scholarly journals Five Facts about Beliefs and Portfolios

2021 ◽  
Vol 111 (5) ◽  
pp. 1481-1522
Author(s):  
Stefano Giglio ◽  
Matteo Maggiori ◽  
Johannes Stroebel ◽  
Stephen Utkus
Keyword(s):  
Demographic Characteristics ◽  
Individual Heterogeneity ◽  
Expected Returns ◽  
Trading Frequency ◽  
Portfolio Composition ◽  
Rare Disasters ◽  
Retail Investors ◽  
Large Panel ◽  
Macro Finance ◽  
Belief Changes

We study a newly designed survey administered to a large panel of wealthy retail investors. The survey elicits beliefs that are important for macroeconomics and finance, and matches respondents with administrative data on their portfolio composition, their trading activity, and their login behavior. We establish five facts inthese data. (i) Beliefs are reflected in portfolio allocations. The sensitivity of portfolios to beliefs is small on average, but varies significantly with investor wealth, attention, trading frequency, and confidence. (ii) Belief changes do not predict when investors trade, but conditional on trading, they affect both the direction and the magnitude of trades. (iii) Beliefs are mostly characterized by large and persistent individual heterogeneity. Demographic characteristics explain only asmall part of why some individuals are optimistic and some are pessimistic. (iv) Expected cash flow growth and expected returns are positively related, both within and across investors. (v) Expected returns and the subjective probability of rare disasters are negatively related, both within and across investors. These five facts provide useful guidance for the design of macro-finance models. (JEL D83, E23, G11, G12, G41, G51)

Unspanned Global Macro Risks in Bond Returns

2021 ◽  
Author(s):  
Feng Zhao ◽  
Guofu Zhou ◽  
Xiaoneng Zhu
Keyword(s):  
Term Structure ◽  
Predictive Power ◽  
International Markets ◽  
Bond Markets ◽  
Market Prices ◽  
Macroeconomic Variables ◽  
Bond Returns ◽  
Large Panel ◽  
Term Premia ◽  
Macro Finance

We examine the macro-spanning hypothesis for bond returns in international markets. Based on a large panel of real-time macroeconomic variables that are not subject to revisions, we find that global macro factors have predictive power for bond returns unspanned by yield factors. Furthermore, we estimate macro-finance term structure models with the unspanned global macro factors and find that the global macro factors influence the market prices of level and slope risks and induce comovements in forward term premia in global bond markets. This paper was accepted by David Simchi-Levi, finance.

scholarly journals Robust optimization of time series momentum portfolios

2021 ◽  
Vol 19 (1) ◽  
pp. 52-69
Author(s):  
Jeremy Fague ◽  
Caio Almeida
Keyword(s):  
Time Series ◽  
Robust Optimization ◽  
Transaction Costs ◽  
Historical Data ◽  
Expected Returns ◽  
Trading Costs ◽  
Portfolio Composition ◽  
Mean Variance

Mean-Variance Optimization (MVO) is well-known to be extremely sensitive to slight differences in the expected returns and covariances: if these measures change day to day, MVO can specify very different portfolios. Making wholesale changes in portfolio composition can cause the incremental gains to be negated by trading costs. We present a method for regularizing portfolio turnover by using the ℓ1 penalty, with the amount of penalization informed by recent historical data. We find that this method dramatically reduces turnover, while preserving the efficiency of mean-variance optimization in terms of risk-adjusted return. Factoring in reasonable estimates of transaction costs, the turnover-regularized MVO portfolio substantially outperforms a leverage-constrained MVO approach, in terms of risk-adjusted return.

Does individual heterogeneity shape retail investor behaviour?

2017 ◽  
Vol 44 (5) ◽  
pp. 578-593 ◽  
Author(s):  
Abhijeet Chandra ◽  
Kantesha Sanningammanavara ◽  
A. Satya Nandini
Keyword(s):  
Portfolio Management ◽  
Critical Role ◽  
Individual Heterogeneity ◽  
Personal Factors ◽  
Content Type ◽  
Retail Investors ◽  
Retail Investor ◽  
Investor Behaviour ◽  
Trading Behaviour ◽  
Investment Behaviour

