Showing papers in "Quantitative Finance in 2019"
TL;DR: Using a large-scale deep learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, the authors uncover nonparametric evidence for the exis...
Abstract: Using a large-scale Deep Learning approach applied to a high-frequency database containing billions of market quotes and transactions for US equities, we uncover nonparametric evidence for the exis...
128 citations
TL;DR: This work explores the attention mechanism in Long–Short-Term Memory (LSTM) network based stock price movement prediction and proposes a model that significantly enhances the LSTM prediction performance in the Hong Kong stock market.
Abstract: State-of-the-art methods using attention mechanism in Recurrent Neural Networks have shown exceptional performance targeting sequential predictions and classifications. We explore the attention mec...
76 citations
TL;DR: In this article, the authors focus on the dynamics of the gold price against bonds, stocks and exchange rates based on a disaggregation of the underlying relationships across different frequencies applying a wavel.
Abstract: This study focuses on the dynamics of the gold price against bonds, stocks and exchange rates based on a disaggregation of the underlying relationships across different frequencies applying a wavel
69 citations
TL;DR: In this paper, a new neural network architecture for modeling spatial distributions (i.e. distributions on Rd) which is more computationally efficient than a traditional fully-connected feed-forward model was developed.
Abstract: This paper develops a new neural network architecture for modeling spatial distributions (i.e. distributions on Rd) which is more computationally efficient than a traditional fully-connected feedfo...
67 citations
TL;DR: In this article, the authors extend the Ito calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the process.
Abstract: We extend some results of the Ito calculus to functionals of the current path of a process to reflect the fact that often the impact of randomness is cumulative and depends on the history of the pr...
57 citations
TL;DR: The second edition of Optimization Methods in Finance comes 11 years after the successful first edition with a relevant extension to include material on mean-variance portfolio selection and multi-... as discussed by the authors.
Abstract: The second edition of Optimization Methods in Finance comes 11 years after the successful first edition with a relevant extension to include material on mean-variance portfolio selection and multi-...
54 citations
TL;DR: In this paper, the authors consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the "rough" regime of Hurst parameter H < 1/2.
Abstract: We consider rough stochastic volatility models where the driving noise of volatility has fractional scaling, in the ‘rough’ regime of Hurst parameter H<1/2. This regime recently attracted a lot of ...
49 citations
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TL;DR: In this paper, a lifted version of the Heston model with n multi-factors, sharing the same Brownian motion but mean reverting at different speeds, is introduced.
Abstract: How to reconcile the classical Heston model with its rough counterpart? We introduce a lifted version of the Heston model with n multi-factors, sharing the same Brownian motion but mean reverting at different speeds. Our model nests as extreme cases the classical Heston model (when n = 1), and the rough Heston model (when n goes to infinity). We show that the lifted model enjoys the best of both worlds: Markovianity and satisfactory fits of implied volatility smiles for short maturities with very few parameters. Further, our approach speeds up the calibration time and opens the door to time-efficient simulation schemes.
41 citations
Abstract: Investor sentiment has become an important factor affecting oil price volatility and extreme risk. Therefore, we utilise a VaR-GARCH model to detect the extreme risk of the crude oil market during ...
40 citations
TL;DR: Judea Pearl is on a mission to change the way we interpret data as mentioned in this paper, and he has documented his research and opinions in scholarly books and papers, such as books and articles.
Abstract: Judea Pearl is on a mission to change the way we interpret data. An eminent professor of computer science, Pearl has documented his research and opinions in scholarly books and papers. Now, he has ...
40 citations
TL;DR: In this paper, a new neural network architecture was proposed to predict short-term price movements in stock markets, based on limit order book data, which can be used to predict stock market prices.
Abstract: The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for pr...
TL;DR: In this article, the authors extend the Gatheral framework to the multi-dimensional case where trading in one asset has a cross-impact on other assets, and extend it to the multidimensional case.
Abstract: We extend the ‘No-dynamic-arbitrage and market impact’-framework of Gatheral [Quant. Finance, 2010, 10(7), 749–759] to the multi-dimensional case where trading in one asset has a cross-impact on th...
TL;DR: An extension to the distributionally robust optimization model for a robust mean-CVaR portfolio selection model is developed that allows the model to capture a zero net adjustment via a linear constraint in the mean return, which can be cast as a tractable conic programme.
Abstract: In this paper, we present a computationally tractable optimization method for a robust mean-CVaR portfolio selection model under the condition of distribution ambiguity. We develop an extension that allows the model to capture a zero net adjustment via a linear constraint in the mean return, which can be cast as a tractable conic programme. Also, we adopt a nonparametric bootstrap approach to calibrate the levels of ambiguity and show that the portfolio strategies are relatively immune to variations in input values. Finally, we show that the resulting robust portfolio is very well diversified and superior to its non-robust counterpart in terms of portfolio stability, expected returns and turnover. The results of numerical experiments with simulated and real market data shed light on the established behaviour of our distributionally robust optimization model.
TL;DR: An empirical study to assess the effectiveness of various machine learning topologies trained with big data approaches and qualitative, rather than quantitative, information as input variables generates outperforming results compared to traditional methods for bankruptcy forecasting and risk measurement.
Abstract: Bankruptcy prediction has received a growing interest in corporate finance and risk management recently. Although numerous studies in the literature have dealt with various statistical and artifici...
TL;DR: This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015 to determine the optimal non-linear mapping and the corresponding lead–lag structure between two time series.
