Regime-Based Strategic Asset Allocation
Eric Bouyé and Jerome Teiletche (World Bank)
April 2024
What should investors do in the presence of economic regimes? Researchers and practitioners usually address this topic from a tactical asset allocation point of view. In this article, we depart from the literature by tackling the issue strategically and analytically. Modeling economic regimes as a mixture of distributions, we first investigate what happens to moments of the distribution of returns. We next deduct the implications for portfolios built under popular asset allocation methodologies (mean-variance-optimization, risk budgeting). Using these analytical results, we define new portfolio construction methodologies seeking to exploit the information in macroeconomic (macro) regimes through the composition of optimal portfolios for each regime, the risk structure of these portfolios, and the long-term probability of the regimes. We empirically show that macro regime-based portfolios can outperform traditional asset-based portfolios, for both multi-asset and equity factor universes, over a sample of more than fifty years.

Do Oil Price Shocks Drive Risk Premia in Stock Markets? A Novel Investment Application
Riza Demirer (Southern Illinois University Edwardsville), et al.
March 2024
Motivated by the evidence from the investment-based theories in the asset pricing literature that links asset pricing factors to economic shocks, this paper examines the effect of disentangled oil price shocks on factor returns in a large set of 62 stock markets. Our findings show that oil market shocks capture significant predictive information regarding the size and direction of factor returns in global stock markets although we observe a great deal of heterogeneity in the response of factors to these shocks, depending on the market classification and the type of oil shock. We show that oil supply and precautionary demand shocks possess the greatest predictive power over systematic risk premia, particularly for value and momentum. We argue that time varying investor sentiment and the flexibility of firms to respond to economic shocks drive the responses of factors to oil market shocks. Further examining the economic implications of the predictive patterns observed, we show that a conditional global factor investing strategy wherein the investment positions are tilted towards factor-based portfolios conditional on the size and direction of the oil price shock yields significant improvements in portfolio returns and diversification over the passive investment strategy. Our findings show that the performance of smart beta strategies can be significantly improved by conditioning factor positions based on the size and direction of shocks.

Bond Risk Premiums at the Zero Lower Bound
Martin M. Andreasen (Aarhus University), et al.
March 2024
We document that the spread between long- and short-term government bond yields is a stronger predictor of excess bond returns when the U.S. economy is at the zero lower bound (ZLB) than away from this bound. The Gaussian shadow rate model with a linear or quadratic shadow rate is unable to explain this change in return predictability. The same holds for the quadratic term structure model and the autoregressive gamma-zero model that also enforce the ZLB. In contrast, the linear-rational square-root model explains our new empirical finding because the model allows for unspanned stochastic volatility as seen in bond yields.

Market Neutrality and Beta Crashes
Xia Xu (ESSCA School of Management)
March 2024
Market neutrality is a key feature of Frazzini and Pedersen (2014)’s betting against beta (BAB) strategy. However, we find that BAB fails to systematically remain market neutral, and the deviations often arrive in the shape of crashes. Specifically, BAB effectuates negative market timing and negative volatility timing amid volatile markets, promoting BAB crashes. The concern of imperfect market neutrality is shared by a broad range of market neutral beta arbitrage strategies. Their particular vulnerability to bull markets is not explained by the liquidity and leverage rationale. Managing beta crashes significantly improves performance net of transaction costs.

China’s footprint in global financial markets
David Lodge (European Central Bank), et al.
February 2024
Using daily data since 2017, we disentangle China-specific structural shocks driving
Chinese financial markets and examine spillovers across global markets. The novelty of this paper consists of simultaneously identifying China shocks with shocks
emanating from the United States and shocks to global risk sentiment – two major
forces driving global financial markets – to ensure that China spillover estimates do
not reflect common factors. Our results show that shocks originating in China have
material impacts on global equity markets, although spillovers are much smaller
than those following shocks in the United States, or those triggered by shifts in
global risk sentiment. By contrast, shocks from China account for a significant
proportion of variation in global commodity prices, more on a par with those of the
United States. Nevertheless, spillovers from China can be significantly amplified in
an environment of heightened global volatility, or when the shocks are large.

Institutional Herding and Investor Sentiment
Xu Guo (Shenzhen University)
February 2024
We investigate the role of investor sentiment in institutional herding behavior and its impact on stock prices. We find that institutional investors exhibit more herding behavior during periods of high sentiment, which has a significant impact on stock prices. Our results show that herding has a stabilizing effect on the stock market when investor sentiment is low, while it causes price distortions when sentiment is high. We also show that the impact of sentiment on price is particularly pronounced for small, non-profitable, low tangibility, high-growth firms.

Efficacy of a Mean Reversion Trading Strategy Using True Strength Index
Daniel Requejo (independent researcher)
January 27, 2024
This paper presents a comprehensive analysis of a mean reversion trading strategy, centered around the True Strength Index (TSI), applied to the SPY (S&P 500) and QQQ (Nasdaq Index) ETFs. The study spans historical data from 1996 to 2022, encompassing various market conditions to assess the strategy’s robustness. The core methodology involves generating open and close signals based on the TSI, with supplementary insights from the Relative Strength Index (RSI) and other technical indicators.
Through rigorous backtesting, the paper evaluates key performance metrics such as Compound Annual Growth Rate (CAGR), Maximum Drawdown (Max DD), Sharpe Ratio, and Sortino Ratio. These metrics illuminate the strategy’s profitability, risk management efficiency, and overall effectiveness. The strategy’s adaptability is further demonstrated through a detailed walk-forward analysis, highlighting its performance over sequential three-year periods.
The paper aims to contribute to the financial market trading strategies literature, offering a nuanced understanding of the applicability and sustainability of mean reversion approaches in the dynamic landscape of equity markets. It offers a balanced view, discussing the strategy’s strengths, limitations, and broader implications for investors and traders. This research is significant for those seeking systematic methods to navigate the complexities of financial trading, underlining the importance of continuous adaptation and risk-awareness in investment practices.

Learn To Use R For Portfolio Analysis
Quantitative Investment Portfolio Analytics In R:
An Introduction To R For Modeling Portfolio Risk and Return
By James Picerno