Purpose The purpose of this paper is to survey retail investors to study the determinants of their investment behaviour and show that individual heterogeneity and financial factors such as gender, age, educational status, income, and investment levels determine their trading behaviour across three domains; however, features such as marital status and occupation do not play any significant role in shaping their trading behaviour. Design/methodology/approach Structured surveys are conducted on retail and small investors using the brokerage services of a firm. Data collected from primary methods are used for statistical analysis in ANOVA and multiple regression frameworks. Findings The authors also report that retail investors’ self-perceived confidence as a function of both expected and unexpected changes in the market and personal factors largely determines trading behaviour of retail investors and that self-perceived confidence level and self-reported portfolio size are positively associated implying that (over-)confident retail investors tend to believe that their investment skills being superior are bound to perform better and thus they typically hold larger than average investment portfolios. Practical implications These findings are significant because research on cross-sectional variance of individual investment behaviour explains how investor heterogeneity plays a critical role in investment and asset allocation decisions. Investors, researchers, and practitioners would use the results for financial decision making specifically related to personal finance, behavioural portfolio management, and investment advisory. Originality/value This paper is an empirical approach to explore the retail investor behaviour using psychometric approach with respect to self-perceived confidence and other perceived measures of investor behaviour. The authors contribute to the emerging set of literature on investor behaviour and behavioural finance.

Analisis Portofolio Optimal Dana Pengembangan Pendidikan Nasional pada Lembaga Pengelola Dana Pendidikan

2017 ◽  
Vol 2 (4) ◽  
pp. 65-72
Author(s):  
NFN Zulkifli
Keyword(s):  
Optimal Investment ◽  
Optimal Portfolio ◽  
Expected Returns ◽  
Investment Risk ◽  
Investment Portfolio ◽  
Investment Activity ◽  
Development Fund ◽  
Portfolio Composition ◽  
Time Deposits ◽  
Selection Of

One of the main tasks and functions of Indonesian Endowment Fund for Education (LPDP) is to manage the National Educational Development Fund (DPPN) through short term investment. In investment activity, the selection of investment portfolio will impact on investment returns andand risks. Therefore, an optimal investment portfolio is needed through combination selection of a number of assets so that the risk can be minimized without reducing the expected returns.  This research aimsto answer management question whether the LPDP’s actual investment portfolio is an optimal portfolio. The results shows that actual LPDP’s investment portfolio generates investment risk equal to 0,00385 and expected returns equal to 8,04% for portfolio composition of bonds 63,31% and time deposits 36,69%. This actual portfolio was not an optimal portfolio because with the same risk investment that LPDP bears, a higher expected returns can be generated by 8,15% for the portfolio composition of bonds 6,07% and time deposits 93,93%. On the other hand, portfolio investment with three assets –bonds, time deposit and stocks, optimum portfolio can be generated with the portfolio composition for bonds 74,72%, time deposits 24,81%, and stocks 0,47%, resulting investment risk equal to 0,00537 and expected return equal to 8,06%. Abstrak Salah satu tugas dan fungsi Lembaga Pengelola Dana Pendidikan (LPDP) adalah melakukan pengelolaan Dana Pengembangan Pendidikan Nasional (DPPN) melalui instrument investasi jangka pendek. Dalam berinvestasi, pemilihan portofolio investasi akan berdampak besar pada hasil investasi yang akan diterima serta risiko yang akan dihadapi. Untuk itu, diperlukan penentuan portofolio optimal yang dapat membentuk portofolio sedemikian rupa sehingga risiko dapat diminimalkan tanpa mengurangi return harapan investasi. Penelitian ini dilakukan untuk menjawab pertanyaan manajemen apakah portofolio investasi aktual yang dimiliki LPDP merupakan portofolio yang optimal. Hasil penelitian menunjukkan bahwa portofolio investasi aktual LPDP menghasilkan risiko investasi sebesar 0,00385 dan return harapan sebesar 8,04% dengan komposisi obligasi 63,31% dan deposito 36,69%. Portofolio aktual tersebut bukanlah merupakan portofolio optimal karena dengan risiko investasi yang sama, return harapan yang lebih besar dapat dicapai sebesar 8,15% pada komposisi obligasi 6,07% dan deposito 93,93%. Jika LPDP memasukkan saham dalam portofolio investasi, portofolio optimal dapat dihasilkan dengan komposisi obligasi 74,72%, deposito 24,81% dan saham 0,47% yang menghasilkan risiko investasi sebesar 0,00537 dan return harapan sebesar 8,06%.

scholarly journals Predictability and the Cross-Section of Expected Returns: A Challenge for Asset Pricing Models

2021 ◽  
Author(s):  
Christian Schlag ◽  
Michael Semenischev ◽  
Julian Thimme
Keyword(s):  
Cross Section ◽  
Risk Premium ◽  
Empirical Test ◽  
Expected Returns ◽  
State Variables ◽  
Expected Return ◽  
Long Run ◽  
The Cross ◽  
Long Run Risks ◽  
Macro Finance

Many modern macro finance models imply that excess returns on arbitrary assets are predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples. This paper was accepted by David Simchi-Levi, finance.

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