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ISSN: 2405-9188

Research frontiers of the Chinese financial markets

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OptionNet: A multiscale residual deep learning model with confidence interval to predict option price

Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult...

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Investigating the impact financial content structure has on consumer appreciation: An empirical study of Australian statement of advice documents

This study investigates the impact of financial content structure on consumer appreciation in Australian Statement of Advice (SOA) documents. SOAs are essential for regulatory adherence and consumer...

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The great wall of debt: Real estate, political risk, and Chinese local government financing cost

Chengtou bond is the only asset with market prices that can capture the funding cost of Chinese local government debt. In contrast to the U.S. municipal bonds, Chengtou bonds are issued by private corporations...

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What do we learn from stock price reactions to China's first announcement of anti-corruption reforms?

China's markets gained 3.86% around December 4, 2012, when the Party announced anti-corruption reforms. State-owned enterprises (SOEs) with higher past entertainment and travel costs (ETC) gained more....

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Asset allocation using a Markov process of clustered efficient frontier coefficients states

We propose a novel asset allocation model using a Markov process of states defined by clustered efficient frontier coefficients. While most research in Markov models of the market characterize regimes...

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Topological tail dependence: Evidence from forecasting realized volatility

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study...

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Does one size fit all? Comparing the determinants of the FinTech market segments expansion

The paper aims to indentify and compare the determinants of the overall FinTech market expansion and its major segments – cryptocurrency and peer-to-peer lending markets – in a dataset, which covers...

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An analysis of conditional mean-variance portfolio performance using hierarchical clustering

This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance...

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Data-driven estimation of economic indicators with search big data in discontinuous situation

Economic indicators are essential for policymaking and strategic decisions in both the public and private sectors. However, due to delays in the release of government indicators based on macroeconomic...

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The applications of big data in the insurance industry: A bibliometric and systematic review of relevant literature

The insurance industry has changed rapidly over the last few decades. One factor in this change is the continuous growth of massive amounts of data that need to be processed properly to be optimally...

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Making it into a successful series A funding: An analysis of Crunchbase and LinkedIn data

Startups are a key force driving economic development, and the success of these high-risk ventures can bring huge profits to venture capital firms. The ability to predict the success of startups is...

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Stock pledged loans and market crash risk: Evidence from China

Stock pledged loans have become prevalent among large shareholders of listed firms in China. The largest shareholder pledges a greater fraction of her holdings as collateral for credit when the firm...

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Hedging using reinforcement learning: Contextual k-armed bandit versus Q-learning

The construction of replication strategies for contingent claims in the presence of risk and market friction is a key problem of financial engineering. In real markets, continuous replication, such...

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Expert aggregation for financial forecasting

Machine learning algorithms dedicated to financial time series forecasting have gained a lot of interest. But choosing between several algorithms can be challenging, as their estimation accuracy may...

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Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets

This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using...

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Accounting in an age of big data

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Performance attribution of machine learning methods for stock returns prediction

We analyze the performance of investable portfolios built using predicted stock returns from machine learning methods and attribute their performance to linear, marginal non-linear and interaction effects....

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Big data, accounting information, and valuation

This paper reviews research that uses big data and/or machine learning methods to provide insight relevant for equity valuation. Given the huge volume of research in this area, the review focuses on...

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The use of predictive analytics in finance

Statistical and computational methods are being increasingly integrated into Decision Support Systems to aid management and help with strategic decisions. Researchers need to fully understand the use...

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Trading the FX volatility risk premium with machine learning and alternative data

In this study, we show how both machine learning and alternative data can be successfully leveraged to improve and develop trading strategies. Starting from a trading strategy that harvests the EUR/USD...

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A causal approach to test empirical capital structure regularities

Capital structure theories are often formulated as causal narratives to explain which factors drive financing choices. These narratives are usually examined by estimating cross–sectional relations between...

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