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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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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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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 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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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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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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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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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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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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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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FinLex: An effective use of word embeddings for financial lexicon generation

We present a simple and effective methodology for the generation of lexicons (word lists) that may be used in natural language scoring applications. In particular, in the finance industry, word lists...

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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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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 new measure of corporate bond liquidity using survival analysis

We define liquidity for corporate bonds as the expected waiting time to reduce a risk position. Our methodology addresses the fact that many bonds are liquidated quickly despite having few trades in...

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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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Predicting bitcoin returns using high-dimensional technical indicators

There has been much debate about whether returns on financial assets, such as stock returns or commodity returns, are predictable; however, few studies have investigated cryptocurrency return predictability....

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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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Twitter as a tool for forecasting stock market movements: A short-window event study

In order to explore the relationship between politics-related sentiment and FTSE 100 movements, we conducted a short-window event study of a UK based political event. We collected a sample of over 60,000...

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Improving insurers’ loss reserve error prediction: Adopting combined unsupervised-supervised machine learning techniques in risk management

Emerging literature focuses on insurers' earnings management using estimated liability for unpaid claims, known as loss reserve. An insurance company generally uses the traditional estimation methods...

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An overview on data representation learning: From traditional feature learning to recent deep learning

Since about 100 years ago, to learn the intrinsic structure of data, many representation learning approaches have been proposed, either linear or nonlinear, either supervised or unsupervised, either...

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Term structure of interest rates with short-run and long-run risks

We find that interest rate variance risk premium (IRVRP) — the difference between implied and realized variances of interest rates — is a strong predictor of U.S. Treasury bond returns of maturities...

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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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Are there trade-offs with mandating timely disclosure of cybersecurity incidents? Evidence from state-level data breach disclosure laws

On March 23, 2022, the SEC proposed that firms publicly disclose their cybersecurity incidents within four days of discovery. In the U.S., state-level data breach disclosure laws require firms to disclose...

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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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