2,378 research outputs found
Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction
In this paper we develop a novel neural network model for predicting implied
volatility surface. Prior financial domain knowledge is taken into account. A
new activation function that incorporates volatility smile is proposed, which
is used for the hidden nodes that process the underlying asset price. In
addition, financial conditions, such as the absence of arbitrage, the
boundaries and the asymptotic slope, are embedded into the loss function. This
is one of the very first studies which discuss a methodological framework that
incorporates prior financial domain knowledge into neural network architecture
design and model training. The proposed model outperforms the benchmarked
models with the option data on the S&P 500 index over 20 years. More
importantly, the domain knowledge is satisfied empirically, showing the model
is consistent with the existing financial theories and conditions related to
implied volatility surface.Comment: 8 pages, SIGKDD 202
Earnings prediction using machine learning methods and analyst comparison
In the course of this dissertation we propose an experimental study on how technical,
macroeconomic, and financial variables, alongside analysts’ forecasts, can be used to
optimize the prediction for the subsequent quarter’s earnings results using machine learning,
comparing the performance of the models to analysts’ forecasts. The dissertation includes
three steps. In step one, an event study is conducted to test abnormal returns in firms’ stock
prices in the day following earnings announcement, grouped by earnings per share (EPS)
growth in classes of size 3, 6 and 9, computed for each quarter. In step two, several machine
learning models are built to maximize the accuracy of EPS predictions. In the last step,
investment strategies are constructed to take advantage of investors’ expectations, which are
closely correlated with analysts’ predictions. In the backdrop of an exhaustive analysis on
quarterly earnings predictions using machine learning methods, conclusions are drawn
related to the superiority of the CatBoost classifier. All machine learning models tested
underperform analyst predictions, which could be explained by the time and privileged
information at analysts’ disposal, as well as their selection of firms to cover. Regardless,
machine learning models can be used as a confirmation for analyst predictions, and
statistically significant investment strategies are pursued with those fundamentals.
Importantly, high confidence predictions by machine learning models are significantly more
accurate than the average accuracy of forecasts.No decorrer desta dissertação, realiza-se um estudo experimental sobre a forma como
análises técnicas, macroeconómicas, fundamentais e as previsões dos analistas podem ser
utilizadas em conjunto para otimizar a previsĂŁo dos resultados de lucros do prĂłximo
trimestre de empresas A dissertação inclui três etapas. Na primeira etapa, é efetuado um
estudo de evento para testar os retornos anormais nas ações no dia seguinte aos anúncios de
lucros, sendo estes agrupados pelo crescimento do lucro por ação nas classes de 3, 6 e 9,
calculado para cada trimestre. Na etapa dois, vários modelos de machine learning (ML) são
concebidos para maximizar a precisão das previsões de crescimento de lucros de empresas.
Na Ăşltima etapa, estratĂ©gias de investimento sĂŁo construĂdas para tirar proveito das
expectativas do investidor, que estão relacionadas com as previsões dos analistas. Uma vez
que um dos projetos de pesquisa mais exaustivos sobre previsões de lucros para o próximo
trimestre, conclusões podem ser retiradas relacionadas com a superioridade do modelo
CatBoost nas previsões de lucros. Todos os modelos de testados apresentam desempenho
inferior às previsões dos analistas, o que pode ser explicado pelo tempo e pelas informações
privilegiadas a que os analistas tĂŞm acesso, bem como pela escolha da empresa sob a qual
as suas previsões incidem. Os modelos de podem ser utilizados como uma confirmação para
as previsões dos analistas criando estratégias de investimento estatisticamente significativas.
Além disso, as previsões com alta confiança por modelos de são mais precisas do que a
precisão média das previsões dos analistas
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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
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Machine Learning Stock Market Prediction Studies: Review and Research Directions
Stock market investment strategies are complex and rely on an evaluation of vast amounts of data. In recent years, machine learning techniques have increasingly been examined to assess whether they can improve market forecasting when compared with traditional approaches. The objective for this study is to identify directions for future machine learning stock market prediction research based upon a review of current literature. A systematic literature review methodology is used to identify relevant peer-reviewed journal articles from the past twenty years and categorize studies that have similar methods and contexts. Four categories emerge: artificial neural network studies, support vector machine studies, studies using genetic algorithms combined with other techniques, and studies using hybrid or other artificial intelligence approaches. Studies in each category are reviewed to identify common findings, unique findings, limitations, and areas that need further investigation. The final section provides overall conclusions and directions for future research
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