Abstract: This paper develops the optimal causal path algorithm and applies it within a fully-fledged statistical arbitrage framework to minute-by-minute data of the S&P 500 constituents from 1998 to 2015. S...
TL;DR: The authors argue that the ability to read and write is without question an essential skill to understand the world and that data literacy is equally crucial, advocates David Spiegelhalter, in his latest book The Art of Data Literacy.
Abstract: Literacy, the ability to read and write, is without question an essential skill to understand the world. Data literacy is equally crucial, advocates David Spiegelhalter, in his latest book The Art ...
TL;DR: In this paper, the authors investigated the effects of stock market uncertainty on economic fundamentals, represented by economic activities and systemic risk, in China and found that an uncertainty shock generates a short-term decline in industrial production, a rapid drop and rebound in the composite leading indicator, and an increase in systemic risk.
Abstract: This study investigates the effects of stock market uncertainty on economic fundamentals, represented by economic activities and systemic risk, in China. To capture the uncertainty in the Chinese stock market precisely, we use the entropy measure through symbolic time-series analysis. The empirical findings reveal strong spillover effects from stock market uncertainty to economic fundamentals. Specifically, an uncertainty shock generates (i) a short-term decline in industrial production, (ii) a rapid drop and rebound in the composite leading indicator, and (iii) an increase in systemic risk. To understand these findings, we suggest and validate the transmission channel through changes in consumption and investment.
TL;DR: In this paper, the authors introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fashion.
Abstract: We introduce extensions of the Realized Exponential GARCH model (REGARCH) that capture the evident high persistence typically observed in measures of financial market volatility in a tractable fash...
TL;DR: In this paper, the authors exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the Hawkes process for financial point process models.
Abstract: The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of th...
TL;DR: This study is the first one to make use of social media data for predicting minute-by-minute stock returns, namely the ones of the S&P 500 stock constituents, and it shows that the approach applying the adaptive-order algorithm outperforms classical approaches with respect to a multitude of criteria.
Abstract: Over the past 15 years, there have been a number of studies using text mining for predicting stock market data. Two recent publications employed support vector machines and second-order Factorizati...
TL;DR: The authors examined the predictability of positive and negative stock market bubbles via an application of the LPPLS Confidence Multi-scale Indicators to the S&P500, FTSE and NIKKEI indexes.
Abstract: We examine the predictability of positive and negative stock market bubbles via an application of the LPPLS Confidence Multi-scale Indicators to the S&P500, FTSE and NIKKEI indexes. We find that th...
TL;DR: The out-of-sample computational results show that a regime-switching risk parity portfolio can consistently outperform its nominal counterpart, maintaining a similar ex post level of risk while delivering higher-than-nominal returns over a long-term investment horizon.
Abstract: We formulate and solve a risk parity optimization problem under a Markov regime-switching framework to improve parameter estimation and to systematically mitigate the sensitivity of optimal portfol...
TL;DR: This paper applies some types of convolutional neural network architectures to order-based features to predict the direction of mid-price trends and shows that smoothing filters which it proposes to employ rather than embedding features of orders improve accuracy.
Abstract: Predicting the price trends of stocks based on deep learning and high-frequency data has been studied intensively in recent years. Especially, the limit order book which describes supply-demand bal...
TL;DR: In this paper, the effects of cointegration on optimal investment and consumption strategies for an investor with exponential utility were studied, and a Hamilton-Jacobi-Bellman (HJB) equation was derived.
Abstract: In this paper, we study the effects of cointegration on optimal investment and consumption strategies for an investor with exponential utility. A Hamilton-Jacobi-Bellman (HJB) equation is derived f...
TL;DR: In this paper, joint tests of contagion are derived which are designed to have power where contagion operates simultaneously through coskewness, cokurtosis and covolatility.
Abstract: Joint tests of contagion are derived which are designed to have power where contagion operates simultaneously through coskewness, cokurtosis and covolatility. Finite sample properties of the new tests are evaluated and compared with existing tests of contagion that focus on a single channel. Applying the tests to daily euro zone equity returns from 2005 to 2014 shows that contagion operated mainly through higher order moment channels during the GFC and the European debt crisis, which were not necessarily detected by traditional tests based on correlations. The empirical results have important implications for pricing risk and constructing well diversified portfolios.
TL;DR: In this paper, an unsupervised learning algorithm was proposed to determine the number of selection bias under multiple testing in the context of investment strategies. But this algorithm is not suitable for the problem of multi-testing.
Abstract: In this paper we address the problem of selection bias under multiple testing in the context of investment strategies. We introduce an unsupervised learning algorithm that determines the number of ...
TL;DR: This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015 to propose a high-frequency pair selection and trading strategy.
Abstract: This paper develops the regime classification algorithm and applies it within a fully-fledged pairs trading framework on minute-by-minute data of the S&P 500 constituents from 1998 to 2015. Specifi...
TL;DR: In this paper, market states are identified by a reference sparse precision matrix and a vector of expectation values, and a novel methodology is proposed to define, analyze and forecast market states.
Abstract: We propose a novel methodology to define, analyze and forecast market states. In our approach, market states are identified by a reference sparse precision matrix and a vector of expectation values...
TL;DR: This article introduced a new factor model for log volatilities that considers contributions, and performs dimensionality reduction, at a global level through the market, and at a local level through clusters.
Abstract: We introduce a new factor model for log volatilities that considers contributions, and performs dimensionality reduction, at a global level through the market, and at a local level through clusters...
TL;DR: In this paper, the authors investigate market selection and bet pricing in a repeated prediction market model and derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information information.
Abstract: We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information...