當(dāng)期望股票收益由 風(fēng)險(xiǎn)暴露

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摘要翻譯：
人們普遍認(rèn)為，當(dāng)經(jīng)典的最優(yōu)策略應(yīng)用于從數(shù)據(jù)中估計(jì)的參數(shù)時，所得的投資組合權(quán)重隨時間的推移具有顯著的波動性和不穩(wěn)定性。對此的主要解釋是難以準(zhǔn)確估計(jì)預(yù)期收益。本文通過引入一種新的漂移率參數(shù)化，對$N$stock Black-Scholes模型進(jìn)行了修正。在此框架下，我們解決了Markowitz的連續(xù)時間投資組合問題。最優(yōu)投資組合權(quán)重對應(yīng)于在每一個$N$布朗運(yùn)動中保持1/N$財(cái)富投資于股票。該策略是在樣本外應(yīng)用于一個大數(shù)據(jù)集。投資組合的權(quán)重隨著時間的推移是穩(wěn)定的，并獲得明顯高于經(jīng)典的$1/N$策略的夏普比率。
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英文標(biāo)題：
《Portfolio optimization when expected stock returns are determined by
  exposure to risk》
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作者：
Carl Lindberg
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最新提交年份：
2009
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分類信息：

一級分類：Mathematics        數(shù)學(xué)
二級分類：Statistics Theory        統(tǒng)計(jì)理論
分類描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
應(yīng)用統(tǒng)計(jì)、計(jì)算統(tǒng)計(jì)和理論統(tǒng)計(jì)：例如統(tǒng)計(jì)推斷、回歸、時間序列、多元分析、數(shù)據(jù)分析、馬爾可夫鏈蒙特卡羅、實(shí)驗(yàn)設(shè)計(jì)、案例研究
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一級分類：Quantitative Finance        數(shù)量金融學(xué)
二級分類：Portfolio Management        項(xiàng)目組合管理
分類描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
證券選擇與優(yōu)化、資本配置、投資策略與績效評價
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一級分類：Statistics        統(tǒng)計(jì)學(xué)
二級分類：Statistics Theory        統(tǒng)計(jì)理論
分類描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的別名。漸近，貝葉斯推論，決策理論，估計(jì)，基礎(chǔ)，推論，檢驗(yàn)。
--

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英文摘要：
  It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the difficulty of estimating expected returns accurately. In this paper, we modify the $n$ stock Black--Scholes model by introducing a new parametrization of the drift rates. We solve Markowitz' continuous time portfolio problem in this framework. The optimal portfolio weights correspond to keeping $1/n$ of the wealth invested in stocks in each of the $n$ Brownian motions. The strategy is applied out-of-sample to a large data set. The portfolio weights are stable over time and obtain a significantly higher Sharpe ratio than the classical $1/n$ strategy.
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PDF鏈接：
https://arxiv.org/pdf/0906.2271

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關(guān)鍵詞：股票收益 Optimization Quantitative Multivariate asymptotics 進(jìn)行 推移 weights 收益 股票