股票选择：股权管理定量分析策略

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"Equity Management Quantitative Analysis for Stock Selection" 是一本由Bruce I. Jacobs和Kenneth N. Levy合著的书籍，前言由诺贝尔奖得主Harry M. Markowitz撰写，由McGraw-Hill出版。这本书主要探讨了股票选择中的股权管理定量分析方法，涉及股票分析和量化分析等主题。在股票投资领域，股权管理定量分析是投资者用来做出明智决策的关键工具。这种分析方法依赖于数学和统计模型，以评估和预测股票的表现。它超越了传统的基本面分析，通过大量的数据处理和复杂的算法来识别具有潜在价值的投资机会。 1. **股权管理**：股权管理是投资组合管理的重要组成部分，它涉及到如何分配和调整投资者的权益资产，以实现最大化回报和控制风险的目标。在定量分析的框架下，这通常包括对各种股票的风险-回报特性进行建模和比较。 2. **股票分析**：股票分析旨在评估股票的价值，以确定其是否被市场低估或高估。定量分析方法包括使用财务比率、历史价格数据和预测模型，如市盈率(P/E)、收益率、增长模型(如股息贴现模型)等。 3. **定量分析**：这种方法侧重于使用数学和统计技术来分析市场数据，寻找可以指导投资决策的模式和趋势。它包括回归分析、时间序列分析、因子模型、机器学习等，用于预测股票价格、估计风险和构建投资策略。 4. **量化分析**：量化分析是定量分析的一个子集，更专注于利用计算机程序来自动化分析过程。这包括构建投资算法，执行高频交易，以及使用大数据和人工智能技术来发现市场微结构和非线性关系。在"Equity Management Quantitative Analysis for Stock Selection"一书中，作者可能深入讨论了如何构建和应用这些模型，以及如何解读分析结果以做出有效的投资决策。此外，书中的内容可能还包括风险管理、投资组合优化、市场效率理论以及与实际投资环境相关的案例研究。这本书对于投资者、金融分析师、基金经理以及其他对量化投资策略感兴趣的读者来说，都是一份宝贵的资源。通过学习和理解其中的知识，投资者可以更好地理解和利用数据驱动的方法来优化他们的股票投资组合。

Foreword

practice, of course, additional constraints are often imposed.

For example, DPOS, which had tracking error versus a Japanese

market index as its measure of risk, constrained the optimizer not to

hold individual positions that were “too much” different from their

benchmark weights, and not to hold sector totals that deviated too

much from the corresponding benchmark totals.

am not privy to

the models used in practice by Jacobs and

Levy,

but I imagine that

the outputs of even their most sophisticated model of expected re-

turns,

when

run

through an optimizer subject to minimal con-

straints, will sometimes result in

ante

efficient portfolios with

uncomfortably large positions. To avoid

this,

the optimizer can be

told to restrict maximum holdings.

But such constraints result in (the same

or)

less efficient portfo-

lios,

ante,

given estimated means, variances, and covariances of

security returns. How are we to reconcile theory and practice?

Chapter

of Markowitz

(1959)

presents the mean-variance in-

vestor as approximating a rational decision maker acting under un-

certainty. Examination of

this

analysis highlights limits of the

approximation, and suggests reasons why one might add con-

straints in practice beyond minimally required restraints.

The mean-variance analysis may implicitly be less averse to

an extreme downside move than the true expected utility maximi-

zation. [See Table

Levy

and Markowitz

(1979).]

It is therefore

possible that adding constraints to a minimally constrained mean-

variance analysis may produce a portfolio that gives higher true ex-

pected utility, even though it gives a lower value to

mean-variance

approximation.

The rational decision maker

@DM)

of Markowitz

(1959)

a human decision maker

(HDM)

in that both must make decisions un-

der uncertainty. It differs, however, in that the former

assumed

have unlimited computing capability; for example, it can instantly

compute the billionth place of

Nor does the RDM make up hypothe

ses

about the world

goes

along. Rather, it has

astronomically

long list of possible hypotheses about the nature of the world, attaches

probability beliefs to these hypotheses, and alters these beliefs accord-

ing to Bayes‘s rule

evidence accumulates.

choosing between two possible decisions, the RDM does not

act as

the hypothesis that it currently considers most probable

is,

in fact, certain. Rather, with its unlimited computing capacity, for

xvi

Foreword

each decision the RDM evaluates the expected utility of that deci-

sion

each alternative.hypothesis were true. For a given decision,

the

RDM

computes a weighted

sum

these expected utilities,

weighting each hypothesis by its probability. The (grand total) util-

ity the RDM attaches to the decision

this

probability-weighted

sum.

particular, if one decision (for example, choice of portfolio)

would have high utility

the hypothesis that is considered most

likely were true, but would be disastrous

some different, not too

implausible, hypothesis were true, the decision’s (grand total) ex-

pected utility would be less than that of a decision that would do al-

most as well

the more likely hypothesis were true and not too

badly

the less likely hypothesis were true.

