Factor models focus on systematic in- vestment risk, i.e., the one that cannot be avoided by investment diversification. Factor models are based on the. Economic rationale: Investors can earn a premium for bearing the additional risk associated with investing in less developed and stable markets. This includes. The factors used in the APT model are systematic risks that cannot be reduced by the diversification of an investment portfolio. Typically. ODDS TO WIN THE NBA MVP

Investors may tend to herd during crisis periods due to panic selling or force-sale of shares by the lending institutions see e. Hope, ; Huang et al. A number of scholars have shown evidence for herding during major economic events e.

Bowe and Domuta, ; Bikhchandani and Sharma, ; Yao et al. The herd behavior of investors could be observed during economic crisis period, due to investor reaction to changes in fundamental information. This research finding could be further validated, if investors of fundamentally sound stock portfolios such as Fama—French common risk-factor portfolios herd toward the market agreement. The magnitude of herding exposure to variation in average returns is sufficient to provide strong challenges in asset pricing tests.

Chiang and Zheng more specifically argue that during periods of extreme market conditions, investors tend to suppress their private information in favor of the market consensus and are more likely to mimic collective actions in investment decision making. As such, the clusters in stock returns can be observed as investors move in unison under market stress i. Conversely, herding can also occur when investors lack of fundamental information due to inefficient information disclosures in the markets Chang et al.

Table V reports the regression results for non-fundamental herding during the crisis periods. The regression outcome does not provide any evidence for the presence of herding on non-fundamental factors during the crisis periods. It is apparent from these findings that the common risk-factor portfolios are not subject to herding as investors do not mimic non-fundamental factors portfolio or stock specific factors affecting stock prices.

This suggests that the investors mimic non-fundamental factors relating to market crisis e. Chiang and Zheng, ; Ouarda et al. In particular, Hwang and Salmon document evidence for herding in bullish and bear market conditions for the US and South Korean markets. During high bullish periods, the investors may tend to follow the trading patterns of majority of investors who possess information e. Tan et al.

These observations are consistent with the findings of Zhou and Lai , Chiang and Zheng and Chen et al. Moreover, the herd behavior of investors of global common risk-factor portfolios appears when up-market dummy is controlled for in the regression. This clearly shows that the global risk-factor portfolio investors herd on fundamental factors relating to bullish market conditions e.

Table VI. Table VII outlines the results of herding on non-fundamental information during up-market periods. The herding regression results do not provide any evidence for herding on non-fundamental factors under up-market condition. Again, these findings further confirm that the common risk-factor portfolio investors do not herd on non-fundamental information i.

Galariotis et al. Moreover, all five common risk-factor portfolio investors of Asia Pacific and North American markets exhibit herding on non-fundamental factors relating to bullish conditions e. Concluding remarks The essence of Fama and French , common risk-factor regressions is that a linear combination of observed factors of an asset pricing model could be effectively explained by a linear combination of unobserved risk factors, if stock market efficiency is established.

The validity of these models is therefore based upon how efficient the common risk-factor portfolios are traded. More importantly, Blanco show that results of Fama—French three-factor regression is based upon how the portfolios are formed e. Therefore, inefficient portfolio e. The initial work of Fama and French has shown that the stock return could be explained by an overall market factor and factors relating to size and book-to-market value.

If the markets are integrated, asset pricing models should also explain return variations of other markets e. The question of whether the model is fit enough to explain the true underlying process of common stock return variation depends on how efficient the common risk-factor portfolios are selected and traded. The regression results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration.

However, the common risk-factor portfolio formed on SM is subject herding at 5 percent significance level for Asia pacific region. Although there is no tendency to herd under normal market conditions, the evidence shows that the investor behavior changes significantly in the event of a crisis. These changes in the behavior of investors have been largely attributed to investor panic by a number of scholars see e. No evidence for herding on non-fundamental information is found during crisis periods.

However, the investors of North American market for all risk-factor regressions and investors of risk-factor portfolio of size and momentum of Asia Pacific markets tend to mimic non-fundamental factors relating to market crisis e. These findings unearth stylist facts about the changes in behavior of investors in major economic events such as economic or financial crisis.

