Downside Risk and the Momentum Effect

Stocks with greater downside risk, which is measured by higher correlations conditional on downside moves of the market, have higher returns. After controlling for the market beta, the size effect and the book-to-market effect, the average rate of return on stocks with the greatest downside risk exceeds the average rate of return on stocks with the least downside risk by 6.55% per annum. Downside risk is important for explaining the cross-section of expected returns. In particular of the profitability of investing in momentum strategies can be explained as compensation for bearing high exposure to downside risk.

Downside Risk

Economists have long recognized that investors care differently about downside losses versus upside gains. Agents who place greater weight on downside risk demand additional compensation for holding stocks with high sensitivities to downside market movements. We show that the cross-section of stock returns reflects a premium for downside risk. Specifically, stocks that covary strongly with the market when the market declines have high average returns. We estimate that the downside risk premium is approximately 6% per annum. The reward for bearing downside risk is not simply compensation for regular market beta, nor is it explained by coskewness or liquidity risk, or size, book-to-market, and momentum characteristics.

Uncovered Interest Rate Parity and the Term Structure

This paper examines uncovered interest rate parity (UIRP) and the expectations hypotheses of the term structure (EHTS) at both short and long horizons. The statistical evidence against UIRP is mixed and is currency- not horizon-dependent. Economically, the deviations from UIRP are less pronounced than previously documented. The evidence against the EHTS is statistically more uniform, but, economically, actual spreads and theoretical spreads (spreads constructed under the null of the EHTS) do not behave very differently, especially at long horizons. Partly because of this, the deviations from the EHTS only play a minor role in explaining deviations from UIRP at long horizons. A random walk model for both exchange rates and interest rates fits the data marginally better than the UIRP-EHTS model.

Risk, Uncertainty and Asset Prices

We identify the relative importance of changes in the conditional variance of fundamentals (which we call %u201Cuncertainty%u201D) and changes in risk aversion (%u201Crisk%u201D for short) in the determination of the term structure, equity prices and risk premiums. Theoretically, we introduce persistent time-varying uncertainty about the fundamentals in an external habit model. The model matches the dynamics of dividend and consumption growth, including their volatility dynamics and many salient asset market phenomena. While the variation in dividend yields and the equity risk premium is primarily driven by risk, uncertainty plays a large role in the term structure and is the driver of counter-cyclical volatility of asset returns.

The Cross-Section of Volatility and Expected Returns

We examine the pricing of aggregate volatility risk in the cross-section of stock returns. Consistent with theory, we find that stocks with high sensitivities to innovations in aggregate volatility have low average returns. In addition, we find that stocks with high idiosyncratic volatility relative to the Fama and French (1993) model have abysmally low average returns. This phenomenon cannot be explained by exposure to aggregate volatility risk. Size, book-to-market, momentum, and liquidity effects cannot account for either the low average returns earned by stocks with high exposure to systematic volatility risk or for the low average returns of stocks with high idiosyncratic volatility.

Value versus Growth: Time-Varying Expected Stock Returns

Is the value premium predictable? We study time-variations of the expected value premium using a two-state Markov switching model. We find that when conditional volatilities are high, the expected excess returns of value stocks are more sensitive to aggregate economic conditions than the expected excess returns of growth stocks. As a result, the expected value premium is time-varying: it spikes upward in the high-volatility state, only to decline more gradually in the ensuring periods. However, out-of-sample predictability of the value premium is close to nonexistent.

Build America Bonds

Build America Bonds (BABs) are a new form of municipal financing introduced in 2009. Investors in BAB municipal bonds receive interest payments that are taxable, but issuers receive a subsidy from the U.S. Treasury. The BAB program has succeeded in lowering the cost of funding for state and local governments with BAB issuers obtaining finance 54 basis points lower, on average, compared to issuing regular municipal bonds. For institutional investors, BAB issue yields are 116 basis points higher than comparable Treasuries and 88 basis points higher than comparable highly rated corporate bonds. For individual investors, BABs have lower yields than regular municipal bonds. Thus, on average the Federal government subsidy disadvantages individual U.S. taxpayers, who are the main holders of municipal bonds, and benefits new entrants in the municipal bond market.

Taxes on Tax-Exempt Bonds

Implicit tax rates priced in the cross section of municipal bonds are approximately two to three times as high as statutory income tax rates, with implicit tax rates close to 100% using retail trades and above 70% for interdealer trades. These implied tax rates can be identified on the cross section of municipal bonds because a portion of secondary market municipal bond trades involve income taxes. After valuing the tax payments, market discount bonds, which carry income tax liabilities, trade at yields around 25 basis points higher than comparable municipal bonds not subject to any taxes. The high sensitivities of municipal bond prices to tax rates can be traced to individual retail traders dominating dealers and other institutions.

Knowledge Restore and Transfer for Multi-label Class-Incremental Learning

Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution. In this paper, we propose a knowledge restore and transfer (KRT) framework for MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Besides, we propose a token loss to jointly optimize the incremental cross-attention module. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks