. Our meta-model can process data as a stream and learn sequentially; this helps it cope with the nonstationary environment. In chapter 7, Sequential asset ranking in nonstationary time series, we create an online learning long/short portfolio selection algorithm that can detect the best and worst performing portfolio constituents that change over time; in particular, we successfully handle the higher transaction costs associated with using daily-sampled data, and achieve higher total and risk-adjusted returns than the long-only holding of the S&P 500 index with hindsight

    Integrating Big Data Analytics with U.S. SEC Financial Statement Datasets and the Critical Examination of the Altman Z’-Score Model

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    The main aim of this thesis is to document the process of developing Big Data analytical applications and their integration with financial statement datasets. These datasets are publicly available on the U.S. SEC (Security and Exchange Commission) website which contains the annual and quarterly reports of approximately 8000 companies. Through its Electronic Data Gathering, Analysis and Retrieval (EDGAR) system, the SEC receives several terabytes of data in the mandatory filings from its registrants. This vast amount of data can potentially provide a valuable resource for those parties (such as investors, analysts, regulators and researchers) who are interested in assessing the financial performance and position of companies. Traditionally, the quarterly and annual reports were submitted in standard PDF, HTML and Text files. The data from these files could be manually extracted and analysed, but this process (still used by some analysts and researchers) is costly and time-consuming. In 2009, the SEC mandated all listed companies to use a digital reporting format known as XBRL (eXtensible Business Reporting Language). The intention of this was to improve financial reporting in terms of transparency and efficiency. In order to take advantage of structured data contained in the XBRL format, a variety of methods such as novel extraction algorithms and data mining techniques have been developed. However, several limitations and issues have emerged. These include a lack of automated connectivity between the EDGAR web interface and the terms used in structured taxonomies, and the inability to provide access to multiple files in a single query. Given the challenging and complex nature of these issues, this research project used the financial statement datasets available on the SEC website to extract relevant financial information from the company’s annual reports. The novel aspect of this research is providing big data analytical applications using cloud technologies that can efficiently perform datasets integration and transformation into a format suitable for further analysis. The result of this is that the extracted financial data can be analysed to assess the performance of companies, and this facilitates the critical examination of widely used credit assessment models such as the Altman Z’-Score

    Machine Learning-Driven Decision Making based on Financial Time Series

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Environmental concern, regulations and board diversity

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    Essays on financial disclosure and innovation

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    Essays in High Frequency Trading and Market Structure

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    High Frequency Trading (HFT) is the use of algorithmic trading technology to gain a speed advantage when operating in financial markets. The increasing gap between the fastest and the slowest players in financial markets raises questions around the efficiency of markets, the strategies players must use to trade effectively and the overall fairness of markets which regulators must maintain. This research explores markets affected by HFT activity from three perspectives. Firstly an updated microstructure model is proposed to allow for empirical exploration of current levels of noise in financial markets, this illustrates current noise levels are not disruptive to dominant trading strategies. Second, a ARCH type model is used to de-compose market data into a series of traders working price levels to demonstrate that in cases of suspected market abuse, regulators can assess the impact individual traders make on price even in fast markets. Finally, a review of various HFT control measures are examined in terms of effectiveness and in light of an ordoliberal benchmark of fairness. The work illustrates the extents to which HFT activity is not yet disruptive, but also shows where HFT can be a conduit for market abuse and provides a series of recommendations around use of circuit breakers, algorithmic governance standards and additional considerations where assets are dual listed in different countries
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