Subjective Return Expectations, Perceptions, and Portfolio Choice

Exploiting a representative sample of the French population by age, wealth, and asset classes, we document novel facts about their expectations and perceptions of stock market returns. Both expectations and perceptions of returns are very dispersed, significantly lower than their data counterparts, and a substantial portion of the variation in the former is explained by dispersion in the latter. Consistent with portfolio choice models under incomplete information, a conditional risk-return trade-off explains the intensive margin, while at the extensive margin, only expected returns matter. Despite accounting for survey measurement error in subjective return expectations, ’muted sensitivities’ at both portfolio choice margins obtain, getting consistently (i) bigger when excluding informed non-participants, and (ii) smaller, for inertial and professionally delegated portfolios.

METODE SIMULASI HISTORIS UNTUK PERHITUNGAN NILAI VALUE AT RISK PADA PORTOFOLIO DENGAN MODEL MARKOWITZ

A portfolio concerns the formation of the composition of multiple assets to obtain optimum results. At the same time, Value at Risk is a technique in risk management to measure and assess parametrically (variant and co-variant), Monte-Carlo, and historical simulation. This research employed historic simulation because normal distribution is not required from returns and is a Value at Risk calculation model that is determined by the past value on produced return asset, in which this research aimed to determine the Markowitz model positive shares and Value at Risk in the portfolio by using historical simulation. The Markowitz model found eight shares with positive expected returns, which are as follows: BBCA, BBRI, BRPT, EXCL, ICBP, INDF, MNCN, and TPIA. The BBCA has the most significant exposure of all the shares with the amount of Rp 2.287.200.440.000, while the TPIA has the smallest exposure of all the shares with the amount of Rp 58.899.375.000. Further, the EXCL has the largest VaR with the amount of Rp 236.189.538.497, while the TPIA and ICBP had no VaR losses because the VaR of TPIA and ICBP is Rp 0 and Rp -1.407.719.893, respectively, along with the INDF as the share with the smallest VaR of Rp 18.513.213.620. The most significant exposure average is Rp 719.246.318.375, while the largest VaR average is Rp 76.827.608.341,3. As long as the VaR did not exceed the exposure value, the investors will be safe and have no loss.

Predictive Value of Estimated Beta

The Capital Asset Pricing Model (CAPM) is widely used in corporate finance to assess expected returns of securities and return on equity, and beta, a measure of systematic risk, is a component of the CAPM equation. Previous studies appear not to have addressed whether beta as a stand-alone metric allows individual investors to effectively assess returns relative to the market, and this study aims to address this. Exchange-traded funds (ETFs) reflecting a range of expected volatilities relative to the S&P 500 index were selected. Betas of XLK (Technology sector), XLE (Energy sector), XLU (Utilities sector), and XLY (Consumer Staples sector) were estimated by regressing their weekly returns over five years against those of the S&P 500 index. Three five-year periods were used (ending in 2005, 2010, and 2015). The betas largely conformed to anticipated values with the exception of that of XLY which was surprisingly greater than the market beta. Estimated and observed betas were compared using a two-tailed paired T-test and no difference was found, suggesting that estimated beta is statistically a good proxy for actual beta. In practical terms though, there were relatatively large variances in several instances between estimated and observed betas, and this could be a concern for investors. Returns using estimated beta and actual returns were also compared over one, two, three, four, and five years with regard to the three five-year periods. Significant variation was observed for expected minus observed returns both in sign and magnitude. A two-tailed paired T-test suggested there was a difference between returns using estimated beta and actual ones over the three five-year periods for all funds except XLE. The observations suggest betas are volatile and individual investors should incorporate additional metrics to forecast returns relative to the market.

PENENTUAN KEPUTUSAN INVESTASI SAHAM MENGGUNAKAN CAPITAL ASSET PRICING MODEL (CAPM) DENGAN PENAKSIR PARAMETER STOKASTIK

CAPM is a method of determining efficient or inefficient stocks based on the differences between individual returns and expected returns based on the CAPM’s positive value for efficient and negative value for inefficient stocks. The move to share prices in the process can influence investors's decisions in investing funds, so that it can be formulated in stochastic differential equations that form the Geometric Brownian Motion model (GBM). The purpose of the study is to determine return value using the CAPM based on share estimates and historical stock prices. The study uses secondary data that data a monthly closing of stock prices from December 2017 to December 2020. The GBG model's estimated stock price is used to determine the expected value return using the CAPM. In this case, it is called CAPM-Stochastic. Then the results of the CAPM-Stochastic was compared to the results of the CAPM-Historical to define efficient stocks and inefficient stocks. The results of research using CAPM-Stochastic obtained that HMSP, ICBP, KLBF, and WOOD shares are efficient stock while UNVR shares are inefficient. The results of CAPM-Historical obtained that HMSP, ICBP, KLBF, and UNVR shares are inefficient stocks and WOOD is an efficient stocks.

