Relevant bibliographies by topics / Risk and Return

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Risk and Return'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 January 2023

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Journal articles on the topic "Risk and Return"

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Gambeta, Vaughn, and Roy Kwon. "Risk Return Trade-Off in Relaxed Risk Parity Portfolio Optimization." Journal of Risk and Financial Management 13, no. 10 (October 4, 2020): 237. http://dx.doi.org/10.3390/jrfm13100237.

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This paper formulates a relaxed risk parity optimization model to control the balance of risk parity violation against the total portfolio performance. Risk parity has been criticized as being overly conservative and it is improved by re-introducing the asset expected returns into the model and permitting the portfolio to violate the risk parity condition. This paper proposes the incorporation of an explicit target return goal with an intuitive target return approach into a second-order-cone model of a risk parity optimization. When the target return is greater than risk parity return, a violation to risk parity allocations occurs that is controlled using a computational construct to obtain near-risk parity portfolios to retain as much risk parity-like traits as possible. This model is used to demonstrate empirically that higher returns can be achieved than risk parity without the risk contributions deviating dramatically from the risk parity allocations. Furthermore, this study reveals that the relaxed risk parity model exhibits advantageous traits of robustness to expected returns, which should not deter the use of expected returns in risk parity model.
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Miller, Kent D., and Michael J. Leiblein. "Corporate Risk-Return Relations: Returns Variability Versus Downside Risk." Academy of Management Journal 39, no. 1 (February 1996): 91–122. http://dx.doi.org/10.5465/256632.

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Miller, K. D., and M. J. Leiblein. "CORPORATE RISK-RETURN RELATIONS: RETURNS VARIABILITY VERSUS DOWNSIDE RISK." Academy of Management Journal 39, no. 1 (February 1, 1996): 91–122. http://dx.doi.org/10.2307/256632.

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Huang, Wei, Qianqiu Liu, S. Ghon Rhee, and Liang Zhang. "Return Reversals, Idiosyncratic Risk, and Expected Returns." Review of Financial Studies 23, no. 1 (March 25, 2009): 147–68. http://dx.doi.org/10.1093/rfs/hhp015.

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Aslanidis, Nektarios, Charlotte Christiansen, and Christos S. Savva. "Quantile Risk–Return Trade-Off." Journal of Risk and Financial Management 14, no. 6 (June 3, 2021): 249. http://dx.doi.org/10.3390/jrfm14060249.

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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.
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Odutola Omokehinde, Joshua. "Mutual funds behavior and risk-adjusted performance in Nigeria." Investment Management and Financial Innovations 18, no. 3 (September 9, 2021): 277–94. http://dx.doi.org/10.21511/imfi.18(3).2021.24.

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The paper investigates the behavior of mutual funds and their risk-adjusted performance in the financial markets of Nigeria between April 2016 and May 31, 2019, using descriptive statistics, as well as CAPM, Jensen’s alpha, and other risk-adjusted portfolio performance measures such as Sharpe and Treynor ratios, as well as Fama decomposition of return. The descriptive tests revealed that 80.77% of the funds were superior to market returns, while 13.46% were riskier. The market and the fund returns behaved abnormally with asymptotic and leptokurtic characteristics as their skewness and kurtosis varied from the normal requirements. Diagnostically, the normality test by Jacque-Berra showed that the return was not normally distributed at a 1% significance level. The market was more aggressive relative to the funds. The average risk-free rate was 6.75% above the market’s return. The risk-adjusted portfolio returns measured by Sharpe and Treynor ratios showed that 67.31% of the funds underperformed the market compared to 40.38% that outperformed the market using Jensen’s alpha. Fama decomposition of return revealed that the fund managers are risk-averse with 48% superior selection ability and rationally invested over 85% of investors’ funds in schemes with fixed income securities at a given risk-free return that cushioned the negative effects of the systematic and idiosyncratic risks and consequently threw the total returns into positive territories. Overall, the fund managers possessed 52% of inferior selection abilities that only earned 33% of superior risk-adjusted returns and hence, failed to achieve the desired diversification in the relevant period.
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Cochrane, John H., and Monika Piazzesi. "Bond Risk Premia." American Economic Review 95, no. 1 (February 1, 2005): 138–60. http://dx.doi.org/10.1257/0002828053828581.

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We study time variation in expected excess bond returns. We run regressions of one-year excess returns on initial forward rates. We find that a single factor, a single tent-shaped linear combination of forward rates, predicts excess returns on one-to five-year maturity bonds with R2 up to 0.44. The return-forecasting factor is countercyclical and forecasts stock returns. An important component of the return-forecasting factor is unrelated to the level, slope, and curvature movements described by most term structure models. We document that measurement errors do not affect our central results.
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BAYAT, Fikret, and Şule Yüksel YİĞİTER. "COMPARISON OF DOWN-SIDE RISK MEASUREMENTS AND MODERN PORTFOLIO THEORY: THE EXAMPLE OF BORSA ISTANBUL." Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi 13, no. 25 (June 29, 2022): 1–23. http://dx.doi.org/10.36543/kauiibfd.2022.001.

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The concept of risk entered the portfolio world with the work of Harry Markowitz. By considering risk and return together, Markowitz accepts the return distribution symmetrically to create optimal portfolios so that investors can obtain the least risk (variance) and the highest return. When the return distribution is symmetrical, variance can give accurate results as an indicator of risk. But what if the returns show an asymmetrical distribution, can this be the case? Based on this question, the purpose of our research is to compare the portfolio return, risk and covariances of 10 different stocks traded in BIST100 between 1.1.2011-31.4.2021 according to Modern Portfolio theory and Downside risk criteria. In our study, it has been found that Modern Portfolio does not diversify sufficiently, creates portfolios from stocks with high return-risk features, and when the returns do not show a symmetrical distribution, it is insufficient. On the contrary, it has been understood that portfolios created against downside risk measures contain less risk and that more accurate results can be achieved with downside risk measures in asymmetric return distribution.
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Shaw, Frances, Fergal O’Brien, and Finbarr Murphy. "European Corporate Credit Returns: A Risk Return Analysis." International Review of Business Research Papers 11, no. 1 (March 2015): 11–24. http://dx.doi.org/10.21102/irbrp.2015.03.111.02.

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Marston, Felicia, and Robert S. Harris. "Risk and Return: A Revisit Using Expected Returns." Financial Review 28, no. 1 (February 1993): 117–37. http://dx.doi.org/10.1111/j.1540-6288.1993.tb01341.x.

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Dissertations / Theses on the topic "Risk and Return"

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Gilbert, Emmeleen Ulita. "Risk-return portfolio modelling." Master's thesis, University of Cape Town, 2007. http://hdl.handle.net/11427/19030.

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Markowitz introduced the concept of modelling the risk associated with a given security as the variance of the expected return and showed how under certain conditions an investors portfolio can be managed by balancing the expected return of the portfolio and its variance. Building on Markowitz original framework, William Sharpe, extended these ideas by connecting a portfolio to a risky asset. This extension became known as the Sharpe Index Model. There are number of assumptions governing the residuals of the Sharpe index model, one being that the error terms of the stocks are uncorrelated. The Troskie-Hossain innovation to the Sharpe Index model relaxes this assumption. We evaluate the Troskie-Hossain model relative to the Sharpe Index Model and Markowitz portfolio, and find that the Troskie-Hossain model approximates the Markowitz efficient frontier and optimal portfolio very closely. Further examining the residuals, we find evidence of autocorrelation and heteroskedasticity. Using ARMA to model the autocorrelation of the residuals has very little impact on the efficient frontier when working with log returns. However when working with simple returns the ARMA shifts the efficient frontier to the left. We find that GARCH(l , 1) models capture most of the autocorrelation in the squared residuals for both simple returns and log returns and shifts the efficient frontier to the left. Modelling a non-constant conditional mean and non-constant conditional variance (ARMA and GARCH) has proven difficult. The more complex a model becomes the more difficult the estimation. We investigate the effects of dividend yields on the efficient frontier, as well as using simple returns vs log returns in portfolio construction. Including dividend yields in our return data shifts the efficient frontier upwards. However only the a's are increased, and the f3's and f3 t-statistics of the shares remain the same. This shift effect of dividends has no impact on the time series or heteroskedastic models. The simple returns efficient frontier lies above that of the log returns efficient frontier. The a 's for simple returns are very different to those of log returns, however the f3's lie in a similar region to those of log returns.
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Chapman, Zaneta Anne. "Risk, Return and Credit." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/82992.

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Business Administration
Ph.D.
This dissertation investigates the role of credit in the evaluation of risk and return. The research comprises three essays, which analyze the use of credit from different perspectives. Chapter 1: The first essay proposes a comprehensive theory for the assessment and implementation of "acceptable" underwriting and rating variables. While the use of personal credit was the driving force behind the essay, we extend our theory and models to include all controversial rating classifications. It is shown that a rating classification would be appropriate when the cost to society is relatively small. The use of personal credit in the automobile insurance industry is provided as an application of the proposed models, and other considerations are explored. Chapter 2: For many years, gamblers have developed strategies to reach specific monetary and survival goals. In the second essay, a strategy is introduced in which a speculator engages in bet doubling to increase his chances of walking home a winner. It is shown that with enough credit it is quite possible to become a winner with a high degree of certainty--99.9%, even while facing a losing proposition. However, huge returns require huge risks, and so adopting such a strategy would eventually lead to large losses and negative expected profits. It is also shown that limited liability and a cost of obtaining credit are important factors to consider when analyzing expected gains. Chapter 3: "Hazardously immoral" contracts force external parties to bear significant losses without their consent. Abuses are particularly likely to occur when the threat of system-wide disruption is sufficient to make governments and international agencies bail out the offending organizations in order to limit total damages. The models provided in chapter 2 are presented in the third essay as strategies for externalizing extreme risks, and several results are derived.
Temple University--Theses
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Money, Alex Luxman Narayanan. "Corporate water risk - and return." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ddc0441c-ac54-471a-9741-301cb6b21c4a.

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Corporate water risk is a function of resource dependence, which exposes firms to uncertainty. Firms rationally seek to reduce this risk, and this shapes their disclosure strategies. However, the consequence is that corporate water risk disclosure is becoming increasingly unfit for purpose. As current approaches begin to acquire institutional legitimacy and the path-dependent label of best practice, a status quo is becoming embedded, reinforced through mimetic behaviour. The agency problem that this creates is unchecked; in part because of the legitimacy acquired by the disclosure strategies, but also because of temporal myopia exhibited by investors, which contributes to unpredictable decision-making. The status quo also results in sub-optimal resource allocation, a problem that is compounded by the large and growing global infrastructure deficit for water supply and services. This thesis sets out a framework by which the disclosure of corporate water risk can be meaningfully evaluated by investors and other stakeholders; and proposes how the water infrastructure investment gap could be narrowed by the development and application of the corporate water return concept. The research builds on empirical foundations to offer new approaches that address the problems of the status quo. First, it empirically explores perceptions of best practice in terms of water risk disclosure, from the perspective of both listed firms and leading institutional investors (Chapters 3 and 4). Second, it proposes a methodology through which firms can disclose water risks in a systematic format; and advances corporate water return as a complementary concept to water risk, in order to catalyse corporate investment in water infrastructure (Chapters 5 and 6). Resource dependence theory, institutional theory, and stakeholder theory are combined to create a trio of integrative, explicative conceptual narratives that form the overarching thesis structure. The research also draws on other themes from economic geography, including proximity; strategic cognition; transaction costs; and real options theory.
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Mårtensson, Jonathan. "Portfolio optimisation : improved risk-adjusted return?" Thesis, Uppsala University, Department of Economics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6397.

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In this thesis, portfolio optimisation is used to evaluate if a specific sample of portfolios have

a higher risk level or lower expected return, compared to what may be obtained through

optimisation. It also compares the return of optimised portfolios with the return of the original

portfolios. The risk analysis software Aegis Portfolio Manager developed by Barra is used for

the optimisations. With the expected return and risk level used in this thesis, all portfolios can

obtain a higher expected return and a lower risk. Over a six-month period, the optimised

portfolios do not consistently outperform the original portfolios and therefore it seems as

though the optimisation do not improve the return of the portfolios. This might be due to the

uncertainty of the expected returns used in this thesis.