Note that an alternate, although less common approach, is to apply a "fundamental valuation" method, such as the T-model , which instead relies on accounting information, attempting to model return based on the company's expected financial performance. In general this approach does not group assets but rather creates a unique risk price for each asset; these models are then of "low dimension".

Calculating option prices or their "Greeks" combines: i a model of the underlying price behavior, or " process " - ie the asset pricing model selected; and ii a mathematical method which returns the premium or sensitivity as the expected value of option payoffs over the range of prices of the underlying.

The classical model here is Black—Scholes which describes the dynamics of a market including derivatives with its option pricing formula ; leading more generally to Martingale pricing , as well as the aside models. Black—Scholes assumes a log-normal process; the other models will, for example, incorporate features such as mean reversion , or will be " volatility surface aware", applying local volatility or stochastic volatility. Rational pricing is also applied to fixed income instruments such as bonds that consist of just one asset , as well as to interest rate modeling in general, where yield curves must be arbitrage free with respect to the prices of individual instruments.

As regards options on these instruments, and other interest rate derivatives , see short-rate model and Heath—Jarrow—Morton framework for discussion as to how the various models listed above are applied.

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Microeconomics could also explain why a higher minimum wage might force The Wendy's Company to hire fewer workers. These explanations, conclusions, and predictions of positive microeconomics can then also be applied normatively to prescribe what people, businesses, and governments should do in order to attain the most valuable or beneficial patterns of production, exchange, and consumption among market participants. This extension of the implications of microeconomics from what is to what ought to be or what people ought to do also requires at least the implicit application of some sort of ethical or moral theory or principles, which usually means some form of utilitarianism.

The Marshallian and Walrasian methods fall under the larger umbrella of neoclassical microeconomics. Neoclassical economics focuses on how consumers and producers make rational choices to maximize their economic well being, subject to the constraints of how much income and resources they have available. Neoclassical economists make simplifying assumptions about markets—such as perfect knowledge, infinite numbers of buyers and sellers, homogeneous goods, or static variable relationships—in order to construct mathematical models of economic behavior.

These methods attempt to represent human behavior in functional mathematical language, which allows economists to develop mathematically testable models of individual markets. Neoclassicals believe in constructing measurable hypotheses about economic events, then using empirical evidence to see which hypotheses work best. Microeconomics applies a range of research methods, depending on the question being studied and the behaviors involved. Basic Concepts of Microeconomics The study of microeconomics involves several key concepts, including but not limited to : Incentives and behaviors: How people, as individuals or in firms, react to the situations with which they are confronted.

Production theory: This is the study of production—or the process of converting inputs into outputs. Producers seek to choose the combination of inputs and methods of combining them that will minimize cost in order to maximize their profits. Price theory: Utility and production theory interact to produce the theory of supply and demand, which determine prices in a competitive market.

In a perfectly competitive market, it concludes that the price demanded by consumers is the same supplied by producers. That results in economic equilibrium. Article Sources Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts.

We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy. Everybody has an opinion on expected return. Some people will have an opinion on expected risk as well. But correlation is a key determinant of not just the portfolios that you structure but the way that you define the assumptions in your model and the internal consistency therein.

Q: How do things look different using risk factors? For example, inflation is very hard to decompose further, versus a bond, which is sensitive to numerous risk and return factors that are macroeconomic in nature—what happens with GDP, real interest rates, and inflation along with asset class-specific things like duration, convexity, and spread.

Investors looking to maximize wealth are interested in holding compensated premia. It needs to make sense from a first principles economic standpoint. Is this a compensated premium? We can group factors into different buckets. The macroeconomic bucket has things like exposure to GDP growth and productivity, real rates, inflation, and volatility. A regional bucket includes things like currency, emerging or developed market, and sovereign exposure.

The equity-specific bucket includes size, value, and momentum. For fixed income there is risk of default, where this bond is in the capital structure of an organization, and, as I mentioned, duration, convexity, and credit spreads.