Structural innovation in state variables and expected stock returns

PurposeThe purpose of this study is to investigate whether the surprise components of systematic risk, which are useful in forecasting future investment opportunities, help explain the cross-section of average returns associated with portfolios sorted on size, book-to-market and accruals. This study also aims to examine the mispricing attributes of the size, value and accrual effects by investigating the relative economic relevance of aggregate risk factors, which are related to exogenous shocks in state variables, in the cross-sectional returns of triple-sorted portfolios.Design/methodology/approachThis study uses innovations of systematic risk, which affect the cash flows and risk-adjusted discount rates of all firms in an economy and determines the expected returns of portfolios based on firm characteristics. This study uses independent sorts based on size, book-to-market and total accruals – all of which are measured at the firm level – and construct three-dimensional test portfolios. For unobserved innovations, this study estimates a triangular structural vector autoregressive system and obtain the exogenous innovations in state variables. The author uses Fama-MacBeth two-pass cross-sectional regressions and examines whether the structural innovations explain a significant part of the cross-sectional variation in the average returns of the test portfolios.FindingsThis study finds that variations in expected returns of testing assets are determined by differences in the underlying assets’ exposure to systematic risk innovation. The empirical evidence also shows that exogenous innovation in Fama-French (FF) risk factors leaves out important cross-sectional information about expected returns, and additionally, the FF-factor betas have lower cross-sectional power than the proxy for innovation betas. The cross-sectional differences in the test portfolios’ sensitivity to instruments such as the short-term Treasury bill rate and term spread survive the presence of FF-factor betas.Originality/valueIn contrast to the existing literature, this study uses structural innovations that are uncorrelated and thus exogenous in nature. The author creates test portfolios that display a wide range of average returns and are unlikely to show spurious variability in risk exposures. Unlike the existing research, where size, value and accrual anomalies have been analyzed in isolation, this study examine these pricing patterns jointly, focusing on the possible contributing role of structural innovation in economy-wide predictor variables. To the best of the author’s knowledge, this paper is the first attempt to link the sensitivity of portfolios sorted on size, book-to-market and accruals to exogenous structural innovation.

FINANCIAL SECTOR STOCKS REACTION TO COVID-19 EVENTS

Price fluctuations in the financial sector are often of major interest when projecting the general performance and state of the economy. The implications of the COVID-19 pandemic in the sector are analyzed through the event study method. A random sample portfolio of 20 financial sector stocks listed on the NYSE is used and its reaction on 15 different events throughout 2020 is observed. Results indicate that events in the earlier stage of the pandemic exhibit both higher abnormal returns and significance, compared to the ones at the latter stages, with a larger proportion of them being bad news. The financial sector is perceived to react significantly in such cases, usually anticipating them beforehand. As adjustment windows are rarely significant, the market’s reaction is deemed as efficient. The general conclusion is that the financial sector stocks react to important COVID-19 news, generating abnormal rather than expected returns.

Asset Allocation via Machine Learning

In this paper, we document a novel machine learning-based numerical framework to solve static and dynamic portfolio optimization problems, with, potentially, an extremely large number of assets. The framework proposed applies to general constrained optimization problems and overcomes many major difficulties arising in current literature. We not only empirically test our methods in U.S. and China A-share equity markets, but also run a horse-race comparison of some optimization schemes documented in (Homescu, 2014). We record significant excess returns, relative to the selected benchmarks, in both U.S. and China equity markets using popular schemes solved by our framework, where the conditional expected returns are obtained via machine learning regression, inspired by (Gu, Kelly & Xiu, 2020) and (Leippold, Wang & Zhou, 2021), of future returns on pricing factors carefully chosen.

Incentive Constrained Risk Sharing, Segmentation, and Asset Pricing

Incentive problems make securities’ payoffs imperfectly pledgeable, limiting agents’ ability to issue liabilities. We analyze the equilibrium consequences of such endogenous incompleteness in a dynamic exchange economy. Because markets are endogenously incomplete, agents have different intertemporal marginal rates of substitution, so that they value assets differently. Consequently, agents hold different portfolios. This leads to endogenous markets segmentation, which we characterize with optimal transport methods. Moreover, there is a basis going always in the same direction: the price of a security is lower than that of replicating portfolios of long positions. Finally, equilibrium expected returns are concave in factor loadings. (JEL D51, D52, G11, G12)

THE INFLUENCE OF THE COVID-19 PANDEMIC ON ALLOCATION OF ASSETS IN INVESTORS PORTFOLIO

Investing during a pandemic is very challenging. Even in these difficult times, the investor must appropriately allocate assets into his portfolio. In this article, we discuss investing in the stock market. We are interested in creating portfolios of shares that consist of financial assets. The individual methods we use are designed to provide an allocation of funds in between individual shares. In the modern portfolio theory, the Markowitz model (Markowitz, 1952) is being used to solve these problems. The paper's main goal is to propose an efficient, robust approach to solve the Markowitz optimization problem adjusted for periods of a global decline in financial markets. In our research, we focus on robust optimization. Instead of precisely given input parameters, we propose a set of parameters from which we always select the worst possible parameter (so-called worst-case optimization). The robustness of optimization is achieved using so-called filter matrices. These matrices are used to modify historical data directly during optimization. The proposed model modifies the data by using different lengths of historical returns. Our proposed model is then compared with the original Markowitz non-robust model. We compare these two models using the properties of the second derivative of the optimization problem. Our results are visualized for different levels of investor’s risk aversion. We present our methods on historical price data of five randomly selected companies traded on the US market. By comparing the proposed robust approach with the non-robust one, we show that different lengths of historical returns capture volatility changes earlier. The investor can thus reduce his risk aversion and increase his expected returns.