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Ghunmi, Diana Nawwash Abed El-Hafeth Abu. "Stock return, risk and asset pricing." Thesis, Durham University, 2008. http://etheses.dur.ac.uk/2908/.

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This thesis attempts to address a number of issues that have been identified in the asset pricing literature as essential for shaping stock returns. These issues include the need to uncover the link between the macroeconomic variables and stock returns. In addition to this, is the need to decide, in light of the findings of the literature, whether to advise investors to include idiosyncratic risk and downside risk as risk factors in their asset pricing models. The results presented here suggest, consistent with other previous studies, that stock returns are a function of a number of previously identified risk factors along with the wider set of macroeconomic variables. These macroeconomic variables could be represented by a number of estimated macro factors. However, only one of these estimated factors emerged as significant in explaining the cross-section of stock returns. Nevertheless, it is important to note that the size (SMB) and value (HML) factors remain important factors in explaining the cross sectional returns on UK stocks, even with the existence of the other risk factors. This finding of inability of the examined macroeconomic variables to capture the pricing power of the SMB and the HML may cast doubt on the possibility of finding more macroeconomic factors that are able to account for these two factors in the cross section of returns in the UK. Interestingly, this conclusion seems to contradict previous authors' findings of potential links in the UK market. The results also support past studies that find that downside risk is an important risk factor and by allowing the downside risk premium to vary with business cycle conditions, downside risk might be a better measure of risk than market risk. Nevertheless, this thesis shows that although this finding is applicable in times of economic expansion, during recession, there is no conclusive relationship between . downside risk and stock returns. Furthermore, this thesis supports the studies which find that idiosyncratic risk is not significant in pricing stocks. However in contrast to other studies, it reveals this by showing that time-varying risk could be the reason behind the potentially illusive findings of idiosyncratic risk effect. This thesis confirms that, for London Stock Exchange investors, macroeconomic variables should never be overlooked when estimating stock returns and downside risk could be an influential risk factor.
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Saldanha, Liesl. "Risk and return in stock markets." Thesis, Glasgow Caledonian University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263381.

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Hossain, Nafees. "Accurate portfolio risk-return structure modelling." Doctoral thesis, University of Cape Town, 2006. http://hdl.handle.net/11427/18423.

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Markowitz's modem portfolio theory has played a vital role in investment portfolio management, which is constantly pushing the development on volatility models. Particularly, the stochastic volatility model which reveals the dynamics of conditional volatility. Financial time series and volatility models has become one of the hot spots in operations research. In this thesis, one of the areas we explore is the theoretical formulation of the optimal portfolio selection problem under Ito calculus framework. Particularly, a stochastic variation calculus problem, i.e., seeking the optimal stochastic volatility diffusion family for facilitating the best portfolio selection identified under the continuous-time stochastic optimal control theoretical settings. One of the properties this study examines is the left-shifting role of the GARCH(1, 1) (General Autoregressive Conditional Heteroskedastic) model's efficient frontier. This study considers many instances where the left shifting superior behaviour of the GARCH(1, 1) is observed. One such instance is when GARCH(1, 1) is compared within the volatility modelling extensions of the GARCH environ in a single index framework. This study will demonstrate the persistence of the superiority of the G ARCH ( 1, 1) frontier within a multiple and single index context of modem portfolio theory. Many portfolio optimization models are investigated, particularly the Markowitz model and the Sharpe Multiple and Single index models. Includes bibliographical references (p. 313-323).
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Lundh, Hampus. "Corporate Spinoffs- A Risk and Return Perspective." Thesis, Jönköping University, JIBS, Economics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-811.

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Spinoffs are an increasing phenomenon on the Swedish stock market. In this report one can read about factors that trigger spinoffs as well as about the short and medium term risk and return that spinoffs yield. I have observed 17 pre-spinoff companies that become 34 post-spinoff companies which continued to be traded on the stock market.

For the purpose of the investigation I use time-series regression, and my model is the sin-gle-factor market model. I use this model to estimate the beta and the firm specific factor. Supporting theories are: efficiency, portfolio theory, valuation method and asymmetry all those topics are central parts in a spinoff.

