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Moving Forward from Predictive Regressions: Boosting Asset Allocation Decisions
... These include parametric portfolio policies (Brandt et al., 2009;DeMiguel et al., 2020), a boosting approach (Nevasalmi and Nyberg, 2021), a subset combination approach (Maasoumi et al., 2022), a genetic programming approach (Liu and Zhou, 2024), and an approach that using deep reinforcement learning (Cong et al., 2024). The above mentioned techniques involve optimizing economic utility for specific portfolio choice problems at the individual asset level, while our approach is about maximizing utility one level up by combining PRs. ...
We propose an ensemble framework for combining heterogeneous portfolio rules that cannot be accommodated by previously proposed combination methods. Using our approach, researchers and investors can take advantage of established and ongoing advances in portfolio choice by diversifying the idiosyncratic risks of alternative rules. Our ensemble approach maximizes the utility jointly generated by the candidate portfolio rules, while allowing learning about their time-varying relative performance. Based on out-of-sample evaluations of over forty years, we document substantial utility gains in extensive applications to cross-sections of stocks and to market timing.
Applications of Machine Learning in Quantitative Investment and Wealth Management:
-- when it works well
-- when it does not work well
-- why it does not work well
I describe specific opportunities and challenges of leveraging machine learning within context of quantitative investment and wealth management. Such applications include forecasting of financial time series, classification of alternative data, identification of market regimes, clustering-based portfolio diversification, assessment of risk factors.
The QWIM applications are categorized into areas and topics:
1) classification and pattern recognition, such as:
-- classification and partition of the investment universe
-- investing based on alternative data
-- text analysis of company and regulatory documents
-- sentiment analysis of news and social media
-- ESG (Environmental, Social and Governance) investing
-- fund decomposition, inference and replication
2) network analysis and clustering, such as:
-- clustering-based portfolio optimization
-- network-based portfolio optimization
-- analysis of interconnectedness risk
-- network effects on investment portfolios.
3) time series forecasting, such as:
-- forecasting of financial time series
-- empirical asset pricing
4) reinforcement learning, such as:
-- pricing and hedging of financial derivatives
-- optimal dynamic trading strategies
-- portfolio construction
-- goals-based investing
5) Other practical ML applications in QWIM include:
-- synthetic financial data generation
-- testing investment strategies and portfolios
-- factor-based investment strategies
-- nowcasting
-- incorporating market states and regimes into investment portfolios.
The presentation also describes some of the the challenges, including
-- lack of sufficient data
-- need to satisfy privacy, fairness and regulatory requirements
-- model overfitting
-- causality
-- explainability and interpretability
-- hyperparameter tuning
When a rate of return is regressed on a lagged stochastic regressor, such as a dividend yield, the regression disturbance is correlated with the regressor's innovation. The OLS estimator's finite-sample properties, derived here, can depart substantially from the standard regression setting. Bayesian posterior distributions for the regression parameters are obtained under specifications that differ with respect to (i) prior beliefs about the autocorrelation of the regressor and (ii) whether the initial observation of the regressor is specified as fixed or stochastic. The posteriors differ across such specifications asset allocations in the presence of estimation risk exhibit sensitivity to those differences.
We survey the literature on stock return forecasting, highlighting the challenges faced by forecasters as well as strategies for improving return forecasts. We focus on U.S. equity premium forecastability and illustrate key issues via an empirical application based on updated data. Some studies argue that, despite extensive in-sample evidence of equity premium predictability, popular predictors from the literature fail to outperform the simple historical average benchmark forecast in out-of-sample tests. Recent studies, however, provide improved forecasting strategies that deliver statistically and economically significant out-of-sample gains relative to the historical average benchmark. These strategies - including economically motivated model restrictions, forecast combination, diffusion indices, and regime shifts - improve forecasting performance by addressing the substantial model uncertainty and parameter instability surrounding the data-generating process for stock returns. In addition to the U.S. equity premium, we succinctly survey out-of-sample evidence supporting U.S. cross-sectional and international stock return forecastability. The significant evidence of stock return forecastability worldwide has important implications for the development of both asset pricing models and investment management strategies.
Classification trees based on exhaustive search algorithms tend to be biased towards selecting variables that afford more splits. As a result, such trees should be interpreted with caution. This article presents an algorithm called QUEST that has negligible bias. Its split selection strategy shares similarities with the FACT method, but it yields binary splits and the final tree can be selected by a direct stopping rule or by pruning. Real and simulated data are used to compare QUEST with the exhaustive search approach. QUEST is shown to be substantially faster and the size and classification accuracy of its trees are typically comparable to those of exhaustive search.
Financial–market risk, commonly measured in terms of asset–return volatility, plays a fundamental role in investment decisions, risk management and regulation. In this paper, we investigate a new modeling strategy that helps to better understand the forces that drive market risk. We use componentwise gradient boosting techniques to identify financial and macroeconomic factors influencing volatility and to assess the specific nature of their influence. Componentwise boosting is capable of producing parsimonious models from a, possibly, large number of predictors and—in contrast to other related techniques—allows a straightforward interpretation of the parameter estimates.
