### Trending academic research:

See also: Most popular SSRN papers

"- Investing for the Long Run
- Currency Hedging
- New Bid-Ask Spread Estimators from Daily High and Low Prices
- Lucas Paradox in the Short-Run
- Clustering Algorithms for Risk-Adjusted Portfolio Construction
- Expected Stock Returns
- The Decline in Asset Return Predictability and Macroeconomic Volatility
- 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.
- Are REITs a Distinct Asset Class?
- How to Predict Financial Stress? An Assessment of Markov Switching Models
- Accounting Quality, Information Risk and the Term Structure of Implied Volatility around Earnings Announcements
- Leontief Meets Shannon - Measuring the Complexity of the Economic System abstract We develop a complexity measure for large-scale economic systems based on Shannon's concept of entropy. By adopting Leontief's perspective of the production process as a circular flow, we formulate the process as a Markov chain. Then we derive a measure of economic complexity as the average number of bits required to encode the flow of goods and services in the production process. We illustrate this measure using data from seven national economies, spanning several decades.
- Sparse Precision Matrices for Minimum Variance Portfolios
- 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.
- News and Social Media Emotions in the Commodity Market
- 2017 Global Cryptocurrency Benchmarking Study
- Safe Assets
- Do Financial Analysts Generate Value-Relevant Interpretive Information from 10-K Filings?
- 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.