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Ultra-Simple Shiller's CAPE : How One Year's Data Can Predict Equity Market Returns Better Than Ten (Presentation Slides)

Philips, Thomas K. - 2019

Campbell and Shiller average 10 years of real S&P 500 earnings to construct its Cyclically Adjusted P/E ratio, or CAPE, which they then use to forecast its future 10-year returns. In essence, Campbell and Shiller kill two birds with one large stone - they use the 10-year average to reduce noise...

Persistent link: https://www.econbiz.de/10012864087

Ultra-Simple Shiller’s CAPE : How One Year’s Data Can Predict Equity Market Returns Better Than Ten

Philips, Thomas K. - 2020

Persistent link: https://www.econbiz.de/10012847032

Information Ratio Analysis of Momentum Strategies

Ferreira, Fernando - 2015

In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of...

Persistent link: https://www.econbiz.de/10013034189

Statistical Arbitrage and High-Frequency Data with an Application to Eurostoxx 50 Equities

Rudy, Jozef - 2013

data and compare its profit potential to the standard sampling frequency of daily closing prices. We use a simple trading … data sampling frequencies. The frequencies observed range from a 5-minute interval, to prices recorded at the close of each … (e.g. above 3 for an average pair sampled at the high-frequency interval and above 1 for a daily sampling frequency) …

Persistent link: https://www.econbiz.de/10013081228

Is Your Covariance Matrix Still Relevant? An Asset Allocation-Based Analysis of Dynamic Volatility Models

Colon, James - 2013

Ever since Harry Markowitz published his seminal paper on portfolio selection, investors have incorporated estimates of future volatilities and correlations into their asset allocation process. While portfolio construction methods continue to evolve, many investors continue to forecast...

Persistent link: https://www.econbiz.de/10013086014

Mean Reversion Based on Autocorrelation : A Comparison Using the S&P 100 Constituent Stocks and the 100 Most Liquid ETFs

Dunis, Christian - 2014

improved when we use half-daily (open-close-open-...) sampling frequency as opposed to the daily one (close-close). Information … ratios after accounting for transaction costs (TC) range between 1.4 and 2.8 for ETF pairs at half-daily sampling frequency …

Persistent link: https://www.econbiz.de/10013063822

Understanding Jumps in High Frequency Digital Asset Markets

Saef, Danial; Nagy, Odett; Sizov, Sergej; Härdle, Wolfgang - 2021

While attention is a predictor for digital asset prices, and jumps in Bitcoin prices are well-known, we know little about its alternatives. Studying high frequency crypto data gives us the unique possibility to confirm that cross market digital asset returns are driven by high frequency jumps...

Persistent link: https://www.econbiz.de/10013323741

Factor Glut in Asset Pricing through a Modern Optimization Lens

Bakshi, Gurdip; Christensen, Timothy; Crosby, John; … - 2023

This paper addresses questions regarding the dimensionality of the stochastic discount factor and the selection of the best factors that enter it. We analyze these questions theoretically and empirically with a novel methodology which performs both (i) estimation of factor loadings and (ii) best...

Persistent link: https://www.econbiz.de/10014350213

Hidden Interactions in Financial Markets

Stavroglou, Stavros K. - 2020

The hidden nature of causality is a puzzling, yet critical notion for effective decision-making. Financial markets are characterized by fluctuating interdependencies which seldom give rise to emergent phenomena such as bubbles or crashes. In this paper, we propose a method based on symbolic...

Persistent link: https://www.econbiz.de/10012853068

Deepvol : Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

Moreno-Pino, Fernando; Zohren, Stefan - 2022

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques, based on machine learning, can readily be employed when treating volatility as a univariate, daily time-series. However, econometric studies have shown that...

Persistent link: https://www.econbiz.de/10014236547