Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach...

This study quantifies and sorts the disciplinarity of finance journals by developing and employing three new measures of journal specialization based on publication keywords. We retrieved 32,232 articles and 116,271 keywords from journals included in the finance section of the 2021 Academic...

This study empirically tests and compares the performances of three famous financial asset valuation models in the Moroccan stock exchange: CAPM, the Fama and French three-factor model, and the Fama and French five-factor model. Our sample considers monthly data covering the sample period of...

We study the nexus between endogenous growth and asset prices. We show that endogenous growth models with either horizontal and vertical innovation match financial data well due to countercyclical dividends which are either procyclical or acyclical in US data. Countercyclical dividends...

We study the nexus between endogenous growth and asset prices. Endogenous growth models with either horizontal or vertical innovation exhibit a good match to financial data due to countercyclical dividends which are either procyclical or acyclical in US data. In the model technology shocks...

The study applies the effective transfer entropy (ETE) and Renyi transfer entropy (RTE) to quantify the information flow between government bonds and equity market of G7 countries. The magnitude and direction of information flow between these two markets are dynamic across the state of the...

Evolutionary Finance (EF) explores financial markets as evolving biological systems. Investors pursuing diverse investment strategies compete for the market capital. Some "survive" and some "become extinct". A central goal is to identify evolutionary stable (in one sense or another) investment...

Artificial Intelligence (AI) models have been recently studied in finance to discover data patterns. However, generative AI, particularly in image synthesis, remains relatively unexplored. In this paper, we investigate the potential of generative AI to model unknown dynamics using stock chart...

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered in this Primer on Empirical Asset Pricing include the basic questions in empirical asset pricing, how to make factors, how to use factors in factor models, regressions (time series, cross-sectional,...

These lecture notes are part of a course on Big Data Asset Pricing. The topics covered include fundamentals of asset pricing with frictions, transaction costs and market liquidity risk, funding liquidity risk, margin requirements, new directions, and machine learning with frictions.For more...

In March 2020, safe asset markets experienced surprising and unprecedented price crashes. We explain how strategic investor behavior can create such market fragility in a model with investors valuing safety, investors valuing liquidity, and constrained dealers. While safety investors and...

We consider the classical multi-asset Merton investment problem under drift uncertainty, i.e. the asset price dynamics are given by geometric Brownian motions with constant but unknown drift coefficients. The investor assumes a prior drift distribution and is able to learn by observing the asset...

We present a full (to my opinion) set of models for big data in nance, including HFT (e.g., LOB). All these models were introduced, pro-posed, and then studied after investigating many HFT data (see [12, 13, 7, 4]), which supported and justi ed our models. For quantitative and comparative...

Uncertainty is known to be crucial in asset pricing, yet evidence from comprehensive analysis of various uncertainty measures remains sparse. This paper investigates the predictability of stock returns based on economic fundamentals uncertainty by constructing a novel uncertainty index derived...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...

We develop a novel empirical asset pricing framework to estimate time-varying risk premia, building on the recently introduced score-driven conditional betas model. First, we extend the conditional betas theory by establishing the asymptotic distribution of standard tests statistics for...

I present evidence that a moving average (MA) trading strategy has a greater average return and skewness as well as a lower variance compared to buying and holding the underlying asset using monthly returns of value-weighted US decile portfolios sorted by market size, book-to-market, and...

Stock prices are well known to exhibit behaviors that are difficult to model mathematically. Individual stocks are observed to exhibit short term price reversals and long term momentum, while their industries only exhibit momentum.Here we show that individual stocks can be modeled by simple mean...

Theory suggests that traders will be more reluctant to trade on negative private information about an ongoing merger if their trading will cause the merger to be canceled. This paper provides evidence on the existence of such endogenous limits to arbitrage and it's consequence on the...

This paper studies the welfare impact of information spillover in divisible-good markets with heterogeneous traders and interdependent values. In a setting in which two groups of traders trade two distinct but correlated assets, one within each group, the information content in the price of one...

In recent years, the argument on capital market efficiency has shifted to alternative forms of the hypothesis. This research examines one of such alternatives, the Adaptive Market Hypothesis (AMH) with an aim to offer a framework for testing the dynamic notion of market efficiency. Using the...

This study investigates the pattern of mothly liquidity and volatility of Taiwan stock market from January 1998 to December 2015. In this study, the market liquidity is measured as the reciprocal value of Amihud illiquidity. The empirical results by using the VAR approach show that the...

Copula modelling is a popular tool in analysing the dependencies between variables. Copula modelling allows the investigation of tail dependencies, which is of particular interest in risk and survival applications. Copula modelling is also of specific interest to economic and financial modelling...

Research finds strong links between credit booms and macroeconomic outcomes like financial crises and output growth. Are impacts also seen in financial asset prices? We document this robust and significant connection for the first time using a large sample of historical data for many countries....