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ISSN: 2405-9188

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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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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Persistence in factor-based supervised learning models

In this paper, we document the importance of memory in machine learning (ML)-based models relying on firm characteristics for asset pricing. We find that predictive algorithms perform best when they...

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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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Betting against noisy beta

Strategies that overweight low beta stocks and underweight high beta stocks earn positive alphas. Price noise is known to affect high beta stocks, hence, noise trading can be expected to significantly...

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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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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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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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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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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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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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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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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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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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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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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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Measuring tail risks

Value-at-Risk (VaR) and Expected Shortfall (ES) are common high quantile-based risk measures adopted in financial regulations and risk management. In this paper, we propose a tail risk measure based...

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Vine copula based dependence modeling in sustainable finance

Climate change and sustainability have become societal focal points in the last decade. Consequently, companies have been increasingly characterized by non-financial information, such as environmental,...

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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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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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Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning

The VSTOXX index tracks the expected 30-day volatility of the EURO STOXX 50 equity index. Futures on the VSTOXX index can, therefore, be used to hedge against economic uncertainty. We investigate the...

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Enhanced PD-implied ratings by targeting the credit rating migration matrix

A high-quality and granular probability of default (PD) model is on many practical dimensions far superior to any categorical credit rating system. Business adoption of a PD model, however, needs to...

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Pairwise acquisition prediction with SHAP value interpretation

Predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate...

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