From my research I can not prove that spinoffs increase shareholders wealth. That means that the new units created through a spinoff are not more worth than the old corporation as such the new units do not outperform the old conglomerate structures expected return. However, the new units beta is not equal the old conglomerate structures beta, and this may due to change in capital structure. The weighted beta increase in half of the times, as such, it suggests a higher level of debt financing.

By comparing the spinoff company and the parent company in the post-spinoff scenario it can be concluded that the company who is performing the best is also the riskier alternative and the spinoff performs better than the parent company in eleven out of seventeen times. There is also a correlation between risk and return - when higher return is observed it also brings higher risk, and it holds true in all samples except one.

Further, at group level the spinoff group performs better than the market return and the spinoff group performs on average better than the parent group. Thus, if an outside inves-tor is to invest in either a spinoff company or a parent company one should buy the spinoff company at preferred weight according to the investors risk preferences.

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Mayr, Dominik Stephan. "Return and risk analysis in multinational firms /." [S.l. : s.n.], 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016429887&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Feng, Guoliang. "Essays on Local Housing Risk and Return." Thesis, The George Washington University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3716188.

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Local returns to housing investment across the U.S. cities are estimated and applied to explain the stockholding puzzle, i.e. the tendency for US homeowners to hold only housing and risk free assets in their portfolios. Several empirical problems exist in the previous studies: first, rental returns are always ignored or just assumed to be constant across cities; second, the CAP rates at the city level are often based on the problematic BLS Rent Index (the BLS CAP rate) which is questioned by Ambrose et al (2014).

Using micro data from American Housing Survey (AHS), CAP rates for 38 of the largest MSAs in the U.S. (the AHS CAP rate) are estimated. Pooled OLS methods are used to control the heterogeneity in individual housing characteristics and quality differences across tenure types. As expected based on Ambrose et al (2014), AHS CAP rates are much more volatile than BLS CAP rates. Standard deviations of annual AHS CAP rates (national average value is 2.27%) are much larger than those of BLS CAP rates (national average value is 0.57%). Moreover, in inland cities, especially those in Rust Belt, AHS CAP rates reflect more rental risk than BLS CAP rates do. This divergence is smaller in coastal cities where housing price appreciation is more volatile. This implies that past research using the BLS Rent Index to analyze rental risk may be biased.

After formulating CAP rate measures for a panel of cities, this data is used to test the dividend pricing hypothesis (DPH) in housing by studying the trade-off between the capitalization rate and subsequent house price appreciation. In previous tests, even allowing for the fact that actual appreciation does not equal expected appreciation, evidence for the DPH has not been strong. This research has included an implicit assumption that risks associated with housing investment are common across housing markets. In addition, many previous tests have used BLS CAP rates or assumed that the CAP rate was constant across cities and/or over time. In this second essay, statistically constructed estimates of the AHS CAP rate and the variance in total return are used to conduct tests of the DPH. The result is far stronger than those obtained in previous studies of a cross section of U.S. cities. But, when the BLS Rent Index is used to measure CAP rates and risk, the results are not consistent with the DPH.

Finally, these findings about total return to housing investment are used to explain the stockholding paradox. Homeowners tend to hold housing and risk-free assets, but not equities or bonds in their personal portfolios. This has been called the "stockholding paradox" and has been explained by observing that the correlation between the rate of appreciation of national housing prices and returns to the S&P; 500 is relatively high. The common conclusion in the literature has been that homeowners derive only modest diversification benefits from holding stocks and choose instead to amortize their mortgages. In contrast to the empirical literature on the stockholding paradox, Brueckner (1997) has demonstrated the theoretical proposition that consumption constrained households, those whose wealth is a fraction of housing value, will not find holding the market portfolio efficient. This research proceeds from Brueckner's observation. First, total return to homeownership, including both appreciation and AHS CAP rate is measured. Second, properties of optimal portfolios for households under various degrees of consumption constraints are identified. Third, optimal portfolios of individual stocks are determined. The results show that portfolios of individual stocks, which vary by city, are far more attractive than the market portfolio for homeowners. This suggests a resolution to the stockholding puzzle. Homeowners could benefit from holding portfolios designed to offset the unique risk of the cities where they live but they lack information on what these portfolios might be. Given this information gap, holding the market portfolio is not particularly attractive for most homeowners.

