Professional Portfolio Managers - a Setting for Momentum Strategies

2008 ◽  
Author(s):  
Jose L. B. Fernandes ◽  
Juan Ignacio Peña ◽  
Benjamin M. Tabak ◽  
Jose Renato Haas Ornelas
Keyword(s):  
Momentum Strategies ◽  
Portfolio Managers

The Interaction of Value and Momentum Strategies

CFA Digest ◽  
1997 ◽  
Vol 27 (4) ◽  
pp. 38-40
Author(s):  
S. Brooks Marshall
Keyword(s):  
Momentum Strategies

Evidence on the Compensation of Portfolio Managers

CFA Digest ◽  
2007 ◽  
Vol 37 (1) ◽  
pp. 91-91
Author(s):  
Stephen M. Horan
Keyword(s):  
Portfolio Managers

Risk-Managed Momentum: An Evidence from Indian Stock Market

2021 ◽  
pp. 231971452110230
Author(s):  
Simarjeet Singh ◽  
Nidhi Walia ◽  
Pradiptarathi Panda ◽  
Sanjay Gupta
Keyword(s):  
Stock Market ◽  
Asset Management ◽  
Stock Exchange ◽  
Sharpe Ratio ◽  
Relative Momentum ◽  
Extended Time ◽  
Momentum Strategies ◽  
Momentum Profits ◽  
Indian Stock Market ◽  
Time Frames

Relative momentum strategies yield large and substantial profits in the Indian Stock Market. Nevertheless, relative momentum profits are negatively skewed and prone to occasional severe losses. By taking into consideration 450 stocks listed on the Bombay Stock Exchange, the present study predicts the timing of these huge momentum losses and proposes a simple risk-managed momentum approach to avoid these losses. The proposed risk-managed momentum approach not only doubles the adjusted Sharpe ratio but also results in significant improvements in downside risks. In contrast to relative momentum payoffs, risk-managed momentum payoffs remain substantial even in extended time frames. The study’s findings are particularly relevant for asset management companies, fund houses and financial academicians working in the area of asset anomalies.

scholarly journals Multivariate Analysis of Cryptocurrencies

Econometrics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 28
Author(s):  
Vincenzo Candila
Keyword(s):  
Great Recession ◽  
Market Value ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Mixed Frequency ◽  
The Great Recession ◽  
The World ◽  
Asymmetric Garch ◽  
Portfolio Managers ◽  
Points Of View

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

scholarly journals Can Economic Factors Improve Momentum Trading Strategies? The Case of Managed Futures during the COVID-19 Pandemic

Economies ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 86
Author(s):  
Renata Guobužaitė ◽  
Deimantė Teresienė
Keyword(s):  
Financial Markets ◽  
Risk Premium ◽  
Trading Strategies ◽  
Economic Factors ◽  
Momentum Strategies ◽  
New Approach ◽  
Managed Futures ◽  
Asset Classes ◽  
Momentum Trading ◽  
Insight Into

Systematic momentum trading is a prevalent risk premium strategy in different portfolios. This paper focuses on the performance of the managed futures strategy based on the momentum signal across different economic regimes, focusing on the COVID-19 pandemic period. COVID-19 had a solid but short-lived impact on financial markets, and therefore gives a unique insight into momentum strategies’ performance during such critical moments of market stress. We offer a new approach to implementing momentum strategies by adding macroeconomic variables to the model. We test a managed futures strategy’s performance with a well-diversified futures portfolio across different asset classes. The research concludes that constructing a portfolio based on academically/economically sound momentum signals with its allocation timing based on broader economic factors significantly improves managed futures strategies and adds significant diversification benefits to the investors’ portfolios.

scholarly journals Multi-Period Portfolio Optimization with Investor Views under Regime Switching

2020 ◽  
Vol 14 (1) ◽  
pp. 3
Author(s):  
Razvan Oprisor ◽  
Roy Kwon
Keyword(s):  
Regime Switching ◽  
Portfolio Allocation ◽  
Ease Of Use ◽  
Future Research ◽  
Expected Return ◽  
Asset Return ◽  
Market Portfolio ◽  
New Information ◽  
Trading Model ◽  
Portfolio Managers

We propose a novel multi-period trading model that allows portfolio managers to perform optimal portfolio allocation while incorporating their interpretable investment views. This model’s significant advantage is its intuitive and reactive design that incorporates the latest asset return regimes to quantitatively solve managers’ question: how certain should one be that a given investment view is occurring? First, we describe a framework for multi-period portfolio allocation formulated as a convex optimization problem that trades off expected return, risk and transaction costs. Using a framework borrowed from model predictive control introduced by Boyd et al., we employ optimization to plan a sequence of trades using forecasts of future quantities, only the first set being executed. Multi-period trading lends itself to dynamic readjustment of the portfolio when gaining new information. Second, we use the Black-Litterman model to combine investment views specified in a simple linear combination based format with the market portfolio. A data-driven method to adjust the confidence in the manager’s views by comparing them to dynamically updated regime-switching forecasts is proposed. Our contribution is to incorporate both multi-period trading and interpretable investment views into one framework and offer a novel method of using regime-switching to determine each view’s confidence. This method replaces portfolio managers’ need to provide estimated confidence levels for their views, substituting them with a dynamic quantitative approach. The framework is reactive, tractable and tested on 15 years of daily historical data. In a numerical example, this method’s benefits are found to deliver higher excess returns for the same degree of risk in both the case when an investment view proves to be correct, but, more notably, also the case when a view proves to be incorrect. To facilitate ease of use and future research, we also developed an open-source software library that replicates our results.

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