The human decision maker cannot

perform

a similar calculation,

at least not on an astronomically long list of alternative models of the

world. By imposing constraints that rule out extreme solutions, like

too large bets on particular securities, the HDM may

seen

intu-

itively emulating the

RDM

by avoiding actions with

dire

cons+

quences under not-too-implausible

scenarios

and hypotheses.

Chapter

of Markowitz

(1959)

discusses a many-period

consumption-investment game assuming perfectly liquid assets.

Lip service is given to the illiquid case, but only to recognize that the

problem is important and hard.

practice (for example, with

DPOS), transaction costs, including estimated market impact, and

constraints, such as upper bounds on portfolio tumover and on the

increase or decrease in holdings of a security at any one time, at-

tempt to achieve reasonable, if not optimal, policies in light of

illiquidity.

The inability of human decision makers to fully emulate RDMs

in maximizing expected utility in the face of uncertainty and

illiquidity is a manifestation of what Herbert Simon

(1997)

calls

“bounded rationality.” The imposition of more

than

minimally

re-

quired constraints, however, is not an example of what Simon calls

”satisficing” behavior. The investor does not add constraints that

lower

ante

efficiency because the investor

“satisfied” with less

efficiency. Constraints are added (in part, at least) because the in-

vestor seeks protection against contingencies whose probability of

”disutility” is underrated by mean-variance approximation or, pos-

sibly, by the parameter estimation procedure. We may view such

constraints as an effort by the

HDM

to achieve intuitively

policy

Foreword

xvii

that an

RDM

would consider superior to that provided by the

mini-

mally constrained mean-variance procedure.

By and large,

still

believe, as

did in

1952,

that mean-variance

analysis can provide “the ’right kind‘ of diversification for the ’right

reason.’

”

Diversification makes sense, and proper diversification

depends on a consideration of covariances.

This

is in contrast to the

view that the decision to “buy, sell,

hold” can be determined by

studying the security itself and not in relation to other securities. It

seems to me obvious, as well as an implication of mean-variance

analysis, that the buy-hold-sell decision should depend on the de-

sirability of alternative investments itnd the investor’s risk aversion,

as well as the covariances of the security in question with other

available securities, subject to the caveat that mean-variance analy-

sis should not be considered a black box that can be set on automatic

and allowed to

run

portfolios on

its

own.

The virtues of an ”integrated portfolio approach” are further

described in Parts

and

this

volume. Jacobs and

Levy

are to be

acknowledged for bridging the gap between theory and practice in

the world of money management and thanked

for

bringing together

their cogent observations on the virtues of integrated portfolios and

the estimation of required inputs.

REFERENCES

Bloch,

M. et al. 1993.

“A

comparison

some aspects

the

U.S.

and Japanese eq-

uity

markets.”

Japan and

the

World Economy

5 (1): 3-26.

.Jacobs,

and

Levy.

1988. “Disentangling

equity

retum regularities: New in-

sights

and investment opportunities.”

Financial

Analysts]oumal44

(3).

Jacobs,

B.,

Levy

and

Starer. 1999. “Longshort portfolio management: Aninte-

grated approach.”

Journal

Portfolio

Management

26 (2).

Levy,

and

Markowik. 1979. ”Approximating Expected

Utility

Function

Mean and Variance.“

American Economic

Rmim

69: 308-317.

Markowik,

1952. “Portfolio selection.”]ournal

Finance

7 (2): 77-91.

1956. “The optimization

a quadratic function subject

linear con-

1959.

Porffolio

Selection: Eficient DiversiFcation

ofInveshnents.

New York

Sion,

1997.

Models

Bounded Rationality.

Cambridge, Massachusetts

MIT

straints.“

Naval Research Logistics Quarterly

IIk

111-133.

John

Wiley

Sons.

Press.

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