Tests of asset pricing with time‐varying factor loads

This paper investigates an optimal investment problem for an open-end fund with dynamic flows. The accumulated fund inflow and outflow processes are modeled by two independent compound Poisson processes, respectively. The fund manager allocates the fund capital between a risk-free asset and a risky asset. The correlation between the fund inflow process and the risky asset's price process is under consideration. Inspired by Berk and Van Binsbergen [(2015). Measuring skill in the mutual fund industry. Journal of Financial Economics, 118(1), 1–20], we first introduce the accumulated value added process to measure the fund performance. The manager aims to maximize the expected utility of the fund's accumulated value added. We derive the optimal investment strategy explicitly via dynamic programming approach. Furthermore, a verification theorem is provided and some properties of the optimal strategy are analyzed. Finally, sensitivity analysis is given to illustrate the effects of model parameters on the optimal strategy.

References and Further Reading

This paper compares out-of-sample, ex-ante risk and returns of arbitrage pricing theory (APT) risk-factor based, zero-cost portfolios with characteristic-based, zero-cost portfolios. In particular the Haugen and Baker characteristic-based model framework is used in a comparison with the capital asset pricing model (CAPM) (Haugen, R. A., & Baker, L. N. (1996). Commonality in the determinants of expected stocks returns. Journal of Financial Economics, 41, 401–439), and three-factor Fama and French APT model portfolios to analyse returns of stocks listed on the Johannesburg Stock Exchange (Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33, 3–56). The finding that cross-sectional characteristic-based models have yielded portfolios with higher excess monthly returns but lower risk than their arbitrage pricing theory counterparts is discussed. Under the assumption of general no arbitrage conditions, it is argued that evidence in favour of characteristic-based pricing provides insight into the non-linear nature in which information is assimilated into pricing kernels for the market considered.

The seasonal patterns observed on Monday stock returns are still unexplained by different asset pricing models. We attempt to fill this gap in the finance literature by using the Fama-French (Journal of Financial Economics 33:3–56, 1993) risk factors to explain the Monday seasonal. The results in the study show that Monday returns are explained by risk factors such as the market return, the size of the firms, and the book-to-market ratios of firms.

By this study, we want to provide tools for an economic agent who wants to maintain her maximized intertemporal consumption rate over time period regularly by trading activity in capital market. She would use the diversification principle to compose portfolio that is consisted of risky and risk free assets, with initial wealth by using principle of dynamic programming in stochastic control problem. While our target function is to maximize the discounted utility function over consumption rate, the agent may use this portion of wealth for hedonistic lifestyle without thinking the bequest aim. Motivated by observation that seminal work of Samuelson (1969)’s optimal investment on risky asset was independent of time and wealth process, we were replacing the source of uncertainty in the risky asset by binomial process in order to improve Samuelson's solution. The risky asset price evolution is determined to move in two possible directions of up and down in the next time instantly with certain probability, following binomial trees scheme. Working within this setting, we manage to obtain trading strategies as explicit solution for the optimal investment and consumption decisions which were being expressed as time-dependent functions as well as wealth process feedback form. This solution goes along with inter-temporal concept that our consumption and investment strategies tomorrow will be affected with our wealth today. We exemplify the results by numerical examples and perform Monte Carlo simulation.

We investigate the risk–return trade-off on the US and European stock markets. We investigate the non-linear risk–return trade-off with a special eye to the tails of the stock returns using quantile regressions. We first consider the US stock market portfolio. We find that the risk–return trade-off is significantly positive at the upper tail (0.9 quantile), where the upper tail is large positive excess returns. The positive trade-off is as expected from asset pricing models. For the lower tail (0.1 quantile), that is for large negative stock returns, the trade-off is significantly negative. Additionally, for the median (0.5 quantile), the risk–return trade-off is insignificant. These results are recovered for the US industry portfolios and for Eurozone stock market portfolios.

Multifactor Risk Loadings and Abnormal Returns Under Uncertainty and Learning

This paper proposes energy consumption in the US as a new measure for the consumption capital asset pricing model. We find that (i) industrial energy growth produces reasonable values for the relative risk aversion coefficient and the implied risk‐free rate; (ii) compared to alternative consumption measures, industrial energy performs well in explaining the cross‐sectional variation in stock returns with the lowest implied risk aversion and pricing errors; (iii) the industrial energy consumption risk model performs equally well as the Fama–French three‐factor model in the cross‐sectional asset pricing tests; and (iv) total energy consumption risk is priced in the presence of the Fama–French factor risks.

The influence of Bitcoin on Portfolio Diversification and Design

The influence of Bitcoin on portfolio diversification and design

It is well known that in simple linear regression, measurement errors in the explanatory variable lead to a downward bias in the OLS slope estimator. In two-pass regression tests of asset-pricing models, one is confronted with such measurement errors as the second-pass cross-sectional regression uses as explanatory variables imprecise estimates of asset betas extracted from the first-pass time-series regression. The slope estimator of the second-pass regression is used to get an estimate of the pricing-model’s factor risk-premium. Since the significance of this estimate is decisive for the validity of the model, knowledge of the properties of the slope estimator, in particular, its bias, is crucial. First, we show that cross-sectional correlations in the idiosyncratic errors of the first-pass time-series regression lead to correlated measurement errors in the betas used in the second-pass cross-sectional regression. We then study the effect of correlated measurement errors on the bias of the OLS slope estimator. Using Taylor approximation, we develop an analytic expression for the bias in the slope estimator of the second-pass regression with a finite number of test assets N and a finite time-series sample size T. The bias is found to depend in a non-trivial way not only on the size and correlations of the measurement errors but also on the distribution of the true values of the explanatory variable (the betas). In fact, while the bias increases with the size of the errors, it decreases the more the errors are correlated. We illustrate and validate our result using a simulation approach based on empirical return data commonly used in asset-pricing tests. In particular, we show that correlations seen in empirical returns (e.g., due to industry effects in sorted portfolios) substantially suppress the bias.

Continuous trading in an exchange economy under discontinuous dynamics: A resolution of the equity premium puzzle

Information, Diversification, and Cost of Capital

Pricing Factors and Causal Networks for US Industry Portfolios

In the existing literature, the representativeness heuristic is commonly viewed as a cause of asset price overreaction to new information. This article proves that the representativeness heuristic can cause asset price underreaction to new information in a competitive securities market. Specifically, there is one risk-free asset and one risky asset. Both rational and heuristic traders trade can against each other or against noise traders, whose demand is random. The payoff of the risky asset is unknown but all traders receive an informational signal about the risky asset’s payoff before any trading takes place. Due to the representativeness heuristic, the updated mean of the risky asset’s payoff for heuristic traders is higher (lower) than that for rational traders when the realization of the informational signal is above (below) the expected payoff of the risky asset. The results of this study suggest that regardless of noise traders being net buyers or sellers, the representativeness heuristic causes the asset price to overreact to new information close to the expected payoff of the risky asset and causes the asset price to underreact to new information far above or below the expected payoff of the risky asset. <b>TOPICS:</b>Statistical methods, risk management, in markets