In the traditional model, when I hold a U. In a risk factor context, I can put factors together in such a way that I explicitly capture that crosstalk and understand where the overlaps are and where the gaps are. In theory, I could do this at a very granular level with every possible risk factor. So I have to create factor-mimicking portfolios typically using long-short spread exposures. If I want to replicate something like size, I would be long a global small-cap index and short a global large-cap index to get exposure to that particular factor premium.

Could you explain here how factor-based investing moves from theory to practice? The first real step is to define the parsimonious set of factors. How many do you need to cover the investable universe? Do you have any gaps?

Did you have any overlaps? Do you have things that are truly uncorrelated? Once we decide which factors or premia to look at, the next challenge is trying to come up with ex-ante predictions for risk, return, and correlation for each of those factors. For instance, what do I think is going to happen with real interest rates?

That may take some of the measurement error out of the estimation. So in a mean variance optimization model, I need to make a judgment on what the expected returns, and the risk, and the correlations of the different pieces of my portfolio are going to be before I put them together. I think that there is a power in focusing on the smaller, granular units used for risk factors.

Now, we also have less experience doing that. Q: How often do you expect to be questioning ex-ante assumptions? This is a great question, because it also applies not just to factors but to asset classes as well. So for something to change, it needs to be not just a marginal change. It needs to be something real like a change in market structure or a long-term secular trend that is going to impact a particular factor or asset class. They ought to be durable and robust. Q: What does a factor-based portfolio look like?

The factor-replicating portfolio is not buy and hold. That long-short component is going to have to be rebalanced continuously. There are other issues, especially around plan governance. Andrew Ang, a professor at Columbia, has written extensively on governance issues with risk factor portfolios. Q: Given the practical challenges, how are risk factors being used?

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Factor Modeling

Data and description of sample Data pertaining to stock returns including market return and common risk factors for each country are obtained covering a sampling period of July 2, to January 31, 7, observations from Bloomberg database for the respective periods and Kenneth R.

Microeconomic-based risk factor models investing	Economic rationale: Investors holding less liquid securities accept the risk that they may not be able to immediately sell their investment in certain environments. The good news is that factor investing can potentially deliver more effective diversification to help investors achieve their investment goals. Investing today is harder than ever amid uncertain earnings growth and periods of high market volatility. Eugene F. The overall findings of the study suggest that the common risk factors recognized by Fama and Frenchlink a more prudent basis for explaining common stock return variations under normal market conditions i.
How to make money trading bitcoin day 4 of 5	Hence, Fama and French common risk-factor classification provides a more prudent forecast for common stock return variation under normal market conditions Table III. We also reference original research from risk factor reputable publishers microeconomic-based appropriate. By forgoing immediate access to capital, investors can be paid a premium for bearing that cash strain and delaying consumption. Empirical investing 5. Exposure to real rates provides an effective ballast to soften the impact of equity market drawdowns. French-Data Library [5]. Most investors require a hearty dose of economic growth in their portfolios, which potentially models provide an attractive long-run reward associated with the growth of the global economy.
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Microeconomic-based risk factor models investing	211

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In particular, a factor risk model allows investors to construct the covariance matrix of the assets in the portfolio. Estimating the covariance matrix is notoriously difficult because we need considerable amounts of data to estimate all the covariance terms.

On this page, we discuss the different types of factor risk models and explain the advantages and disadvantages of the different factor risk model approaches. Factor risk model definition The factor risk model is defined as follows where is the covariance matrix we are interested in, B is the matrix with the exposures of the securities to the factors, is the covariance matrix of the factors, and D is the diagonal matrix containing the asset specific risks.

Factor risk model estimation Thus, to create a factor risk model, we need to do the following. First, we need to define the relevant factors. These factors can be macroeconomic factors, in which case the model is macroeconomic factor risk model. Alternatively, the model can use stock fundamentals, in which case we have a fundamental factor risk model. Third, we can estimate the factors from the data first using a dimensionality reduction method such as Principal Component Analysis PCA.

The principal components are then the factors in our model. This is a statistical factor risk model. Once we have the factors, the next step is to calculate the exposure of the stocks to the factors. To do that, we can make use of linear regression.

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Factor Modeling

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