Considering a wide range of potential risk drivers, we apply boosting to derive monthly volatility predictions for the equity market represented by S&P 500 index. Comparisons with commonly–used GARCH and EGARCH benchmark models show that our approach substantially improves out–of–sample volatility forecasts for short– and longer–run horizons. The results indicate that risk drivers affect future volatility in a nonlinear fashion
We propose a new method for constructing the hedge component in Merton's ICAPM that uses a daily summary measure of economic
activity to track time-varying investment opportunities. We then use nonparametric projections to compute a robust estimate
of the conditional covariance between stock market returns and our daily economic activity index. We find that the new conditional
covariance risk measure plays an important role in explaining time variation in the equity risk premium. Specification tests
as well as out-of-sample forecasts of aggregate stock returns suggest that the new covariance risk measure performs well compared
to alternative covariance measures previously proposed in the literature.
We present a recent innovation to portfolio construction called full-scale optimisation. In contrast to mean–variance analysis, which assumes that returns are normally distributed or that investors have quadratic utility, full-scale optimisation identifies the optimal portfolio given any set of return distributions and any description of investor preferences. It therefore yields the truly optimal portfolio in sample, whereas mean–variance analysis provides an approximation to the in-sample truth. Both approaches, however, suffer from estimation error. We employ a bootstrapping procedure to compare the estimation error of full-scale optimisation to the combined approximation and estimation error of mean–variance analysis. We find that, to a significant degree, the in-sample superiority of full-scale optimisation prevails out of sample.Journal of Asset Management (2007) 7, 302–311. doi:10.1057/palgrave.jam.2250042
Recent explanations of aggregate stock market fluctuations suggest that countercyclical stock market volatility is consistent with rational asset evaluations. In this paper, I develop a framework to study the causes of countercyclical stock market volatility. I find that countercyclical risk premia do not imply countercyclical return volatility. Instead, countercyclical stock volatility occurs if risk premia increase more in bad times than they decrease in good times, thereby inducing price–dividend ratios to fluctuate more in bad times than in good. The business cycle asymmetry in the investors’ attitude toward discounting future cash flows plays a novel and critical role in many rational explanations of asset price fluctuations.
Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess
returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return,
once weak restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample explanatory power
is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing
the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of
volatile stock returns.
Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to
be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS)
and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and
other statistics; and these models would not have helped an investor with access only to available information to profitably
time the market.
We compare the Sharpe rations of investment funds which combine one riskless and one risky asset following: i) timing strategies which forecast excess returns using simple regressions; ii) a strategy which uses multiple regression instead; and iii) a passive allocation which combines the funds in i) with constant weightings. We show that iii) dominates i) and ii), as it implicitly uses the linear forecasting rule that maximizes the Sharpe ratio of actively traded portfolios, but the relative ranking of i) and ii) is generally unclear. We also discuss under what circumstances the performance of ii) and iii) coincides.
This paper develops a nonparametric approach to examine how portfolio and consumption choice depends on variables that forecast time-varying investment opportunities. I estimate single-period and multiperiod portfolio and consumption rules of an investor with constant relative risk aversion and a one-month to 20-year horizon. The investor allocates wealth to the NYSE index and a 30-day Treasury bill. I find that the portfolio choice varies significantly with the dividend yield, default premium, term premium, and lagged excess return. Furthermore, the optimal decisions depend on the investor's horizon and rebalancing frequency. Copyright The American Finance Association 1999.
This article examines the robustness of the evidence on predictability of U.S. stock returns, and addresses the issue of whether this predictability could have been historically exploited by investors to earn profits in excess of a buy-and-hold strategy in the market index. We find that the predictive power of various economic factors over stock returns changes through time and tends to vary with the volatility of returns. The degree to which stock returns were predictable seemed quite low during the relatively calm markets in the 1960s but increased to a level where, net of transaction costs it could have been exploited by investors in the volatile markets of the 1970s. Copyright 1995 by American Finance Association.
This paper provides a survey on studies that analyze the macroeconomic effects of intellectual property rights (IPR). The first part of this paper introduces different patent policy instruments and reviews their effects on R&D and economic growth. This part also discusses the distortionary effects and distributional consequences of IPR protection as well as empirical evidence on the effects of patent rights. Then, the second part considers the international aspects of IPR protection. In summary, this paper draws the following conclusions from the literature. Firstly, different patent policy instruments have different effects on R&D and growth. Secondly, there is empirical evidence supporting a positive relationship between IPR protection and innovation, but the evidence is stronger for developed countries than for developing countries. Thirdly, the optimal level of IPR protection should tradeoff the social benefits of enhanced innovation against the social costs of multiple distortions and income inequality. Finally, in an open economy, achieving the globally optimal level of protection requires an international coordination (rather than the harmonization) of IPR protection.
Forecasting the Equity Risk Premium: The Role of Technical Indicators
- C J Neely
Reconciling the return predictability evidence
- M Lettau
Forecasting stock returns under economic constraints
- D Pettenuzzo
Out-of-sample equity premium prediction: Combination forecasts and links to the real economy
- D E Rapach