A neural network enhanced volatility component model

Volatility prediction, a central issue in financial econometrics, attracts increasing attention in the data science literature as advances in computational methods enable us to develop models with great forecasting precision. In this paper, we draw upon both strands of the literature and develop a novel two-component volatility model. The realized volatility is decomposed by a nonparametric filter into long- and short-run components, which are modeled by an artificial neural network and an ARMA process, respectively. We use intraday data on four major exchange rates and a Chinese stock index to construct daily realized volatility and perform out-of-sample evaluation of volatility forecasts generated by our model and well-established alternatives. Empirical results show that our model outperforms alternative models across all statistical metrics and over different forecasting horizons. Furthermore, volatility forecasts from our model offer economic gain to a mean-variance utility investor with higher portfolio returns and Sharpe ratio

Adaptive Hidden Markov Model With Anomaly States for Price Manipulation Detection

Price manipulation refers to the activities of those traders who use carefully designed trading behaviors to manually push up or down the underlying equity prices for making profits. With increasing volumes and frequency of trading, price manipulation can be extremely damaging to the proper functioning and integrity of capital markets. The existing literature focuses on either empirical studies of market abuse cases or analysis of particular manipulation types based on certain assumptions. Effective approaches for analyzing and detecting price manipulation in real time are yet to be developed. This paper proposes a novel approach, called adaptive hidden Markov model with anomaly states (AHMMAS) for modeling and detecting price manipulation activities. Together with wavelet transformations and gradients as the feature extraction methods, the AHMMAS model caters to price manipulation detection and basic manipulation type recognition. The evaluation experiments conducted on seven stock tick data from NASDAQ and the London Stock Exchange and 10 simulated stock prices by stochastic differential equation show that the proposed AHMMAS model can effectively detect price manipulation patterns and outperforms the selected benchmark models

Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Soft Computing Techniques for Stock Market Prediction: A Literature Survey

Stock market trading is an unending investment exercise globally. It has potentials to generate high returns on investors’ investment. However, it is characterized by high risk of investment hence, having knowledge and ability to predict stock price or market movement is invaluable to investors in the stock market. Over the years, several soft computing techniques have been used to analyze various stock markets to retrieve knowledge to guide investors on when to buy or sell. This paper surveys over 100 published articles that focus on the application of soft computing techniques to forecast stock markets. The aim of this paper is to present a coherent of information on various soft computing techniques employed for stock market prediction. This research work will enable researchers in this field to know the current trend as well as help to inform their future research efforts. From the surveyed articles, it is evident that researchers have firmly focused on the development of hybrid prediction models and substantial work has also been done on the use of social media data for stock market prediction. It is also revealing that most studies have focused on the prediction of stock prices in emerging market

Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM

Abstract: As stocks have been developing over decades, the trend and the price of a stock are more often used for predictions in stock market analysis. In the field of finance, an accurate stock future trending can not only help decision-makers estimate the possibility of profit, but also help them avoid risks. In this research, we present a quantitative approach to predicting the trend of stocks in which a clustering model is employed to mine the stock trends patterns from historical stock price data. Stock series clustering is a special kind of time series clustering. We aim to find out the trend types, e.g. rising, falling and others, of a stock at time intervals, and then make use of the past trends to predict its future trend. The proposed prediction method is based on Graph Convolutional Neural Network for clustering and Long Short-Term Memory model for prediction. This method is suitable for the data clustering of unbalanced classes too. The experiments on real-world stock data demonstrate that our method can yield accurate forecasts. In the long run, the proposed method can be used to explore new possibilities in the research field of time series clustering, such as using other graph neural networks to predict stock trends.Comme les prix des actions évoluent au fil des décennies, la tendance et le prix d’une action sont souvent utilisés pour effectuer des prévisions en bourse. Bien anticiper la tendance future des actions peut non seulement aider les décideurs à mieux estimer les possibilités de profit, mais aussi les risques. Dans cette thèse, une approche quantitative est présentée pour prédire les fluctuations d’actions. L’approche se base sur une méthode de clustering pour identifier la tendance des actions à partir de leurs données historiques. C’est un type particulier de clustering appliqué sur des séries chronologiques. Il consiste à découvrir les tendances des actions sur des intervalles de temps, tel que des tendances haussières, des tendances baissières, et ensuite d’utiliser ces tendances pour prédire leurs états futurs. La méthode de prédiction proposée se base sur les réseaux de neurones convolutionnels graphiques et des réseaux récurrents mémoire pour la prédiction. Cette méthode fonctionne également sur des ensembles de données où la proportion des classes est déséquilibrée. Les données historiques des actions démontrent que la méthode proposée effectue des prévisions précises. La méthode proposée peut ouvrir une nouvelle perspective de recherche pour le clustering de séries chronologiques, notamment l’utilisation d‘autres réseaux de neurones graphiques pour prédire les tendances des actions