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Books on the topic "Risk and Return"

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Campbell, John Y. Understanding risk and return. Cambridge, MA: National Bureau of Economic Research, 1993.

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Ang, Andrew. Risk, return and dividends. Cambridge, MA: National Bureau of Economic Research, 2007.

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Lyng Jensen, Jesper, and Susanne Sublett. Redefining Risk & Return. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41369-3.

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Tuller, Lawrence W. High-risk, high-return investing. New York: J. Wiley & Sons, 1994.

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Scott, David Logan. Understanding and managing investment risk & return. Chicago, Ill: Probus Pub., 1990.

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Chan, Louis K. C. The risk and return from factors. Cambridge, MA: National Bureau of Economic Research, 1997.

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Understanding and managing investment risk & return. London: McGraw-Hill, 1990.

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Risk, Return, and the Indigo Autumn. Charleston WV: Tim McGhee, 2007.

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Dhankar, Raj S. Risk-Return Relationship and Portfolio Management. New Delhi: Springer India, 2019. http://dx.doi.org/10.1007/978-81-322-3950-5.

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FitzGerald, Adrian. Re-assessing the equity risk premium. Edinburgh: University of Edinburgh, Centre for Financial Markets Research, Dept. of Business Studies, 1997.

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Book chapters on the topic "Risk and Return"

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Shonkwiler, Ronald W. "Return and Risk." In Finance with Monte Carlo, 33–75. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8511-7_2.

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Rutterford, Janette. "Risk and return." In Introduction to Stock Exchange Investment, 27–61. London: Macmillan Education UK, 1993. http://dx.doi.org/10.1007/978-1-349-23045-7_2.

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Rönnbäck, Klas, and Oskar Broberg. "Risk and Return." In Capital and Colonialism, 125–44. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-19711-7_7.

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Mishra, Chandra S. "Risk and Return." In Getting Funded, 193–218. New York: Palgrave Macmillan US, 2015. http://dx.doi.org/10.1057/9781137384508_8.

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Shah, Atul. "Risk and return." In Jainism and Ethical Finance, 56–73. Abingdon, Oxon ; New York, NY : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315626178-4.

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Jensen, Jesper Lyng, and Susanne Sublett. "Risk and Uncertainty." In Redefining Risk & Return, 19–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41369-3_4.

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Huang, Ji-Ping. "Risk Management: Unusual Risk-Return Relationship." In Experimental Econophysics, 155–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-44234-0_11.

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Shivaani, M. V., P. K. Jain, and Surendra S. Yadav. "Examining Risk–Return Relationship." In India Studies in Business and Economics, 205–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-8141-6_6.

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Rutterford, Janette, and Marcus Davison. "Investment return and risk." In An Introduction to Stock Exchange Investment, 40–66. London: Macmillan Education UK, 2007. http://dx.doi.org/10.1007/978-0-230-21350-0_2.

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Bolder, David Jamieson. "Combining Risk and Return." In Fixed-Income Portfolio Analytics, 419–45. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12667-8_13.

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Conference papers on the topic "Risk and Return"

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Chuang, Wu-Jen, Liang-Yuh Ou-Yang, and Wen-Chen Lo. "Dynamic Return-Volume Relation and Future Returns - Implication for Reducing Investing Risk." In 2009 International Association of Computer Science and Information Technology - Spring Conference. IEEE, 2009. http://dx.doi.org/10.1109/iacsit-sc.2009.37.

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"RISK RETURN ANALYSIS OF NSE LISTED STOCKS." In International Conference on Research in Business management & Information Technology. ELK ASIA PACIFIC JOURNAL, 2015. http://dx.doi.org/10.16962/elkapj/si.bm.icrbit-2015.10.

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Fang, Shuhong. "On Optimal Risk/Return-Efficient Arbitrage Portfolio." In 2009 International Conference on Business Intelligence and Financial Engineering (BIFE). IEEE, 2009. http://dx.doi.org/10.1109/bife.2009.69.

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FAN, LONGZHEN. "BOND RISK AND RETURN IN THE SSE." In Advances in Data Mining and Modeling. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704955_0013.

