Clustering Algorithms for Risk-Adjusted Portfolio Construction
Expected Stock Returns
New Bid-Ask Spread Estimators from Daily High and Low Prices
Investing for the Long Run
Factors vs. Sectors in Asset Allocation: Stronger Together?
Lucas Paradox in the Short-Run
How to Predict Financial Stress? An Assessment of Markov Switching Models
Arbitrage and Its Physical Limits
Murphy Diagrams: Forecast Evaluation of Expected Shortfall abstract Motivated by the Basel 3 regulations, recent studies have considered joint forecasts of Value-at-Risk and Expected Shortfall. A large family of scoring functions can be used to evaluate forecast performance in this context. However, little intuitive or empirical guidance is currently available, which renders the choice of scoring function awkward in practice. We therefore develop graphical checks (Murphy diagrams) of whether one forecast method dominates another under a relevant class of scoring functions, and propose an associated hypothesis test. We illustrate these tools with simulation examples and an empirical analysis of S&P 500 and DAX returns.
Are REITs a Distinct Asset Class?
Benchmark Dataset for Mid-Price Prediction of Limit Order Book data abstract Presently, managing prediction of metrics in high frequency financial markets is a challenging task. An efficient way to do it is by monitoring the dynamics of a limit order book and try to identify the information edge. This paper describes a new benchmark dataset of high-frequency limit order markets for mid-price prediction. We make publicly available normalized representations of high frequency data for five stocks extracted from the NASDAQ Nordic stock market. Furthermore, we define an experimental protocol that can be used in order to evaluate the performance of related research methods. Baseline results based on linear and nonlinear regression models are also provided and show the potential that these methods have for mid-price prediction.
Optimal Rényi Entropy Portfolios
Do Financial Analysts Generate Value-Relevant Interpretive Information from 10-K Filings?
Sparse Precision Matrices for Minimum Variance Portfolios
News and Social Media Emotions in the Commodity Market
2017 Global Cryptocurrency Benchmarking Study
Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves abstract This paper considers how to optimally allocate investments in a portfolio of competing technologies. We introduce a simple model representing the underlying trade-off - between investing enough effort in any one project to spur rapid progress, and diversifying effort over many projects simultaneously to hedge against failure. We use stochastic experience curves to model the idea that investing more in a technology reduces its unit costs, and we use a mean-variance objective function to understand the effects of risk aversion. In contrast to portfolio theory for standard financial assets, the feedback from the experience curves results in multiple local optima of the objective function, so different optimal portfolios may exist simultaneously. We study the two-technology case and characterize the optimal diversification as a function of relative progress rates, variability, initial cost and experience, risk aversion and total demand. There are critical regions of the parameter space in which the globally optimal portfolio changes sharply from one local minimum to another, even though the underlying parameters change only marginally, so a good understanding of the parameter space is essential. We use the efficient frontier framework to visualize technology portfolios and show that the feedback leads to nonlinear distortions of the feasible set.
Dynamic Properties of the Bitcoin and the US Market
An Empirical Analysis of the Dynamic Relation among Investment, Earnings and Dividends
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