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ISSN: 2405-9188

Selecting appropriate methodological framework for time series data analysis

Economists face method selection problem while working with time series data. As time series data may possess specific properties such as trend and structural break, common methods used to analyze other...

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Credit scoring methods: Latest trends and points to consider

Credit risk is the most significant risk by impact for any bank and financial institution. Accurate credit risk assessment affects an organisation's balance sheet and income statement, since credit...

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Machine learning for cryptocurrency market prediction and trading

We employ and analyze various machine learning models for daily cryptocurrency market prediction and trading. We train the models to predict binary relative daily market movements of the 100 largest...

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Short-term bitcoin market prediction via machine learning

We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform...

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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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Machine learning portfolio allocation

We find economically and statistically significant gains when using machine learning for portfolio allocation between the market index and risk-free asset. Optimal portfolio rules for time-varying expected...

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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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Audit data analytics, machine learning, and full population testing

Emerging technologies like data analytics and machine learning are impacting the accounting profession. In particular, significant changes are anticipated in audit and assurance procedures because of...

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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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Stock price prediction using support vector regression on daily and up to the minute prices

The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH)...

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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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CapitalVX: A machine learning model for startup selection and exit prediction

Using a big data set of venture capital financing and related startup firms from Crunchbase, this paper develops a machine-learning model called CapitalVX (for “Capital Venture eXchange”) to predict...

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Forecasting earnings and returns: A review of recent advancements

We selectively review recent advancements in research on predictive models of earnings and returns. We discuss why applying statistical, econometric, and machine learning advancements to forecasting...

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Accounting in an age of big data

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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 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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A hybrid stock trading framework integrating technical analysis with machine learning techniques

In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN) and a set of rules is proposed to generate the trading decisions more...

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Big data based fraud risk management at Alibaba

With development of mobile internet and finance, fraud risk comes in all shapes and sizes. This paper is to introduce the Fraud Risk Management at Alibaba under big data. Alibaba has built a fraud risk...

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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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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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The extent of voluntary disclosure and its determinants in emerging markets: Evidence from Egypt

The primary objective of this study is to test a theoretical framework relating eight major corporate governance determinants with the extent of the voluntary disclosure provided by listed firms listed...

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The effects of mergers and acquisitions on stock price behavior in banking sector of Pakistan

Mergers and Acquisitions are considered as one of the useful strategies for growth and expansion of businesses. These strategies have widely been adopted in developed economies while are quite often...

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Financial news predicts stock market volatility better than close price

The behaviour of time series data from financial markets is influenced by a rich mixture of quantitative information from the dynamics of the system, captured in its past behaviour, and qualitative...

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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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Sustainable investing and the cross-section of returns and maximum drawdown

We use supervised learning to identify factors that predict the cross-section of returns and maximum drawdown for stocks in the US equity market. Our data run from January 1970 to December 2019 and...

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