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BROTONS, JOSÉ M. "RETURN RISK MAP IN A FUZZY ENVIRONMENT." In Proceedings of the 4th International ISKE Conference on Intelligent Systems and Knowledge Engineering. WORLD SCIENTIFIC, 2009. http://dx.doi.org/10.1142/9789814295062_0017.

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Dankanich, John W., Laura M. Burke, and Joseph A. Hemminger. "Mars sample return Orbiter/Earth Return Vehicle technology needs and mission risk assessment." In 2010 IEEE Aerospace Conference. IEEE, 2010. http://dx.doi.org/10.1109/aero.2010.5446767.

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BROTONS, JOSE M., ANTONIO TERCEÑO, and M. GLORIA BARBERÁ–MARINÉ. "RETURN AND RISK: THE SPANISH PUBLIC DEBT MARKET." In Proceedings of the XVII SIGEF Congress. WORLD SCIENTIFIC, 2012. http://dx.doi.org/10.1142/9789814415774_0007.

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Suyanto, Mr, and Florens Natalia Handayani Sibarani. "Stock investment analysis, idiosyncratic risk and abnormal return." In 15th International Symposium on Management (INSYMA 2018). Paris, France: Atlantis Press, 2018. http://dx.doi.org/10.2991/insyma-18.2018.22.

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Lee, Wei-Long, Ching-Tang Hsieh, Jui-Chan Huang, and Tzu-Jung Wu. "The influence of jumping risk and volatility risk on TAIEX option return." In APPLIED MATHEMATICS AND COMPUTER SCIENCE: Proceedings of the 1st International Conference on Applied Mathematics and Computer Science. Author(s), 2017. http://dx.doi.org/10.1063/1.4981953.

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Parsa, H., M. Jin, and X. Liang. "A multi-period return-risk measure portfolio optimization problem incorporating risk strategies." In EM). IEEE, 2010. http://dx.doi.org/10.1109/ieem.2010.5674625.

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Reports on the topic "Risk and Return"

1

Campbell, John. Understanding Risk and Return. Cambridge, MA: National Bureau of Economic Research, November 1993. http://dx.doi.org/10.3386/w4554.

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Ang, Andrew, and Jun Liu. Risk, Return and Dividends. Cambridge, MA: National Bureau of Economic Research, January 2007. http://dx.doi.org/10.3386/w12843.

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Chan, Louis K., Jason Karceski, and Josef Lakonishok. The Risk and Return from Factors. Cambridge, MA: National Bureau of Economic Research, July 1997. http://dx.doi.org/10.3386/w6098.

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Pindyck, Robert. Risk and Return in Environmental Economics. Cambridge, MA: National Bureau of Economic Research, July 2012. http://dx.doi.org/10.3386/w18262.

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Samphantharak, Krislert, and Robert Townsend. Risk and Return in Village Economies. Cambridge, MA: National Bureau of Economic Research, December 2013. http://dx.doi.org/10.3386/w19738.

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Daniel, Kent, Lira Mota, Simon Rottke, and Tano Santos. The Cross-Section of Risk and Return. Cambridge, MA: National Bureau of Economic Research, December 2017. http://dx.doi.org/10.3386/w24164.

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Cochrane, John. The Risk and Return of Venture Capital. Cambridge, MA: National Bureau of Economic Research, January 2001. http://dx.doi.org/10.3386/w8066.

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Ghysels, Eric, Pedro Santa-Clara, and Rossen Valkanov. There is a Risk-Return Tradeoff After All. Cambridge, MA: National Bureau of Economic Research, November 2004. http://dx.doi.org/10.3386/w10913.

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Campbell, John, and Luis Viceira. The Term Structure of the Risk-Return Tradeoff. Cambridge, MA: National Bureau of Economic Research, February 2005. http://dx.doi.org/10.3386/w11119.

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Guo, Hui, and Robert Whitelaw. Uncovering the Risk-Return Relation in the stock Market. Federal Reserve Bank of St. Louis, 2001. http://dx.doi.org/10.20955/wp.2001.001.

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You might also be interested in the extended bibliographies on the topic 'Risk and Return' for particular source types:
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