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The present research aimed to investigate the effects of monetary policy on the US stock market between December 2008 and December 2021. This period had a significant absolute return advantage for growth stocks over value stocks, in contrast to research that has historically shown an advantage for value stocks over growth stocks. For this reason, we sought to examine whether the monetary policy of the period would have benefited growth stocks through the application of a vector autoregressive (VAR) model with an error correction vector (VEC model), using as variables four exchange-traded funds (ETFs) that passively follow indices, two of which are value and two growth. The total assets on the Federal Reserve (Fed) balance sheet were utilized to analyze the effects of monetary policy in this period. The results demonstrated, by the generalized impulse-response functions, the greater long-term elasticity of growth ETFs to shocks in the value of Fed assets, confirming the hypothesis that monetary stimuli positively influenced the absolute returns of stock price growth and value in the demarcated period, with greater benefits for growth stocks.

The weak value-growth premium of the Spanish stock market highlights the importance of enhancing the accounting-based fundamental strength of the value-growth strategy. This accounting strength is needed to detect potential errors in market expectations that result in mispriced stocks. When we select value-growth stocks whose accounting strength is incongruent with the market expectation reflected by their book-to-market ratio, the value-growth strategy becomes highly profitable. Our results are consistent with the evidence in the US market and demonstrate that stock markets with a weak value-growth premium are not necessarily free of errors in market expectations. We also demonstrate that the momentum effect allows better timing of this strategy, indicating the best time to buy and sell mispriced stocks. This effect increases profits and reduces the time needed to hold stocks to achieve these profits.

We propose a latent variables approach within a present-value model to estimate the expected returns and expected dividend growth rates of the aggregate stock market. This approach aggregates information contained in the history of price-dividend ratios and dividend growth rates to predict future returns and dividend growth rates. We find that returns and dividend growth rates are predictable with R² values ranging from 8.2% to 8.9% for returns and 13.9% to 31.6% for dividend growth rates. Both expected returns and expected dividend growth rates have a persistent component, but expected returns are more persistent than expected dividend growth rates.

We show that consumption-based asset pricing models with time-separable preferences generate realistic amounts of stock price volatility if one allows for small deviations from rational expectations. Rational investors with subjective beliefs about price behavior optimally learn from past price observations. This imparts momentum and mean reversion into stock prices. The model quantitatively accounts for the volatility of returns, the volatility and persistence of the price-dividend ratio, and the predictability of long-horizon returns. It passes a formal statistical test for the overall fit of a set of moments provided one excludes the equity premium.

We test for firm-level asset investment effects in returns by examining the cross-sectional relation between firm asset growth and subsequent stock returns. Asset growth rates are strong predictors of future abnormal returns. Asset growth retains its forecasting ability even on large capitalization stocks. When we compare asset growth rates with the previously documented determinants of the cross-section of returns (i.e., book-to-market ratios, firm capitalization, lagged returns, accruals, and other growth measures), we find that a firm's annual asset growth rate emerges as an economically and statistically significant predictor of the cross-section of U.S. stock returns.

We present a simple model of asset pricing in which payoff salience drives investors' demand for risky assets. The key implication is that extreme payoffs receive disproportionate weight in the market valuation of assets. The model accounts for several puzzles in finance in an intuitive way, including preference for assets with a chance of very high payoffs, an aggregate equity premium, and countercyclical variation in stock market returns.

We explore the impact of investment-specific technology (IST) shocks on the cross section of stock returns. Using a structural model, we show that IST shocks have a differential effect on the value of assets in place and the value of growth opportunities. This differential sensitivity to IST shocks has two main implications. First, firm risk premia depend on the contribution of growth opportunities to firm value. Second, firms with similar levels of growth opportunities comove with each other, giving rise to the value factor in stock returns and the failure of the conditional CAPM. Our empirical tests confirm the model's predictions.

We examine international stock return comovements using country-industry and country-style portfolios as the base portfolios. We first establish that parsimonious risk-based factor models capture the data covariance structure better than the popular Heston–Rouwenhorst (1994) model. We then establish the following stylized facts regarding stock return comovements. First, there is no evidence for an upward trend in return correlations, except for the European stock markets. Second, the increasing importance of industry factors relative to country factors was a short-lived phenomenon. Third, large growth stocks are more correlated across countries than are small value stocks, and the difference has increased over time.

Residential GHG emissions in the United States are driven in part by a housing stock where onsite fossil combustion is common, home sizes are large by international standards, energy efficiency potential is large and electricity generation in many regions is GHG intensive. In this analysis, we assess decarbonization pathways for the US residential sector to 2060, through 108 scenarios describing housing stock evolution, new housing characteristics, renovation levels and clean electricity. The lowest emission pathways involve very rapid decarbonization of electricity supply alongside extensive renovations to existing homes, including improving thermal envelopes and heat pump electrification of heating. Reducing the size and increasing the electrification of new homes provide further emission cuts and combining all strategies enables reductions of 91% between 2020 and 2050. The potential of individual mitigation strategies shows great regional variation. Reaching zero emissions will require simultaneous deployment of multiple strategies and greater reduction of embodied emissions.Residential sector decarbonization is an essential part of mitigation, especially in the United States where per capita energy use is high by global standards. This article shows the emission reduction potential from individual and combined strategies applied to existing and new homes and to electricity supply.

Returns and cash flow growth for the aggregate U.S. stock market are highly and robustly predictable. Using a single factor extracted from the cross-section of book-tomarket ratios, we find an out-of-sample return forecasting R² of 13% at the annual frequency (0.9% monthly). We document similar out-of-sample predictability for returns on value, size, momentum, and industry portfolios. We present a model linking aggregate market expectations to disaggregated valuation ratios in a latent factor system. Spreads in value portfolios' exposures to economic shocks are key to identifying predictability and are consistent with duration-based theories of the value premium.